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-rwxr-xr-xMass_-_Transfer_Operations/Chapter1.ipynb76
-rwxr-xr-xMass_-_Transfer_Operations/Chapter10.ipynb1344
-rwxr-xr-xMass_-_Transfer_Operations/Chapter10_1.ipynb1366
-rwxr-xr-xMass_-_Transfer_Operations/Chapter10_2.ipynb1366
-rwxr-xr-xMass_-_Transfer_Operations/Chapter11.ipynb1207
-rwxr-xr-xMass_-_Transfer_Operations/Chapter11_1.ipynb1235
-rwxr-xr-xMass_-_Transfer_Operations/Chapter11_2.ipynb1235
-rwxr-xr-xMass_-_Transfer_Operations/Chapter12.ipynb922
-rwxr-xr-xMass_-_Transfer_Operations/Chapter12_1.ipynb932
-rwxr-xr-xMass_-_Transfer_Operations/Chapter12_2.ipynb932
-rwxr-xr-xMass_-_Transfer_Operations/Chapter13.ipynb443
-rwxr-xr-xMass_-_Transfer_Operations/Chapter13_1.ipynb438
-rwxr-xr-xMass_-_Transfer_Operations/Chapter13_2.ipynb438
-rwxr-xr-xMass_-_Transfer_Operations/Chapter1_1.ipynb76
-rwxr-xr-xMass_-_Transfer_Operations/Chapter1_2.ipynb76
-rwxr-xr-xMass_-_Transfer_Operations/Chapter2.ipynb333
-rwxr-xr-xMass_-_Transfer_Operations/Chapter2_1.ipynb333
-rwxr-xr-xMass_-_Transfer_Operations/Chapter2_2.ipynb333
-rwxr-xr-xMass_-_Transfer_Operations/Chapter3.ipynb628
-rwxr-xr-xMass_-_Transfer_Operations/Chapter3_1.ipynb628
-rwxr-xr-xMass_-_Transfer_Operations/Chapter3_2.ipynb628
-rwxr-xr-xMass_-_Transfer_Operations/Chapter4.ipynb471
-rwxr-xr-xMass_-_Transfer_Operations/Chapter4_1.ipynb471
-rwxr-xr-xMass_-_Transfer_Operations/Chapter4_2.ipynb471
-rwxr-xr-xMass_-_Transfer_Operations/Chapter5.ipynb384
-rwxr-xr-xMass_-_Transfer_Operations/Chapter5_1.ipynb385
-rwxr-xr-xMass_-_Transfer_Operations/Chapter5_2.ipynb385
-rwxr-xr-xMass_-_Transfer_Operations/Chapter6.ipynb1050
-rwxr-xr-xMass_-_Transfer_Operations/Chapter6_1.ipynb1059
-rwxr-xr-xMass_-_Transfer_Operations/Chapter6_2.ipynb1059
-rwxr-xr-xMass_-_Transfer_Operations/Chapter7.ipynb1071
-rwxr-xr-xMass_-_Transfer_Operations/Chapter7_1.ipynb1071
-rwxr-xr-xMass_-_Transfer_Operations/Chapter7_2.ipynb1092
-rwxr-xr-xMass_-_Transfer_Operations/Chapter8.ipynb1462
-rwxr-xr-xMass_-_Transfer_Operations/Chapter8_1.ipynb1312
-rwxr-xr-xMass_-_Transfer_Operations/Chapter8_2.ipynb1312
-rwxr-xr-xMass_-_Transfer_Operations/Chapter9.ipynb2153
-rwxr-xr-xMass_-_Transfer_Operations/Chapter9_1.ipynb2154
-rwxr-xr-xMass_-_Transfer_Operations/Chapter9_2.ipynb2154
-rwxr-xr-xMass_-_Transfer_Operations/README.txt10
-rwxr-xr-xMass_-_Transfer_Operations/screenshots/illustration13.3.pngbin0 -> 76410 bytes
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-rwxr-xr-xMass_-_Transfer_Operations/screenshots/illustration5.1.pngbin0 -> 87422 bytes
-rwxr-xr-xMass_-_Transfer_Operations/screenshots/illustration5.1_1.pngbin0 -> 87422 bytes
-rwxr-xr-xMass_-_Transfer_Operations/screenshots/illustration5.1_2.pngbin0 -> 87422 bytes
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diff --git a/Mass_-_Transfer_Operations/Chapter1.ipynb b/Mass_-_Transfer_Operations/Chapter1.ipynb
new file mode 100755
index 00000000..3d150815
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter1.ipynb
@@ -0,0 +1,76 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:a429839b93fda73811b080ddf18c202362ff24ca9ec207904626c7313297633e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter1 : The Mass-Transfer Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex1.1: Page 17"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 1.1\n",
+ "# Page: 17\n",
+ "\n",
+ "print'Illustration 1.1 - Page: 17\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# Taking conversion factor from table 1.5 (Pg 15)\n",
+ "# viscosity: [(lb/ft.h)]*4.134*10^(-4) [kg/m.s] (Pg 15)\n",
+ "# time: [h] = 3600.0 [s]\n",
+ "# Density: [lb/cubic feet]*16.09 = [kg/cubic m] (Pg 15)\n",
+ "# Length: [ft]*0.3048 = [m]\n",
+ "N = (2.778*10**(-4))*(30600/(1/(0.3048**(3.0/2))))*((1/(4.134*(10**(-4))*16.019))**0.111)*(((1/16.019)/(1/16.019))**0.26);\n",
+ "print'The coeffecient for S.I. Unit is',round(N,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 1.1 - Page: 17\n",
+ "\n",
+ "\n",
+ "The coeffecient for S.I. Unit is 2.5\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter10.ipynb b/Mass_-_Transfer_Operations/Chapter10.ipynb
new file mode 100755
index 00000000..69678c20
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter10.ipynb
@@ -0,0 +1,1344 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:aee8bd5a480bd89ea66526c53a540baecd3d515c3125767ad2bbf6eed92072f7"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 10: Liquid Extraction"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.1: Page 494"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.1\n",
+ "# Page: 494\n",
+ "\n",
+ "print'Illustration 10.1 - Page: 494\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "xF = 0.30;# [mol fraction]\n",
+ "yS = 0;# [mol fraction]\n",
+ "S1 = 40.0;# [kg]\n",
+ "B1 = 40.0;# [kg]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data at 20 OC:\n",
+ "# Wa: Wt. percent of a\n",
+ "# Wb: Wt. percent of b\n",
+ "# Wc: Wt. percent of c\n",
+ "# Data1 = [Wc Wa Wb]\n",
+ "# Data1: water layer\n",
+ "Data1 = numpy.array([(0.69 ,98.1, 1.2),(1.41, 97.1 ,1.5),(2.89 ,95.5 ,1.6),(6.42 ,91.7 ,1.9),(13.30, 84.4, 2.3),(25.50 ,71.1 ,3.4),(36.70 ,58.9 ,4.4),(44.30 ,45.1 ,10.6),(46.40 ,37.1 ,16.5)])\n",
+ "# Data2: isopropyl ether layer\n",
+ "Data2 = numpy.array([(0.18 ,0.5 ,99.3),(0.37, 0.7 ,98.9),(0.79, 0.8, 98.4),(1.93 ,1, 97.1),(4.82, 1.9, 93.3),(11.40, 3.9, 84.7),(21.60, 6.9, 71.5),(31.10, 10.8, 58.1),(36.20 ,15.1 ,48.7)])\n",
+ "\n",
+ "plt.plot((Data1[:,2])/100,(Data1[:,0])/100,label=\"x Vs fraction ether\")\n",
+ "plt.plot((Data2[:,2])/100,(Data2[:,0])/100,label=\"y Vs fraction ether\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='lower center');\n",
+ "ax=pylab.gca()\n",
+ "ax.set_xlabel(\"Wt fraction of isopropyl ether\");\n",
+ "ax.set_ylabel(\"Wt fraction of acetic acid\");\n",
+ "plt.ylim((0,0.3))\n",
+ "plt.xlim((0,1))\n",
+ "plt.show();\n",
+ "# x: Wt fraction of acetic acid in water layer.\n",
+ "# y: Wt fraction of acetic acid in isopropyl layer.\n",
+ "\n",
+ "# The rectangular coordinates of Fig 10.9(a) will be used but only upto x = 0.30\n",
+ "\n",
+ "# Stage 1:\n",
+ "F = 100;# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "M1 = F+S1;# [kg]\n",
+ "# From Eqn. 10.5:\n",
+ "xM1 = ((F*xF)+(S1*yS))/M1;\n",
+ "# From Fig. 10.15 (Pg 495):\n",
+ "# Point M1 is located on the line FB and with the help of tie line passing through M1:\n",
+ "x1 = 0.258;# [mol fraction]\n",
+ "y1 = 0.117;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E1 = (M1*(xM1-x1)/(y1-x1));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R1 = M1-E1;# [kg]\n",
+ "\n",
+ "# Stage 2:\n",
+ "S2 = 40;# [kg]\n",
+ "B2 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M2 = R1+B2;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM2 = ((R1*x1)+(S2*yS))/M2;\n",
+ "# Point M2 is located on the line R1B and the tie line passing through R2E2 through M2:\n",
+ "x2 = 0.227;\n",
+ "y2 = 0.095;\n",
+ "# From Eqn. 10.8:\n",
+ "E2 = (M2*(xM2-x2)/(y2-x2));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R2 = M2-E2;# [kg]\n",
+ "\n",
+ "# Stage 3:\n",
+ "S3 = 40;# [kg]\n",
+ "B3 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M3 = R2+B3;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM3 = ((R2*x2)+(S3*yS))/M3;\n",
+ "# Point M3 is located on the line R2B and the tie line passing through R3E3 through M3:\n",
+ "x3 = 0.20;# [mol fraction]\n",
+ "y3 = 0.078;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E3 = (M3*(xM3-x3)/(y3-x3));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R3 = M3-E3;# [kg]\n",
+ "Ac = x3*R3;\n",
+ "print\"The composited extract is\",round((E1+E2+E3),2),\" kg\\n\"\n",
+ "print\"The acid content is \",round(((E1*y1)+(E2*y2)+(E3*y3)),2),\" kg\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# If an extraction to give the same final raffinate concentration were to be done in single stage, the point M would be at the intersection of tie line R3E3 and the line BF.\n",
+ "x = 0.20;# [mol fraction]\n",
+ "xM = 0.12;# [mol fraction]\n",
+ "# From Eqn. 10.6:\n",
+ "S = F*(xF-xM)/(xM-yS);# [kg]\n",
+ "print round(S,2),\"kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.1 - Page: 494\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F6AM8U4J9C5SIVVLGmMhIrpzM4+c+Tp+P+3BAY2J4oDLjdTyM0cB+4BxgHDDB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\niRUYxhhjPPl/ptoLDFmMWJUAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x789db00>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The composited extract is 135.05 kg\n",
+ "\n",
+ "The acid content is 13.01 kg\n",
+ "\n",
+ "\n",
+ "\n",
+ "150.0 kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.2: Page 497"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.2\n",
+ "# Page: 497\n",
+ "\n",
+ "print'Illustration 10.2 - Page: 497\\n\\n'\n",
+ "\n",
+ "print'Illustration 10.2 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:kerosene c:Nicotine\n",
+ "xF = 0.01;# [wt fraction nicotine]\n",
+ "F = 100.0;# [kg]\n",
+ "B = 150.0;# [kg]\n",
+ "#******#\n",
+ "\n",
+ "# Equilibrium data:\n",
+ "# x_prime = kg nicotine/kg water\n",
+ "# y_prime = kg nicotine/kg kerosene\n",
+ "# Data = [x_prime y_prme]\n",
+ "Data = numpy.array([[0 ,0],[0.001011 ,0.000807],[0.00246, 0.001961],[0.00502, 0.00456],[0.00751, 0.00686],[0.00998 ,0.00913],[0.0204, 0.01870]])\n",
+ "xF_prime = xF/(1-xF);# kg nicotine/kg water\n",
+ "A = F*(1-xF);# [kg]\n",
+ "AbyB = A/B;\n",
+ "\n",
+ "def f64(x):\n",
+ " return -AbyB*(x-xF)\n",
+ "x = numpy.arange(0,0.01+0.001,0.001);\n",
+ "plt.plot(Data[:,0],Data[:,1],label=\"Equilibrium line\")\n",
+ "plt.plot(x,f64(x),label=\"Operating Line\");\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.2(a)\")\n",
+ "plt.show()\n",
+ "# The operating line and equilibrium line intersect at:\n",
+ "x1_prime = 0.00425;# [kg nicotine/kg water]\n",
+ "y1_prime = 0.00380;# [kg nicotine/kg water]\n",
+ "extract = A*(0.01011-x1_prime);\n",
+ "print extract*100,\"% of nicotine is extracted.\\n\\n\"\n",
+ "\n",
+ "print'Illustration 10.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "B = 50.0;# [kg]\n",
+ "# For each stage:\n",
+ "AbyB = A/B;\n",
+ "def f65(x1):\n",
+ " return -AbyB*(x1-xF)\n",
+ "x1 = numpy.arange(0,0.01+0.001,0.001)\n",
+ "def f66(x2):\n",
+ " return -AbyB*(x2-0.007)\n",
+ "x2 = numpy.arange(0,0.01+0.001,0.001)\n",
+ "def f67(x3) :\n",
+ " return -AbyB*(x3-0.005)\n",
+ "x3 =numpy.arange(0,0.01+0.001,0.001)\n",
+ "\n",
+ "plot(Data[:,0],Data[:,1],label=\"Equilibrium line\")\n",
+ "plt.plot(x1,f65(x1),label=\"Operating Line from xF\")\n",
+ "plt.plot(x2,f66(x2),label=\"Operating Line from 0.007\")\n",
+ "plt.plot(x3,f67(x3),label=\"Operating Line from 0.005\")\n",
+ "plt.grid('on');\n",
+ "legend(loc=\"upper right\");\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0, 0.010))\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.2(b)\")\n",
+ "plt.show()\n",
+ "# The final raffinate composition:\n",
+ "x3_prime = 0.0034;# [kg nicotine/kg water]\n",
+ "extract = A*(0.01011-x3_prime);\n",
+ "print extract*100,\" % of nicotine is extracted.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.2 - Page: 497\n",
+ "\n",
+ "\n",
+ "Illustration 10.2 (a)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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BVX8MWpXsQSzGI1bn7IimcvjjD7jvPhg6FD7+2GnkD6doKguvWVl4K5Qk86OI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\nKouYui5EpKqIfCQi34jI1yLSJ2D7mLo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+ "text": [
+ "<matplotlib.figure.Figure at 0x7b9c278>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "58.014 % of nicotine is extracted.\n",
+ "\n",
+ "\n",
+ "Illustration 10.2 (b)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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VGDx4sMdtjRw5Ml3GOS1XXWhoKEePHvXKuDzBkyeZGcBXWHtXALYBj3vLoECm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\nRK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa275160>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "66.429 % of nicotine is extracted.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.3: Page 502"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.3\n",
+ "# Page: 502\n",
+ "\n",
+ "print'Illustration 10.3 - Page: 502\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "F = 8000;# [kg/h]\n",
+ "xF = 0.30;# [wt. fraction acetic acid]\n",
+ "#*******#\n",
+ "\n",
+ "# From Illustration 10.1 (Pg 494)\n",
+ "# Equilibrium Data:\n",
+ "# Eqb = [y_star*100 x*100]\n",
+ "Eqb = numpy.array([[0.18 ,0.69],[0.37 ,1.41],[0.79 ,2.89],[1.93, 6.42],[4.82, 13.30],[11.40, 25.50],[21.60 ,36.70],[31.10 ,44.30],[36.20, 46.40]]);\n",
+ "\n",
+ "# Solution(a)\n",
+ "\n",
+ "# From Figure 10.23 (Pg 503):\n",
+ "# For minimum solvent rate:\n",
+ "y1 = 0.143;# [Wt fraction of acetic acid in isopropyl ether layer]\n",
+ "xM = 0.114;# [Wt fraction of acetic acid in water layer]\n",
+ "# From Eqn. 10.24:\n",
+ "Bm = (F*xF/xM)-F;# [kg/h]\n",
+ "print\"Minimum solvent rate: \",Bm,\" kg/h\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "B = 20000.0;# [kg solvent/h]\n",
+ "yS = 0;\n",
+ "S = B;\n",
+ "# From Eqn 10.24:\n",
+ "xM = ((F*xF)+(S*yS))/(F+S);\n",
+ "# From Fig. 10.23 (Pg 503):\n",
+ "y1 = 0.10;\n",
+ "# Operating curve data:\n",
+ "# Operat = [YsPlus1 Xs]\n",
+ "Operat = numpy.array([[0 ,0.02],[0.01 ,0.055],[0.02 ,0.09],[0.04 ,0.150],[0.06, 0.205],[0.08, 0.250],[0.1 ,0.3]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,1]/100,Eqb[:,0]/100,label=\"Operating Line\")\n",
+ "plt.plot(Operat[:,1],Operat[:,0],label=\"Equilibrium Line\")\n",
+ "plt.grid('on');\n",
+ "plt.ylim((0,y1));\n",
+ "plt.xlim((0,xF));\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"Wt. fraction acetic acid in water solution\");\n",
+ "plt.ylabel(\"Wt. fraction acetic acid in isopropyl ether solution\");\n",
+ "plt.title(\"Solution 10.3\")\n",
+ "plt.show()\n",
+ "# From Figure scf(22):\n",
+ "xNp = 0.02;\n",
+ "Np = 7.6;\n",
+ "# By acid balance:\n",
+ "M = B+F;\n",
+ "E1 = M*(xM-xNp)/(y1-xNp);# [kg/h]\n",
+ "RNp = M-E1;# [kg/h]\n",
+ "print\"Number of theoretical Stages: \\n\",Np\n",
+ "print\"Weight of the extract:\",E1,\"kg/h\\n\"\n",
+ "print\"Weight of the raffinate \",RNp,\" kg/h\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.3 - Page: 502\n",
+ "\n",
+ "\n",
+ "Minimum solvent rate: 13052.6315789 kg/h\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Mw0gCW7fCBRfAxReXHcMxavkorp54NRMvn2iGI4X4ivNQ1a+Be4ABwJnAMyLy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\nLJF/2E+RvpBzHifLPoDv5JBHrf6GbQz4UBrLU8B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+ "text": [
+ "<matplotlib.figure.Figure at 0x987d7f0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical Stages: \n",
+ "7.6\n",
+ "Weight of the extract: 23000.0 kg/h\n",
+ "\n",
+ "Weight of the raffinate 5000.0 kg/h\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.4: Page 506"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.4\n",
+ "# Page: 506\n",
+ "\n",
+ "print'Illustration 10.4 - Page: 506\\n\\n'\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "import numpy\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:kerosene c:Nicotine\n",
+ "F = 1000.0;# [kg/h]\n",
+ "xF = 0.01;# [wt. fraction acetic acid]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data:\n",
+ "# x_prime = kg nicotine/kg water\n",
+ "# y_prime = kg nicotine/kg kerosene\n",
+ "# Eqb = [x_prime y_prme]\n",
+ "Eqb = numpy.array([[0 ,0],[0.001011, 0.000807],[0.00246, 0.001961],[0.00502 ,0.00456],[0.00751, 0.00686],[0.00998, 0.00913],[0.0204 ,0.01870]]);\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "A = 1000*(1-xF);# [kg water/h]\n",
+ "yS = 0;\n",
+ "yS_prime = 0;\n",
+ "y1_prime = 0;\n",
+ "xF_prime = xF/(1-xF);# [kg nicotine/kg water]\n",
+ "# For xF_prime = 0.0101:\n",
+ "yk = 0.0093;\n",
+ "xNp = 0.001;# [wt. fraction acetic acid]\n",
+ "xNp_prime = xNp/(1-xNp);# [kg nicotine/kg water]\n",
+ "# For infinite stages:\n",
+ "# Operating Line should pass through (xNp_prime,y1_prime) & (xF_prime,yk)\n",
+ "Operat = numpy.array([[xNp_prime, y1_prime],[xF_prime ,yk]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1],label=\"equilibrium Line\")\n",
+ "plt.plot(Operat[:,0],Operat[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.4(a)\")\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0,0.01))\n",
+ "plt.show()\n",
+ "AbyBm = (yk-y1_prime)/(xF_prime-xNp_prime);\n",
+ "Bm = A/AbyBm;# [kg kerosene/h];\n",
+ "print\"Mininmum kerosene rate: \",round(Bm,2),\" kg kerosene/h \\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "B = 1150.0;# [kg/h]\n",
+ "AbyB = A/B;\n",
+ "# From Eqn. 10.36:\n",
+ "y2_prime = ((xF_prime-xNp_prime)*AbyB)+yS_prime;# [kg nicotine/kg kerosene]\n",
+ "# Operating Line should pass through (xNp_prime,y1_prime) & (xF_prime,y2_prime)\n",
+ "Operat = numpy.array([[xNp_prime, y1_prime],[xF_prime, y2_prime]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1],label=\"equilibrium Line\")\n",
+ "plt.plot(Operat[:,0],Operat[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine/kg water\");\n",
+ "plt.ylabel(\"kg nicotine/kg kerosene\");\n",
+ "plt.title(\"Solution 10.4(b)\")\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0,0.01))\n",
+ "plt.show()\n",
+ "# From Figure:\n",
+ "Np = 8.3;\n",
+ "print\"Number of theoretical stages: \\n\",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.4 - Page: 506\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3MyhjTGg4dgx69XIeUV640OmHAViwdQGdpnfijSZv0OWGLsEN0oQ0v+YuSy0s\nUhQop6rfuxdSYFifjDHZ8+OPzriXmjWdR5QLFABVZfCqwQxaNYjJrSdzc/mbgx2mCbAc75MRkeXA\n3Z6y64B/ROQbVbXVMY2JQKrw2Wfw4oswaJDTRAZwMvEk3WZ346e/f2J1l9WUL1Q+uIGasOBPn0wh\nVT0MtATGqmod4DZ3wzK+RHK7cCTXDUK7focOOXcvH38MX3/9vwSz+8huGo1uxKmkU3z90NeZJphQ\nrl8gRHr9As2fJBMlIqWANsBczz5rhzImwqxdCzVqwKWXwpo1cIVnBak1u9ZQZ1gdWlzRgon3TeSi\nvBcFN1ATVvwZJ9MaeAn4RlV7iMhlwNuqel9OBHiurE/GGP8kJ8O778KAAfDJJ3Cf1//ZY78fyzOL\nnmFEzAjuvuLu4AVpckyg+2Sy1PEfTizJGOPbvn0QG+v8O2ECVKrk7E9KTqLPf/swY9MMZrabydXF\nrw5qnCbnBGPRsnIiMl1E/vG8polI2UAFYM5NJLcLR3LdIHTq99VXTvPYVVfBihX/SzAHTx6k+YTm\nbPhrA/Fd47OcYEKlfm6J9PoFmj99MqOAWUBpz2u2Z58xJgwlJTkLirVt6zxF9vbbkNezjtiv+36l\n7vC6XF70chY8sICiFxYNbrAm7Pk1d5mqXu9rX6ix5jJjzrZ7t7Pui6oz91jp0v97b/6W+Tw440He\nuu0tHqrxUPCCNEGV481lwH4R6SgiUSJynog8AOwLVADGmJyxYIEzsLJxY/jvf/+XYFSVgd8M5OFZ\nDzO97XRLMCag/EkynXEeX/4L2AO09uwzQRTJ7cKRXDfI+folJECfPtC1qzMH2csvQ1SU896JhBN0\nmtGJiT9PZE2XNdxU/qZsX88+P+Mt0xH/nhmX31BVe3bRmDC0Ywe0bw9FizoLi1166f/e+/Pwn9w7\n6V4uK3oZKzqvsPEvxhX+9Ml8DTRR1VM5E1JgWJ+Mye2+/BIeecS5i3nyScjj1W6xetdqWk1uxWO1\nH6PvzX1tgTGTKhjryWwHvhaRWcBxzz5V1cGBCsIYEzgnT8IzzzirV86eDXXrnvn+mA1j6L24NyNb\njKT55c2DE6TJNfzpk/kNZzqZPMDFnlcBN4MyvkVyu3Ak1w3crd/mzc66L3v3Os1j3gkmMTmRpxc+\nzWsrXiMuNs61BGOfn/Hm805GVfsDiEh+VT3mekTGmHPy+efw1FPw6qvQvTt4t4AdOHGAdtPakazJ\nrOmyxsabjGJ4AAAgAElEQVS/mBzjT59MfZxFygqoajkRuR7orqqP+jy5SDQwBIgChqvqgHTKvA/c\nidMUF6uq630dKyKPA48CScBcVe2TznmtT8bkCkePQs+ezqSWkybBdded+f7GfzbSYmIL7qp6FwNv\nH8h5efxpJTe5VTDGyQwBovGMjfEsWNbI10EiEgV86Dn2KqC9iFRLU6YZUEVVqwLdgE98HSsitwAx\nwHWqeg3wjh91MCYi/fAD1K7t3LV8++3ZCWbelnk0Gt2I525+jnej37UEY3KcP0kGVd2ZZleiH4fV\nAbaq6g5VTQAmAi3SlIkBxniusQYoLCIlfRzbA3jTsx9V/cefOkSaSG4XjuS6QWDqpwpDh0KTJvD8\n8zBqFOTP7/2+MuDrAXSd3ZWZ7WbSuUbODW2zz8948+fPmp0ichOAiJwPPAFs9OO4MsAfXtu7gLp+\nlCmDM0daRsdWBRqKyBvASeAZVf3Wj3iMiQgHDzoDK7dscRYWS1n3JcWJhBN0md2FX/f9ypouayhb\n0OazNcHjz51MD+AxnC//P4Eanm1f/O0QyWrb33lAEVWtB/QGJmfx+IjQuHHjYIfgmkiuG2SvfvHx\ncMMNUKIErF59doLZdXgXDUc3BGBF5xVBSTD2+Rlv/tzJ1FLV+713iMgjwFAfx/0JlPPaLodzR5JZ\nmbKeMnkzOXYX8CWAqq4VkWQRuURV96cNIDY2looVKwJQuHBhqlevnvoLknLLa9u2HQ7bS5fGMWUK\nTJvWmKFDoWjROFavPrP8z3//zBu73qBX3V7UPl2bNd+sCZn4bTt0t+Pi4hg9ejRA6vdlQKlqpi9g\nJc6I/5TtZ4EFfhx3Hs4Ym4rA+cAGoFqaMs2AeZ6f6wGrfR0LdAde8fx8ObAzg+trJFu2bFmwQ3BN\nJNdNNev1+/tv1WbNVOvVU92+Pf0yI78bqZe+fanO+XVOtuPLLvv8wpvnu9NnbvD35c+dTAwwR0RO\n4zztdaVnn6/klSgiPYGFOI8hj1DVjSLS3fP+p6o6T0SaichW4BieiTczOtZz6pHASBH5ETgNdPKj\nDsaEpeXLnan5778fXnvtf+u+pEhMTqT3ot7M3TKX5bHLqXZptfRPZEyQ+LX8sogUB5YA3wIPqT8H\nBZmNkzHhLCnJSSpDhzpPjkVHn13m3xP/0m5qO0SEifdNpMiFRXI+UBNxAj1OJsMkIyJHObPz/nwg\nwbNPVbVgoIJwgyUZE65274YOHZyxL+PGnbmwWIqN/2wkZmIMMZfHMKDpABv/YgImxwZjqurFqlrA\n65XPa19IJ5jcIKXjLhJFct0g8/qlLCx2662weHH6CWbO5jk0Gt2IFxu8yKA7BoVcgsnNn585W2j9\ndhqTSyUkwAsvwIQJztQwDRueXUZVGfDNAD6I/4BZ7WdRr2y9nA/UmCzyq08mHFlzmQkXO3ZAu3ZQ\nrBiMHu38m9bxhON0mdWFrf9uZXrb6ZQpWCanwzS5RDDmLjPGuGTaNKhTB9q0gVmz0k8wuw7vouGo\nhkTliWJ57HJLMCas+EwyIlI0nVdeX8cZd0Vyu3Ak1w2c+p08CY8+Cr17w5w5zhT9edL5v3HlHyup\nO7wuba9uy9h7xnJh3gtzPuAsyg2fn/GfP3cy3+HMwLzF89oH/C4i34lITTeDMyYS7dzpLCy2bx+s\nX+/cyaRn5PqR3DPxHobdPYzeN/W2JZJNWPJnPZlhwFRVXejZvh1oBYwC3lPVDP4XCS7rkzGhaOxY\nePppZwxMt25nLiyWImUFywW/LWBmu5lcWezKnA/U5Fo5Nk7G64I/qbNui/e+H1X1WhHZoKrVAxVM\nIFmSMaHk6FF47DFngsvJk+Haa9Mv9++Jf2k7tS1REsXEVhMpfEHhnA3U5HrB6PjfIyJ9RKSCiFQU\nkWeBvZ6FxZIDFYjJmkhuF460un3/PdSqBVFRzsJi+/fHpVvu579/ps6wOlxf4nrm3j83bBNMpH1+\naUV6/QLNnyRzP84syDOA6UB5oD3OnGJt3AvNmPCmCp98ArfdBi++CCNHnrmwmLfZv87mljG38HKj\nl3nn9neIyhOVs8Ea4xJ/mssqqer2NPtqq+paVyPLJmsuM8F08CB06QK//eYMrrz88vTLqSpvfv0m\nH6/9mGltplG3bNp1/YzJWcFoLpsmIqkrH4lII5xOf2NMOtasgRo1oFQpWLUq4wRzPOE47ae1Z+av\nM4nvGm8JxkQkf5JMd2CGiJQUkWbA+8Cd7oZlfInkduFwrVtyMrzzDsTEwODB8MEHcMEFZ5eLi4tj\n56Gd3DzyZs6POp/lscspXSCdScrCVLh+fv6K9PoFms+5y9RZffIJYDFwAmiqqn+7HpkxYeSff+DB\nB+HAAecJsgoVMi77494fuX/4/Tx949M8deNTNv7FRLTMpvqfnWZXNWAPcBBnqn+fC5cFk/XJmJwS\nF+csLPbAA/Dqq2cvLOZt+HfDeX7J84y9dyzRVdJZJMaYIAt0n0xmdzKD0tmngHDmOjPG5EpJSU5S\n+fRTZ2LLO+7IuGxCUgJPL3qaRb8tYkXnFVxR7Ioci9OYYMpsPZm4dF7LU/7NySDN2SK5XTgc6vbn\nn9CkCaxYAd99l3mC2X98P9Hjo9ny7xZWd1nNnp/25FygQRAOn192RHr9As1mYTYmi+bPdwZXNmkC\nixY5T5Fl5Ke/f6LO8DrULFWTOe3nhO0AS2POla0nY4yfTp92FhabOBHGj09/YTFvMzfNpOvsrgy+\nYzAPXPdAzgRpTDblWJ+MiHwGzAf+q6pHAnVBY8LR9u3OwmLFizszJ6e37ksKVeWNFW8wdN1Q5tw/\nhzplQnIOWWNyRGbNZSOB6sA8EVnqmb/s+hyKy/gQye3CoVa3qVOhbl0nyWS0sFiKY6eP0W5aO2Zv\nnk18l/h0E0yo1S/QrH7GW4Z3Mqq6GlgN9BORYsDtwNMich2wHpivqpNzJkxjct7Jk85iYgsXwty5\nULt25uV3HtpJi4ktuL7E9cTFxnHBeemMxDQml8lyn4w4I8dqAneo6uuuRBUA1idjsmPTJmjbFq68\nEj77DAoVyrz8it9X0GZqG56t/yz/V+//bIClCVs5vp5MuLIkY87VmDHwzDPw+uvQtWv6C4t5G7Zu\nGC8ue5Gx94zljiqZPMtsTBgIxgSZJgRFcrtwsOp29Ch06gQDBsDSpRmvXJkiISmBnvN6MmjVIFZ0\nXuF3gonkzw6sfuZMlmSMATZsgJo1nSlh1q7NeOXKFPuO7+OOcXew/eB21nRZw+WXZDDVsjG5nD/r\nyeQHngLKq2pXEakKXKGqc3IiwHNlzWXGH6rOtDAvvQRDhkCHDr6P+XHvj7SY2II2V7fh9VtftwXG\nTETJybnLUowC1gH1Pdu7galASCcZY3w5dQp69nTWfPnmm4zXffE2Y9MMus7uypA7htDhOj8ykjG5\nnD/NZZep6gDgNICqHnM3JOOPSG4Xzom6/fUX3Hor7N+f+cJiKVSVV5e/yuPzH2d+h/nZSjCR/NmB\n1c+cyZ8kc0pELkzZEJHLgFPuhWSMu779FurUgdtvdwZaFiiQefljp4/RZmob5m2dR3yXeGqVrpUz\ngRoTAfzpk7kdeAG4CmfhspuAWFVd5n545876ZEx6xo2DJ590xr7ce6/v8r8f/J0WE1tQo1QNht41\nlHzn5XM/SGOCKCjjZDwj/ut5Nler6r5ABeAWSzLGW1IS9O0LX34JM2fCNdf4Puar37+i7dS29Lmp\nD73q9rIBliZXCNY4mXzAAeAIcJWI+Jh/1rgtktuFA123AwegWTPnMeW1a/1LMJ9++ymtp7RmzD1j\nAj6CP5I/O7D6mTP5fLpMRAYAbYFfgCSvt75yKyhjAuWXX6BFC2jeHAYOhPN8/MYnJCXQa0Ev4nbE\n8XXnr6l6SdWcCdSYCOVPn8xm4FpVDavOfmsuM7Nnw8MPw9tvQ2ys7/L7ju+j9ZTW5M+bny/u+4KC\n+Qq6HqMxoSYYzWW/Aeefy8lFJFpENonIFhHpk0GZ9z3vfy8iNfw9VkSeFpFkESl6LrGZyKXqzDvW\no4eTaPxJMD/s/YE6w+pQr0w9ZrabaQnGmADxJ8mcADaIyGci8oHn9b6vg0QkCvgQiMZ5Mq29iFRL\nU6YZUEVVqwLdgE/8OVZEygFNgd/9iD8iRXK7cHbqdvQotGnjJJf4eGcdGF++3PglTcY24fVbX+fN\n2950fQR/JH92YPUzZ/JnxP8sz8ubP+1QdYCtqroDQEQmAi2AjV5lYoAxAKq6RkQKi0hJoJKPYwcD\nzwIz/YjD5BLbt8M998ANN0BcHFzgYzmXZE3m1eWvMmL9CBZ0WEDN0jVzJE5jchOfSUZVR5/jucsA\nf3ht7wLS/l2ZXpkyQOmMjhWRFsAuVf0hNz9S2rhx42CH4JpzqduyZdC+PTz/PDz+uO/p+Y+ePkrs\njFj2HN1DfNd4Sl5c8tyCPQeR/NmB1c+cKcMkIyJTVLW1iPyYztuqqtf5OLe/ve5+ZwrPzAPP4zSV\nZfl4E3lU4aOP4LXXYPx4aNLE9zE7Du6gxcQW1CpVi/Etx9sAS2NclNmdTC/Pv805+4vcnwTyJ1DO\na7sczh1JZmXKesrkzeDYy4CKwPeeu5iywDoRqaOqf6cNIDY2looVKwJQuHBhqlevnvpXSEq7arhu\nDxkyJKLq473t3eadWfnTp2Hy5MasWQODB8cRFQWQ+fmlotBuWjtaXdiKlgVbpiaYUKxfuG5b/cJr\nOy4ujtGjRwOkfl8GlKpm+gIG+LMvnTLn4TyZVhHn6bQNQLU0ZZoB8zw/18OZTcCvYz3ltgNFM7i+\nRrJly5YFOwTX+FO33btVb7xRtWVL1SNH/Dvvx/Efa/GBxXXxb4uzF2A2RfJnp2r1C3ee706fucHf\nlz/jZNarao00+35UVR/LOoGI3AkMAaKAEar6poh092SATz1lUp4iOwZ0VtXvMjo2nfNvA2qp6r/p\nvKe+6mbC09q10LKls3LlCy9AHh/PSJ5OOk2v+b34audXzGw3kypFq+RMoMaEoRybu0xEegCP4jRR\n/eb1VgHgG1UN6cU0LMlEps8/h6eegmHDnCfJfPnn2D+0mtKKQvkKMa7lOBv/YowPOTkY8wvgbpzH\nl5t7vWqGeoLJDbzbhSNNenVLTIRnnoFXXnGeJPMnwXz/1/fUGV6Hm8vdzIx2M0ImwUTyZwdWP3Om\nDDv+VfUQcAhoJyLVgQY4Hf4rgP05E54xzgSX7do5T5LFx0NRP+Z4mPbLNHrM7cEHd35A22vauh+k\nMSZd/vTJ9AK6Al/iPGV2DzBMVX2O+g8may6LDCkTXMbEwIABvie4TNZk/rP8P4zaMIrpbadzQ6kb\nciZQYyJEjq8n4xknU089yy6LSH6cp8B8dvwHkyWZ8DdzJnTtCu+8A506+S5/9PRROk3vxN/H/mZa\nm2mUuLiE+0EaE2GCtZ5McgY/myCJ5HbhpUvjePVV6NkT5szxL8FsP7Cd+iPqU/TCoizptCSkE0wk\nf3Zg9TNn8mfuslHAGhHxbi4b6WpUJtc6etTp3D992ul/KVXK9zHLti+j/bT2vNDgBXrW6WkrWBoT\nQvxdfrkmcDOejn9VXe92YNllzWXhZ/t2p/+ldm34+GPI52O2F1Xlk28/4T/L/8P4luNpUtmPOWWM\nMZkKRp9MPeAXVT3s2S6IM/p+TaCCcIMlmfCydCncfz+8+CI89pjvCS5PJ53m8XmP880f3zCz3Uwu\nK3pZzgRqTIQLRp/MUOCI1/Yxzz4TRJHSLqwKH3zgJJgvvnD6YZYvj8v0mL+P/c1tY2/jr2N/serh\nVWGXYCLls8uI1c9486vj3/uWQFWTcKZ6MSZbTp2CLl2c0furVsGtt/o+ZsNfG6gzrA6NKjRietvp\nFMhXwP1AjTHnzJ/msunAMpxVKwXoAdyiqn6MuQ4eay4LbXv2OPOPlSkDo0fDxRf7PmbKz1N4bN5j\nfNTsI1pf3dr1GI3JjYLRXPYIcBPOtPy7cGZL7haoAEzuEx8PderAXXfBlCm+E0yyJvPyspfpvbg3\nCx9YaAnGmDDiM8mo6l5VbauqxT2v9prO2i0mZ4Vru/DYsdC8OXz4odPJn14Hv3fdjpw6wn2T72PZ\njmXEd42nRqkaZx8QZsL1s/OX1c94y2xlzD6qOkBEPkjnbVXVJ1yMy0SYxER49lmYPduZ4PLqq30f\ns+3ANmImxFC/XH0mtZrE+VHnux+oMSagMpvq/25VnS0isem8rao6xtXIssn6ZELHv/9C27bOXcvE\nif5NcLl0+1Lun3Y/LzV8iUdrP2oDLI3JITk+TiZcWZIJDT//7AywvOceeOst3xNcqiofrf2I1756\njS/u+4JbK/nxyJkxJmByvONfRK4QkWEislhElnleSwMVgDk34dAuPGMG3HIL9O/vTHLpK8GcTjpN\nt9ndGDRhECsfXhmxCSYcPrvssPoZb/7MXTYF5/Hl4UCSZ5/dIpgMJSfDa68541/mznWmifHl72N/\nc9/k+yh2UTE+uvMjKhep7H6gxhjX+TNOZp2q1syheALGmsuC4+hRePBBZxzMl19CyZK+j1m/Zz33\nTLqH2Otj6de4H3nE38nBjTGBFoxxMrNF5DERKSUiRVNegQrARI5t2+DGG6FIEecJMn8SzOSfJ3PH\nuDsYdPsgXrnlFUswxkQYf/6PjgWeAVYC6zyvb12Myfgh1NqFlyyB+vWhe3enmczXDMrJmsyLS1/k\n2cXPsqjjIlpd1Sr1vVCrW6BZ/cJbpNcv0Hz2yahqxRyIw4SplAku33jDeTy5cWPfxxw+dZiO0zty\n4MQB4rvGUzx/cdfjNMYEhz3CbM7ZqVPQowesW+cslVyxou9jfvv3N2ImxtCgfAPev/N9G2BpTIgJ\n1vLLxpxhzx7nruXIEVi50r8Es2TbEuqPrE/P2j0Z2nyoJRhjcgFLMmEqmO3Ca9Y4E1w2bw6TJ0P+\n/JmXV1XeX/M+Hb7swKRWk+hRu0em5SO9zdvqF94ivX6B5rNPxrP0ctp2p0PA76qa6EpUJmSNGQO9\ne8Pw4RAT47v8qcRTPDbvMeL/jGfVw6uoVKSS+0EaY0KGP+NkVgM1gR88u64FfgYKAT1UdaGrEZ4j\n65MJrMREJ7nMnev0v1Sr5vuYvUf30nJyS0rkL8HYe8dy8fl+LBpjjAmqYPTJ7Aaqq2pNz6DM6sA2\noCnwdqACMaFr/36IjoaNG52mMn8SzHd7vqPO8DrcXvl2praZagnGmFzKnyRzhar+nLKhqr8AV6rq\nb9j0MkGTU+3CP/3k9L/ccINzF1OkiO9jJv00iehx0Qy+ffA5jeCP9DZvq194i/T6BZo/c5f9LCKf\nABNxll9uA/wiIvmABDeDM8E1fTp06wbvvgsPPOC7fMoAywk/TWBxx8VcX/J694M0xoQ0f/pkLgIe\nxVmCGeAb4GPgJJBfVY+4GuE5sj6Zc5ecDK++CiNGOPOP1arl+5jDpw7T4csOHDl1hCmtp3Bp/kvd\nD9QYE3A5vp6MiNRU1XVp9jVX1TmBCsINlmTOzZEjzgSXe/fCtGn+zT+29d+txEyIoXHFxrwX/R55\no/K6H6gxxhXB6PgfJiLXegXQHng5UAGYc+NGu/C2bc78Y5dcAkuX+pdg/rvtv9w08iaeqPsEH9/1\ncUASTKS3eVv9wluk1y/Q/EkyrYAxInKliHTFaTpr6m5YJqelTHDZowd89pnvCS5VlfdWv0fH6R2Z\n3Goyj9R6JGcCNcaEFb/mLhORK4AZwO9AS1U97nZg2WXNZf5RhffegwEDYMIE/ya4PJV4ih5ze7Bu\nzzpmtptJxcIV3Q7TGJNDAt1cluHTZSLyY5pdRXHufNZ4vsCvC1QQJjhOnoRHHoENG2D1aqhQwfcx\nfx39i5aTWlK6QGlWPrSS/Of7mFPGGJOrZdZcdneaV13gDs/Pfkwo4hCRaBHZJCJbRKRPBmXe97z/\nvYjU8HWsiAwUkY2e8l+KSCF/44kU2W0X3r0bGjWCEyfgm2/8SzDrdq+jzrA6RFeJZnLrya4lmEhv\n87b6hbdIr1+gZZhkVHVHZi9/Ti4iUcCHQDRwFdBeRKqlKdMMqKKqVYFuwCd+HLsIuFpVrwc2A8/5\nX2WzerUzwPKee5w1YHxNcAkw4ccJRI+P5r3o93i50cu2gqUxxi+uricjIjcC/VQ12rPdF0BV3/Iq\nMxRYpqqTPNubgMZAJV/HevbfC9ynqg+k2W99MukYPRqefRZGjnRmUfYlKTmJF5e+yKSfJzGz3Uyu\nLXGt74OMMWErx/pkAqQM8IfX9i6cZjdfZcoApf04FuAhYEK2I41wiYnwzDMwbx4sX+7f/GOHTh6i\nw5cdOJZwjPiu8RS7qJj7gRpjIorbScbfW4lzypoi8gJwWlW/SO/92NhYKnpW0ypcuDDVq1ensefx\nqZR21XDdHjJkiN/12bMHmjePIyoK4uMbU7iw7/OPmzmO55c+z923382Q6CF8s+KbHKufd5t3qPz3\ntvpZ/SK1fnFxcYwePRog9fsyoFTVtRdQD1jgtf0c0CdNmaFAO6/tTUAJX8cCsThT3FyQwbU1ki1b\ntsxnmaQk1U8/VS1WTPWll1QTE/0798KtC7X4wOL66befZi/Ic+RP3cKZ1S+8RXr9PN+dAcsDbvfJ\nnAf8CjTBWTIgHmivqhu9yjQDeqpqMxGpBwxR1XqZHSsi0cAgoJGq7svg2upm3ULdr786k1ueOgXD\nhsG1fnSlqCpDVg9h4MqBTGo1iQYVGrgfqDEmpIRVn4yqJopIT2AhEAWM8CSJ7p73P1XVeSLSTES2\nAseAzpkd6zn1B8D5wGIRAVilqo+6WZdwcfq0M7Dyvffg5ZfhsccgKsr3cacST/HI3EfY8NcGVj28\nigqF/Xim2RhjfHD1TiaYIv1OJi4uLrV9NcWqVdC1K1SsCB9/DOXL+3euPUf20HJyS8oVLMeoFqOC\nPsAyvbpFEqtfeIv0+gVjgkwT4o4cgccfh5Yt4aWXYPZs/xPM2j/XUmd4He6qeheTWk0KeoIxxkQW\nu5MJc7NnO01iTZvCwIFQtKj/x47/YTxPLnySz+7+jHuuvMe9II0xYSOs+mSMe/76C554AtavhzFj\n4JZb/D82KTmJ55c8z9SNU1n64FKuKX6Ne4EaY3I1ay4LM6owfDhceWUcVarADz9kLcEcOnmImIkx\nrN29lvgu8SGZYLzHIUQiq194i/T6BZrdyYSRzZudx5KPH4d33oEuXbJ4/P7NxEyIoWnlpgy+Y7Ct\nYGmMcZ31yYSBhASnv2XwYHjxRaeT35/Hkr0t3LqQTjM68dotr9G1Zld3AjXGhD3rk8ll1qxxHksu\nWxbWrfNvSn5vqsq7q9/lnZXvMK3NNG4uf7M7gRpjTDqsTyZEHTkCvXo50/E/9xzMnXtmgvGnXfhk\n4kliZ8Yy7odxrO6yOmwSTKS3eVv9wluk1y/QLMmEoLlz4Zpr4PBh+OknaN8eJIs3r7uP7Kbx6Mac\nTDzJ1w99TflCfg6cMcaYALI+mRCyd69z97J2LXz6Kdx227mdZ+2fa2k5uSU9avXguZufQ7KaoYwx\nuZaN+I9Aqs4iYtde6zSJ/fjjuSeYcT+M464v7uKjZh/xfIPnLcEYY4LKkkyQbd3qJJSPP4aFC53J\nLS+6yPdxaduFk5KT6L2oN/3i+rHswWXEXBHjTsA5INLbvK1+4S3S6xdolmSCJCEB3noL6tWDu+6C\n1auhRo1zO9fBkwdpPqE53/31HfFd4rm6+NWBDdYYY86R9ckEwdq1zmPJJUvCJ59ApUrnfq5f9/1K\nzMQYoi+LZtAdgzgvjz2Vbow5d9YnE8aOHoUnn4S774bevWH+/OwlmPlb5tNgVAOerf8s7935niUY\nY0zIsSSTQ+bPdx5L3r/feSy5Q4esP5acQlXp8WEPHp71MNPbTufhGx4ObLBBFult3la/8Bbp9Qs0\n+9PXZX//Df/3f06fy2efwe23Z+98JxNP0nV2V9ZsX8Oal9dQrlC5wARqjDEusD4Zl6jC2LHw7LPQ\nqRP07w/5s7ke2O4ju7l30r1ULlKZETEjuCivH4+hGWNMFtjcZWHgt9/gkUecprH58+GGG7J/zjW7\n1nDf5Pt4rPZj9L25r41/McaEBeuTCaDERHj7bahb12kWi48PTIIZ+/1Y7p5wN5/c9QnPNXBG8Edy\nu3Ak1w2sfuEu0usXaHYnEyDr1jnruxQr5iSXypWzf87E5ET6LO7DzF9nEhcbx1WXXpX9kxpjTA6y\nPplsOnYMXn4Zxo1z1nzp2PHcnxrzduDEAdpPa0+SJjGp1SSKXlg0+yc1xhgfbJxMCFm0yJlvbO9e\n57HkTp0Ck2A27dtE3eF1ubLYlczvMN8SjDEmbFmSOQf79jkJpVs3Z86xcePg0ksDc+55W+bRcFRD\nnrv5OYZED8lwgGUktwtHct3A6hfuIr1+gWZJJgtUYfx4Z1BlsWLO3Ut0dKDOrbz9zdt0nd2Vme1m\n0rlG58Cc2Bhjgsj6ZPy0Y4fzWPKePTB8ONSuHbBTcyLhBF1nd2XTvk3MaDeDsgXLBu7kxhiTBdYn\nk8OSkuDdd6FWLWjcGL79NrAJ5s/Df9JwdEOSNZkVnVdYgjHGRBRLMpn4/nu48UaYNQtWrYK+fSFv\n3sCdf/Wu1dQZXodW1VoxvuV4Lsx7od/HRnK7cCTXDax+4S7S6xdolmTSceIEPP88NG0K3bvD0qVQ\ntWpgrzF6w2hiJsTwafNP6XNzHxvBb4yJSNYnk8ayZc5TYzVqwPvvO2u+BFJiciLPLn6W2ZtnM6vd\nLKpdWi2wFzDGmGywuctccuCAs8bLwoXw0UcQ48LqxQdOHKDt1LYAxHeJp8iFRQJ/EWOMCSG5vrlM\nFaZMgauvhnz54Oef3UkwG//ZSJ3hdbim+DXM6zAv2wkmktuFI7luYPULd5Fev0DL1Xcyu3bBo4/C\n1q0wdSrUr+/OdeZunkvnmZ0Z2HQgD1Z/0J2LGGNMCMqVfTLJyfDJJ84aLz17Ok+N5csX+BhSBlh+\nEAnk/LoAAArXSURBVP8BU9tMpV7ZeoG/iDHGBJD1yWTTL79A167Oz8uXw1UuTWx8IuEEXWZ3YfP+\nzazpsoYyBcu4cyFjjAlhrvbJiEi0iGwSkS0i0ieDMu973v9eRGr4OlZEiorIYhHZLCKLRKSwP7Gc\nOuXcuTRqBA88ACtWuJdgdh3eRYNRDRCEr2K/ciXBRHK7cCTXDax+4S7S6xdoriUZEYkCPgSigauA\n9iJSLU2ZZkAVVa0KdAM+8ePYvsBiVb0cWOLZztQ33ziPJG/YAOvXQ48ekMelmq/8YyV1h9elzdVt\n+Pzez7M0wDIrNmzY4Mp5Q0Ek1w2sfuEu0usXaG42l9UBtqrqDgARmQi0ADZ6lYkBxgCo6hoRKSwi\nJYFKmRwbAzTyHD8GiCODRHP4sNPfMnOmM+alZcvATMWfkZHrR9L3v30Zfc9omlVt5t6FgIMHD7p6\n/mCK5LqB1S/cRXr9As3NJFMG+MNrexdQ148yZYDSmRxbQlX3en7eC5TIKICrr4Y773RmSy7i4pCU\nxOREnln0DPO2zOOrzl9xZbEr3buYMcaEETeTjL+PrflzbyHpnU9VVUQyvM7nnzuTWropWZO5e8Ld\nqCpruqzJsQGWO3bsyJHrBEMk1w2sfuEu0usXcKrqyguoByzw2n4O6JOmzFCgndf2Jpw7kwyP9ZQp\n6fm5FLApg+urvexlL3vZK+uvQOYCN+9kvgWqikhFYDfQFmifpswsoCcwUUTqAQdVda+I7M/k2FnA\ng8AAz78z0rt4IJ/zNsYYc25cSzKqmigiPYGFQBQwQlU3ikh3z/ufquo8EWkmIluBY0DnzI71nPot\nYLKIPAzsANq4VQdjjDHZE7Ej/o0xxgRfWEyQGUqDOt3gUv0GishGT/kvRaRQTtQlPW7Uz+v9p0Uk\nWUSKulmHjLhVNxF53PP5/SQiA9yuR0Zc+t2sIyLxIrJeRNaKSADXms2abNZvpIjsFZEf05SPlO+W\njOqXte8Wtzr+A/gAQRSwFagI5AU2ANXSlGkGzPP8XBdY7etY4G3gWc/PfYC3Iqx+TYE8np/firT6\ned4vBywAtgNFI6VuwC3AYiCvZ/vSSPrscMa23eH5+U5gWbjVz7PdAKgB/JjmmLD/bvFRvyx9t4TD\nnUzqoE5VTQBSBmZ6O2NQJ5AyqDOzY1OP8fx7j7vVyJAr9VPVxaqa7Dl+DVDW/aqky63PD2Aw8Kzb\nFciEW3XrAbzp2Y+q/uN+VdLlVv32ACl//RYG/nS3GhnKTv1Q1RXAgXTOGwnfLRnWL6vfLeGQZDIa\nsOlPmfQGdaYc6/egTpe5VT9vDwHzsh3puXGlfiLSAtilqj8EOuAscOuzqwo0FJHVIhInIrUCGvX/\nt3f+MVJVVxz/fMsPQawKgq3WIkoKEbVFVsgitooo0YQa26KmxCjGGKJ/SCAl1Zr6I8ZY0hCbUmwt\ntpIYNSJum2qiVmhEkRJhgRWXiIS2kmLQSmKLv6Alp3/cM3EY3uzMLO+5O7vnk0zm7p177zvnvs09\nufe8d079FKXf7cASSbuBn5NeUegJjka/rugLa0u91FxbmsHI1PtkwlG91NnAdfImT/2O7CTdCRw0\nsye60z8HctdP0lDgJ8Dd3emfI0Xdu4HAcDNrBRYBKxvsnxdF6fc74DYzGw0sAH7fYP+86K5+da8V\nTbq21NWv3rWlGUL97yGdvZf4OsnadtXmNG8zKKO+tDV/T9JXzWyvpFOA93OVun7y1O+wvpLmks5c\nZ+QnbsMUod9Y0jlzh1IwutOAdklTzOyLvI9F3bt/Am0AZrbRH2w4ycz25Sh7PRSl3xQzu9TLq4BH\n8hK4QbqrX63jvWZfW2oeXza0tvSEQ6pB59VAYBdpURlMbedVK587H6v2JTnnSlEEbqfnnHNF6Xc5\n0AmM7Iv3r6J/Tzn+i7p384B7vTwO2N2X7h2wGbjIyzOAjc2mX9nvY8h2/Df12lJDv4bWli9c8W5O\n1hXADtKTEnd43TxgXlmbX/nvHcCkrvp6/QhgNfA28GfgxD6m307gHWCLfx7qS/pVjP83esDIFHjv\nBgGPAduAduDivnTvgPNJDuOtwF+B85pUvydJEUkOkPwaN3p9X1lbqunX0NoSL2MGQRAEhdEMjv8g\nCIKgSQkjEwRBEBRGGJkgCIKgMMLIBEEQBIURRiYIgiAojDAyQRAEQWGEkQl6LZLGVIYZz2nc71YL\ne16j3wmSbin7+1RJT+coV6uk31bUXSzp2byu0YAsN/jb6kFwVISRCfodZvasmXUnR8tw4Naycd41\ns6vzk4wrgOdzHO9omEsKclk3kgYUI0rQzISRCZoCSWdK2iypRdKxklZK6vSkSRsktWT0+YekeyS1\nS3pD0nivnytpqZe/IukPkrb6p9XrF0ra5p/5PuTPgLGebGuxpNNLOy0fs03S856sanGZHDMlrXc5\nVkoaVkXNS0hvilebg8k+B2dIGuWJsd6UtNx1HVHR/mpJS7w8X9Kusrlc5+W7lBKIbZP0sNfNJr2V\n/7hfb4jP+8uSNkl6oRQO3uselLQRuK3Lmxj0S8LIBL0eNw6rgBvMrJ20m9hnZmcDPwVayI4ca8C/\nzKwF+DXwo4w2vyQlzZpIStC03Q3WXFI+jlbgZkkTSQmodpnZeWb2Y46MXvst4BrgXOBaSV+TNBK4\nE5jhcrQDCzN0HAn818z2V5mDC1yHK83s76QI1KvN7Byfm9EZ3V4hJZ7Cvz+QdKqX13r9UjObYmbn\nAkMlzTKzVcAmYI6ZTQIOAUuBH5jZ+cCjwP3e30jJ1Sab2YNZsgf9m2aIwhz0b04G/gh8z8ze8rpp\nwC8AzKxTUlc5Zdr8ezPw/YzfpwPX+VgG/EfShUCbmX0KIKmNtDD/qYasa0pGQtJ2UnDB4cAEYL1H\njB4MrM/oOxN4scq4ZwEPA5eZ2V6vm4YnwzKzFyVlJZd6T9Jxko4jRdd9AvgOcCHwjDe7RNIi4FhS\nzK03gef8t5IRHQ+cDax2HQaQYlqVeKqK3EEQRibo9XxICsb3beCtsvp6c5gc8O9DVP9/z8qnoYrf\n6wnyd6CsXH69l8xsTo2+lwNLMuqNlEnyGGAShyeIqmcO1gM3koIkrgNuAqYCCyUNAZYBLWa2R9Ld\nwJCKa5eu02lmF1S5xsd1yBH0U+K4LOjtHCTtQK6X9EOve410LIWkCaTjqe6yhpTuGEkDJB0PvApc\nJWmo+0+u8rqPgC83MLYBG4Bpksb6NYZJ+kZ5I6XtwTfNrCNjDJEM7SzgAUkXeX35HMwk7ZiyeJWU\n+GwtKWLudOAz33GVDMo+3+2UP8SwHzjeyzuAUWX+qkE+70FQkzAyQW/HzOwT0iK7QNIs4CHSotcJ\n3EfKbfHvrL4VZcsozwem+5HbJlK+jS3ACuB1kpFYbmYdlpKGveZO8sVdjFku/Ack/86TkjpIO4vx\nFc1aSAYgU3+fg/d9DpZJmgzcC8z0Bw9mA3tJhqGSdaR0uq9Yysu+2+swsw+B5aQjshdI4fdLrAB+\nI2kzaZ2YDSyWtNVlnVpF3iA4jAj1HzQdkr5EcjYf8B3CS8A4M/tfD4vWLZTS2O40s7rTLEsaDBwy\ns0OSpgLL3EkfBL2K8MkEzcgw4C+SBpGOk25pVgMDYGb31251BKOBlW5wDwI35ytVEORD7GSCIAiC\nwgifTBAEQVAYYWSCIAiCwggjEwRBEBRGGJkgCIKgMMLIBEEQBIURRiYIgiAojP8D2/x1wBuTrN4A\nAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa2780b8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Mininmum kerosene rate: 968.71 kg kerosene/h \n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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7ygTgHQgG8G6cTGfgWaAE8BdQ2bNt/CiU24VDuW4Q2PU7etS5ehkyBL7//vIS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\nua7xbQHGGNcFW59MCeDPRNu7gaTT6Sa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+ "text": [
+ "<matplotlib.figure.Figure at 0x981e198>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical stages: \n",
+ "8.3\n"
+ ]
+ }
+ ],
+ "prompt_number": 29
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.5: Page 510"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.5\n",
+ "# Page: 510\n",
+ "\n",
+ "print'Illustration 10.5 - Page: 510\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "import numpy.linalg as lin\n",
+ "import numpy\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:ethylbenzne b:diethylene glycol c:styrene\n",
+ "F = 1000;# [kg/h]\n",
+ "xF = 0.5;# [Wt. fraction styrene]\n",
+ "xPE = 0.9;# [kg styrene/kg hydrocarbon]\n",
+ "xRNp = 0.1;# [kg styrene/kg hydrocarbon]\n",
+ "#******#\n",
+ "\n",
+ "# X: kg styrene/kg hydrocarbon\n",
+ "# Y: kg styrene/kg hydrocarbon\n",
+ "# N:kg glycol/kg hydrocarbon\n",
+ "# Equilibrium data:\n",
+ "# Hydrocarbon rich solutions:\n",
+ "# Eqb1 = [X N]\n",
+ "Eqb1 = numpy.array([[0 ,0.00675],[0.0870 ,0.00817],[0.1833, 0.00938],[0.288 ,0.01010],[0.384 ,0.01101],[0.458, 0.01215],[0.464 ,0.01215],[0.561 ,0.01410],[0.573, 0.01405],[0.781 ,0.01833],[1 ,0.0256]]);\n",
+ "# Solvent rich solutions:\n",
+ "# Eqb2 = [Y_star N]\n",
+ "Eqb2 = numpy.array([[0 ,8.62],[0.1429 ,7.71],[0.273, 6.81],[0.386, 6.04],[0.480, 5.44],[0.557, 5.02],[0.565, 4.95],[0.655, 4.46],[0.674, 4.37],[0.833, 3.47],[1 ,2.69]]);\n",
+ "\n",
+ "plt.plot(Eqb1[:,0],Eqb1[:,1],label=\"X Vs N\")\n",
+ "plt.plot(Eqb2[:,0],Eqb2[:,1],label=\"Y Vs N\")\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper right');\n",
+ "plt.xlabel(\"kg styrene / kg hydrocarbon\");\n",
+ "plt.ylabel(\"kg diethylene glycol / kg hydrocarbon\");\n",
+ "plt.title(\"Equilibrium Data\")\n",
+ "# In Fig. 10.31 (Pg 512):\n",
+ "# Point E1 is located.\n",
+ "NE1 = 3.10;\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "# From Fig. 10.30 (Pg 511):\n",
+ "Np = 9.5;\n",
+ "print\"Minimum number of theoretical stages:\\n\",Np\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "# The tie line when extended passes through F provides the minimum reflux ratio.\n",
+ "# From the plot:\n",
+ "N_deltaEm = 20.76;\n",
+ "# From Eqn. 10.48:\n",
+ "Ratiom = (N_deltaEm-NE1)/NE1;# [kg reflux/kg extract product]\n",
+ "print\"Minimum extract reflux ratio: \",round(Ratiom,3),\"kg reflux/kg extract product\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (c)\n",
+ "\n",
+ "Ratio = 1.5*Ratiom;# [kg reflux/kg extract product]\n",
+ "# From Eqn. 10.48;\n",
+ "N_deltaE = (Ratio*NE1)+NE1;\n",
+ "# Point deltaE is plotted.\n",
+ "# A straight line from deltaE through F intersects line X = 0.10 at deltaR.\n",
+ "N_deltaR = -29.6;\n",
+ "# In Fig. 10.31 (Pg 512):\n",
+ "# Random lines are drawn from deltaE for the concentrations to the right of F, and from deltaR for those to the left,and intersection of these with the solubility curves provide the coordinates of the opeating curve.\n",
+ "# The tie line data are plotted directly to provide the equilibrium curve.\n",
+ "# From Fig. 10.32 (Pg 513):\n",
+ "Np = 15.5;\n",
+ "# Feed is to be introduced in the seventh from the extract product end of cascade.\n",
+ "# From Fig. 10.31 (Pg 512):\n",
+ "XRNp = 0.10;\n",
+ "NRNp = 0.0082;\n",
+ "# Basis:1 hour.\n",
+ "# Overall plant balance:\n",
+ "# (1): PE_prime+RNp_prime = F\n",
+ "# C Balance\n",
+ "# (2): PE_prime*(1-XRNp)+RNp_prime*XRNp = F*xF\n",
+ "# Solving (1) & (2) simultaneously:\n",
+ "a = numpy.array([[1 ,1],[(1-XRNp), XRNp]]);\n",
+ "b = numpy.array([F,F*xF]);\n",
+ "soln =lin.solve(a,b)\n",
+ "PE_prime = soln[0];# [kg/h]\n",
+ "RNp_prime = soln[1];# [kg/h]\n",
+ "RO_prime = Ratio*PE_prime;# [kg/h]\n",
+ "# From Eqn 10.39:\n",
+ "E1_prime = RO_prime+PE_prime;# [kg/h]\n",
+ "BE = E1_prime*NE1;# [kg/h]\n",
+ "E1 = BE+E1_prime;# [kg/h]\n",
+ "RNp = RNp_prime*(1+NRNp);# [kg/h]\n",
+ "S = BE+(RNp_prime*NRNp);# [kg/h]\n",
+ "print\"Number of theoretical stages: \\n\",Np\n",
+ "print\"Extract Flow Rate: \",round(E1,2),\" kg/h\\n\"\n",
+ "print\"solvent Flow Rate: \",S,\" kg/h\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.5 - Page: 510\n",
+ "\n",
+ "\n",
+ "Minimum number of theoretical stages:\n",
+ "9.5\n",
+ "\n",
+ "\n",
+ "Minimum extract reflux ratio: 5.697 kg reflux/kg extract product\n",
+ "\n",
+ "\n",
+ "\n",
+ "Number of theoretical stages: \n",
+ "15.5\n",
+ "Extract Flow Rate: 19567.58 kg/h\n",
+ "\n",
+ "solvent Flow Rate: 14799.1 kg/h\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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idIF0+NepajdVna+qH6hqd6CRqg7AGRzNmByhyRVNmNdlHl/0/IJNhzZx5egr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\nH2OMyU4uyn8R7Wu0p32N9hw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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c60cc0>"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.6: Page 516"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.6\n",
+ "# Page: 516\n",
+ "\n",
+ "print'Illustration 10.6 - Page: 516\\n\\n'\n",
+ "\n",
+ "import numpy.linalg as lin\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:heptane b:p-chloronitrobenzene c:o-chloronitrobenzene d:aq. methanol\n",
+ "xb = 0.4;# [Wt fraction]\n",
+ "xC = 0.60;# [Wt fraction]\n",
+ "F = 100;# [kg]\n",
+ "# The para isomer(b) favours the heptane(a) and the ortho isomer(c) favours the methanol(d).\n",
+ "# Basis: 1 hour.\n",
+ "A = 2400;# [kg/h]\n",
+ "D = 2760;# [kg/h]\n",
+ "xbW = 0.8;# [Wt fraction]\n",
+ "xbZ = 0.15;# [Wt fraction]\n",
+ "kb=1.35;\n",
+ "kc=0.835;\n",
+ "#*******#\n",
+ "\n",
+ "B = xb*F;# [kg]\n",
+ "C = F-B;# [kg]\n",
+ "# W = kg A rich product, after solvent removal\n",
+ "# Z = kg D rich product, after solvent removal\n",
+ "# B balance:\n",
+ "# (1): (0.80*W)+(0.15*Z) = B\n",
+ "# C balance:\n",
+ "# (2): (0.20*W)+(0.85*Z) = C\n",
+ "# Solving (1) & (2) simultaneously:\n",
+ "a = numpy.array([[0.80, 0.15],[0.20, 0.85]]);\n",
+ "b = [B,C];\n",
+ "soln = lin.solve(a,b)\n",
+ "W = soln[0];\n",
+ "Z = soln[1];\n",
+ "Wb = xbW*W;# [kg]\n",
+ "Wc = W-Wb;# [kg]\n",
+ "Zb = xbZ*Z;# [kg]\n",
+ "Zc = Z-Zb;# [kg]\n",
+ "xB1_prime = Zb/D;\n",
+ "xC1_prime = Zc/D;\n",
+ "yB1_prime = Wb/D;\n",
+ "yC1_prime = Wc/D;\n",
+ "DbyA = D/A;\n",
+ "# Equilibrium curve:\n",
+ "# First distribution coeffecient: yB_star/xB_prime = 1.35\n",
+ "def f68(x1):\n",
+ " return kb*x1\n",
+ "x1 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Second distribution coeffecient: yC_star/xC_prime = 0.835\n",
+ "def f69(x2):\n",
+ " return kc*x2\n",
+ "x2 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Operating Line, corresponding to First distribution coeffecient:\n",
+ "def f70(x3):\n",
+ " return (DbyA*x3)+yB1_prime\n",
+ "x3 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "def f71(x4):\n",
+ " return DbyA*(x4-xB1_prime)\n",
+ "x4 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Operating Line, corresponding to Second distribution coeffecient:\n",
+ "def f72(x5):\n",
+ " return (DbyA*x5)+yC1_prime\n",
+ "x5 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "def f73(x6):\n",
+ " return (DbyA)*(x6-xC1_prime);\n",
+ "x6 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "\n",
+ "\n",
+ "plot(x1,f68(x1),label=\"Equilibrium curve\")\n",
+ "plt.plot(x3,f70(x3),label=\"Operating curve\")\n",
+ "plt.plot(x4,f71(x4),label=\"Operating curve\");\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"xB_prime\");\n",
+ "plt.ylabel(\"yB_prime\");\n",
+ "plt.title(\"yB_star/xB_prime = 1.35\");\n",
+ "plt.xlim((0,0.05))\n",
+ "plt.ylim((0,0.07))\n",
+ "plt.show()\n",
+ "\n",
+ "plot(x2,f69(x2),label=\"Equilibrium curve\")\n",
+ "plt.plot(x5,f72(x5),label=\"Operating curve\")\n",
+ "plt.plot(x6,f73(x6),label=\"Operating curve\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"xC_prime\");\n",
+ "plt.ylabel(\"yC_prime\");\n",
+ "plt.title(\"yC_star/xC_prime = 0.835\");\n",
+ "plt.xlim((0,0.06))\n",
+ "plt.ylim((0,0.07))\n",
+ "plt.show()\n",
+ "# The stages are constructed.\n",
+ "# The feed matching is shown on Fig. 10.37 (Pg 518):\n",
+ "f_prime = 6.6;\n",
+ "fstage = 4.6;\n",
+ "print\"Number of ideal stage is \\n\",fstage+f_prime-1\n",
+ "print\"The feed stage is \",fstage,\"th from the solvent-D inlet\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.6 - Page: 516\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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aoCslS5T0ttJxgs3CigBmaWuWtkbwBOMQmOU1Xv/5+kxdPTXba7z7xd0teBQC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\n0jwM9TGCwOb75xCOtkg/nJ7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EMAzDKBI2hGUYhmEUCQsghmEYRpGwAGIYhmEU\nCQsghmEYRpGwAGIYhmEUif8fHq0g8rWtPKEAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7b64fd0>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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FpCpwNnBviu01DCMNsfk5/ogqTJvmAsfmzXDHHXDZZbDPPkm9SMoHO+wKfA2s\nBO70tvUF+kaUecLbvwho621rAiz0liX5xxZy/iIHCrPFlmQvJWX27NklPiZdCIJvP/zyg/ab2k9r\nP1RbR302Snfn7E7auYPgXyLk5Ki++qrqMceotmql+tprbls03vNcqvd7ysfCUtUZwIyobWOi1vsX\nctwqIGExQ0OSagh7W/Sw+2ekhuj+HNk3ZBfZn6O8sHs3vPgiPPggHHQQPPAAdO2a8JivcRHaOdEN\nwwgfajrHH/jlF3j2WRg+HI44AgYNglNPLT5wBL4joWEYRrIwnWNvtm2DJ5+EUaPglFPgzTehhINr\nl5ry3U04DQhzW3Qw/9KZsvLNr3nIg3rvNm1ytYymTeHrr2H2bJg8ueyDB1gAMQwjoNg85Hvz/fcw\nYAAcfrirfcyfDy+84NJWfmEaiGEYgcJ0jr1ZsQKGDYN//xv++le4+WZIxnQ2poEYhhEqTOf4nYUL\nXUuq2bOhf3/45huImKkgEFgKK+AENQ+bLMy/9CWZvvmlc8TCr3v38cdwzjnQrRu0awerVsE99wQv\neIAFEMMwfMR0DocqvPsudOwIl18O3bu7wHHLLVCtmt/WFY1pIIZhlDmmczjy8py2MXQo/Pqra13V\nsydULANxwTQQwzDSDtM5YM8eePVV12u8WjW4+25X60i3+dfSzNzyR5hz6GD+pTMl9S2IOkcsUnHv\ndu2Cp5+GFi1g/HjXCXDuXDj//PQLHmABxDCMFGM6B/z8Mzz8MDRpAjNmwCuvwH/+A2eemdqxqlKN\naSCGYaQE0zngxx9dLeOpp1ywuPNOOOYYv61ymAZiGEYgKe86x/r1MGIEjBsHF10En3wCzZv7bVXy\nsRRWwAlzDh3Mv3SmMN/STeeIRSL3btUquO46OOoo18Lqq69g7NhwBg+wAGIYRhIo7zrHkiVutr92\n7eDAA90gh48+Cg0a+G1ZajENxDCMhCnvOse8eW64kU8/hb/9Da6/HmrU8Nuq+DANxDAM3yivOocq\nZGW5zn8rVsBtt8HLL0OVKn5bVvZYCivghDmHDuZfOpKvc5x8z8mc2+LctNY5YhF971Th7bfhpJOc\nztG7txvgsH//8hk8wAKIYRhxEq1zTDh/AgNOGBB6nSM31/Uab9XKDWo4cCAsWwZXXgn77OO3df5i\nGohhGDEprzrHb7/Biy+6uTjq1IG77oIuXdK7418kpoEYhpFSyqPO8csvruntiBFw5JHw/PPQoYPf\nVgWTuFP6hjKTAAAgAElEQVRYIlJFRA5LpTHGHwljDj0S8y+YxNOfI119K4pt2+D++91wIx99BHff\nncU771jwiEVcAUREugNfAjO99TYiMiXOY7uIyHIR+UZEbi+izChv/yIRaRO1L0NEvhSRt+O5nmEY\niVMe+3Ns3OiGGGnaFFaudC2sJk2Cw8L8c3nWrKScJi4NREQWAKcDs1W1jbdtiaoeVcxxGcDXwJnA\nOuBzoJeqZkeU6Qb0V9VuInIC8Jiqto/YPxA4Fqiuqt0LuYZpIIZRSsqjzvHddzB8uGuC27s33Hor\nNG7st1Up5ptv3CxVS5ciq1aVWgOJN4W1R1W3RW3Li+O4dsBKVV2tqnuAiUCPqDLdgQkAqjoXqCki\ndQBEpAHQDXgWCIl0ZRjBYummpXR+qTO3vXcbIzuPZMalM0IdPL7+2rWgatvWNb9dtgyeeCLkwWPb\nNhc4TjzR5eSWLUvKaeMNIEtF5FKgoog0F5HHgU/iOK4+sCZifa23Ld4yjwK3EV+wCiVhyzNHY/75\nR2nHrQqyb4Xx5Zdw8cXu/dmkiUtXDRsGBx9cePl0869QcnLcBCSHHw47dsDSpa7n4777JuX08bbC\nuhG4C/gNeBWnhdwXx3Hx5paiaxciIucCm1T1SxHpFOvgPn360Nj7+VCzZk1at25Np07ukPyHIF3X\nFy5cGCh7zL/0929P7h6WVFnC0I+G0iGvA2NbjaXHCT0CY1+y1xcvhhkzOrFoEZx/fhYTJkDXrsGx\nL2Xrs2aRdc01UKMGnWbOJGvrVsbf7mToxkmqbqW0H4iItAeGqGoXb/1OIE9Vh0WUGQ1kqepEb305\n0AkYAFwO5ACVgf2Byar6l6hrmAZiGHFQnnQOVZg50w03sm4d3H47XHFF0n54B5t8nWPZMify9OhR\naOeVZPQDiVdEPx4YBDTm91qLqmrMqVFEpCJORD8DWA/MI7aI3h4YGSmie2U6Areq6nmFXMMCiGEU\nQ2R/jkfOfiS0/Tny8uDNN13g+O03GDTIpa0qloceb9u2wX33wYQJLmIOGBAzYiYjgMSrgbwMjAMu\nAs7zlj+0iIpGVXOA/riU1zLgNVXNFpG+ItLXKzMdWCUiK4ExQL+iThenraEiv0oaVsy/1JLK+Tn8\n9i2SPXvce/PII+Ghh2DwYDcXR+/eiQePIPkXkxTrHLGI96v9QVXj6vcRjarOAGZEbRsTtd6/mHN8\nAHyQyPUNozyyO3c3T857kqEfDaXXUb3IviGbA6oc4LdZSefXX11P8Ycfdv04nngCTj89PMONFMus\nWXDzzW4Skpkz3YBdZUi8KayzgZ7ALGC3t1lV9Y0U2hYXlsIyjN8pLzrH9u0werSbtKldO9cRsH37\n4o8LDXHqHLEoy7GwrgAO88pHNqn1PYAYhuEoD+NWbd4Mo0bBU09B587w7rtw9NF+W1WGROsc//qX\nry0D4tVAjgOOV9UrVPXK/CWVhhmOtMnDJoj5V3r8moe8LO/dunVuGPUWLeB//4PPPnM9yFMZPAL1\nbPqoc8Qi3gDyCXBEKg0xDKNklIdxq1auhGuv/T1QfPUVPPMMNGvmr11lyqxZ0KYNvP660znGjHHj\nyweAeDWQ5UBT4L+4zoQQRzPessA0EKO8UR50jsWL4cEH3fvy+uvhppucTlyuSILOEYuy1EC6lOYi\nhmEkh7DrHHPnuj4cc+e6xkVPPw377++3VWVMwHSOWMRMYYlI/q3bXsRipJhA5WFTgPkXH37pHLFI\nlm+q8P77cMYZrtPf2WfDf//r3p1+Bo8yfzYDqnPEorgayKvAOcAC/tiRT4EmqTDKMAxHmPtz5OXB\n1KmuxrFtm2uK27s3VAqPhBM/PvfnSJRiNRAREaChqn5fNiaVDNNAjDCiqkxdMZVb3r2FprWahkrn\nyMlxevADD7hgMWgQXHABZGT4bZkPpFjniEVZaiDTgZiTRxmGkRyWbFrCwJkDWbN9DY91ecz3VFWy\n+O03l9YfNgzq13e9xzt3Lke9xiNJI50jFsU24/V+3n8hIu3KwB4jCtMI0puS+Ld552b6TevH6RNO\nD4zOEYt4ffvlF9djvGlTN9Dh+PEwZw506RLs4JGSZzMNdY5YxFsDaQ9cJiLfAb942wLRjNcw0p2w\n6hxbt7qxqR5/HDp2hClT3CyA5ZY01TliEW8/kMbex/zCAqCqq1NhVEkwDcRIV8Kqc2zcCI88As8+\nC927uwzN4Yf7bZWP+KhzxKLMNBBVXS0ixwKn4MbC+lhVF5TmwoZRngmjzvHdd07XeOUVuPRSWLAA\nGjXy2yofCYnOEYu4hjIRkXuA8UAtoDYwTkT+L4V2GR6mEaQ30f6lm84Ri3zfli+HPn1ceqpaNcjO\ndmmrdA8eCT+bIdM5YhGvBnIZcIyq7gIQkQeARcQ3L7phlHvCqHOsWOE0jjlz3OR3K1dCZqbfVvlM\nCHWOWMSrgcwGLlTVrd56Jm5+8tNTbF+xmAZiBJkw6hwffgj//CcsWQK33grXXANVq/ptlc8EVOeI\nRVn2A9kOLBWRd731s4B5IvI4rjXWgNIYYRhhJEw6hyq8847rNb5hg0vpv/VWKLMyJaMc6ByxiHc4\n9zeBQUAWMBu4C/g38IW3GCmivGkEYSBf5zhtwmkc9vNhaa1z5Oa6d2LbtvD3v0O/fk7zuOYa+PTT\nLL/NSykxn81ypHPEIt5WWONj7ReRyap6UVIsMow0JVrnWH7DchbPW5yW83Ps2eMmbHrwQahZE/7x\nDzjnHKgQ70/OMFPOdI5YxKWBFHsSkS9VtU0S7Enk2qaBGL4SJp3j11/huedcc9zmzd04VaedFvh0\nftmQhjpHLMpSAzEMoxDConP89JPLyIwcCe3bu8EOTzjBb6sCQjnXOWJhFdKAE0aNIJJ09S/e/hxB\n9++HH+Duu904VUuWuOzMv/8dX/AIum+lJev9903nKIaUBxAR6SIiy0XkGxG5vYgyo7z9i0Skjbet\nsojMFZGFIrLM63tiGL4SOQ95xQoV03Ye8rVrXRr/sMNcEJk7F156CY6yMbcds2bB1VcHch7yQKGq\nRS7AQcCRhWw/Eqgdsd65iOMzgJVAY6ASsBBoGVWmGzDd+3wC8FnEvire34rAZ8AphVxDDSPV5OXl\n6ZTlU7T5qOba5aUuumzTMr9NSohvvlG9+mrVzEzVgQNV167126KAsWKF6nnnqTZpovrGG6p5eX5b\nlDK8d2fMGFDcUlwN5HGgsKnsDwAeiwhCM4s4vh2wUlVXq+oeYCLQI6pMd2CCd565QE0RqeOt7/TK\n7OMFoy3F2GsYSWfJpiV0fqkzf5/1dx7r8hgzLp2RdiL5V19Br15O36hXz/UiHzHCzcth4HSOW26B\nE0+EDh2cUH7BBWktkpcFxQWQZqr6QfRGVZ0DxNN2rT6wJmJ9rbetuDINAEQkQ0QWAhuB2aq6LI5r\nhorQ55kD7F8yxq3y27/PPnMj4nbuDG3awKpVcO+9rgVqafHbt6SQ35/jsMPg55/30jlC4V+KKa4V\nVvUY++JJ+sbbvjY6zOfnpnKB1iJSA5gpIp1UNSv64D59+tC4cWMAatasSevWrenUqRPw+0OerusL\nFy4MlD3lwb89uXtYUmUJQz8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+ "text": [
+ "<matplotlib.figure.Figure at 0xa272668>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of ideal stage is \n",
+ "10.2\n",
+ "The feed stage is 4.6 th from the solvent-D inlet\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 47
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.7: Page 525"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.7\n",
+ "# Page: 525\n",
+ "\n",
+ "print'Illustration 10.7 - Page: 525\\n\\n'\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# c:Water d:Toulene \n",
+ "Density_c = 998;# [kg/cubic m]\n",
+ "viscosity_c = 0.95*10**(-3);# [kg/m.s]\n",
+ "Dc = 2.2*10**(-9);# [square m/s]\n",
+ "Density_d = 865;# [kg/cubic m]\n",
+ "viscosity_d = 0.59*10**(-3);# [kg/m.s]\n",
+ "Dd = 1.5*10**(-9);# [square m/s]\n",
+ "sigma = 0.022;# [N/m]\n",
+ "Dist = 20.8;# [Distribution Coeffecient]\n",
+ "d = 0.5;# [m]\n",
+ "h = 0.5;# [m]\n",
+ "di = 0.15;# [m]\n",
+ "N = 13.3;# [r/s]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "qC = 3*10**(-3);# [cubic m/s]\n",
+ "qD = 3*10**(-4);# [cubic m/s]\n",
+ "#********#\n",
+ "\n",
+ "V = math.pi*h*d**2/4;# [Vessel volume,cubic m]\n",
+ "phi_DF = qD/(qD+qC);# [Volume fraction toulene]\n",
+ "# Assume:\n",
+ "phi_Dbyphi_DF = 0.9;\n",
+ "phi_D = phi_Dbyphi_DF*phi_DF;\n",
+ "phi_W = 1-phi_D;\n",
+ "# From Eqn. 10.56:\n",
+ "Density_M = (Density_c*phi_W)+(Density_d*phi_D);# [kg/cubic m]\n",
+ "if phi_W>0.4:\n",
+ " viscosity_M = (viscosity_c/phi_W)*(1+(6*viscosity_d*phi_D/(viscosity_d+viscosity_c)));# [kg/m s]\n",
+ "else:\n",
+ " viscosity_M = (viscosity_c/phi_D)*(1-(1.5*viscosity_c*phi_W/(viscosity_d+viscosity_c)));# [kg/m s]\n",
+ "\n",
+ "# Impeller Reynold's Number:\n",
+ "IRe = (di**2*N*Density_M/viscosity_M);\n",
+ "# From Fig 6.5 (Pg 152), curve g:\n",
+ "Po = 0.72;\n",
+ "P = Po*Density_M*N**3*di**5;# [W]\n",
+ "# From Eqn. 10.61:\n",
+ "Value1 = P*qD*viscosity_c**2/(V*sigma**3);\n",
+ "Value2 = viscosity_c**3/(qD*Density_c**2*sigma);\n",
+ "Value3 = Density_c/(Density_c-Density_d);\n",
+ "Value4 = sigma**3*Density_c/(viscosity_c**4*g);\n",
+ "Value5 = viscosity_d/viscosity_c;\n",
+ "phi_Dbyphi_DF = 3.39*Value1**0.247*Value2**0.427*Value3**0.430*Value4**0.401*Value5**0.0987;\n",
+ "# The value of phi_Dbyphi_DF is sufficiently close to the value 0.90 assumed earlier.\n",
+ "phi_D = phi_Dbyphi_DF*phi_DF;\n",
+ "# From Eqn. 10.6:\n",
+ "Value6 = viscosity_c/Density_c;# [square m/s]\n",
+ "Value7 = P/(V*Density_M);\n",
+ "Value8 = sigma/Density_c;\n",
+ "dp = 10**(-2.066+(0.732*phi_D))*Value6**0.0473*Value7**(-0.204)*Value8**(0.274);# [m]\n",
+ "a = 6*phi_D/dp;# [square m/cubic m]\n",
+ "Sca = viscosity_c/(Density_c*Dc);\n",
+ "# From Eqn. 10.65:\n",
+ "Shc = 65.3;\n",
+ "kLc = Shc*Dc/dp;# [kmol/square m s (kmol/cubic m)]\n",
+ "thetha = V/(qD+qC);# [s]\n",
+ "# From Table 10.1 (Pg 524):\n",
+ "# lambda = [lambda1 lambda2 lambda3]\n",
+ "Lambda = [1.359 ,7.23, 17.9];\n",
+ "# B = [B1 B2 B3]\n",
+ "B = [1.42 ,0.603 ,0.317];\n",
+ "Val = numpy.zeros(3);\n",
+ "Sum = 0;\n",
+ "for n in range(0,3):\n",
+ " Val[n] = (B[n]**2)*exp((-Lambda[n])*64*Dd*thetha/dp**2);\n",
+ " Sum = Sum+Val[n];\n",
+ "\n",
+ "# From Eqn. 10.66:\n",
+ "kLd = -(dp/(6*thetha))*math.log((3.0/8)*Sum);\n",
+ "mCD = 1.0/Dist;\n",
+ "# From Eqn. 10.67:\n",
+ "KLd = 1/((1/kLd)+(1/(mCD*kLc)));# [kmol/square m s (kmol/cubic m)]\n",
+ "Z = 0.5;# [m]\n",
+ "Vd = qD/(math.pi*Z**2.0/4);# [m/s]\n",
+ "# From Eqn.10.70:\n",
+ "NtoD = Z/(Vd/(KLd*a));\n",
+ "# From Eqn. 10.71:\n",
+ "EMD = NtoD/(NtoD+1);\n",
+ "print\"Expected stage efficiency: \\n\",round(EMD,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.7 - Page: 525\n",
+ "\n",
+ "\n",
+ "Expected stage efficiency: \n",
+ "0.93\n"
+ ]
+ }
+ ],
+ "prompt_number": 41
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.8: Pg-539"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.8\n",
+ "# Page: 539\n",
+ "\n",
+ "print'Illustration 10.8 - Page: 539\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:acetic acid c:Water d:Isopropylether layer\n",
+ "# Water solution (continuous):\n",
+ "C = 8000.0;# [kg/h]\n",
+ "xCn = 0.175;# [mass fraction]\n",
+ "Density_c = 1009.0;# [kg/cubic m]\n",
+ "viscosity_c = 3.1*10**(-3);# [kg/m.s]\n",
+ "Dc = 1.24*10**(-9);# [square m/s]\n",
+ "\n",
+ "# Isopropyl Ethr Layer:\n",
+ "D = 20000.0;# [kg/h]\n",
+ "xDnPlus1 = 0.05;# [mass fraction]\n",
+ "Density_d = 730;# [kg/cubic m]\n",
+ "viscosity_d = 0.9*10**(-3);# [kg/m.s]\n",
+ "Dd = 1.96*10**(-9);# [square m/s]\n",
+ "\n",
+ "sigma = 0.013;# [/N/m]\n",
+ "m = 2.68;# [Distributon coeffecient]\n",
+ "#*******#\n",
+ "\n",
+ "Ma = 60.1;\n",
+ "g = 9.81;# [m/square s]\n",
+ "cCn = xCn*Density_c/Ma;# [kmol/cubic m]\n",
+ "cDnPlus1 = xDnPlus1*Density_d/Ma;# [kmol/cubic m]\n",
+ "mCD = m*(Density_c/Density_d);# [(kmol/cubic min ether)/(kmol/cubic m in water)]\n",
+ "\n",
+ "# Perforations:\n",
+ "Do = 0.006;# [m]\n",
+ "pitch = 0.015;# [m]\n",
+ "qD = D/(3600.0*Density_d);# [cubic m/s]\n",
+ "delta_Density = Density_c-Density_d;# [kg/cubic m]\n",
+ "Value1 = Do/(sigma/(delta_Density*g))**0.5;\n",
+ "if Value1<0.1785:\n",
+ " # From Eqn. 10.74(a):\n",
+ " doBydj = (0.485*Value1**2)+1;\n",
+ "else:\n",
+ " # From Eqn. 10.74(b)\n",
+ " doBydj = (1.51*Value1)+0.12;\n",
+ "\n",
+ "dj = Do/doBydj;# [m]\n",
+ "Vomax = 2.69*((dj/Do)**2)*(sigma/(dj*((0.5137*Density_d)+(0.4719*Density_c))))**0.5;# [m/s]\n",
+ "# Since Vomax is less than 0.1:\n",
+ "Vo = 0.1;# [m/s]\n",
+ "Ao = qD/Vo;# [square m]\n",
+ "No = Ao/(math.pi*Do**2.0/4);# [square m]\n",
+ "# From Eqn. 6.30:\n",
+ "# Plate area for perforation:\n",
+ "Aa = Ao/(0.907*(Do/pitch)**2);# [square m]\n",
+ "\n",
+ "# Downspout:\n",
+ "dp = 0.0007;# [m]\n",
+ "# From Eqn. 10.75:\n",
+ "U = Density_c**2*sigma**3/(g*viscosity_c**4*delta_Density);\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 1.515;\n",
+ "abcissa = 0.62;\n",
+ "def f74(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vt = fsolve(f74,7);# [m/s]\n",
+ "Vd = Vt[0];# [m/s]\n",
+ "qC = C/(Density_c*3600);# [cubic m/s]\n",
+ "Ad = qC/Vd;# [square m]\n",
+ "# From Table 6.2 (Pg 169):\n",
+ "# Allowing for supports and unperforated area:\n",
+ "At = Aa/0.65;# [square m]\n",
+ "T = (At*4/math.pi)**0.5;# [m]\n",
+ "An = At-Ad;# [square m]\n",
+ "\n",
+ "\n",
+ "# Drop Size:\n",
+ "alpha1 = 10.76;\n",
+ "alpha2 = 52560;\n",
+ "alpha3 = 1.24*10**6;\n",
+ "alpha4 = 3.281;\n",
+ "abcissa = (alpha2*sigma*Do/delta_Density)+(alpha3*Do**1.12*Vo**0.547*viscosity_c**0.279/delta_Density**1.5);\n",
+ "Parameter = alpha1*Density_d*Vo**2/(delta_Density);\n",
+ "ordinate = 0.024;\n",
+ "dp = ordinate/alpha4;\n",
+ "\n",
+ "# Coalesced layer:\n",
+ "Vn = qD/An;# [m/s]\n",
+ "# From Eqn. 10.80:\n",
+ "ho = (Vo**2-Vn**2)*Density_d/(2*g*0.67**2*delta_Density);# [m]\n",
+ "hD = ho;\n",
+ "# From Eqn. 10.82:\n",
+ "hC = 4.5*Vd**2*Density_c/(2*g*delta_Density);# [m]\n",
+ "# From Eqn. 10.78:\n",
+ "h = hC+hD;\n",
+ "# Since this is very shallow, increase it by placing an orifice at the bottom of the downspout.\n",
+ "# VR: Velocity through the restriction.\n",
+ "# hR: Corresponding depth of the coalesced layer.\n",
+ "# Assume:\n",
+ "Vr = 0.332;# [m/s]\n",
+ "hr = (Vr**2-Vd**2)*Density_c/(2*0.67**2*delta_Density);\n",
+ "Ar = qC/Vr;# [square m]\n",
+ "dr = (4*Ar/math.pi)**0.5;# [m]\n",
+ "h = h+hr;# [m]\n",
+ "# The above results are satisfacyory.\n",
+ "Z = 0.35;# [m]\n",
+ "# Lead the downspout apron to within 0.1 m of the tray below.\n",
+ "\n",
+ "# Dispersed-phase holdup:\n",
+ "# From Eqn. 10.48:\n",
+ "Vsphi_D = Vn;\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 165.2;\n",
+ "abcissa = 30.0;\n",
+ "def f75(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vtl = fsolve(f75,7);# [m/s]\n",
+ "# For solids:\n",
+ "# From Fig. 10.48 (Pg 536):\n",
+ "abcissa = dp/(3*viscosity_c**2/(4*Density_c*delta_Density*g))**(1.0/3);\n",
+ "phi_D = [0, 0.1 ,0.2 ,0.3];\n",
+ "# Corresponding ordinates, from Fig. 10.48 (Pg 536):\n",
+ "ordinate1 = [8.8, 5.9 ,4.3 ,3.0];\n",
+ "Value1 = 1.0/(4*viscosity_c*delta_Density*g/(3*Density_c**2))**(1.0/3);\n",
+ "Val = numpy.zeros((4,7));\n",
+ "# Val = [phi_D ordinate Vs(1-phi_D) (Vs for solids) Vs/Vt (Vs for liquids) (Vs*phi_D (for liquids))]\n",
+ "for i in range(0,4):\n",
+ " Val[i,0] = phi_D[i];\n",
+ " Val[i,1] = ordinate1[i];\n",
+ " Val[i,2] = Val[i,1]/Value1;\n",
+ " Val[i,3] = Val[i,2]/(1-Val[i,0]);\n",
+ " Val[i,4] = Val[i,3]/Val[0,3];\n",
+ " Val[i,5] = Vtl*Val[i,4];\n",
+ " Val[i,6] = Val[i,5]*Val[i,0];\n",
+ "\n",
+ "\n",
+ "# By Interpolation:\n",
+ "Phi_D = 0.1;\n",
+ "\n",
+ "thetha_f =0.2498 # s\n",
+ "# From Eqn. 10.87:\n",
+ "const = 1.5;\n",
+ "kLDf = const*(Dd/(math.pi*thetha_f))**0.5;# [m/s]\n",
+ "# From Eqn. 10.86\n",
+ "KLDf = 1.0/((1.0/kLDf)*(1+((1.0/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# The ordinate of Fig. 10.47 for the drops larger than 70. Hence mass transfer coeffecient during drop rise is given by Eqn. 10.89:\n",
+ "# From Eqn. 10.91:\n",
+ "b = 1.052*dp**0.225;\n",
+ "# From Eqn. 10.90:\n",
+ "omega = (1.0/(2*math.pi))*math.sqrt(192*sigma*b/(dp**3*((3*Density_d)+(2*Density_c))));# [1/s]\n",
+ "Del = 0.2;\n",
+ "kLDr = math.sqrt((4.0*Dd*omega/math.pi)*(1+Del+(1.0/2)*Del**2));\n",
+ "KLDr = 1.0/1/((1/kLDr)*(1+((1/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# From Eqn. 10.98:\n",
+ "EMD = ((4.4*KLDf/Vo)*(dp/Do)**2)+(6*KLDr*Phi_D*(Z-h)/(dp*Vn))/(1+((0.4*KLDf/Vo)*(dp*1.0/Do)**2)+(3*KLDr*Phi_D*(Z-h)/(dp*Vn)));\n",
+ "print\"Stage Efficiency: \",round(-EMD,3)\n",
+ "# The solution in the textbook is incorrect\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.8 - Page: 539\n",
+ "\n",
+ "\n",
+ "Stage Efficiency: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 0.057\n"
+ ]
+ }
+ ],
+ "prompt_number": 42
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.9: Pg-551"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.9\n",
+ "# Page: 551\n",
+ "\n",
+ "print('Illustration 10.9 - Page: 551\\n\\n');\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "#****Data****#\n",
+ "B = 20000;# [kg/h]\n",
+ "#******#\n",
+ "\n",
+ "# x and y are taken in weight fraction acetic acid.\n",
+ "x1 = 0.30;# [Wt fraction]\n",
+ "xF = 0.30;# [Wt fraction]\n",
+ "y2 = 0;# [Wt fraction]\n",
+ "x2 = 0.02;# [Wt fraction]\n",
+ "y1 = 0.10;# [Wt fraction]\n",
+ "# The operating diagram is plotted in Fig. 10.23:\n",
+ "# Data = [x x_star]\n",
+ "# From Fig. 10.23 (Pg 503):\n",
+ "Data = numpy.array([[0.30 ,0.230],[0.25 ,0.192],[0.20 ,0.154],[0.15, 0.114],[0.10, 0.075],[0.05, 0.030],[0.02, 0]]);\n",
+ "Val = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " Val[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x\");\n",
+ "plt.ylabel(\"1/(x-x*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# From Area Under the curve:\n",
+ "Area = 8.40;\n",
+ "# The mutual solubility of water and isopropyl ether is very small.\n",
+ "Ma = 18.0;# [kg/kmol water]\n",
+ "Mb = 60.0;# [kg/kmol isopropyl ether]\n",
+ "r = Ma/Mb;\n",
+ "# From Eqn. 10.110:\n",
+ "NtoR = Area+(1.0/2)*math.log(1-x2/(1-x1))+(1.0/2)*math.log(x2*(r-1)+1.0/(x1*(r-1)+1));\n",
+ "# Since the operating line and equilibrium line are parallel:\n",
+ "Np = NtoR;\n",
+ "print\"Number of theoretical Units: \\n\",round(NtoR,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.9 - Page: 551\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3Tpd0PNDm5rNZa3j99eSS17POgqeeSm6Y+8xnfMNco5ShxzAB+DTwQUlz069J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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x9890390>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical Units: \n",
+ "8.5\n"
+ ]
+ }
+ ],
+ "prompt_number": 43
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.10:pg-552"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.10\n",
+ "# Page: 552\n",
+ "\n",
+ "print('Illustration 10.10 - Page: 552\\n\\n');\n",
+ "\n",
+ "# Solution\n",
+ "import math\n",
+ "#****Data****#\n",
+ "B = 1150;# [kg/h]\n",
+ "#*******#\n",
+ "\n",
+ "# x and y are taken in weight ratio.\n",
+ "x1_prime = 0.0101;# [Wt. fraction]\n",
+ "xF_prime = 0.0101;# [Wt. fraction]\n",
+ "y2_prime = 0;# [Wt. fraction]\n",
+ "x2_prime = 0.001001;# [Wt. fraction]\n",
+ "y1_prime = 0.0782;# [Wt. fraction]\n",
+ "# From Illustration 10.4:\n",
+ "A = 990.0;# [kg/h]\n",
+ "# At the dilute end:\n",
+ "m1_prime = 0.798;\n",
+ "Value1 = m1_prime*B/A;\n",
+ "# At the concentrated end:\n",
+ "m2_prime = 0.953;\n",
+ "Value2 = m2_prime*B/A;\n",
+ "ValueAv = (Value1*Value2)**0.5;\n",
+ "# From Eqn. 10.116:\n",
+ "# Since y2_prime = 0\n",
+ "Value3 = x2_prime/x1_prime;\n",
+ "NtoR = (math.log((1.0/Value3)*(1-(1/ValueAv))+(1/ValueAv)))/(1-(1/ValueAv));\n",
+ "print\"Number of theoretical Unit : \",round(NtoR,1),\"\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.10 - Page: 552\n",
+ "\n",
+ "\n",
+ "Number of theoretical Unit : 8.6 \n"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter10_1.ipynb b/Mass_-_Transfer_Operations/Chapter10_1.ipynb
new file mode 100755
index 00000000..df27b4e6
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter10_1.ipynb
@@ -0,0 +1,1366 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:9fbb8ba54aec06ad3c69036b535306d5fcf2bbf41aaf2bb73bb23d87c87b8396"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 10: Liquid Extraction"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.1: Page 494"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.1\n",
+ "# Page: 494\n",
+ "\n",
+ "print'Illustration 10.1 - Page: 494\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "xF = 0.30;# [mol fraction]\n",
+ "yS = 0;# [mol fraction]\n",
+ "S1 = 40.0;# [kg]\n",
+ "B1 = 40.0;# [kg]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data at 20 OC:\n",
+ "# Wa: Wt. percent of a\n",
+ "# Wb: Wt. percent of b\n",
+ "# Wc: Wt. percent of c\n",
+ "# Data1 = [Wc Wa Wb]\n",
+ "# Data1: water layer\n",
+ "Data1 = numpy.array([(0.69 ,98.1, 1.2),(1.41, 97.1 ,1.5),(2.89 ,95.5 ,1.6),(6.42 ,91.7 ,1.9),(13.30, 84.4, 2.3),(25.50 ,71.1 ,3.4),(36.70 ,58.9 ,4.4),(44.30 ,45.1 ,10.6),(46.40 ,37.1 ,16.5)])\n",
+ "# Data2: isopropyl ether layer\n",
+ "Data2 = numpy.array([(0.18 ,0.5 ,99.3),(0.37, 0.7 ,98.9),(0.79, 0.8, 98.4),(1.93 ,1, 97.1),(4.82, 1.9, 93.3),(11.40, 3.9, 84.7),(21.60, 6.9, 71.5),(31.10, 10.8, 58.1),(36.20 ,15.1 ,48.7)])\n",
+ "\n",
+ "plt.plot((Data1[:,2])/100,(Data1[:,0])/100,label=\"x Vs fraction ether\")\n",
+ "plt.plot((Data2[:,2])/100,(Data2[:,0])/100,label=\"y Vs fraction ether\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='lower center');\n",
+ "ax=pylab.gca()\n",
+ "ax.set_xlabel(\"Wt fraction of isopropyl ether\");\n",
+ "ax.set_ylabel(\"Wt fraction of acetic acid\");\n",
+ "plt.ylim((0,0.3))\n",
+ "plt.xlim((0,1))\n",
+ "plt.show();\n",
+ "# x: Wt fraction of acetic acid in water layer.\n",
+ "# y: Wt fraction of acetic acid in isopropyl layer.\n",
+ "\n",
+ "# The rectangular coordinates of Fig 10.9(a) will be used but only upto x = 0.30\n",
+ "\n",
+ "# Stage 1:\n",
+ "F = 100;# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "M1 = F+S1;# [kg]\n",
+ "# From Eqn. 10.5:\n",
+ "xM1 = ((F*xF)+(S1*yS))/M1;\n",
+ "# From Fig. 10.15 (Pg 495):\n",
+ "# Point M1 is located on the line FB and with the help of tie line passing through M1:\n",
+ "x1 = 0.258;# [mol fraction]\n",
+ "y1 = 0.117;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E1 = (M1*(xM1-x1)/(y1-x1));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R1 = M1-E1;# [kg]\n",
+ "\n",
+ "# Stage 2:\n",
+ "S2 = 40;# [kg]\n",
+ "B2 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M2 = R1+B2;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM2 = ((R1*x1)+(S2*yS))/M2;\n",
+ "# Point M2 is located on the line R1B and the tie line passing through R2E2 through M2:\n",
+ "x2 = 0.227;\n",
+ "y2 = 0.095;\n",
+ "# From Eqn. 10.8:\n",
+ "E2 = (M2*(xM2-x2)/(y2-x2));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R2 = M2-E2;# [kg]\n",
+ "\n",
+ "# Stage 3:\n",
+ "S3 = 40;# [kg]\n",
+ "B3 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M3 = R2+B3;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM3 = ((R2*x2)+(S3*yS))/M3;\n",
+ "# Point M3 is located on the line R2B and the tie line passing through R3E3 through M3:\n",
+ "x3 = 0.20;# [mol fraction]\n",
+ "y3 = 0.078;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E3 = (M3*(xM3-x3)/(y3-x3));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R3 = M3-E3;# [kg]\n",
+ "Ac = x3*R3;\n",
+ "print\"The composited extract is\",round((E1+E2+E3),2),\" kg\\n\"\n",
+ "print\"The acid content is \",round(((E1*y1)+(E2*y2)+(E3*y3)),2),\" kg\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# If an extraction to give the same final raffinate concentration were to be done in single stage, the point M would be at the intersection of tie line R3E3 and the line BF.\n",
+ "x = 0.20;# [mol fraction]\n",
+ "xM = 0.12;# [mol fraction]\n",
+ "# From Eqn. 10.6:\n",
+ "S = F*(xF-xM)/(xM-yS);# [kg]\n",
+ "print round(S,2),\"kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.1 - Page: 494\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F6AM8U4J9C5SIVVLGmMhIrpzM4+c+Tp+P+3BAY2J4oDLjdTyM0cB+4BxgHDDB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\niRUYxhhjPPl/ptoLDFmMWJUAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7727f28>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The composited extract is 135.05 kg\n",
+ "\n",
+ "The acid content is 13.01 kg\n",
+ "\n",
+ "\n",
+ "\n",
+ "150.0 kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.2: Page 497"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.2\n",
+ "# Page: 497\n",
+ "\n",
+ "print'Illustration 10.2 - Page: 497\\n\\n'\n",
+ "\n",
+ "print'Illustration 10.2 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:kerosene c:Nicotine\n",
+ "xF = 0.01;# [wt fraction nicotine]\n",
+ "F = 100.0;# [kg]\n",
+ "B = 150.0;# [kg]\n",
+ "#******#\n",
+ "\n",
+ "# Equilibrium data:\n",
+ "# x_prime = kg nicotine/kg water\n",
+ "# y_prime = kg nicotine/kg kerosene\n",
+ "# Data = [x_prime y_prme]\n",
+ "Data = numpy.array([[0 ,0],[0.001011 ,0.000807],[0.00246, 0.001961],[0.00502, 0.00456],[0.00751, 0.00686],[0.00998 ,0.00913],[0.0204, 0.01870]])\n",
+ "xF_prime = xF/(1-xF);# kg nicotine/kg water\n",
+ "A = F*(1-xF);# [kg]\n",
+ "AbyB = A/B;\n",
+ "\n",
+ "def f64(x):\n",
+ " return -AbyB*(x-xF)\n",
+ "x = numpy.arange(0,0.01+0.001,0.001);\n",
+ "plt.plot(Data[:,0],Data[:,1],label=\"Equilibrium line\")\n",
+ "plt.plot(x,f64(x),label=\"Operating Line\");\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.2(a)\")\n",
+ "plt.show()\n",
+ "# The operating line and equilibrium line intersect at:\n",
+ "x1_prime = 0.00425;# [kg nicotine/kg water]\n",
+ "y1_prime = 0.00380;# [kg nicotine/kg water]\n",
+ "extract = A*(0.01011-x1_prime);\n",
+ "print extract*100,\"% of nicotine is extracted.\\n\\n\"\n",
+ "\n",
+ "print'Illustration 10.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "B = 50.0;# [kg]\n",
+ "# For each stage:\n",
+ "AbyB = A/B;\n",
+ "def f65(x1):\n",
+ " return -AbyB*(x1-xF)\n",
+ "x1 = numpy.arange(0,0.01+0.001,0.001)\n",
+ "def f66(x2):\n",
+ " return -AbyB*(x2-0.007)\n",
+ "x2 = numpy.arange(0,0.01+0.001,0.001)\n",
+ "def f67(x3) :\n",
+ " return -AbyB*(x3-0.005)\n",
+ "x3 =numpy.arange(0,0.01+0.001,0.001)\n",
+ "\n",
+ "plot(Data[:,0],Data[:,1],label=\"Equilibrium line\")\n",
+ "plt.plot(x1,f65(x1),label=\"Operating Line from xF\")\n",
+ "plt.plot(x2,f66(x2),label=\"Operating Line from 0.007\")\n",
+ "plt.plot(x3,f67(x3),label=\"Operating Line from 0.005\")\n",
+ "plt.grid('on');\n",
+ "legend(loc=\"upper right\");\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0, 0.010))\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.2(b)\")\n",
+ "plt.show()\n",
+ "# The final raffinate composition:\n",
+ "x3_prime = 0.0034;# [kg nicotine/kg water]\n",
+ "extract = A*(0.01011-x3_prime);\n",
+ "print extract*100,\" % of nicotine is extracted.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.2 - Page: 497\n",
+ "\n",
+ "\n",
+ "Illustration 10.2 (a)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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BVX8MWpXsQSzGI1bn7IimcvjjD7jvPhg6FD7+2GnkD6doKguvWVl4K5Qk86OI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\nJGOMMcYzlmSMMcZ4xpKMMcYYz/w/SP1Cq7DN7uIAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x77359b0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "58.014 % of nicotine is extracted.\n",
+ "\n",
+ "\n",
+ "Illustration 10.2 (b)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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VGDx4sMdtjRw5Ml3GOS1XXWhoKEePHvXKuDzBkyeZGcBXWHtXALYBj3vLoECm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\nRK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x79b5f28>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "66.429 % of nicotine is extracted.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.3: Page 502"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.3\n",
+ "# Page: 502\n",
+ "\n",
+ "print'Illustration 10.3 - Page: 502\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "F = 8000;# [kg/h]\n",
+ "xF = 0.30;# [wt. fraction acetic acid]\n",
+ "#*******#\n",
+ "\n",
+ "# From Illustration 10.1 (Pg 494)\n",
+ "# Equilibrium Data:\n",
+ "# Eqb = [y_star*100 x*100]\n",
+ "Eqb = numpy.array([[0.18 ,0.69],[0.37 ,1.41],[0.79 ,2.89],[1.93, 6.42],[4.82, 13.30],[11.40, 25.50],[21.60 ,36.70],[31.10 ,44.30],[36.20, 46.40]]);\n",
+ "\n",
+ "# Solution(a)\n",
+ "\n",
+ "# From Figure 10.23 (Pg 503):\n",
+ "# For minimum solvent rate:\n",
+ "y1 = 0.143;# [Wt fraction of acetic acid in isopropyl ether layer]\n",
+ "xM = 0.114;# [Wt fraction of acetic acid in water layer]\n",
+ "# From Eqn. 10.24:\n",
+ "Bm = (F*xF/xM)-F;# [kg/h]\n",
+ "print\"Minimum solvent rate: \",Bm,\" kg/h\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "B = 20000.0;# [kg solvent/h]\n",
+ "yS = 0;\n",
+ "S = B;\n",
+ "# From Eqn 10.24:\n",
+ "xM = ((F*xF)+(S*yS))/(F+S);\n",
+ "# From Fig. 10.23 (Pg 503):\n",
+ "y1 = 0.10;\n",
+ "# Operating curve data:\n",
+ "# Operat = [YsPlus1 Xs]\n",
+ "Operat = numpy.array([[0 ,0.02],[0.01 ,0.055],[0.02 ,0.09],[0.04 ,0.150],[0.06, 0.205],[0.08, 0.250],[0.1 ,0.3]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,1]/100,Eqb[:,0]/100,label=\"Operating Line\")\n",
+ "plt.plot(Operat[:,1],Operat[:,0],label=\"Equilibrium Line\")\n",
+ "plt.grid('on');\n",
+ "plt.ylim((0,y1));\n",
+ "plt.xlim((0,xF));\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"Wt. fraction acetic acid in water solution\");\n",
+ "plt.ylabel(\"Wt. fraction acetic acid in isopropyl ether solution\");\n",
+ "plt.title(\"Solution 10.3\")\n",
+ "plt.show()\n",
+ "# From Figure scf(22):\n",
+ "xNp = 0.02;\n",
+ "Np = 7.6;\n",
+ "# By acid balance:\n",
+ "M = B+F;\n",
+ "E1 = M*(xM-xNp)/(y1-xNp);# [kg/h]\n",
+ "RNp = M-E1;# [kg/h]\n",
+ "print\"Number of theoretical Stages: \\n\",Np\n",
+ "print\"Weight of the extract:\",E1,\"kg/h\\n\"\n",
+ "print\"Weight of the raffinate \",RNp,\" kg/h\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.3 - Page: 502\n",
+ "\n",
+ "\n",
+ "Minimum solvent rate: 13052.6315789 kg/h\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Mw0gCW7fCBRfAxReXHcMxavkorp54NRMvn2iGI4X4ivNQ1a+Be4ABwJnAMyLy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\nq+bgYWZm1Rw8zMysmoOHmZlV+ws92v6jnNxHLAAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x78da7f0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical Stages: \n",
+ "7.6\n",
+ "Weight of the extract: 23000.0 kg/h\n",
+ "\n",
+ "Weight of the raffinate 5000.0 kg/h\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.4: Page 506"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.4\n",
+ "# Page: 506\n",
+ "\n",
+ "print'Illustration 10.4 - Page: 506\\n\\n'\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "import numpy\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:kerosene c:Nicotine\n",
+ "F = 1000.0;# [kg/h]\n",
+ "xF = 0.01;# [wt. fraction acetic acid]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data:\n",
+ "# x_prime = kg nicotine/kg water\n",
+ "# y_prime = kg nicotine/kg kerosene\n",
+ "# Eqb = [x_prime y_prme]\n",
+ "Eqb = numpy.array([[0 ,0],[0.001011, 0.000807],[0.00246, 0.001961],[0.00502 ,0.00456],[0.00751, 0.00686],[0.00998, 0.00913],[0.0204 ,0.01870]]);\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "A = 1000*(1-xF);# [kg water/h]\n",
+ "yS = 0;\n",
+ "yS_prime = 0;\n",
+ "y1_prime = 0;\n",
+ "xF_prime = xF/(1-xF);# [kg nicotine/kg water]\n",
+ "# For xF_prime = 0.0101:\n",
+ "yk = 0.0093;\n",
+ "xNp = 0.001;# [wt. fraction acetic acid]\n",
+ "xNp_prime = xNp/(1-xNp);# [kg nicotine/kg water]\n",
+ "# For infinite stages:\n",
+ "# Operating Line should pass through (xNp_prime,y1_prime) & (xF_prime,yk)\n",
+ "Operat = numpy.array([[xNp_prime, y1_prime],[xF_prime ,yk]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1],label=\"equilibrium Line\")\n",
+ "plt.plot(Operat[:,0],Operat[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.4(a)\")\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0,0.01))\n",
+ "plt.show()\n",
+ "AbyBm = (yk-y1_prime)/(xF_prime-xNp_prime);\n",
+ "Bm = A/AbyBm;# [kg kerosene/h];\n",
+ "print\"Mininmum kerosene rate: \",round(Bm,2),\" kg kerosene/h \\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "B = 1150.0;# [kg/h]\n",
+ "AbyB = A/B;\n",
+ "# From Eqn. 10.36:\n",
+ "y2_prime = ((xF_prime-xNp_prime)*AbyB)+yS_prime;# [kg nicotine/kg kerosene]\n",
+ "# Operating Line should pass through (xNp_prime,y1_prime) & (xF_prime,y2_prime)\n",
+ "Operat = numpy.array([[xNp_prime, y1_prime],[xF_prime, y2_prime]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1],label=\"equilibrium Line\")\n",
+ "plt.plot(Operat[:,0],Operat[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine/kg water\");\n",
+ "plt.ylabel(\"kg nicotine/kg kerosene\");\n",
+ "plt.title(\"Solution 10.4(b)\")\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0,0.01))\n",
+ "plt.show()\n",
+ "# From Figure:\n",
+ "Np = 8.3;\n",
+ "print\"Number of theoretical stages: \\n\",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.4 - Page: 506\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3MyhjTGg4dgx69XIeUV640OmHAViwdQGdpnfijSZv0OWGLsEN0oQ0v+YuSy0s\nUhQop6rfuxdSYFifjDHZ8+OPzriXmjWdR5QLFABVZfCqwQxaNYjJrSdzc/mbgx2mCbAc75MRkeXA\n3Z6y64B/ROQbVbXVMY2JQKrw2Wfw4oswaJDTRAZwMvEk3WZ346e/f2J1l9WUL1Q+uIGasOBPn0wh\nVT0MtATGqmod4DZ3wzK+RHK7cCTXDUK7focOOXcvH38MX3/9vwSz+8huGo1uxKmkU3z90NeZJphQ\nrl8gRHr9As2fJBMlIqWANsBczz5rhzImwqxdCzVqwKWXwpo1cIVnBak1u9ZQZ1gdWlzRgon3TeSi\nvBcFN1ATVvwZJ9MaeAn4RlV7iMhlwNuqel9OBHiurE/GGP8kJ8O778KAAfDJJ3Cf1//ZY78fyzOL\nnmFEzAjuvuLu4AVpckyg+2Sy1PEfTizJGOPbvn0QG+v8O2ECVKrk7E9KTqLPf/swY9MMZrabydXF\nrw5qnCbnBGPRsnIiMl1E/vG8polI2UAFYM5NJLcLR3LdIHTq99VXTvPYVVfBihX/SzAHTx6k+YTm\nbPhrA/Fd47OcYEKlfm6J9PoFmj99MqOAWUBpz2u2Z58xJgwlJTkLirVt6zxF9vbbkNezjtiv+36l\n7vC6XF70chY8sICiFxYNbrAm7Pk1d5mqXu9rX6ix5jJjzrZ7t7Pui6oz91jp0v97b/6W+Tw440He\nuu0tHqrxUPCCNEGV481lwH4R6SgiUSJynog8AOwLVADGmJyxYIEzsLJxY/jvf/+XYFSVgd8M5OFZ\nDzO97XRLMCag/EkynXEeX/4L2AO09uwzQRTJ7cKRXDfI+folJECfPtC1qzMH2csvQ1SU896JhBN0\nmtGJiT9PZE2XNdxU/qZsX88+P+Mt0xH/nhmX31BVe3bRmDC0Ywe0bw9FizoLi1166f/e+/Pwn9w7\n6V4uK3oZKzqvsPEvxhX+9Ml8DTRR1VM5E1JgWJ+Mye2+/BIeecS5i3nyScjj1W6xetdqWk1uxWO1\nH6PvzX1tgTGTKhjryWwHvhaRWcBxzz5V1cGBCsIYEzgnT8IzzzirV86eDXXrnvn+mA1j6L24NyNb\njKT55c2DE6TJNfzpk/kNZzqZPMDFnlcBN4MyvkVyu3Ak1w3crd/mzc66L3v3Os1j3gkmMTmRpxc+\nzWsrXiMuNs61BGOfn/Hm805GVfsDiEh+VT3mekTGmHPy+efw1FPw6qvQvTt4t4AdOHGAdtPakazJ\nrOmyxsabjGJ4AAAgAElEQVS/mBzjT59MfZxFygqoajkRuR7orqqP+jy5SDQwBIgChqvqgHTKvA/c\nidMUF6uq630dKyKPA48CScBcVe2TznmtT8bkCkePQs+ezqSWkybBdded+f7GfzbSYmIL7qp6FwNv\nH8h5efxpJTe5VTDGyQwBovGMjfEsWNbI10EiEgV86Dn2KqC9iFRLU6YZUEVVqwLdgE98HSsitwAx\nwHWqeg3wjh91MCYi/fAD1K7t3LV8++3ZCWbelnk0Gt2I525+jnej37UEY3KcP0kGVd2ZZleiH4fV\nAbaq6g5VTQAmAi3SlIkBxniusQYoLCIlfRzbA3jTsx9V/cefOkSaSG4XjuS6QWDqpwpDh0KTJvD8\n8zBqFOTP7/2+MuDrAXSd3ZWZ7WbSuUbODW2zz8948+fPmp0ichOAiJwPPAFs9OO4MsAfXtu7gLp+\nlCmDM0daRsdWBRqKyBvASeAZVf3Wj3iMiQgHDzoDK7dscRYWS1n3JcWJhBN0md2FX/f9ypouayhb\n0OazNcHjz51MD+AxnC//P4Eanm1f/O0QyWrb33lAEVWtB/QGJmfx+IjQuHHjYIfgmkiuG2SvfvHx\ncMMNUKIErF59doLZdXgXDUc3BGBF5xVBSTD2+Rlv/tzJ1FLV+713iMgjwFAfx/0JlPPaLodzR5JZ\nmbKeMnkzOXYX8CWAqq4VkWQRuURV96cNIDY2looVKwJQuHBhqlevnvoLknLLa9u2HQ7bS5fGMWUK\nTJvWmKFDoWjROFavPrP8z3//zBu73qBX3V7UPl2bNd+sCZn4bTt0t+Pi4hg9ejRA6vdlQKlqpi9g\nJc6I/5TtZ4EFfhx3Hs4Ym4rA+cAGoFqaMs2AeZ6f6wGrfR0LdAde8fx8ObAzg+trJFu2bFmwQ3BN\nJNdNNev1+/tv1WbNVOvVU92+Pf0yI78bqZe+fanO+XVOtuPLLvv8wpvnu9NnbvD35c+dTAwwR0RO\n4zztdaVnn6/klSgiPYGFOI8hj1DVjSLS3fP+p6o6T0SaichW4BieiTczOtZz6pHASBH5ETgNdPKj\nDsaEpeXLnan5778fXnvtf+u+pEhMTqT3ot7M3TKX5bHLqXZptfRPZEyQ+LX8sogUB5YA3wIPqT8H\nBZmNkzHhLCnJSSpDhzpPjkVHn13m3xP/0m5qO0SEifdNpMiFRXI+UBNxAj1OJsMkIyJHObPz/nwg\nwbNPVbVgoIJwgyUZE65274YOHZyxL+PGnbmwWIqN/2wkZmIMMZfHMKDpABv/YgImxwZjqurFqlrA\n65XPa19IJ5jcIKXjLhJFct0g8/qlLCx2662weHH6CWbO5jk0Gt2IFxu8yKA7BoVcgsnNn585W2j9\ndhqTSyUkwAsvwIQJztQwDRueXUZVGfDNAD6I/4BZ7WdRr2y9nA/UmCzyq08mHFlzmQkXO3ZAu3ZQ\nrBiMHu38m9bxhON0mdWFrf9uZXrb6ZQpWCanwzS5RDDmLjPGuGTaNKhTB9q0gVmz0k8wuw7vouGo\nhkTliWJ57HJLMCas+EwyIlI0nVdeX8cZd0Vyu3Ak1w2c+p08CY8+Cr17w5w5zhT9edL5v3HlHyup\nO7wuba9uy9h7xnJh3gtzPuAsyg2fn/GfP3cy3+HMwLzF89oH/C4i34lITTeDMyYS7dzpLCy2bx+s\nX+/cyaRn5PqR3DPxHobdPYzeN/W2JZJNWPJnPZlhwFRVXejZvh1oBYwC3lPVDP4XCS7rkzGhaOxY\nePppZwxMt25nLiyWImUFywW/LWBmu5lcWezKnA/U5Fo5Nk7G64I/qbNui/e+H1X1WhHZoKrVAxVM\nIFmSMaHk6FF47DFngsvJk+Haa9Mv9++Jf2k7tS1REsXEVhMpfEHhnA3U5HrB6PjfIyJ9RKSCiFQU\nkWeBvZ6FxZIDFYjJmkhuF460un3/PdSqBVFRzsJi+/fHpVvu579/ps6wOlxf4nrm3j83bBNMpH1+\naUV6/QLNnyRzP84syDOA6UB5oD3OnGJt3AvNmPCmCp98ArfdBi++CCNHnrmwmLfZv87mljG38HKj\nl3nn9neIyhOVs8Ea4xJ/mssqqer2NPtqq+paVyPLJmsuM8F08CB06QK//eYMrrz88vTLqSpvfv0m\nH6/9mGltplG3bNp1/YzJWcFoLpsmIqkrH4lII5xOf2NMOtasgRo1oFQpWLUq4wRzPOE47ae1Z+av\nM4nvGm8JxkQkf5JMd2CGiJQUkWbA+8Cd7oZlfInkduFwrVtyMrzzDsTEwODB8MEHcMEFZ5eLi4tj\n56Gd3DzyZs6POp/lscspXSCdScrCVLh+fv6K9PoFms+5y9RZffIJYDFwAmiqqn+7HpkxYeSff+DB\nB+HAAecJsgoVMi77494fuX/4/Tx949M8deNTNv7FRLTMpvqfnWZXNWAPcBBnqn+fC5cFk/XJmJwS\nF+csLPbAA/Dqq2cvLOZt+HfDeX7J84y9dyzRVdJZJMaYIAt0n0xmdzKD0tmngHDmOjPG5EpJSU5S\n+fRTZ2LLO+7IuGxCUgJPL3qaRb8tYkXnFVxR7Ioci9OYYMpsPZm4dF7LU/7NySDN2SK5XTgc6vbn\nn9CkCaxYAd99l3mC2X98P9Hjo9ny7xZWd1nNnp/25FygQRAOn192RHr9As1mYTYmi+bPdwZXNmkC\nixY5T5Fl5Ke/f6LO8DrULFWTOe3nhO0AS2POla0nY4yfTp92FhabOBHGj09/YTFvMzfNpOvsrgy+\nYzAPXPdAzgRpTDblWJ+MiHwGzAf+q6pHAnVBY8LR9u3OwmLFizszJ6e37ksKVeWNFW8wdN1Q5tw/\nhzplQnIOWWNyRGbNZSOB6sA8EVnqmb/s+hyKy/gQye3CoVa3qVOhbl0nyWS0sFiKY6eP0W5aO2Zv\nnk18l/h0E0yo1S/QrH7GW4Z3Mqq6GlgN9BORYsDtwNMich2wHpivqpNzJkxjct7Jk85iYgsXwty5\nULt25uV3HtpJi4ktuL7E9cTFxnHBeemMxDQml8lyn4w4I8dqAneo6uuuRBUA1idjsmPTJmjbFq68\nEj77DAoVyrz8it9X0GZqG56t/yz/V+//bIClCVs5vp5MuLIkY87VmDHwzDPw+uvQtWv6C4t5G7Zu\nGC8ue5Gx94zljiqZPMtsTBgIxgSZJgRFcrtwsOp29Ch06gQDBsDSpRmvXJkiISmBnvN6MmjVIFZ0\nXuF3gonkzw6sfuZMlmSMATZsgJo1nSlh1q7NeOXKFPuO7+OOcXew/eB21nRZw+WXZDDVsjG5nD/r\nyeQHngLKq2pXEakKXKGqc3IiwHNlzWXGH6rOtDAvvQRDhkCHDr6P+XHvj7SY2II2V7fh9VtftwXG\nTETJybnLUowC1gH1Pdu7galASCcZY3w5dQp69nTWfPnmm4zXffE2Y9MMus7uypA7htDhOj8ykjG5\nnD/NZZep6gDgNICqHnM3JOOPSG4Xzom6/fUX3Hor7N+f+cJiKVSVV5e/yuPzH2d+h/nZSjCR/NmB\n1c+cyZ8kc0pELkzZEJHLgFPuhWSMu779FurUgdtvdwZaFiiQefljp4/RZmob5m2dR3yXeGqVrpUz\ngRoTAfzpk7kdeAG4CmfhspuAWFVd5n545876ZEx6xo2DJ590xr7ce6/v8r8f/J0WE1tQo1QNht41\nlHzn5XM/SGOCKCjjZDwj/ut5Nler6r5ABeAWSzLGW1IS9O0LX34JM2fCNdf4Puar37+i7dS29Lmp\nD73q9rIBliZXCNY4mXzAAeAIcJWI+Jh/1rgtktuFA123AwegWTPnMeW1a/1LMJ9++ymtp7RmzD1j\nAj6CP5I/O7D6mTP5fLpMRAYAbYFfgCSvt75yKyhjAuWXX6BFC2jeHAYOhPN8/MYnJCXQa0Ev4nbE\n8XXnr6l6SdWcCdSYCOVPn8xm4FpVDavOfmsuM7Nnw8MPw9tvQ2ys7/L7ju+j9ZTW5M+bny/u+4KC\n+Qq6HqMxoSYYzWW/Aeefy8lFJFpENonIFhHpk0GZ9z3vfy8iNfw9VkSeFpFkESl6LrGZyKXqzDvW\no4eTaPxJMD/s/YE6w+pQr0w9ZrabaQnGmADxJ8mcADaIyGci8oHn9b6vg0QkCvgQiMZ5Mq29iFRL\nU6YZUEVVqwLdgE/8OVZEygFNgd/9iD8iRXK7cHbqdvQotGnjJJf4eGcdGF++3PglTcY24fVbX+fN\n2950fQR/JH92YPUzZ/JnxP8sz8ubP+1QdYCtqroDQEQmAi2AjV5lYoAxAKq6RkQKi0hJoJKPYwcD\nzwIz/YjD5BLbt8M998ANN0BcHFzgYzmXZE3m1eWvMmL9CBZ0WEDN0jVzJE5jchOfSUZVR5/jucsA\nf3ht7wLS/l2ZXpkyQOmMjhWRFsAuVf0hNz9S2rhx42CH4JpzqduyZdC+PTz/PDz+uO/p+Y+ePkrs\njFj2HN1DfNd4Sl5c8tyCPQeR/NmB1c+cKcMkIyJTVLW1iPyYztuqqtf5OLe/ve5+ZwrPzAPP4zSV\nZfl4E3lU4aOP4LXXYPx4aNLE9zE7Du6gxcQW1CpVi/Etx9sAS2NclNmdTC/Pv805+4vcnwTyJ1DO\na7sczh1JZmXKesrkzeDYy4CKwPeeu5iywDoRqaOqf6cNIDY2looVKwJQuHBhqlevnvpXSEq7arhu\nDxkyJKLq473t3eadWfnTp2Hy5MasWQODB8cRFQWQ+fmlotBuWjtaXdiKlgVbpiaYUKxfuG5b/cJr\nOy4ujtGjRwOkfl8GlKpm+gIG+LMvnTLn4TyZVhHn6bQNQLU0ZZoB8zw/18OZTcCvYz3ltgNFM7i+\nRrJly5YFOwTX+FO33btVb7xRtWVL1SNH/Dvvx/Efa/GBxXXxb4uzF2A2RfJnp2r1C3ee706fucHf\nlz/jZNarao00+35UVR/LOoGI3AkMAaKAEar6poh092SATz1lUp4iOwZ0VtXvMjo2nfNvA2qp6r/p\nvKe+6mbC09q10LKls3LlCy9AHh/PSJ5OOk2v+b34audXzGw3kypFq+RMoMaEoRybu0xEegCP4jRR\n/eb1VgHgG1UN6cU0LMlEps8/h6eegmHDnCfJfPnn2D+0mtKKQvkKMa7lOBv/YowPOTkY8wvgbpzH\nl5t7vWqGeoLJDbzbhSNNenVLTIRnnoFXXnGeJPMnwXz/1/fUGV6Hm8vdzIx2M0ImwUTyZwdWP3Om\nDDv+VfUQcAhoJyLVgQY4Hf4rgP05E54xzgSX7do5T5LFx0NRP+Z4mPbLNHrM7cEHd35A22vauh+k\nMSZd/vTJ9AK6Al/iPGV2DzBMVX2O+g8may6LDCkTXMbEwIABvie4TNZk/rP8P4zaMIrpbadzQ6kb\nciZQYyJEjq8n4xknU089yy6LSH6cp8B8dvwHkyWZ8DdzJnTtCu+8A506+S5/9PRROk3vxN/H/mZa\nm2mUuLiE+0EaE2GCtZ5McgY/myCJ5HbhpUvjePVV6NkT5szxL8FsP7Cd+iPqU/TCoizptCSkE0wk\nf3Zg9TNn8mfuslHAGhHxbi4b6WpUJtc6etTp3D992ul/KVXK9zHLti+j/bT2vNDgBXrW6WkrWBoT\nQvxdfrkmcDOejn9VXe92YNllzWXhZ/t2p/+ldm34+GPI52O2F1Xlk28/4T/L/8P4luNpUtmPOWWM\nMZkKRp9MPeAXVT3s2S6IM/p+TaCCcIMlmfCydCncfz+8+CI89pjvCS5PJ53m8XmP880f3zCz3Uwu\nK3pZzgRqTIQLRp/MUOCI1/Yxzz4TRJHSLqwKH3zgJJgvvnD6YZYvj8v0mL+P/c1tY2/jr2N/serh\nVWGXYCLls8uI1c9486vj3/uWQFWTcKZ6MSZbTp2CLl2c0furVsGtt/o+ZsNfG6gzrA6NKjRietvp\nFMhXwP1AjTHnzJ/msunAMpxVKwXoAdyiqn6MuQ4eay4LbXv2OPOPlSkDo0fDxRf7PmbKz1N4bN5j\nfNTsI1pf3dr1GI3JjYLRXPYIcBPOtPy7cGZL7haoAEzuEx8PderAXXfBlCm+E0yyJvPyspfpvbg3\nCx9YaAnGmDDiM8mo6l5VbauqxT2v9prO2i0mZ4Vru/DYsdC8OXz4odPJn14Hv3fdjpw6wn2T72PZ\njmXEd42nRqkaZx8QZsL1s/OX1c94y2xlzD6qOkBEPkjnbVXVJ1yMy0SYxER49lmYPduZ4PLqq30f\ns+3ANmImxFC/XH0mtZrE+VHnux+oMSagMpvq/25VnS0isem8rao6xtXIssn6ZELHv/9C27bOXcvE\nif5NcLl0+1Lun3Y/LzV8iUdrP2oDLI3JITk+TiZcWZIJDT//7AywvOceeOst3xNcqiofrf2I1756\njS/u+4JbK/nxyJkxJmByvONfRK4QkWEislhElnleSwMVgDk34dAuPGMG3HIL9O/vTHLpK8GcTjpN\nt9ndGDRhECsfXhmxCSYcPrvssPoZb/7MXTYF5/Hl4UCSZ5/dIpgMJSfDa68541/mznWmifHl72N/\nc9/k+yh2UTE+uvMjKhep7H6gxhjX+TNOZp2q1syheALGmsuC4+hRePBBZxzMl19CyZK+j1m/Zz33\nTLqH2Otj6de4H3nE38nBjTGBFoxxMrNF5DERKSUiRVNegQrARI5t2+DGG6FIEecJMn8SzOSfJ3PH\nuDsYdPsgXrnlFUswxkQYf/6PjgWeAVYC6zyvb12Myfgh1NqFlyyB+vWhe3enmczXDMrJmsyLS1/k\n2cXPsqjjIlpd1Sr1vVCrW6BZ/cJbpNcv0Hz2yahqxRyIw4SplAku33jDeTy5cWPfxxw+dZiO0zty\n4MQB4rvGUzx/cdfjNMYEhz3CbM7ZqVPQowesW+cslVyxou9jfvv3N2ImxtCgfAPev/N9G2BpTIgJ\n1vLLxpxhzx7nruXIEVi50r8Es2TbEuqPrE/P2j0Z2nyoJRhjcgFLMmEqmO3Ca9Y4E1w2bw6TJ0P+\n/JmXV1XeX/M+Hb7swKRWk+hRu0em5SO9zdvqF94ivX6B5rNPxrP0ctp2p0PA76qa6EpUJmSNGQO9\ne8Pw4RAT47v8qcRTPDbvMeL/jGfVw6uoVKSS+0EaY0KGP+NkVgM1gR88u64FfgYKAT1UdaGrEZ4j\n65MJrMREJ7nMnev0v1Sr5vuYvUf30nJyS0rkL8HYe8dy8fl+LBpjjAmqYPTJ7Aaqq2pNz6DM6sA2\noCnwdqACMaFr/36IjoaNG52mMn8SzHd7vqPO8DrcXvl2praZagnGmFzKnyRzhar+nLKhqr8AV6rq\nb9j0MkGTU+3CP/3k9L/ccINzF1OkiO9jJv00iehx0Qy+ffA5jeCP9DZvq194i/T6BZo/c5f9LCKf\nABNxll9uA/wiIvmABDeDM8E1fTp06wbvvgsPPOC7fMoAywk/TWBxx8VcX/J694M0xoQ0f/pkLgIe\nxVmCGeAb4GPgJJBfVY+4GuE5sj6Zc5ecDK++CiNGOPOP1arl+5jDpw7T4csOHDl1hCmtp3Bp/kvd\nD9QYE3A5vp6MiNRU1XVp9jVX1TmBCsINlmTOzZEjzgSXe/fCtGn+zT+29d+txEyIoXHFxrwX/R55\no/K6H6gxxhXB6PgfJiLXegXQHng5UAGYc+NGu/C2bc78Y5dcAkuX+pdg/rvtv9w08iaeqPsEH9/1\ncUASTKS3eVv9wluk1y/Q/EkyrYAxInKliHTFaTpr6m5YJqelTHDZowd89pnvCS5VlfdWv0fH6R2Z\n3Goyj9R6JGcCNcaEFb/mLhORK4AZwO9AS1U97nZg2WXNZf5RhffegwEDYMIE/ya4PJV4ih5ze7Bu\nzzpmtptJxcIV3Q7TGJNDAt1cluHTZSLyY5pdRXHufNZ4vsCvC1QQJjhOnoRHHoENG2D1aqhQwfcx\nfx39i5aTWlK6QGlWPrSS/Of7mFPGGJOrZdZcdneaV13gDs/Pfkwo4hCRaBHZJCJbRKRPBmXe97z/\nvYjU8HWsiAwUkY2e8l+KSCF/44kU2W0X3r0bGjWCEyfgm2/8SzDrdq+jzrA6RFeJZnLrya4lmEhv\n87b6hbdIr1+gZZhkVHVHZi9/Ti4iUcCHQDRwFdBeRKqlKdMMqKKqVYFuwCd+HLsIuFpVrwc2A8/5\nX2WzerUzwPKee5w1YHxNcAkw4ccJRI+P5r3o93i50cu2gqUxxi+uricjIjcC/VQ12rPdF0BV3/Iq\nMxRYpqqTPNubgMZAJV/HevbfC9ynqg+k2W99MukYPRqefRZGjnRmUfYlKTmJF5e+yKSfJzGz3Uyu\nLXGt74OMMWErx/pkAqQM8IfX9i6cZjdfZcoApf04FuAhYEK2I41wiYnwzDMwbx4sX+7f/GOHTh6i\nw5cdOJZwjPiu8RS7qJj7gRpjIorbScbfW4lzypoi8gJwWlW/SO/92NhYKnpW0ypcuDDVq1ensefx\nqZR21XDdHjJkiN/12bMHmjePIyoK4uMbU7iw7/OPmzmO55c+z923382Q6CF8s+KbHKufd5t3qPz3\ntvpZ/SK1fnFxcYwePRog9fsyoFTVtRdQD1jgtf0c0CdNmaFAO6/tTUAJX8cCsThT3FyQwbU1ki1b\ntsxnmaQk1U8/VS1WTPWll1QTE/0798KtC7X4wOL66befZi/Ic+RP3cKZ1S+8RXr9PN+dAcsDbvfJ\nnAf8CjTBWTIgHmivqhu9yjQDeqpqMxGpBwxR1XqZHSsi0cAgoJGq7svg2upm3ULdr786k1ueOgXD\nhsG1fnSlqCpDVg9h4MqBTGo1iQYVGrgfqDEmpIRVn4yqJopIT2AhEAWM8CSJ7p73P1XVeSLSTES2\nAseAzpkd6zn1B8D5wGIRAVilqo+6WZdwcfq0M7Dyvffg5ZfhsccgKsr3cacST/HI3EfY8NcGVj28\nigqF/Xim2RhjfHD1TiaYIv1OJi4uLrV9NcWqVdC1K1SsCB9/DOXL+3euPUf20HJyS8oVLMeoFqOC\nPsAyvbpFEqtfeIv0+gVjgkwT4o4cgccfh5Yt4aWXYPZs/xPM2j/XUmd4He6qeheTWk0KeoIxxkQW\nu5MJc7NnO01iTZvCwIFQtKj/x47/YTxPLnySz+7+jHuuvMe9II0xYSOs+mSMe/76C554AtavhzFj\n4JZb/D82KTmJ55c8z9SNU1n64FKuKX6Ne4EaY3I1ay4LM6owfDhceWUcVarADz9kLcEcOnmImIkx\nrN29lvgu8SGZYLzHIUQiq194i/T6BZrdyYSRzZudx5KPH4d33oEuXbJ4/P7NxEyIoWnlpgy+Y7Ct\nYGmMcZ31yYSBhASnv2XwYHjxRaeT35/Hkr0t3LqQTjM68dotr9G1Zld3AjXGhD3rk8ll1qxxHksu\nWxbWrfNvSn5vqsq7q9/lnZXvMK3NNG4uf7M7gRpjTDqsTyZEHTkCvXo50/E/9xzMnXtmgvGnXfhk\n4kliZ8Yy7odxrO6yOmwSTKS3eVv9wluk1y/QLMmEoLlz4Zpr4PBh+OknaN8eJIs3r7uP7Kbx6Mac\nTDzJ1w99TflCfg6cMcaYALI+mRCyd69z97J2LXz6Kdx227mdZ+2fa2k5uSU9avXguZufQ7KaoYwx\nuZaN+I9Aqs4iYtde6zSJ/fjjuSeYcT+M464v7uKjZh/xfIPnLcEYY4LKkkyQbd3qJJSPP4aFC53J\nLS+6yPdxaduFk5KT6L2oN/3i+rHswWXEXBHjTsA5INLbvK1+4S3S6xdolmSCJCEB3noL6tWDu+6C\n1auhRo1zO9fBkwdpPqE53/31HfFd4rm6+NWBDdYYY86R9ckEwdq1zmPJJUvCJ59ApUrnfq5f9/1K\nzMQYoi+LZtAdgzgvjz2Vbow5d9YnE8aOHoUnn4S774bevWH+/OwlmPlb5tNgVAOerf8s7935niUY\nY0zIsSSTQ+bPdx5L3r/feSy5Q4esP5acQlXp8WEPHp71MNPbTufhGx4ObLBBFult3la/8Bbp9Qs0\n+9PXZX//Df/3f06fy2efwe23Z+98JxNP0nV2V9ZsX8Oal9dQrlC5wARqjDEusD4Zl6jC2LHw7LPQ\nqRP07w/5s7ke2O4ju7l30r1ULlKZETEjuCivH4+hGWNMFtjcZWHgt9/gkUecprH58+GGG7J/zjW7\n1nDf5Pt4rPZj9L25r41/McaEBeuTCaDERHj7bahb12kWi48PTIIZ+/1Y7p5wN5/c9QnPNXBG8Edy\nu3Ak1w2sfuEu0usXaHYnEyDr1jnruxQr5iSXypWzf87E5ET6LO7DzF9nEhcbx1WXXpX9kxpjTA6y\nPplsOnYMXn4Zxo1z1nzp2PHcnxrzduDEAdpPa0+SJjGp1SSKXlg0+yc1xhgfbJxMCFm0yJlvbO9e\n57HkTp0Ck2A27dtE3eF1ubLYlczvMN8SjDEmbFmSOQf79jkJpVs3Z86xcePg0ksDc+55W+bRcFRD\nnrv5OYZED8lwgGUktwtHct3A6hfuIr1+gWZJJgtUYfx4Z1BlsWLO3Ut0dKDOrbz9zdt0nd2Vme1m\n0rlG58Cc2Bhjgsj6ZPy0Y4fzWPKePTB8ONSuHbBTcyLhBF1nd2XTvk3MaDeDsgXLBu7kxhiTBdYn\nk8OSkuDdd6FWLWjcGL79NrAJ5s/Df9JwdEOSNZkVnVdYgjHGRBRLMpn4/nu48UaYNQtWrYK+fSFv\n3sCdf/Wu1dQZXodW1VoxvuV4Lsx7od/HRnK7cCTXDax+4S7S6xdolmTSceIEPP88NG0K3bvD0qVQ\ntWpgrzF6w2hiJsTwafNP6XNzHxvBb4yJSNYnk8ayZc5TYzVqwPvvO2u+BFJiciLPLn6W2ZtnM6vd\nLKpdWi2wFzDGmGywuctccuCAs8bLwoXw0UcQ48LqxQdOHKDt1LYAxHeJp8iFRQJ/EWOMCSG5vrlM\nFaZMgauvhnz54Oef3UkwG//ZSJ3hdbim+DXM6zAv2wkmktuFI7luYPULd5Fev0DL1Xcyu3bBo4/C\n1q0wdSrUr+/OdeZunkvnmZ0Z2HQgD1Z/0J2LGGNMCMqVfTLJyfDJJ84aLz17Ok+N5csX+BhSBlh+\nEAnk/LoAAArXSURBVP8BU9tMpV7ZeoG/iDHGBJD1yWTTL79A167Oz8uXw1UuTWx8IuEEXWZ3YfP+\nzazpsoYyBcu4cyFjjAlhrvbJiEi0iGwSkS0i0ieDMu973v9eRGr4OlZEiorIYhHZLCKLRKSwP7Gc\nOuXcuTRqBA88ACtWuJdgdh3eRYNRDRCEr2K/ciXBRHK7cCTXDax+4S7S6xdoriUZEYkCPgSigauA\n9iJSLU2ZZkAVVa0KdAM+8ePYvsBiVb0cWOLZztQ33ziPJG/YAOvXQ48ekMelmq/8YyV1h9elzdVt\n+Pzez7M0wDIrNmzY4Mp5Q0Ek1w2sfuEu0usXaG42l9UBtqrqDgARmQi0ADZ6lYkBxgCo6hoRKSwi\nJYFKmRwbAzTyHD8GiCODRHP4sNPfMnOmM+alZcvATMWfkZHrR9L3v30Zfc9omlVt5t6FgIMHD7p6\n/mCK5LqB1S/cRXr9As3NJFMG+MNrexdQ148yZYDSmRxbQlX3en7eC5TIKICrr4Y773RmSy7i4pCU\nxOREnln0DPO2zOOrzl9xZbEr3buYMcaEETeTjL+PrflzbyHpnU9VVUQyvM7nnzuTWropWZO5e8Ld\nqCpruqzJsQGWO3bsyJHrBEMk1w2sfuEu0usXcKrqyguoByzw2n4O6JOmzFCgndf2Jpw7kwyP9ZQp\n6fm5FLApg+urvexlL3vZK+uvQOYCN+9kvgWqikhFYDfQFmifpswsoCcwUUTqAQdVda+I7M/k2FnA\ng8AAz78z0rt4IJ/zNsYYc25cSzKqmigiPYGFQBQwQlU3ikh3z/ufquo8EWkmIluBY0DnzI71nPot\nYLKIPAzsANq4VQdjjDHZE7Ej/o0xxgRfWEyQGUqDOt3gUv0GishGT/kvRaRQTtQlPW7Uz+v9p0Uk\nWUSKulmHjLhVNxF53PP5/SQiA9yuR0Zc+t2sIyLxIrJeRNaKSADXms2abNZvpIjsFZEf05SPlO+W\njOqXte8Wtzr+A/gAQRSwFagI5AU2ANXSlGkGzPP8XBdY7etY4G3gWc/PfYC3Iqx+TYE8np/firT6\ned4vBywAtgNFI6VuwC3AYiCvZ/vSSPrscMa23eH5+U5gWbjVz7PdAKgB/JjmmLD/bvFRvyx9t4TD\nnUzqoE5VTQBSBmZ6O2NQJ5AyqDOzY1OP8fx7j7vVyJAr9VPVxaqa7Dl+DVDW/aqky63PD2Aw8Kzb\nFciEW3XrAbzp2Y+q/uN+VdLlVv32ACl//RYG/nS3GhnKTv1Q1RXAgXTOGwnfLRnWL6vfLeGQZDIa\nsOlPmfQGdaYc6/egTpe5VT9vDwHzsh3puXGlfiLSAtilqj8EOuAscOuzqwo0FJHVIhInIrUCGvX/\nt3f+MVJVVxz/fMsPQawKgq3WIkoKEbVFVsgitooo0YQa26KmxCjGGKJ/SCAl1Zr6I8ZY0hCbUmwt\ntpIYNSJum2qiVmhEkRJhgRWXiIS2kmLQSmKLv6Alp3/cM3EY3uzMLO+5O7vnk0zm7p177zvnvs09\nufe8d079FKXf7cASSbuBn5NeUegJjka/rugLa0u91FxbmsHI1PtkwlG91NnAdfImT/2O7CTdCRw0\nsye60z8HctdP0lDgJ8Dd3emfI0Xdu4HAcDNrBRYBKxvsnxdF6fc74DYzGw0sAH7fYP+86K5+da8V\nTbq21NWv3rWlGUL97yGdvZf4OsnadtXmNG8zKKO+tDV/T9JXzWyvpFOA93OVun7y1O+wvpLmks5c\nZ+QnbsMUod9Y0jlzh1IwutOAdklTzOyLvI9F3bt/Am0AZrbRH2w4ycz25Sh7PRSl3xQzu9TLq4BH\n8hK4QbqrX63jvWZfW2oeXza0tvSEQ6pB59VAYBdpURlMbedVK587H6v2JTnnSlEEbqfnnHNF6Xc5\n0AmM7Iv3r6J/Tzn+i7p384B7vTwO2N2X7h2wGbjIyzOAjc2mX9nvY8h2/Df12lJDv4bWli9c8W5O\n1hXADtKTEnd43TxgXlmbX/nvHcCkrvp6/QhgNfA28GfgxD6m307gHWCLfx7qS/pVjP83esDIFHjv\nBgGPAduAduDivnTvgPNJDuOtwF+B85pUvydJEUkOkPwaN3p9X1lbqunX0NoSL2MGQRAEhdEMjv8g\nCIKgSQkjEwRBEBRGGJkgCIKgMMLIBEEQBIURRiYIgiAojDAyQRAEQWGEkQl6LZLGVIYZz2nc71YL\ne16j3wmSbin7+1RJT+coV6uk31bUXSzp2byu0YAsN/jb6kFwVISRCfodZvasmXUnR8tw4Naycd41\ns6vzk4wrgOdzHO9omEsKclk3kgYUI0rQzISRCZoCSWdK2iypRdKxklZK6vSkSRsktWT0+YekeyS1\nS3pD0nivnytpqZe/IukPkrb6p9XrF0ra5p/5PuTPgLGebGuxpNNLOy0fs03S856sanGZHDMlrXc5\nVkoaVkXNS0hvilebg8k+B2dIGuWJsd6UtNx1HVHR/mpJS7w8X9Kusrlc5+W7lBKIbZP0sNfNJr2V\n/7hfb4jP+8uSNkl6oRQO3uselLQRuK3Lmxj0S8LIBL0eNw6rgBvMrJ20m9hnZmcDPwVayI4ca8C/\nzKwF+DXwo4w2vyQlzZpIStC03Q3WXFI+jlbgZkkTSQmodpnZeWb2Y46MXvst4BrgXOBaSV+TNBK4\nE5jhcrQDCzN0HAn818z2V5mDC1yHK83s76QI1KvN7Byfm9EZ3V4hJZ7Cvz+QdKqX13r9UjObYmbn\nAkMlzTKzVcAmYI6ZTQIOAUuBH5jZ+cCjwP3e30jJ1Sab2YNZsgf9m2aIwhz0b04G/gh8z8ze8rpp\nwC8AzKxTUlc5Zdr8ezPw/YzfpwPX+VgG/EfShUCbmX0KIKmNtDD/qYasa0pGQtJ2UnDB4cAEYL1H\njB4MrM/oOxN4scq4ZwEPA5eZ2V6vm4YnwzKzFyVlJZd6T9Jxko4jRdd9AvgOcCHwjDe7RNIi4FhS\nzK03gef8t5IRHQ+cDax2HQaQYlqVeKqK3EEQRibo9XxICsb3beCtsvp6c5gc8O9DVP9/z8qnoYrf\n6wnyd6CsXH69l8xsTo2+lwNLMuqNlEnyGGAShyeIqmcO1gM3koIkrgNuAqYCCyUNAZYBLWa2R9Ld\nwJCKa5eu02lmF1S5xsd1yBH0U+K4LOjtHCTtQK6X9EOve410LIWkCaTjqe6yhpTuGEkDJB0PvApc\nJWmo+0+u8rqPgC83MLYBG4Bpksb6NYZJ+kZ5I6XtwTfNrCNjDJEM7SzgAUkXeX35HMwk7ZiyeJWU\n+GwtKWLudOAz33GVDMo+3+2UP8SwHzjeyzuAUWX+qkE+70FQkzAyQW/HzOwT0iK7QNIs4CHSotcJ\n3EfKbfHvrL4VZcsozwem+5HbJlK+jS3ACuB1kpFYbmYdlpKGveZO8sVdjFku/Ack/86TkjpIO4vx\nFc1aSAYgU3+fg/d9DpZJmgzcC8z0Bw9mA3tJhqGSdaR0uq9Yysu+2+swsw+B5aQjshdI4fdLrAB+\nI2kzaZ2YDSyWtNVlnVpF3iA4jAj1HzQdkr5EcjYf8B3CS8A4M/tfD4vWLZTS2O40s7rTLEsaDBwy\ns0OSpgLL3EkfBL2K8MkEzcgw4C+SBpGOk25pVgMDYGb31251BKOBlW5wDwI35ytVEORD7GSCIAiC\nwgifTBAEQVAYYWSCIAiCwggjEwRBEBRGGJkgCIKgMMLIBEEQBIURRiYIgiAojP8D2/x1wBuTrN4A\nAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x78b2e48>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Mininmum kerosene rate: 968.71 kg kerosene/h \n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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7ygTgHQgG8G6cTGfgWaAE8BdQ2bNt/CiU24VDuW4Q2PU7etS5ehkyBL7//vIS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\nua7xbQHGGNcFW59MCeDPRNu7gaTT6Sa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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c37160>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical stages: \n",
+ "8.3\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.5: Page 510"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.5\n",
+ "# Page: 510\n",
+ "\n",
+ "print'Illustration 10.5 - Page: 510\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "import numpy.linalg as lin\n",
+ "import numpy\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:ethylbenzne b:diethylene glycol c:styrene\n",
+ "F = 1000;# [kg/h]\n",
+ "xF = 0.5;# [Wt. fraction styrene]\n",
+ "xPE = 0.9;# [kg styrene/kg hydrocarbon]\n",
+ "xRNp = 0.1;# [kg styrene/kg hydrocarbon]\n",
+ "#******#\n",
+ "\n",
+ "# X: kg styrene/kg hydrocarbon\n",
+ "# Y: kg styrene/kg hydrocarbon\n",
+ "# N:kg glycol/kg hydrocarbon\n",
+ "# Equilibrium data:\n",
+ "# Hydrocarbon rich solutions:\n",
+ "# Eqb1 = [X N]\n",
+ "Eqb1 = numpy.array([[0 ,0.00675],[0.0870 ,0.00817],[0.1833, 0.00938],[0.288 ,0.01010],[0.384 ,0.01101],[0.458, 0.01215],[0.464 ,0.01215],[0.561 ,0.01410],[0.573, 0.01405],[0.781 ,0.01833],[1 ,0.0256]]);\n",
+ "# Solvent rich solutions:\n",
+ "# Eqb2 = [Y_star N]\n",
+ "Eqb2 = numpy.array([[0 ,8.62],[0.1429 ,7.71],[0.273, 6.81],[0.386, 6.04],[0.480, 5.44],[0.557, 5.02],[0.565, 4.95],[0.655, 4.46],[0.674, 4.37],[0.833, 3.47],[1 ,2.69]]);\n",
+ "\n",
+ "plt.plot(Eqb1[:,0],Eqb1[:,1],label=\"X Vs N\")\n",
+ "plt.plot(Eqb2[:,0],Eqb2[:,1],label=\"Y Vs N\")\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper right');\n",
+ "plt.xlabel(\"kg styrene / kg hydrocarbon\");\n",
+ "plt.ylabel(\"kg diethylene glycol / kg hydrocarbon\");\n",
+ "plt.title(\"Equilibrium Data\")\n",
+ "# In Fig. 10.31 (Pg 512):\n",
+ "# Point E1 is located.\n",
+ "NE1 = 3.10;\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "# From Fig. 10.30 (Pg 511):\n",
+ "Np = 9.5;\n",
+ "print\"Minimum number of theoretical stages:\\n\",Np\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "# The tie line when extended passes through F provides the minimum reflux ratio.\n",
+ "# From the plot:\n",
+ "N_deltaEm = 20.76;\n",
+ "# From Eqn. 10.48:\n",
+ "Ratiom = (N_deltaEm-NE1)/NE1;# [kg reflux/kg extract product]\n",
+ "print\"Minimum extract reflux ratio: \",round(Ratiom,3),\"kg reflux/kg extract product\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (c)\n",
+ "\n",
+ "Ratio = 1.5*Ratiom;# [kg reflux/kg extract product]\n",
+ "# From Eqn. 10.48;\n",
+ "N_deltaE = (Ratio*NE1)+NE1;\n",
+ "# Point deltaE is plotted.\n",
+ "# A straight line from deltaE through F intersects line X = 0.10 at deltaR.\n",
+ "N_deltaR = -29.6;\n",
+ "# In Fig. 10.31 (Pg 512):\n",
+ "# Random lines are drawn from deltaE for the concentrations to the right of F, and from deltaR for those to the left,and intersection of these with the solubility curves provide the coordinates of the opeating curve.\n",
+ "# The tie line data are plotted directly to provide the equilibrium curve.\n",
+ "# From Fig. 10.32 (Pg 513):\n",
+ "Np = 15.5;\n",
+ "# Feed is to be introduced in the seventh from the extract product end of cascade.\n",
+ "# From Fig. 10.31 (Pg 512):\n",
+ "XRNp = 0.10;\n",
+ "NRNp = 0.0082;\n",
+ "# Basis:1 hour.\n",
+ "# Overall plant balance:\n",
+ "# (1): PE_prime+RNp_prime = F\n",
+ "# C Balance\n",
+ "# (2): PE_prime*(1-XRNp)+RNp_prime*XRNp = F*xF\n",
+ "# Solving (1) & (2) simultaneously:\n",
+ "a = numpy.array([[1 ,1],[(1-XRNp), XRNp]]);\n",
+ "b = numpy.array([F,F*xF]);\n",
+ "soln =lin.solve(a,b)\n",
+ "PE_prime = soln[0];# [kg/h]\n",
+ "RNp_prime = soln[1];# [kg/h]\n",
+ "RO_prime = Ratio*PE_prime;# [kg/h]\n",
+ "# From Eqn 10.39:\n",
+ "E1_prime = RO_prime+PE_prime;# [kg/h]\n",
+ "BE = E1_prime*NE1;# [kg/h]\n",
+ "E1 = BE+E1_prime;# [kg/h]\n",
+ "RNp = RNp_prime*(1+NRNp);# [kg/h]\n",
+ "S = BE+(RNp_prime*NRNp);# [kg/h]\n",
+ "print\"Number of theoretical stages: \\n\",Np\n",
+ "print\"Extract Flow Rate: \",round(E1,2),\" kg/h\\n\"\n",
+ "print\"solvent Flow Rate: \",S,\" kg/h\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.5 - Page: 510\n",
+ "\n",
+ "\n",
+ "Minimum number of theoretical stages:\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "9.5\n",
+ "\n",
+ "\n",
+ "Minimum extract reflux ratio: 5.697 kg reflux/kg extract product\n",
+ "\n",
+ "\n",
+ "\n",
+ "Number of theoretical stages: \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "15.5\n",
+ "Extract Flow Rate: 19567.58 kg/h\n",
+ "\n",
+ "solvent Flow Rate: 14799.1 kg/h\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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idIF0+NepajdVna+qH6hqd6CRqg7AGRzNmByhyRVNmNdlHl/0/IJNhzZx5egr\nefTzR9n5285wh2ZMlgikpPM90FpVf3CnKwKfqmpNEVmjqldnuHEr6Zgw2v37bsasGMOU9VO4uszV\n9GvYj3bV2lmd32R7Xtbw2wDjgV3urCuBe4FFwD2q+nKwjfrt2zp8E3anzp5i9ubZjF89np2/7aT3\n1b35V/1/UTGyYrhDMyZFXtbwP8Wp1w8CHgCqA1+o6vFQOntzIatP+mR1LgrlL0T3q7rz9d1f8/md\nn3Pk9BHqv16fm9+9mflb53Mu8Vz6O/GI/V74WC5CF0iHP0lVT6nqWlVdB+QDPvY4LmPCovZltRl9\n02j2PbiPTjU78fTXTxP1ShRPLX6KH//8MdzhGROSQEo6/wVKquq9InIJ8BEwUVXfCrlxK+mYHGDt\ngbVMWDWBGZtm0DKqJX0b9KVV5VZEiN23aMLD07F0ROQF4G9AA+BZVZ0VfIgp7tc6fJNjHD19lGkb\npzF+1Xh+P/U7fer3odfVvShdtHS4QzN5TKbX8EWkk/vqCHyD8/zaNYC68wINbI+IrBeRNSKyIv0t\n8iarT/pk11wUu6gYfRr0YXWf1bzX+T12/r6TGmNrcPvM21m4a6EnQzhk11yEg+UidGk9LLQdviGR\nAda66yc9k3ZOgG0oEKOqvwUfnjHZj4jQqFwjGpVrxIutXuTt9W/z4IIHOXX2FH0a9CE2OpZShUuF\nO0xj/sLzsXREZDfQUFV/TWGZlXRMrqCqLN+/nAmrJzBvyzzaVmtLvwb9aFahGSJB/+dtTJqy7Xj4\nIrILOILzAPQJqjrRb5l1+CbX+e3kb0xZN4UJqycgCH0b9CU2OpbihYqHOzSTS2Tn8fCbunfj3gQM\nEJHmWdBmjmP1SZ+cnosSF5dgUJNBbL53M6+1fY3l+5dTe1xt5m2ZF/S+cnouMpPlInRpPdP2OmB5\nqIfgqvqz+/WQiMwFGgNfJy2PjY0lKioKgMjISKKjo4mJiQF8P2CbzlvTSbJLPBmdXrx4MQDTO09n\n8Z7FdH+pO6NKjGLcgHHUurRWQPtbu3Zttvl+wj29du3abBVPVk7Hx8cTFxcHcL6/zIi0nmk7HufK\nnG3AJzjj5xwIaucihYF8qnpURIoAnwEjVPUzd7mVdEyecTLhJC8se4Hxq8ZTpUQV+jboS6danSiU\nv1C4QzM5jJdj6dTEKce0AiKBL3GGW1iqqmnecy4ilYC57mR+4B1VHem33Dp8k+cknEvgg60fMGH1\nBNYcWEOUWPnnAAAdaUlEQVTPq3rSp0EfqpeqHu7QTA7hWQ1fVb9X1ZdUtTVwPbAUuB1I95p6Vd2t\nqtHuq45/Z28ulLyckZfl9lwUyFeATrU68dmdn/FN728okK8ALeJacP3k65mxcQZnzp05v25uz0Uw\nLBehC+qkraqeUNWPVPU+VW3gVVDG5BWVS1Tm2RufZd+D++jboC8TVk+g/KjyDP58sI3TbzKdPdPW\nmGxm26/beH3160xeN5mry1xN3wZ9uaX6LTZOvzkv216Hn2bj1uEbk6qkcfonrJ7A9t+20/vq3txT\n/x4bp99k6+vwTQCsPuljuXAUyl+Icr+V46u7v2Jhz4UcPX2U+q/Xp807bZi3ZR5nE8+GO8QsZb8X\noUtr8LRjInI0ldefWRmkMXldrUtr8cpNr7DvwX3cUfsOnlv6HFEvRzE8fjj7/9wf7vBMDmElHWNy\nqPUH1zNh1QSmbZxGswrN6NugL62rtCZfRL5wh2Y85vV4+PWAFjgjX37tPvkqZNbhGxO642eOM33j\ndMavHs8vx3/hnvr30OvqXpQtVjbcoRmPeFbDF5EHgHeAS4HSwNsiMjD4EE1arD7pY7nwCSQXRQoW\noXf93qy8ZyVz75jLviP7qD2uNh1ndGTBjgWejNMfDvZ7EbpATtr+C7hGVZ9U1SeAJsA93oZljMmI\n+pfXZ0K7Cfww6AdaVW7FkIVDqPpqVZ5d8qw9k9cENLTCBqCxqp50py8GVqhq3ZAbt5KOMZ5SVVb+\ntJLXV7/O7O9nc3WZq+lWtxudanbikosvCXd4JoO8HEvnISAW5wlXAnQA4lR1VAbiTL5v6/CNySKn\nzp7i4+0f8+6Gd/l81+f8PervdKvbjZur3UzhAoXDHZ4Jgpdj6bwE3A38DvwKxGZGZ28uZPVJH8uF\nT2bmolD+QnSs2ZFZt89i76C9dKjRgTe+e4Pyo8rz0IKH2PHbjkxrywv2exG6QE7aNgG2q+orqjoa\n2Cki13gfmjHGK8ULFSc2OpbP7vyM1X1WUzBfQa6bdB2t327Nh9s+5FximgPhmhwqkJLOWuDqpNqL\niOQDVrlPsQqtcSvpGJNtnDp7ihkbZzB25VgOnTjEvQ3vpdfVvShZuGS4QzPJeFnDX6uq0cnmrVfV\nq4JtLIV9W4dvTDa04scVjF05lg+2fkCHGh0Y0GgADcs2DHdYxuXlWDq7RWSgiBQQkYLudfm7gg/R\npMXqkz6WC59w5aJxucZM7jCZ7fdvp0bJGnR+rzNN3mjC1HVTOX32dFhist+L0AXS4fcDmgI/Avtx\nrsPvE2gDIpJPRNaIyPyMhWiMCZdShUsxuNlgdg7cyX+a/4e3N7xNhZcr8J+F/2Hvkb3hDs8EKZCS\nTklV/TXDDTiXdTYAiqnqLcmWWUnHmBxm26/bGLdyHFPXT6VFxRYMaDSAGyrdgEjQFQaTQV7W8LcD\na4G3gE+C6aFF5AogDngaeEhV2yVbbh2+MTnUsTPHeGf9O4xdOZYz584woNEAetbrSfFCxcMdWq7n\nZQ2/OjAR6AnsEJGRIlItwP2PAv4N5I7BPDxk9Ukfy4VPds5F0YJF6duwL+v6rWNiu4ks2beESq9U\nov+H/dn4y8ZMby875yKnCOTGq0RV/UxVu+CMoXMXsFJEFovIdaltJyI3A7+o6hqcO3SNMbmQiNC8\nYnNmdJ7Bxns3UqZoGVpNbUVMXAwzN80k4VxCuEM0rkBKOqWA7jhH+AeBN4D5QD1glqpGpbLdM8Cd\nwFmgEPA3YLaq9vRbR++66y6iopxdREZGEh0dTUxMDOD7RLdpm7bpnDWdcC6B/5vyf7y/9X1+vexX\n+jToQ50TdShxcYlsEV9Om46PjycuLg6AqKgoRowY4VkNfxvwNvCmqu5PtmyIqj6bbiMiLYFHrIZv\nTN6z4eAGxq4cy4xNM2hdpTUDGg2gafmmdpI3BJ7W8FX1qeSdPUAgnb3/6kGsm+ckfZoby4W/3JCL\nuqXrMv7m8ex+YDdNyjWh9we9iZ4QzcTVEzl+5njA+8kNuQi3/Kkt8L9uPoVPYk1+iWVaVHUxsDjo\n6IwxuUZkoUgeaPIA919zPwt3LWTMyjE8tvAxetbrSf+G/alasmq4Q8z1Ui3piEhMGtup24mH1riV\ndIzJ03744wfGrxrPpDWTqH95fQY0GkCbqm3subzp8PSZtl6xDt8YA87Abe9teo+xK8fyy/Ff6N+w\nP72v7m0Dt6XCy2fabhCR9e7XpNcSERklIvbTyCRWn/SxXPjklVwUyl+InvV68u2/vuW9zu+x+dBm\nqrxahbvn3c2qn1YBeScXXkq1hu/nU5xLK9/FuZ6+C1AY5xLNOKBdqlsaY0yQGpVrRFy5OA6fOMyk\n7ybR+b3OlC5amhu4gSbNmlAof6Fwh5hjBXJZ5prkY98nzRORDaE829ZKOsaY9JxLPMfH2z9mzMox\nrD2wll7RvejXsB8VIyuGO7Sw8fKyzHz+T7gSkcZ+250NtkFjjAlGvoh8tKvejgU9FvD13V9z8uxJ\n6r9enw7TO/DFri+wg8bABdLh9wYmicgeEdkDTALuEZEiwEgvg8tLrD7pY7nwsVz4xMfHU61kNV5u\n/TJ7B+2lTdU2PPzZw9QcW5PR347myKkj4Q4x2wtkLJ2VqloHiAaiVbWuqq5Q1eOq+p73IRpjzIWK\nFCxCnwZ9WNt3LRPbTWTZvmVEvRLl2cBtuYVdlmmMyRV+Pvozr69+nde/e53Kl1Sme93udK7VOVde\n2mnX4RtjDJBwLoGPtn/EtI3T+HTHpzSr0IwutbvQoUYHil1ULNzhZQovT9qaLGC1Wh/LhY/lwifQ\nXBTIV4AONTowo/MMfnzoR7rX7c7MzTO5YtQVdH6vM7M3z+Zkwklvg82mArnxqoiIPCEiE93pqu5Y\n98YYk60VLViUbnW78UHXD9j9wG5uqnITr616jbIvlaXn3J58sv2TPDVefyDX4b8HrAZ6qmpt9+qc\nZapaL+TGraRjjAmDA8cOMHPTTKZtnMb237bTqWYnutTpQvMKzXPEOD5ePtN2tao28L8BS0TWWYdv\njMkN9vyxhxkbZzB903R+Of4Lt9e6na51u9KobKNsO2a/lzX80yJysV9DlYHTwTZk0ma1Wh/LhY/l\nwserXERFRjG42WDW9F3DF3d+QfFCxblz7p1UebUKQxcOzVWXeQbS4Q/HGU/nChF5F/gSGOxlUMYY\nEw41L63J8JjhbBmwhZm3zSQhMYE277Shzrg6PP3V0+z8bWe4QwxJQJdlus+1beJOfqOqhwPauUgh\nnAefXAQUBOap6mN+y62kY4zJ1hI1keX7ljNt4zRmbp5JxeIV6VKnC3fUvoNyfysXlpg8vQ5fRMoB\nUTijayqAqn4VYGCFVfWEiOQHluA823aJu8w6fGNMjnE28SyLdi9i2sZpvL/lfa4qfRVd6nShc63O\nlCpcKsvi8HI8/OeApcBQ4BHg3+4rIKp6wn1bEMgH/BZskHmB1Wp9LBc+lguf7JCL/BH5+Uflf/Bm\n+zf5+eGfebDJg8Tviafy6Mrc9M5NTFk3hT9P/xnuMFMVyHj4t+I8yDxDJ2pFJAL4DqgMvKaqmzOy\nH2OMyU4uyn8R7Wu0p32N9hw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3fqyIfCAiC4EvcD5ciovIh24Mr4l7O3ca7R0Tkf9z21su\nIudjNjlQuAf5t1f4XzgPm9iAMybJd0Bdd/4jwGvu+9pAAlA/2bYlgS1+039zv74FdPSb/yVQz33/\nDDDAfb8beMR9XwpYDFzsTg8GnvBbL2mb/jhDCyTtq7v7PhLYChRO4Xt8BYhJYf4inHFppgGPufPK\nuu1F4gw/8hUwOoVthwNLcW53LwkcxhkOoCKw2l0nAueBHpcAHYHPcEY4vBxnrPOOKeShLPCDu898\nwEKcYaIvBfYCFZO+X/drMSCf+/5GYJb7PhZn0LWk9WKAk+7PO8KNpVNq7bnbJAJt3ffPAUPD/ftq\nr4y/7AjfJLkMeB/opqpJ9fymOA+gQFU34YzjktwfwCkRmSQit+J0KEn8h259A7jbLVfcDrzrt2yG\n+7UJThlomYiswRkYqoLfenPcr9/hdFoArYAh7vqLgIu4cATBJE2BJSnMF2ACzlOFRrrzGgPxqvqH\nOg/WmJnse0miwIeqmqCqvwK/AKVV9QfgVxGJduP7Tp1RT1sA76rjZ5wPQX9JeWgELFLVX1X1HPCO\nu+01wFfu/lHVP9z1I4FZ7nmYl7iwlPaZ33oAK9R5uEgizodcM6Ch+/0mbw/gjKp+5L5fjS/vJgey\nup5J8gfOUV5znNEIk6Q5Br2qnhORxjjjtHcG7nPfw4X16dk4gz99iXP06z/s83G/95+rardUmjvt\nfj3Hhb+7HTWNceFF5Epgn6b8VCQFlgF/F5EXVfW0O8//+04rB2f83vvH9QbOYFalgTf92kprX8dT\nWS+95wD8F1ioqreKSEUg3m/ZiWTr+v9MhJTPQ/jPT/Cbn4j1GTmaHeGbJGdwSg49RaSrO28pztE4\nIlILqJt8I7fGHqmqnwAP4YzRDnAU32PocDvSBThPr3oz+X5c3wJNxXmoRVJ9vmo6cS8ABvrFk9KD\nnG8CPkljH2/gPFnsPRHJhzNEb0sRiXRPdnYi+BO0c3GeSdzQjRGc0tAd7vmCy4G/p7LtSrf9km48\nXXA68W+AFiISBSAil7jr/w1IOhmd3oiJjcW5KivpP62vgRUptLc4iO/V5BDW4ZskqqongJuBB0Xk\nZmAccKmIbMI5itwEHEm2XTFgvjhD1X4NPOjOnw782z3BWMmd9y7OUeJn/u36BXAIp+48zd3fMpzz\nCn+J1W+7/wIF3BOOG4ERKaz/T+DTdL75UcAaYCpO5/kMTke4BKe+/mdqm6ayvwSc/2beU3UK4Ko6\nF+eBJZtxnkW6LJVtfwaG4JSo1gKrVHW+Os+x7QPMEZG1uOU24HlgpIh8h1ODT4op+VVAivNhMsaN\nYZeqzlXVAym1l8L3F8oVTSYbsOGRTarco8ACqnraPer+HKiWSmkkkP09AhRT1WGZGWc6bV4EfK1B\nPlNARIqo6nH3CH8Ozrjs84LYPgKn5t1ZVXcGFbQxHrF6nElLEeBLESmAU9ftH0JnPxeoBFyfifGl\nyy0lZeQBMsNF5EagELAgyM6+FjAfmGOdvclO7AjfGGPyCKvhG2NMHmEdvjHG5BHW4RtjTB5hHb4x\nxuQR1uEbY0weYR2+McbkEf8PM26glcYuDusAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7e76ac8>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.6: Page 516"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.6\n",
+ "# Page: 516\n",
+ "\n",
+ "print'Illustration 10.6 - Page: 516\\n\\n'\n",
+ "\n",
+ "import numpy.linalg as lin\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:heptane b:p-chloronitrobenzene c:o-chloronitrobenzene d:aq. methanol\n",
+ "xb = 0.4;# [Wt fraction]\n",
+ "xC = 0.60;# [Wt fraction]\n",
+ "F = 100;# [kg]\n",
+ "# The para isomer(b) favours the heptane(a) and the ortho isomer(c) favours the methanol(d).\n",
+ "# Basis: 1 hour.\n",
+ "A = 2400;# [kg/h]\n",
+ "D = 2760;# [kg/h]\n",
+ "xbW = 0.8;# [Wt fraction]\n",
+ "xbZ = 0.15;# [Wt fraction]\n",
+ "kb=1.35;\n",
+ "kc=0.835;\n",
+ "#*******#\n",
+ "\n",
+ "B = xb*F;# [kg]\n",
+ "C = F-B;# [kg]\n",
+ "# W = kg A rich product, after solvent removal\n",
+ "# Z = kg D rich product, after solvent removal\n",
+ "# B balance:\n",
+ "# (1): (0.80*W)+(0.15*Z) = B\n",
+ "# C balance:\n",
+ "# (2): (0.20*W)+(0.85*Z) = C\n",
+ "# Solving (1) & (2) simultaneously:\n",
+ "a = numpy.array([[0.80, 0.15],[0.20, 0.85]]);\n",
+ "b = [B,C];\n",
+ "soln = lin.solve(a,b)\n",
+ "W = soln[0];\n",
+ "Z = soln[1];\n",
+ "Wb = xbW*W;# [kg]\n",
+ "Wc = W-Wb;# [kg]\n",
+ "Zb = xbZ*Z;# [kg]\n",
+ "Zc = Z-Zb;# [kg]\n",
+ "xB1_prime = Zb/D;\n",
+ "xC1_prime = Zc/D;\n",
+ "yB1_prime = Wb/D;\n",
+ "yC1_prime = Wc/D;\n",
+ "DbyA = D/A;\n",
+ "# Equilibrium curve:\n",
+ "# First distribution coeffecient: yB_star/xB_prime = 1.35\n",
+ "def f68(x1):\n",
+ " return kb*x1\n",
+ "x1 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Second distribution coeffecient: yC_star/xC_prime = 0.835\n",
+ "def f69(x2):\n",
+ " return kc*x2\n",
+ "x2 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Operating Line, corresponding to First distribution coeffecient:\n",
+ "def f70(x3):\n",
+ " return (DbyA*x3)+yB1_prime\n",
+ "x3 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "def f71(x4):\n",
+ " return DbyA*(x4-xB1_prime)\n",
+ "x4 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Operating Line, corresponding to Second distribution coeffecient:\n",
+ "def f72(x5):\n",
+ " return (DbyA*x5)+yC1_prime\n",
+ "x5 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "def f73(x6):\n",
+ " return (DbyA)*(x6-xC1_prime);\n",
+ "x6 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "\n",
+ "\n",
+ "plot(x1,f68(x1),label=\"Equilibrium curve\")\n",
+ "plt.plot(x3,f70(x3),label=\"Operating curve\")\n",
+ "plt.plot(x4,f71(x4),label=\"Operating curve\");\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"xB_prime\");\n",
+ "plt.ylabel(\"yB_prime\");\n",
+ "plt.title(\"yB_star/xB_prime = 1.35\");\n",
+ "plt.xlim((0,0.05))\n",
+ "plt.ylim((0,0.07))\n",
+ "plt.show()\n",
+ "\n",
+ "plot(x2,f69(x2),label=\"Equilibrium curve\")\n",
+ "plt.plot(x5,f72(x5),label=\"Operating curve\")\n",
+ "plt.plot(x6,f73(x6),label=\"Operating curve\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"xC_prime\");\n",
+ "plt.ylabel(\"yC_prime\");\n",
+ "plt.title(\"yC_star/xC_prime = 0.835\");\n",
+ "plt.xlim((0,0.06))\n",
+ "plt.ylim((0,0.07))\n",
+ "plt.show()\n",
+ "# The stages are constructed.\n",
+ "# The feed matching is shown on Fig. 10.37 (Pg 518):\n",
+ "f_prime = 6.6;\n",
+ "fstage = 4.6;\n",
+ "print\"Number of ideal stage is \\n\",fstage+f_prime-1\n",
+ "print\"The feed stage is \",fstage,\"th from the solvent-D inlet\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.6 - Page: 516\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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aoCslS5T0ttJxgs3CigBmaWuWtkbwBOMQmOU1Xv/5+kxdPTXba7z7xd0teBQC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\n0jwM9TGCwOb75xCOtkg/nJ7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EMAzDKBI2hGUYhmEUCQsghmEYRpGwAGIYhmEU\nCQsghmEYRpGwAGIYhmEUif8fHq0g8rWtPKEAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7e76a20>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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FpCpwNnBviu01DCMNsfk5/ogqTJvmAsfmzXDHHXDZZbDPPkm9SMoHO+wKfA2s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\nFy4MlD3lwb89uXtYUmUJQz8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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c385c0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of ideal stage is \n",
+ "10.2\n",
+ "The feed stage is 4.6 th from the solvent-D inlet\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.7: Page 525"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.7\n",
+ "# Page: 525\n",
+ "\n",
+ "print'Illustration 10.7 - Page: 525\\n\\n'\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# c:Water d:Toulene \n",
+ "Density_c = 998;# [kg/cubic m]\n",
+ "viscosity_c = 0.95*10**(-3);# [kg/m.s]\n",
+ "Dc = 2.2*10**(-9);# [square m/s]\n",
+ "Density_d = 865;# [kg/cubic m]\n",
+ "viscosity_d = 0.59*10**(-3);# [kg/m.s]\n",
+ "Dd = 1.5*10**(-9);# [square m/s]\n",
+ "sigma = 0.022;# [N/m]\n",
+ "Dist = 20.8;# [Distribution Coeffecient]\n",
+ "d = 0.5;# [m]\n",
+ "h = 0.5;# [m]\n",
+ "di = 0.15;# [m]\n",
+ "N = 13.3;# [r/s]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "qC = 3*10**(-3);# [cubic m/s]\n",
+ "qD = 3*10**(-4);# [cubic m/s]\n",
+ "#********#\n",
+ "\n",
+ "V = math.pi*h*d**2/4;# [Vessel volume,cubic m]\n",
+ "phi_DF = qD/(qD+qC);# [Volume fraction toulene]\n",
+ "# Assume:\n",
+ "phi_Dbyphi_DF = 0.9;\n",
+ "phi_D = phi_Dbyphi_DF*phi_DF;\n",
+ "phi_W = 1-phi_D;\n",
+ "# From Eqn. 10.56:\n",
+ "Density_M = (Density_c*phi_W)+(Density_d*phi_D);# [kg/cubic m]\n",
+ "if phi_W>0.4:\n",
+ " viscosity_M = (viscosity_c/phi_W)*(1+(6*viscosity_d*phi_D/(viscosity_d+viscosity_c)));# [kg/m s]\n",
+ "else:\n",
+ " viscosity_M = (viscosity_c/phi_D)*(1-(1.5*viscosity_c*phi_W/(viscosity_d+viscosity_c)));# [kg/m s]\n",
+ "\n",
+ "# Impeller Reynold's Number:\n",
+ "IRe = (di**2*N*Density_M/viscosity_M);\n",
+ "# From Fig 6.5 (Pg 152), curve g:\n",
+ "Po = 0.72;\n",
+ "P = Po*Density_M*N**3*di**5;# [W]\n",
+ "# From Eqn. 10.61:\n",
+ "Value1 = P*qD*viscosity_c**2/(V*sigma**3);\n",
+ "Value2 = viscosity_c**3/(qD*Density_c**2*sigma);\n",
+ "Value3 = Density_c/(Density_c-Density_d);\n",
+ "Value4 = sigma**3*Density_c/(viscosity_c**4*g);\n",
+ "Value5 = viscosity_d/viscosity_c;\n",
+ "phi_Dbyphi_DF = 3.39*Value1**0.247*Value2**0.427*Value3**0.430*Value4**0.401*Value5**0.0987;\n",
+ "# The value of phi_Dbyphi_DF is sufficiently close to the value 0.90 assumed earlier.\n",
+ "phi_D = phi_Dbyphi_DF*phi_DF;\n",
+ "# From Eqn. 10.6:\n",
+ "Value6 = viscosity_c/Density_c;# [square m/s]\n",
+ "Value7 = P/(V*Density_M);\n",
+ "Value8 = sigma/Density_c;\n",
+ "dp = 10**(-2.066+(0.732*phi_D))*Value6**0.0473*Value7**(-0.204)*Value8**(0.274);# [m]\n",
+ "a = 6*phi_D/dp;# [square m/cubic m]\n",
+ "Sca = viscosity_c/(Density_c*Dc);\n",
+ "# From Eqn. 10.65:\n",
+ "Shc = 65.3;\n",
+ "kLc = Shc*Dc/dp;# [kmol/square m s (kmol/cubic m)]\n",
+ "thetha = V/(qD+qC);# [s]\n",
+ "# From Table 10.1 (Pg 524):\n",
+ "# lambda = [lambda1 lambda2 lambda3]\n",
+ "Lambda = [1.359 ,7.23, 17.9];\n",
+ "# B = [B1 B2 B3]\n",
+ "B = [1.42 ,0.603 ,0.317];\n",
+ "Val = numpy.zeros(3);\n",
+ "Sum = 0;\n",
+ "for n in range(0,3):\n",
+ " Val[n] = (B[n]**2)*exp((-Lambda[n])*64*Dd*thetha/dp**2);\n",
+ " Sum = Sum+Val[n];\n",
+ "\n",
+ "# From Eqn. 10.66:\n",
+ "kLd = -(dp/(6*thetha))*math.log((3.0/8)*Sum);\n",
+ "mCD = 1.0/Dist;\n",
+ "# From Eqn. 10.67:\n",
+ "KLd = 1/((1/kLd)+(1/(mCD*kLc)));# [kmol/square m s (kmol/cubic m)]\n",
+ "Z = 0.5;# [m]\n",
+ "Vd = qD/(math.pi*Z**2.0/4);# [m/s]\n",
+ "# From Eqn.10.70:\n",
+ "NtoD = Z/(Vd/(KLd*a));\n",
+ "# From Eqn. 10.71:\n",
+ "EMD = NtoD/(NtoD+1);\n",
+ "print\"Expected stage efficiency: \\n\",round(EMD,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.7 - Page: 525\n",
+ "\n",
+ "\n",
+ "Expected stage efficiency: \n",
+ "0.93\n"
+ ]
+ }
+ ],
+ "prompt_number": 41
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.8: Pg-539"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.8\n",
+ "# Page: 539\n",
+ "\n",
+ "print'Illustration 10.8 - Page: 539\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:acetic acid c:Water d:Isopropylether layer\n",
+ "# Water solution (continuous):\n",
+ "C = 8000.0;# [kg/h]\n",
+ "xCn = 0.175;# [mass fraction]\n",
+ "Density_c = 1009.0;# [kg/cubic m]\n",
+ "viscosity_c = 3.1*10**(-3);# [kg/m.s]\n",
+ "Dc = 1.24*10**(-9);# [square m/s]\n",
+ "\n",
+ "# Isopropyl Ethr Layer:\n",
+ "D = 20000.0;# [kg/h]\n",
+ "xDnPlus1 = 0.05;# [mass fraction]\n",
+ "Density_d = 730;# [kg/cubic m]\n",
+ "viscosity_d = 0.9*10**(-3);# [kg/m.s]\n",
+ "Dd = 1.96*10**(-9);# [square m/s]\n",
+ "\n",
+ "sigma = 0.013;# [/N/m]\n",
+ "m = 2.68;# [Distributon coeffecient]\n",
+ "#*******#\n",
+ "\n",
+ "Ma = 60.1;\n",
+ "g = 9.81;# [m/square s]\n",
+ "cCn = xCn*Density_c/Ma;# [kmol/cubic m]\n",
+ "cDnPlus1 = xDnPlus1*Density_d/Ma;# [kmol/cubic m]\n",
+ "mCD = m*(Density_c/Density_d);# [(kmol/cubic min ether)/(kmol/cubic m in water)]\n",
+ "\n",
+ "# Perforations:\n",
+ "Do = 0.006;# [m]\n",
+ "pitch = 0.015;# [m]\n",
+ "qD = D/(3600.0*Density_d);# [cubic m/s]\n",
+ "delta_Density = Density_c-Density_d;# [kg/cubic m]\n",
+ "Value1 = Do/(sigma/(delta_Density*g))**0.5;\n",
+ "if Value1<0.1785:\n",
+ " # From Eqn. 10.74(a):\n",
+ " doBydj = (0.485*Value1**2)+1;\n",
+ "else:\n",
+ " # From Eqn. 10.74(b)\n",
+ " doBydj = (1.51*Value1)+0.12;\n",
+ "\n",
+ "dj = Do/doBydj;# [m]\n",
+ "Vomax = 2.69*((dj/Do)**2)*(sigma/(dj*((0.5137*Density_d)+(0.4719*Density_c))))**0.5;# [m/s]\n",
+ "# Since Vomax is less than 0.1:\n",
+ "Vo = 0.1;# [m/s]\n",
+ "Ao = qD/Vo;# [square m]\n",
+ "No = Ao/(math.pi*Do**2.0/4);# [square m]\n",
+ "# From Eqn. 6.30:\n",
+ "# Plate area for perforation:\n",
+ "Aa = Ao/(0.907*(Do/pitch)**2);# [square m]\n",
+ "\n",
+ "# Downspout:\n",
+ "dp = 0.0007;# [m]\n",
+ "# From Eqn. 10.75:\n",
+ "U = Density_c**2*sigma**3/(g*viscosity_c**4*delta_Density);\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 1.515;\n",
+ "abcissa = 0.62;\n",
+ "def f74(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vt = fsolve(f74,7);# [m/s]\n",
+ "Vd = Vt[0];# [m/s]\n",
+ "qC = C/(Density_c*3600);# [cubic m/s]\n",
+ "Ad = qC/Vd;# [square m]\n",
+ "# From Table 6.2 (Pg 169):\n",
+ "# Allowing for supports and unperforated area:\n",
+ "At = Aa/0.65;# [square m]\n",
+ "T = (At*4/math.pi)**0.5;# [m]\n",
+ "An = At-Ad;# [square m]\n",
+ "\n",
+ "\n",
+ "# Drop Size:\n",
+ "alpha1 = 10.76;\n",
+ "alpha2 = 52560;\n",
+ "alpha3 = 1.24*10**6;\n",
+ "alpha4 = 3.281;\n",
+ "abcissa = (alpha2*sigma*Do/delta_Density)+(alpha3*Do**1.12*Vo**0.547*viscosity_c**0.279/delta_Density**1.5);\n",
+ "Parameter = alpha1*Density_d*Vo**2/(delta_Density);\n",
+ "ordinate = 0.024;\n",
+ "dp = ordinate/alpha4;\n",
+ "\n",
+ "# Coalesced layer:\n",
+ "Vn = qD/An;# [m/s]\n",
+ "# From Eqn. 10.80:\n",
+ "ho = (Vo**2-Vn**2)*Density_d/(2*g*0.67**2*delta_Density);# [m]\n",
+ "hD = ho;\n",
+ "# From Eqn. 10.82:\n",
+ "hC = 4.5*Vd**2*Density_c/(2*g*delta_Density);# [m]\n",
+ "# From Eqn. 10.78:\n",
+ "h = hC+hD;\n",
+ "# Since this is very shallow, increase it by placing an orifice at the bottom of the downspout.\n",
+ "# VR: Velocity through the restriction.\n",
+ "# hR: Corresponding depth of the coalesced layer.\n",
+ "# Assume:\n",
+ "Vr = 0.332;# [m/s]\n",
+ "hr = (Vr**2-Vd**2)*Density_c/(2*0.67**2*delta_Density);\n",
+ "Ar = qC/Vr;# [square m]\n",
+ "dr = (4*Ar/math.pi)**0.5;# [m]\n",
+ "h = h+hr;# [m]\n",
+ "# The above results are satisfacyory.\n",
+ "Z = 0.35;# [m]\n",
+ "# Lead the downspout apron to within 0.1 m of the tray below.\n",
+ "\n",
+ "# Dispersed-phase holdup:\n",
+ "# From Eqn. 10.48:\n",
+ "Vsphi_D = Vn;\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 165.2;\n",
+ "abcissa = 30.0;\n",
+ "def f75(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vtl = fsolve(f75,7);# [m/s]\n",
+ "# For solids:\n",
+ "# From Fig. 10.48 (Pg 536):\n",
+ "abcissa = dp/(3*viscosity_c**2/(4*Density_c*delta_Density*g))**(1.0/3);\n",
+ "phi_D = [0, 0.1 ,0.2 ,0.3];\n",
+ "# Corresponding ordinates, from Fig. 10.48 (Pg 536):\n",
+ "ordinate1 = [8.8, 5.9 ,4.3 ,3.0];\n",
+ "Value1 = 1.0/(4*viscosity_c*delta_Density*g/(3*Density_c**2))**(1.0/3);\n",
+ "Val = numpy.zeros((4,7));\n",
+ "# Val = [phi_D ordinate Vs(1-phi_D) (Vs for solids) Vs/Vt (Vs for liquids) (Vs*phi_D (for liquids))]\n",
+ "for i in range(0,4):\n",
+ " Val[i,0] = phi_D[i];\n",
+ " Val[i,1] = ordinate1[i];\n",
+ " Val[i,2] = Val[i,1]/Value1;\n",
+ " Val[i,3] = Val[i,2]/(1-Val[i,0]);\n",
+ " Val[i,4] = Val[i,3]/Val[0,3];\n",
+ " Val[i,5] = Vtl*Val[i,4];\n",
+ " Val[i,6] = Val[i,5]*Val[i,0];\n",
+ "\n",
+ "\n",
+ "# By Interpolation:\n",
+ "Phi_D = 0.1;\n",
+ "\n",
+ "thetha_f =0.2498 # s\n",
+ "# From Eqn. 10.87:\n",
+ "const = 1.5;\n",
+ "kLDf = const*(Dd/(math.pi*thetha_f))**0.5;# [m/s]\n",
+ "# From Eqn. 10.86\n",
+ "KLDf = 1.0/((1.0/kLDf)*(1+((1.0/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# The ordinate of Fig. 10.47 for the drops larger than 70. Hence mass transfer coeffecient during drop rise is given by Eqn. 10.89:\n",
+ "# From Eqn. 10.91:\n",
+ "b = 1.052*dp**0.225;\n",
+ "# From Eqn. 10.90:\n",
+ "omega = (1.0/(2*math.pi))*math.sqrt(192*sigma*b/(dp**3*((3*Density_d)+(2*Density_c))));# [1/s]\n",
+ "Del = 0.2;\n",
+ "kLDr = math.sqrt((4.0*Dd*omega/math.pi)*(1+Del+(1.0/2)*Del**2));\n",
+ "KLDr = 1.0/1/((1/kLDr)*(1+((1/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# From Eqn. 10.98:\n",
+ "EMD = ((4.4*KLDf/Vo)*(dp/Do)**2)+(6*KLDr*Phi_D*(Z-h)/(dp*Vn))/(1+((0.4*KLDf/Vo)*(dp*1.0/Do)**2)+(3*KLDr*Phi_D*(Z-h)/(dp*Vn)));\n",
+ "print\"Stage Efficiency: \",round(-EMD,3)\n",
+ "# The solution in the textbook is incorrect\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.8 - Page: 539\n",
+ "\n",
+ "\n",
+ "Stage Efficiency: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 0.057\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.9: Pg-551"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.9\n",
+ "# Page: 551\n",
+ "\n",
+ "print('Illustration 10.9 - Page: 551\\n\\n');\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#****Data****#\n",
+ "B = 20000;# [kg/h]\n",
+ "#******#\n",
+ "\n",
+ "# x and y are taken in weight fraction acetic acid.\n",
+ "x1 = 0.30;# [Wt fraction]\n",
+ "xF = 0.30;# [Wt fraction]\n",
+ "y2 = 0;# [Wt fraction]\n",
+ "x2 = 0.02;# [Wt fraction]\n",
+ "y1 = 0.10;# [Wt fraction]\n",
+ "# The operating diagram is plotted in Fig. 10.23:\n",
+ "# Data = [x x_star]\n",
+ "# From Fig. 10.23 (Pg 503):\n",
+ "Data = numpy.array([[0.30 ,0.230],[0.25 ,0.192],[0.20 ,0.154],[0.15, 0.114],[0.10, 0.075],[0.05, 0.030],[0.02, 0]]);\n",
+ "Val = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " Val[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x\");\n",
+ "plt.ylabel(\"1/(x-x*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# From Area Under the curve:\n",
+ "Area = 8.40;\n",
+ "# The mutual solubility of water and isopropyl ether is very small.\n",
+ "Ma = 18.0;# [kg/kmol water]\n",
+ "Mb = 60.0;# [kg/kmol isopropyl ether]\n",
+ "r = Ma/Mb;\n",
+ "# From Eqn. 10.110:\n",
+ "NtoR = Area+(1.0/2)*math.log(1-x2/(1-x1))+(1.0/2)*math.log(x2*(r-1)+1.0/(x1*(r-1)+1));\n",
+ "# Since the operating line and equilibrium line are parallel:\n",
+ "Np = NtoR;\n",
+ "print\"Number of theoretical Units: \\n\",round(NtoR,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.9 - Page: 551\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3Tpd0PNDm5rNZa3j99eSS17POgqeeSm6Y+8xnfMNco5ShxzAB+DTwQUlz069J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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x78c3a58>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical Units: \n",
+ "8.5\n"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.10:pg-552"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.10\n",
+ "# Page: 552\n",
+ "\n",
+ "print('Illustration 10.10 - Page: 552\\n\\n');\n",
+ "\n",
+ "# Solution\n",
+ "import math\n",
+ "#****Data****#\n",
+ "B = 1150;# [kg/h]\n",
+ "#*******#\n",
+ "\n",
+ "# x and y are taken in weight ratio.\n",
+ "x1_prime = 0.0101;# [Wt. fraction]\n",
+ "xF_prime = 0.0101;# [Wt. fraction]\n",
+ "y2_prime = 0;# [Wt. fraction]\n",
+ "x2_prime = 0.001001;# [Wt. fraction]\n",
+ "y1_prime = 0.0782;# [Wt. fraction]\n",
+ "# From Illustration 10.4:\n",
+ "A = 990.0;# [kg/h]\n",
+ "# At the dilute end:\n",
+ "m1_prime = 0.798;\n",
+ "Value1 = m1_prime*B/A;\n",
+ "# At the concentrated end:\n",
+ "m2_prime = 0.953;\n",
+ "Value2 = m2_prime*B/A;\n",
+ "ValueAv = (Value1*Value2)**0.5;\n",
+ "# From Eqn. 10.116:\n",
+ "# Since y2_prime = 0\n",
+ "Value3 = x2_prime/x1_prime;\n",
+ "NtoR = (math.log((1.0/Value3)*(1-(1/ValueAv))+(1/ValueAv)))/(1-(1/ValueAv));\n",
+ "print\"Number of theoretical Unit : \",round(NtoR,1),\"\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.10 - Page: 552\n",
+ "\n",
+ "\n",
+ "Number of theoretical Unit : 8.6 \n"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter10_2.ipynb b/Mass_-_Transfer_Operations/Chapter10_2.ipynb
new file mode 100755
index 00000000..df27b4e6
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter10_2.ipynb
@@ -0,0 +1,1366 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:9fbb8ba54aec06ad3c69036b535306d5fcf2bbf41aaf2bb73bb23d87c87b8396"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 10: Liquid Extraction"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.1: Page 494"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.1\n",
+ "# Page: 494\n",
+ "\n",
+ "print'Illustration 10.1 - Page: 494\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "xF = 0.30;# [mol fraction]\n",
+ "yS = 0;# [mol fraction]\n",
+ "S1 = 40.0;# [kg]\n",
+ "B1 = 40.0;# [kg]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data at 20 OC:\n",
+ "# Wa: Wt. percent of a\n",
+ "# Wb: Wt. percent of b\n",
+ "# Wc: Wt. percent of c\n",
+ "# Data1 = [Wc Wa Wb]\n",
+ "# Data1: water layer\n",
+ "Data1 = numpy.array([(0.69 ,98.1, 1.2),(1.41, 97.1 ,1.5),(2.89 ,95.5 ,1.6),(6.42 ,91.7 ,1.9),(13.30, 84.4, 2.3),(25.50 ,71.1 ,3.4),(36.70 ,58.9 ,4.4),(44.30 ,45.1 ,10.6),(46.40 ,37.1 ,16.5)])\n",
+ "# Data2: isopropyl ether layer\n",
+ "Data2 = numpy.array([(0.18 ,0.5 ,99.3),(0.37, 0.7 ,98.9),(0.79, 0.8, 98.4),(1.93 ,1, 97.1),(4.82, 1.9, 93.3),(11.40, 3.9, 84.7),(21.60, 6.9, 71.5),(31.10, 10.8, 58.1),(36.20 ,15.1 ,48.7)])\n",
+ "\n",
+ "plt.plot((Data1[:,2])/100,(Data1[:,0])/100,label=\"x Vs fraction ether\")\n",
+ "plt.plot((Data2[:,2])/100,(Data2[:,0])/100,label=\"y Vs fraction ether\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='lower center');\n",
+ "ax=pylab.gca()\n",
+ "ax.set_xlabel(\"Wt fraction of isopropyl ether\");\n",
+ "ax.set_ylabel(\"Wt fraction of acetic acid\");\n",
+ "plt.ylim((0,0.3))\n",
+ "plt.xlim((0,1))\n",
+ "plt.show();\n",
+ "# x: Wt fraction of acetic acid in water layer.\n",
+ "# y: Wt fraction of acetic acid in isopropyl layer.\n",
+ "\n",
+ "# The rectangular coordinates of Fig 10.9(a) will be used but only upto x = 0.30\n",
+ "\n",
+ "# Stage 1:\n",
+ "F = 100;# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "M1 = F+S1;# [kg]\n",
+ "# From Eqn. 10.5:\n",
+ "xM1 = ((F*xF)+(S1*yS))/M1;\n",
+ "# From Fig. 10.15 (Pg 495):\n",
+ "# Point M1 is located on the line FB and with the help of tie line passing through M1:\n",
+ "x1 = 0.258;# [mol fraction]\n",
+ "y1 = 0.117;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E1 = (M1*(xM1-x1)/(y1-x1));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R1 = M1-E1;# [kg]\n",
+ "\n",
+ "# Stage 2:\n",
+ "S2 = 40;# [kg]\n",
+ "B2 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M2 = R1+B2;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM2 = ((R1*x1)+(S2*yS))/M2;\n",
+ "# Point M2 is located on the line R1B and the tie line passing through R2E2 through M2:\n",
+ "x2 = 0.227;\n",
+ "y2 = 0.095;\n",
+ "# From Eqn. 10.8:\n",
+ "E2 = (M2*(xM2-x2)/(y2-x2));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R2 = M2-E2;# [kg]\n",
+ "\n",
+ "# Stage 3:\n",
+ "S3 = 40;# [kg]\n",
+ "B3 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M3 = R2+B3;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM3 = ((R2*x2)+(S3*yS))/M3;\n",
+ "# Point M3 is located on the line R2B and the tie line passing through R3E3 through M3:\n",
+ "x3 = 0.20;# [mol fraction]\n",
+ "y3 = 0.078;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E3 = (M3*(xM3-x3)/(y3-x3));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R3 = M3-E3;# [kg]\n",
+ "Ac = x3*R3;\n",
+ "print\"The composited extract is\",round((E1+E2+E3),2),\" kg\\n\"\n",
+ "print\"The acid content is \",round(((E1*y1)+(E2*y2)+(E3*y3)),2),\" kg\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# If an extraction to give the same final raffinate concentration were to be done in single stage, the point M would be at the intersection of tie line R3E3 and the line BF.\n",
+ "x = 0.20;# [mol fraction]\n",
+ "xM = 0.12;# [mol fraction]\n",
+ "# From Eqn. 10.6:\n",
+ "S = F*(xF-xM)/(xM-yS);# [kg]\n",
+ "print round(S,2),\"kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.1 - Page: 494\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F6AM8U4J9C5SIVVLGmMhIrpzM4+c+Tp+P+3BAY2J4oDLjdTyM0cB+4BxgHDDB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\niRUYxhhjPPl/ptoLDFmMWJUAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7727f28>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The composited extract is 135.05 kg\n",
+ "\n",
+ "The acid content is 13.01 kg\n",
+ "\n",
+ "\n",
+ "\n",
+ "150.0 kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.2: Page 497"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.2\n",
+ "# Page: 497\n",
+ "\n",
+ "print'Illustration 10.2 - Page: 497\\n\\n'\n",
+ "\n",
+ "print'Illustration 10.2 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:kerosene c:Nicotine\n",
+ "xF = 0.01;# [wt fraction nicotine]\n",
+ "F = 100.0;# [kg]\n",
+ "B = 150.0;# [kg]\n",
+ "#******#\n",
+ "\n",
+ "# Equilibrium data:\n",
+ "# x_prime = kg nicotine/kg water\n",
+ "# y_prime = kg nicotine/kg kerosene\n",
+ "# Data = [x_prime y_prme]\n",
+ "Data = numpy.array([[0 ,0],[0.001011 ,0.000807],[0.00246, 0.001961],[0.00502, 0.00456],[0.00751, 0.00686],[0.00998 ,0.00913],[0.0204, 0.01870]])\n",
+ "xF_prime = xF/(1-xF);# kg nicotine/kg water\n",
+ "A = F*(1-xF);# [kg]\n",
+ "AbyB = A/B;\n",
+ "\n",
+ "def f64(x):\n",
+ " return -AbyB*(x-xF)\n",
+ "x = numpy.arange(0,0.01+0.001,0.001);\n",
+ "plt.plot(Data[:,0],Data[:,1],label=\"Equilibrium line\")\n",
+ "plt.plot(x,f64(x),label=\"Operating Line\");\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.2(a)\")\n",
+ "plt.show()\n",
+ "# The operating line and equilibrium line intersect at:\n",
+ "x1_prime = 0.00425;# [kg nicotine/kg water]\n",
+ "y1_prime = 0.00380;# [kg nicotine/kg water]\n",
+ "extract = A*(0.01011-x1_prime);\n",
+ "print extract*100,\"% of nicotine is extracted.\\n\\n\"\n",
+ "\n",
+ "print'Illustration 10.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "B = 50.0;# [kg]\n",
+ "# For each stage:\n",
+ "AbyB = A/B;\n",
+ "def f65(x1):\n",
+ " return -AbyB*(x1-xF)\n",
+ "x1 = numpy.arange(0,0.01+0.001,0.001)\n",
+ "def f66(x2):\n",
+ " return -AbyB*(x2-0.007)\n",
+ "x2 = numpy.arange(0,0.01+0.001,0.001)\n",
+ "def f67(x3) :\n",
+ " return -AbyB*(x3-0.005)\n",
+ "x3 =numpy.arange(0,0.01+0.001,0.001)\n",
+ "\n",
+ "plot(Data[:,0],Data[:,1],label=\"Equilibrium line\")\n",
+ "plt.plot(x1,f65(x1),label=\"Operating Line from xF\")\n",
+ "plt.plot(x2,f66(x2),label=\"Operating Line from 0.007\")\n",
+ "plt.plot(x3,f67(x3),label=\"Operating Line from 0.005\")\n",
+ "plt.grid('on');\n",
+ "legend(loc=\"upper right\");\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0, 0.010))\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.2(b)\")\n",
+ "plt.show()\n",
+ "# The final raffinate composition:\n",
+ "x3_prime = 0.0034;# [kg nicotine/kg water]\n",
+ "extract = A*(0.01011-x3_prime);\n",
+ "print extract*100,\" % of nicotine is extracted.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.2 - Page: 497\n",
+ "\n",
+ "\n",
+ "Illustration 10.2 (a)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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BVX8MWpXsQSzGI1bn7IimcvjjD7jvPhg6FD7+2GnkD6doKguvWVl4K5Qk86OI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\nJGOMMcYzlmSMMcZ4xpKMMcYYz/w/SP1Cq7DN7uIAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x77359b0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "58.014 % of nicotine is extracted.\n",
+ "\n",
+ "\n",
+ "Illustration 10.2 (b)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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VGDx4sMdtjRw5Ml3GOS1XXWhoKEePHvXKuDzBkyeZGcBXWHtXALYBj3vLoECm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\nRK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x79b5f28>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "66.429 % of nicotine is extracted.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.3: Page 502"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.3\n",
+ "# Page: 502\n",
+ "\n",
+ "print'Illustration 10.3 - Page: 502\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "F = 8000;# [kg/h]\n",
+ "xF = 0.30;# [wt. fraction acetic acid]\n",
+ "#*******#\n",
+ "\n",
+ "# From Illustration 10.1 (Pg 494)\n",
+ "# Equilibrium Data:\n",
+ "# Eqb = [y_star*100 x*100]\n",
+ "Eqb = numpy.array([[0.18 ,0.69],[0.37 ,1.41],[0.79 ,2.89],[1.93, 6.42],[4.82, 13.30],[11.40, 25.50],[21.60 ,36.70],[31.10 ,44.30],[36.20, 46.40]]);\n",
+ "\n",
+ "# Solution(a)\n",
+ "\n",
+ "# From Figure 10.23 (Pg 503):\n",
+ "# For minimum solvent rate:\n",
+ "y1 = 0.143;# [Wt fraction of acetic acid in isopropyl ether layer]\n",
+ "xM = 0.114;# [Wt fraction of acetic acid in water layer]\n",
+ "# From Eqn. 10.24:\n",
+ "Bm = (F*xF/xM)-F;# [kg/h]\n",
+ "print\"Minimum solvent rate: \",Bm,\" kg/h\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "B = 20000.0;# [kg solvent/h]\n",
+ "yS = 0;\n",
+ "S = B;\n",
+ "# From Eqn 10.24:\n",
+ "xM = ((F*xF)+(S*yS))/(F+S);\n",
+ "# From Fig. 10.23 (Pg 503):\n",
+ "y1 = 0.10;\n",
+ "# Operating curve data:\n",
+ "# Operat = [YsPlus1 Xs]\n",
+ "Operat = numpy.array([[0 ,0.02],[0.01 ,0.055],[0.02 ,0.09],[0.04 ,0.150],[0.06, 0.205],[0.08, 0.250],[0.1 ,0.3]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,1]/100,Eqb[:,0]/100,label=\"Operating Line\")\n",
+ "plt.plot(Operat[:,1],Operat[:,0],label=\"Equilibrium Line\")\n",
+ "plt.grid('on');\n",
+ "plt.ylim((0,y1));\n",
+ "plt.xlim((0,xF));\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"Wt. fraction acetic acid in water solution\");\n",
+ "plt.ylabel(\"Wt. fraction acetic acid in isopropyl ether solution\");\n",
+ "plt.title(\"Solution 10.3\")\n",
+ "plt.show()\n",
+ "# From Figure scf(22):\n",
+ "xNp = 0.02;\n",
+ "Np = 7.6;\n",
+ "# By acid balance:\n",
+ "M = B+F;\n",
+ "E1 = M*(xM-xNp)/(y1-xNp);# [kg/h]\n",
+ "RNp = M-E1;# [kg/h]\n",
+ "print\"Number of theoretical Stages: \\n\",Np\n",
+ "print\"Weight of the extract:\",E1,\"kg/h\\n\"\n",
+ "print\"Weight of the raffinate \",RNp,\" kg/h\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.3 - Page: 502\n",
+ "\n",
+ "\n",
+ "Minimum solvent rate: 13052.6315789 kg/h\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Mw0gCW7fCBRfAxReXHcMxavkorp54NRMvn2iGI4X4ivNQ1a+Be4ABwJnAMyLy\nlYh0SKRwZZ2gz7uafrGxcye0b++2jn3kkYR2FZZkP7+CGI5759wbUwyHH4L+2YwHUeM8RKQJ0B24\nCHgPuEhVl4hIHWABMCGhEhqG8Sdyc91+HDVrwvPPBz9DbkEMx/wf5jO/x3zqVKmTapHKPH58Hu8D\nw4HxqrqjyLWrVXVUAuWLiPk8jLJIfr7zb6xb54IBKwR8ZWpBDMfWXVt56/K3LIYjDiQtt5WI7Asc\nASjwlar+EUun8cKMh1HWUHW+jY8+gvfeg8qVUy1RYtmwfQPtxrSjUY1GvNr+VYvhiBNJcZiLyIXA\nKuBZ4DlgtYi0iaVTwx9Bn3c1/UrOo4+6XFVTpqTecCT6+RXEcJxxyBmMuiR+MRx+CPpnMx74yW31\nFHCWqq4CEJHGwDTvMAwjSbzwAgwfDh98AAcckGppEktBDMedLe+k18m9Ui2OEQY/Po+FqnpSSFmA\nT0LPpQqbtjLKCmPHwq23urxVjRunWprEMmPVDK6a6GI4Lj2qjEQ8Jpmk+DxE5EVcjMc471RHYC1u\n5RWq+lYsAsSCGQ+jLDB9OnTrBrNmwbHHplqaxDJy2Uhun+X24WjZoGWqxQksydrPoyLwCy6+40zg\nV+9cW+8wEkTQ511Nv+h8+CF07QoTJ6af4Yjn8wvdhyOnW07KDUfQP5vxIKrPQ1W7J0EOwzCKsGKF\ny1P1+utw6qmpliZx5OXn0WtaL7cPh8VwZAx+pq3qA0OBgp8Cc4GbVfXHBMsWFZu2MoLK6tVwxhnw\n1FNw+eWpliZxbN+9nc4T3D4cFsORPJI1bTUCmATU8Y7J3jnDMBLAunVw7rlw773BNhwbtm/g7FFn\ns3+F/ZnWZZoZjgzDj/GoqaojVHW3d2QDByVYLoPgz7uafn/mf/9zqdWvvx5uuCH+MsWTWJ7fXvtw\nJDmGww9B/2zGAz/GY6OIdBWRLBEpLyJXARsSLZhhlDW2bXObObVuDXfckWppEseS9UtoOaIlvU/u\nzWPnPEY58ZWf1Ugz/Pg8DsFFljf3Ts0Heqvq2gTLFhXzeRhBYdcuaNsW6tVzgYBBTXRoMRzpQcLj\nPESkPDBSVbvE0kmiMONhBIG8PJchd/duGD8eyvvJ+5CBWAxH+pBwh7mq5gKHeIkRjSQT9HlX088l\nOrzxRti4EcaMySzD4ff5pVsMhx+C/tmMB34+qt8BH4jIJGC7d05V9anEiWUYZYO77oJly2D2bKhY\nMdXSxB8GKqrdAAAgAElEQVSL4QgufnweA72XBRUFZzzuj3pzkdbAECALeEVVHw9TZyhwAc4wdVfV\npd756sArwDFe39eq6oIibW3ayshYnnwSRoxw+aoOPDDV0sQfi+FIX+IxbeUnwnyg11k1V9QtPoXL\nwjnazwF+AhaKyCRV/TKkThvgMFX9q4icArzAHsf8M8A0Vb3M870EfOcCoywxfDj8618uQ24QDceG\n7RtoO6YtjWs0ZlzHcWm3FNeIHT/7eZwkIp8CK4BPRWS5iJzo494nA6tUdY2q7gbGAu2L1GkHjARQ\n1Y+B6iJSyzNUp6vqq961XFX9zb9awSDo865lVb8JE+Cf/3T7ctSrl1yZ4klx+qV7DIcfgv7ZjAd+\nFli/CvxDVQ9R1UOAm7xz0agL/BBS/tE7F61OPeBQ4FcRGSEiS0RkmIjs56NPw0hrZs1yDvKpU+Hw\nw1MtTfyxGI6yg58nm6uq8woKqvoBkOujnV9nRNF5N8VNpx0PPK+qxwPbgACHTYWnVatWqRYhoZQ1\n/T7+GDp3hjffhGbNUiNTPCmq3zsr3+H818/n2QuezfgNnIL+2YwHflZbvS8iLwFjvPLl3rnjAVR1\nSTHtfgLqh5Tr40YWkerU884J8KOqLvTOv0kxxqN79+40bNgQgOrVq9O0adPCB18w9LSylVNd/vxz\nuOCCHPr3hzPOSL088Sw3b9mc/jP7M37aeO4/8/7C4L90kc/KrcjJySE7Oxug8PsyVvystsph71GE\nhJZV9axi2pUHvgLOBtYBnwBXhnGY91LVNiLSHBiiqs29a3OB61T1a2/FVyVVHVCkj0CvtsrJySn8\nIASRsqLfmjVw+unw2GPQJS3DbUtHTk4OBx1zEFe8eQVHHngkL7d9meoVq6darLgQ9M9mslZbtSrN\njVU1V0R6ATNwS3WHq+qXInKDd/0lVZ0mIm1EZBVuauqakFv0BkaLSAVgdZFrhpER/Pyzy5B7++3B\nMhyqypSvppD9STaPnfMYPZr1QIKaU8UIi5+RR3XgPuAM71QO8EA6rH4K+sjDyGw2b4ZWreCSS+C+\n+1ItTfzYtGMTPaf05OuNXzO2w1iOqnlUqkUySkiy9vN4FdiC27u8E7AV28/DMCKyfTtcdJHb0One\ne1MtTfyY/8N8mr3UjNqVa/PxdR+b4SjD+DEejVX1PlX9VlVXe0GDjRMsl0Hw15oHVb/du6FjR6hU\nKYchQ4KRITcvP4+H5j7EJW9cwtALhvJsm2dZ8MGC6A0zlKB+NuOJn9VWO0Tk9ILluiLSkj05rgzD\nCCE/H7p1g6wsGDAAygUgzOHHLT9y1VtXISIs6bmEulWLhmsZZRE/Po+mwCigmndqE9BNVZcnWLao\nmM/DSCdUoVcv+OwzmD4dKlVKtUSx887Kd+g5pSd9Tu7DHS3vIKtcVqpFMuJAwvfzKNJZiXJbJQMz\nHkY6ce+9MGUKzJkD1apFr5/O7MzdSf+Z/Zn89WT+3eHfnFr/1FSLZMSRpDjMReQWEamKc5o/7aUL\nOT+WTg1/BH3eNUj6DRkCb7zhRhwFhiNT9fvi1y84edjJ/LztZ5b9fVmxhiNT9fNDkHWLF35mZK/1\nRhvnAQcAVwOPJVQqw8ggRo2Cp55yiQ4POijV0pQeVWXY4mGcMeIM+pzShzcueyMwQX9G/PHj8/hU\nVY/19t3IUdW3RGSpqqY8O49NWxmpZtIk6NnTTVUdlcGrVi12o2yRrDiPxSIyE2gDzPCmsPJj6dQw\ngkBODlx3nfNzZLLhsNgNozT4mrYC7gROVNVtwD5YqpCkEPR510zWb/FiF8sxdiycWMzuNumuX7jY\njYrl/e+Fm+76xUKQdYsXxcZ5iMhRXhLDprhEiI283DV7JUY0jLLGypUuenzYMPjb31ItTemw2A0j\nVor1eYjIMFW9PkxWXaD4bLrJxHweRrJZu9ZlyB04EK7J0PG3xW4YSY3zSEfMeBjJ5NdfneHo2RP6\n9Uu1NCXHYjeMApLlMDdSRNDnXTNJvy1boHVr6NDBv+FIJ/38xm6UhHTSL94EWbd4YcbDMKKwYwe0\nawcnnwwPPZRqaUqGxW4YicKmrQwjArm5brRRqRKMHu0SHmYKFrthFEdSdhL0OqoLNMTtCCi4HFdz\nY+nYMNKd/Hzo0QP++APGj88swzH/h/l0ntCZtoe35bVLXivRElzD8IOf3FaPAx8CdwP9gdu8v0aC\nCfq8azrrp+p8G6tWwZtvQoUKJb9HKvSLNXajJKTz84uVIOsWL/yMPC4BjlDVXYkWxjDShYcegv/8\nB95/HypXTrU0/rDYDSOZ+Mlt9S7QSVW3Jkck/5jPw0gEzz/vEh3OmwcHH5xqafxhsRtGSUiWz2MH\nsExEZgMFow9V1T6xdGwY6ciYMfDIIzB3bmYYjtDYjYmXT7TYDSNp+FmqOwl4EJgPLA45jAQT9HnX\ndNNv2jS45Ra3J0ejRrHfL9H6JSJ2oySk2/OLJ0HWLV5EHXmoanYS5DCMlPLKK3D33fDOO/B//5dq\naSKjqryy5BXunH0nj53zGD2a9cDLO2cYSSNSbqvxqtpRRD4Nc1lV9bjEihYd83kYsZKXBwMGuH05\nJk+GI45ItUSRsdgNIx4k2udxs/e3bSwdGEa6snUrdO4M27bBggVwwAGpligyFrthpBPF+jxUdZ33\nd024I2kSlmGCPu+aSv2+/x5OOw1q13Y+jkQYjnjpl8zYjZIQ5M9nkHWLF74izA0jSCxYAJdeCv37\nOwd5OrsLLHbDSFcst5VRpvj3v53BGDECLrww1dJExmI3jESRtNxWhpHp5Oe7DZxGjYLZs+HYY1Mt\nUfFY7IaRCRTr8xCRTyMcK5IpZFkl6POuydJv+3a44gqYNQs+/jh5hqM0+qU6dqMkBPnzGWTd4kWk\nkUfBKqt/eH9fw2XU7ZJQiQwjjqxfD+3bw+GHu1xVFVPvZw6LxW4YmYaf3FbLVLVpkXNLVbVZQiXz\ngfk8jEgsXeoMR8+eLgAwXb+LLXbDSDbJ2oZWRKRlSOE03AjEMNKWt9+G886DwYPhnnvS13DM/2E+\nzV5qRu3Ktfn4uo/NcBgZgx/jcS3wvIh8LyLfA89754wEE/R510TopwqPPw69esG770LHjnHvwjeR\n9EvX2I2SEOTPZ5B1ixd+clstBo4TkWpe+beES2UYpWDXLrjhBlixwsVy1KuXaonCY7EbRhCIlNuq\nq6q+JiK3AqGVCrahfSoZAkbCfB5GARs2wCWXQM2a8Npr6buBk8VuGOlAon0e+3l/qxQ59vf++hGw\ntYisFJFvRGRAMXWGeteXi0izIteyRGSpiEz2059RNvniCzjlFDj9dLdlbDoajp25O+k9rTc3T7+Z\niZdP5O4z7jbDYWQ2qpqQA8gCVgENgX2AZcBRReq0AaZ5r08BFhS53g8YDUwqpg8NMnPmzEm1CAkl\nHvq9+65qzZqqI0fGLk+8KdDv818+12OfP1Y7juuom3ZsSq1QcSTIn88g66aq6n13xvQdH9VhLiIj\nRaR6SLmGiLzqwy6dDKxSl0hxNzAWaF+kTjtgpGcFPgaqi0gtr596nnF5BVvdZRRBFZ59Fq65Bt56\nC66+OtUS/RlVZdjiYZwx4gz6nNKHNy57g+oVq0dvaBgZgJ/0JE1UdXNBQVU3icjxPtrVBX4IKf+I\nG11Eq1MX+Bl4GugPVPXRVyBp1apVqkVIKKXVb/duuPlmeP99mD8fDj00vnLFg007NvH8huf5+quv\nmXfNvEAuwQ3y5zPIusULv3EeB4QUDsBNSUXDrye76KhCROQi4BdVXRrmulGG2bwZ2rSB775LX8Mx\nc/VMmr7U1GI3jEDjZ+QxGPhIRMbhvsg7Ag/7aPcTUD+kXB83sohUp553rgPQTkTaABWBqiIySlX/\nNDnRvXt3GjZsCED16tVp2rRp4a+GgrXamVoeMmRIoPSJVb/Ro3O480649NJWDBoEH3yQXvpMnTmV\nFxa9wKf7fcqwtsP4YvIXLNhvQdrIl+rnl0nl0DiPdJAnHvpkZ2cDFH5fxowfxwhwDNAb6AUc7bNN\neWA1zmFegegO8+YUcZh7588EJhfTR/w8SGlI0J12JdEvJ0e1Vi3VF15InDyxMGPVDG3wdAO97p3r\ndPOOzapqzy+TCbJuqvFxmPvez8NzZFfEm45S1bU+2lwADMFNcw1X1UdF5Aav/UteneeA1sA24BpV\nXVLkHmcCt6pquzD3V7/yG5nLq6/CnXfC6NFwzjmplmZvtuzawm0zb2PG6hkMazuM8xqfl2qRDCMq\n8Yjz8JMYsR1u6qoO8AtwCPClqh4TS8fxwIxHsMnLgzvucHmqpkyBI45ItUR7M3P1TK6ffD3nNTqP\nQecNolrFaqkWyTB8kazEiA8BLYCvVfVQ4Gzg41g6NfwROu8aRCLpt3WrixhftMilGkknw7Fl1xZ6\nTu7J9ZOvZ1jbYQxrNyys4SjLzy/TCbJu8cKP8ditqhuAciKSpapzgBMTLJdRhlm7Flq2hFq1YMYM\n+MtfUi3RHmaunsmxLxyLqrLi7ytsmsoos/iZtpoFXAI8ChyIm7o6UVVTvsWZTVsFjwULoEMHuPVW\n6Ns3fVKpm2/DCBLJmrZqD2wH+gLTcSlH2kZsYRilYMwYaNcOXnoJ+vVLH8Nhow3D+DNRjYeqblPV\nPFXdrarZqjpUVTcmQ7iyTtDnXQv0U4X77nMrqmbNgosuSq1cBfj1bRRHWXl+QSTIusULPyMPw0gY\nO3bAFVfAzJnw8cdw3HGplshhow3DiIzvOI90xHwemc369W6P8b/+FYYPh4ppsJGe+TaMskBSfB4i\nsr+IZIWUs0QkDXdMMDKJpUvdHhzt2sHrr6eH4bDRhmH4x8+01WygUkh5P+C9xIhjhBLUede334bz\nzoNrr83hnntS7xiP1bdRHEF9fgUEWb8g6xYv/BiPfVX194KCqm5lzy6DhuEbVXjiCejVC6ZNg3TI\nem2jDcMoHX7iPD4E+qjqYq98IvCsqrZIgnwRMZ9H5rBrF/z977B8OUyaBPXqpVYe820YZZl4+Dz8\npGS/BRgnIuu98sHA5bF0apQtNmyASy+FAw+EefNSv8d4aE6qFX9fYTmpDKMU+InzWAgcBdwI/B04\nUlUXJVowIxjzrl984RzjLVvCm2/ubTiSrV+ifBvFEYTnF4kg6xdk3eJFsSMPETlbVWeLSAdcGvaC\nIc7h3pDnraRIaGQsM2ZA167w5JPQrVtqZbHRhmHEl2J9HiJyv6reJyLZhNlSVlWvSbBsUTGfR/ry\n3HPw8MMwfrwbdaQK820Yxp9J1n4ejVT122jnUoEZj/QjNxduvhlycmDyZGjUKHWy2H4bhhGeZCVG\nfDPMufGxdGr4I9PmXTdvhjZt4NtvYf786IYjUfol27dRHJn2/EpKkPULsm7xIpLP4yjgaKC6iFyK\n83koUBW3Ha1hFLJqFbRt64L/Bg+G8n7W8SUA820YRnKI5PNoj9vHoy0wKeTSVmCsqs5PvHiRsWmr\n9OD99+Hyy2HgQBfLkQrMt2EY/kmWz+PUdDAU4TDjkXpGjHD7jI8eDeeckxoZzLdhGCUjWT6PjSIy\nW0Q+9zo9TkTuiaVTwx/pPO+alwf9+8Mjj7iRR2kMR6z6pYtvozjS+fnFgyDrF2Td4oUf4zEMuAv4\nwyt/ClyZMImMtOf3313E+KJFbtvYI49MvgyWk8owUoufaatFqnqiiCxV1WbeuWWq2jQpEkaWzaat\nkszatS6N+kknwb/+BRUqJLd/820YRuwka9rqVxE5LKTTy4D1EeobAeXjj6FFC7j6anj55eQbDhtt\nGEb64Md49AJeAo4QkXVAX1yeKyPBpNO869ixbm/xF1+Efv3isweHX/3S3bdRHOn0/BJBkPULsm7x\nIupqfFVdDZwtIvsD5VR1S+LFMtIFVbj/fsjOhtmzk7/HuMVtGEZ64sfn8SjwuKpu9so1gFtVNeUr\nrsznkVh27IBrroHvv3e7/9Wqlby+zbdhGIkjWT6PCwoMB4CqbgIujKVTI/1Zv97t9FeuHMyZk1zD\nYb4Nw0h//BiPciJSmI5ERCoBSXaVlk1SNe+6bBk0b+58HKNHQ8UEJaMpql+m+jaKI+jz5kHWL8i6\nxQs/xmM0MFtEeojIdcAsYFRixTJSxaRJLj/Vk0/CP/8ZH8e4H2y0YRiZRVSfB4CIXACcg0uM+J6q\nzki0YH4wn0f8UHUGY+hQmDjRxXEkA/NtGEbySdYe5qjqu8C7sXRkpC9//OESGi5d6iLG69VLTr+2\nksowMpeo01Yi0kJEForI7yKyW0TyRcSW6yaBZMy7btgA554LmzbBBx8kx3AU+Da6Pt01EL6N4gj6\nvHmQ9QuybvHCj8/jOaAz8A1uH48ewPOJFMpIDl9+6Rzjp54KEyZA5cqJ7zPUtzG83XCbpjKMDMVP\nnMdiVT1BRFao6nHeOcttleHMnAlXXeX8HN26Jb4/820YRvqQrDiPbSKyL7BcRJ4QkX64XQWNDOVf\n/3IGY8KE5BgOW0llGMHDj/G42qvXC9gO1AM6+O1ARFqLyEoR+UZEBhRTZ6h3fbmIFGTurS8ic0Tk\ncxH5TET6+O0zKMR73jU3F3r1guefhw8/hNNPj+vt/0S0uI2gzyubfplLkHWLF35yW63xXu4ABpbk\n5iKShfOZnAP8BCwUkUmq+mVInTbAYar6VxE5BXgBaA7sBvqq6jIvr9ZiEXkvtK3hn82b3VaxIjB/\nPlRLsH/aVlIZRrDxFedR6puLtADuU9XWXvkOAFV9LKTOi8AcVX3DK68EzlTVn4vc623gWVWdHXLO\nfB4+WL3aRYufey489RSU97VAu3SYb8Mw0p9k+TxioS7wQ0j5R+9ctDp7LRgVkYZAM+DjuEsYcObO\nhZYtoU8fFwCYSMMxY9UM820YRhkhgV8lgItI90NRC1jYzpuyehO4WVV/L9qwe/fuNGzYEIDq1avT\ntGlTWrVqBeyZt8zU8pAhQ2LSZ8CAHF5+GcaNa8W55yZO3rrH1uXWmbeyeP5ibm5+M7e3uz0p+qV7\n2fTL3HKozyMd5ImHPtnZ2QCF35cxo6oRD+AI3D7m7wFzvOM/0dp5bZsD00PKdwIDitR5EbgipLwS\nqOW93geYAdxSzP01yMyZM6dU7bZuVf3HP1QbN1b98sv4yhTKph2btN/0fvqXx/+iT3zwhO7cvbNE\n7UurX6Zg+mUuQdZNVdX77oz6HR7p8BPnsQLnxF4C5O2xObo4mmESkfLAV8DZwDrgE+BK/bPDvJeq\nthGR5sAQVW0uIgKMBDaqat9i7q/R5C9rTJ/uUo2cdZbzb9SoEf8+cvNzGbZ4GPe/fz/tj2jPA2c9\nQK39k5iz3TCMmEhWbqvdqvpCaW6uqrki0gs3esgChqvqlyJyg3f9JVWdJiJtRGQVsA24xmt+GnAV\nsEJElnrn7lTV6aWRJehs3Ah9+7oUI8OGOed4Ipj17Sz6zujLgfsdyIyrZtCkdpPEdGQYRnoTbWiC\nW557E3AwcEDBEeuQJx4HNm2l+fmqY8eq1q6t2rev6u+/J0aWrzd8rW3/3VYbP9NYJ345UfPz82O+\nZ9CnBky/zCXIuqnGZ9rKz8ijO86BfVuozQEaxc2CGaXixx/hH/+Ab79128Seckr8+9i8czMPvv8g\nI5ePZMBpAxjfcTz7lt83/h0ZhpFRJDTOI9GUVZ9Hfj68/LLbrKl3b7jjDqgQ570dza9hGMElKT4P\nEakA3AicgRtxvA+8qKq7Y+nYKB1ffw3XX+/24MjJgWOOiX8f5tcwDCMafoIEXwCOB/7lvT7B+2sk\nmNC15rt3w2OPufTpHTo4x3i8Dcc3G7+h3Zh2/H3K33nwrAf5z9X/SajhCNUviJh+mUuQdYsXfnwe\nJ6mXit1jtrd810gSS5dCjx5QsyYsWgTxivEpwPwahmGUFD9xHkuATqq6yis3Bsar6vFJkC8iQfd5\n7NgB998PI0bAE0/A1Ve7xIbxwvwahlE2SVacR3/gPyLynVduyJ5YDCNBzJ0L110HzZrBihVQK87f\n6ebXMAwjFqL6PNRlsT0c6AP0Bg5X1f8kWrCyypYtcOON0LkzXH11Dm+8EV/DkWy/RiSCPq9s+mUu\nQdYtXhRrPETkbO9vB6ANcBjwV+BCEbk0OeKVLSZPdk7w/Hz47DOXDTdebN65mVtn3EqL4S04vcHp\nfP6Pz7n4yIuReM6DGYZRZijW5yEi96vqfSKSTZjsuKqa8qmroPg8fvnFpUxfvNilFvGSYsYF82sY\nhlGUePg8/DjMG6nqt9HOpYJMNx6q8PrrcNtt0L07DBwIlSrF7/6hfo0h5w8xv4ZhGEDyNoN6M8y5\n8bF0asD338MFF8DgwTBtGjz++J8NR2nnXdPJrxGJoM8rm36ZS5B1ixeRfB5Hef6O6iJyqYh08P52\nByomTcKAkZcHzz4LJ5wAZ54JCxe61/HA/BqGYSSLSD6P9sAlQFtgUsilrcBYVZ2fePEik2nTVl98\n4ZbfZmXBK6/AEUfE577m1zAMoyQky+fRQlU/iqWTRJEpxuOPP9y01NChLujv73+HcnHaPd78GoZh\nlJRk+TxuFJHqIZ3WEJFXY+m0LPHJJ3DiifDxx7BkiUuh7tdwRJp3zRS/RiSCPq9s+mUuQdYtXvj5\nGjtOVTcXFFR1Ey5RohGBbdvg1luhXTuXMn3yZKhfP/b7ml/DMIx0wM+01XLgLFX9n1c+AHhfVY9N\ngnwRSddpq9mzoWdPaNECnn7aJTSMlQK/xsD3B9L+iPY8eNaD5tcwDKNUJCu31WDgIxEZBwjQEXg4\nlk6DyqZNLmbjvffgxRehTZv43Pe91e/Rd0ZfalauycyrZmbc9JRhGMHDT26rUcClwC/Af4FLvHNG\nCG+9Bf/3fy5W4/PP42M4XnvnNdqNaceNU2/kob89lJF+jUgEfV7Z9MtcgqxbvPAz8kBVPxeRDbj4\nDhWRBqq6NrGiZQbr10OvXs5gvPFGfPJRbd65mQfef4Dh04Zzz9X32P4ahmGkHX58Hu1wU1d1cKOP\nQ4AvVTUBG6CWjFT6PPLz3T4bd97p/Bv33AMVYwydDPVrXHzExRavYRhGQkiWz+MhoAXwnqo2E5Gz\ngK6xdJrpzJsHfftC+fLOv9EkDjNJ5tcwDCOT8LNUd7eqbgDKiUiWqs4BTkywXGnJd99Bx47QpQv0\n6wcffRS74fh649fF+jWCPu9q+mU2QdYvyLrFCz/GY5OIVAHmAaNFZCjwe2LFSi+2bIEBA1ywX5Mm\nsHKl26wpltCKzTs3029GP04dfqrFaxiGkXH48XlUBnbiDE0XoCowWlU3Jl68yCTa55GXB8OHw333\nQevW8PDDUKdObPc0v4ZhGKkm4T4PESkPTFHVs4A8IDuWzjKJ2bOdX6NGDZg6FY6PQ0y9+TUMwwgK\nEaetVDUXyA/NbRV0vv7apRS5/no34sjJid1wRPJrRCLo866mX2YTZP2CrFu88LPaahvwqYjMBLZ7\n51RV+yROrOSzaRM88AC89hrcfjuMGxf70tuCeI1Ry0cx4LQBFq9hGEZg8OPz6IZLSwJuL3PBGY+R\nCZYtKvHweeze7VKJPPggXHKJMyC1YnRBmF/DMIx0JqE+DxGZrapnA8eo6u2xdJKOqMK777rMt/Xq\nOR/HsXFI9Wh+DcMwygKRfB4Hi8ipQDsROb7okSwBE8HChXDuuc4h/uSTMHNm7IajtH6NSAR93tX0\ny2yCrF+QdYsXkXwe9wH3AnVx6UmKclZCJEogX3zh0oh88gn8859w7bWwzz6x3dP8GoZhlEX8+Dzu\nVdUHkiRPifDr8/j+exg40C257d/fJTKsVCm2vs2vYRhGppKU3Fbpajj88MsvLrDv9dfd9q/ffAPV\nqsV+X/NrGIZR1vG5m3bpEJHWIrJSRL4RkQHF1BnqXV8uIs1K0jYSY8fCUUe511984VZTxWo4EuHX\niETQ511Nv8wmyPoFWbd4kTDjISJZwHNAa+Bo4EoROapInTbAYar6V6An8ILfttE4+WRYvBieeSb2\npbebdmxKSR6qZcuWJfT+qcb0y2yCrF+QdYsXiRx5nAysUtU1qrobGAu0L1KnHTASQFU/BqqLSG2f\nbSPSqBE0bBibArn5uTy/8HmO/NeRbPtjG5//43P6n9Y/aQ7xzZs3J6WfVGH6ZTZB1i/IusULXzsJ\nFkVEpqrqhVGq1QV+CCn/CJzio05d3MZT0domFPNrGIZhFE+pjAdwnY86fkO/0yoHuarS6c1OLFm/\nhEHnDkppmvQ1a9akpN9kYfplNkHWL8i6xQ1VjXgAN/s5F6ZOc2B6SPlOYECROi8CV4SUVwK1/LT1\nzqsddthhhx0lP6J9h0c7/MR5LFXVZkXOLVPVplHalQe+As4G1gGfAFeq6pchddoAvVS1jYg0B4ao\nanM/bQ3DMIzUESm31ZVAZ+BQEZkccqkKEHUjKFXNFZFewAwgCxiuql+KyA3e9ZdUdZqItBGRVbjs\nvddEals6FQ3DMIx4U+zIQ0QOAQ4FHgMGsMc3sQVY4e31YRiGYZRBIi3VvRS3f8fpqvq+quZ4x5Jk\nGI5UBhgmgxj1WyMiK0RkqYh8kjyp/RFNNxE5UkQ+EpGdInJrSdqmAzHql9bPDnzp18X7TK4QkQ9F\n5Di/bdOBGPULwvNr7+m3VEQWi8jf/LbdiwgO78HAfGATMBd4BLgIOCBWR4sPZ3sWsApoCOwDLAOO\nKlKnDTDNe30KsMBv21Qfsejnlb9LxnNIoG41gROBh4BbS9I21Ucs+qX7syuBfi2Aat7r1gH83wur\nX4CeX+WQ18fiYupK/PyKHXmo6q2qeipQG7fa6X/AtcDnIpJo/0NKAwyTQGn1C42VT6slziFE1U1V\nf1XVRcDukrZNA2LRr4B0fXbgT7+PVPU3r/gxUM9v2zQgFv0KyPTnty2kuD+wwW/bUPxEmFcCqgLV\nvGMdsMCnIqWluOBBP3XCBRgWbZtqYtEP3FK7WSKySESuT5iUpcOPbolomyxilTGdnx2UXL8ewLRS\nttUT5YYAAAi6SURBVE0FsegHAXl+InKxNwh4F+hTkrYFRFptNQyXV2orbqnsfOApVd3kT4eYiLx+\neA/p/AsgErHq11JV14lITeA9EVmpqvPiJFus+NUt3m2TRawynqaq69P02UEJ9BORs3CzEaeVtG0K\niUU/CMjzU9W3gbdF5HTgNRE5sqQdRRp5NAD2Bf4L/OQdyUr48hNQP6RcH2cFI9Wp59Xx0zbVlFa/\nnwBUdZ3391dgIm64mS7E8v4H5dkVi6qu9/6m47MDn/p5TuRhQLuQH5SBeX7F6BeY51eAZ/jKAwd4\n9fw/vyjOl3I4h0pPIBtYDMwEHkiw06c8sBrnuKlAdIdyc/Y47aK2TfURo377AVW815WBD4HzUq1T\nSXQLqTuQvR3mgXh2EfRL62dXgs9mA5xjtXlp35sM1S8oz68xe8I0jgdWl+b5+RWoPnA5MBT4Fvgt\nCW/CBbgo81XAnd65G4AbQuo8511fDhwfqW26HaXVD2jkPdRlwGfpqF803XCLMH4AfsOt5lsL7B+U\nZ1ecfpnw7Hzq9wouUHipd3wSqW26HaXVL0DP73ZP/qXAPOCk0jy/SEGCNwOn4pat5eJ8Hh96fz9T\n1bywDQ3DMIzAEymrbkNgHNBXvTl2wzAMw4AI6UkMwzAMozgSuoe5YRiGEUzMeBiGYRglxoyHYRiG\nUWLMeBiGYRglxoxHQBCRp73l1QXlGV6KmYLyYBHpKyKHiNvoy889O4rIFyIyOw7ytReRo0LK94vI\n2bHeN14kUz4RuUFEuoY531BEPg1zvo6IjE+ELGH62ut9SFKfv/uoc4uIVAopTxWRqomVzIiEGY/g\n8AEuLgcRKQf8BZebrIAWuDidQ3E7RPqhB3Cdqu71JSpum+CSckmoPKp6n6rGbJTiSNLkU7eL5msl\nqL9OVTsmQpYw7PU++EFEsmLs08+Sz5txEd6ugeqFqrolxn6NWEh1NKQdcYsqrQOs9V4fi0snMx2o\njstRtgmXo38BLkfZUuDmCPe7F5cUcyXwBNANmATMBubg0jPMwqWsWYHLAVTQ9mpcVPwyYBTOcG3E\nZSdYgovUzQY6ePXP9s6vAIYDFbzza3ApPgr6OCKMnA1x+80s9o4WIdcGeO2WAY965xrjMoku8tod\ngTO6keQ7CWd4l+FSdO9fRAa/78VI79xAvLQlwAkh158APi1Gx0+9192BtzwdvgYeD1P/JGCC97o9\nblO38kBF9qSiuB6X8HQZ8CYue3bo+7AU90PjT++X1z4beBH3eRpUpP9jvPdpqadbY+98P+BT77g5\npP5W728rYHLI+edwn7vewC7vvZ0d8tk4oLj7eu/Zl8DLuGjqGUDFVP+fBulIuQB2xPFhun/6+rhc\nZDcAD+DSDZwGzPXqnBn6DxrlfnPYkxalOy7lRnWvnMWePD8HAt94r4/BpTco+McuqD8CuDTk3iNw\nu1VWxKXvOMw7PzLkC+A74Cbv9Y3AsDAyVgL29V7/FVjovb4A94VfsYgcs0P6OiXky6g4+Srg8v2c\n4J3fH8gqIkNJ34v7gH7e6xW4LMng33isBqrgfhSsAeoWqV+ePUZiEO6L/FTv2Y/2zh8QUv9BoFcx\n70Nx71c27seEhJF3KNA5RJaKOCO5wntelXFf6E28OsUZj2eBq0M+C6Eyf4dL5hfuvk2992w3cJxX\n/w2gS6r/R4N0lGb6wUhf5uO+JE4FnsLl4j8Vl2PpA69OLGnsZ6pqQWblcsCjXkrnfKCOt1nV34Bx\nqvo/gJD64foW3C//71R1lXduJHAT8IxXfsv7uwT3ZV6UCsBzItIEyMMZEIBzgFdVdWeBHCKyP24U\nNF5EQttHk2+9qi727hNufr407wUiUg23Y13Bs3kNZ/SiMVtVt3r3+AL3RflTwUVVzRWR1V6a7ZNw\nn4UzcEauIH34sSLyEG6Pnv1xo9S93oco75cC49X7Zi7CR8DdIlIPeEtVV4lIS+/1Du/eb3kyLfeh\nb3EIEO6+p+MM23equsKruxj3PhlxwoxHsPgQN8o4FjeE/wG4DWc8Xo3x3oqb/iigC+5X9vGqmici\n3+F+YSrFG6hwXzRFz0mRc7u8v3mE/7z2xX25d/Xm3neG3LeoHOWAzarajPD4kS8cpXkvwuG37q6Q\n13k4o1CUubjMzLtxo4eROP1v865n46bXPhWRbrhf/QUU6Bzt/doe7qSqjhGRBbhtq6eJyA38+b0o\n+pzB5dAL9cNWIjqR7lv0ffJzP8Mn5jD///bu3zWKIAzj+PcpFEQNxkJIpdEioG0aS/+DFGoTRQ7/\nAAWxtLS0CopoJwi2FoJBEEIkRVATjlPLIDYi/tZGRF6LdzZZj7vkxi7h+XS3Ozs7O3c3787MsrOz\nLJF/2E+RvpBzHifLPoDv5JBHrf6GbQz4UBrLU8Bh8k/7FDgj6SCApPGS/kc5pi3IYZ0jko6VbeeB\nhYpyjZFrzkDOLzQN6ROg0zyhI2k8coJ1TdLpsk1l3Yatyjchabocs3/ABHFtXUAO93wDvkpqFhua\nrbjutkFBZxG4DCxFxEfyAYqpiHhV9u8D3kvaBZxjo8Fdr4ct6mt4YaTJiFiLiDngIXkzswjMSNoj\naS8ww0YvqPEWOC5pt6QDZM+tMez7GZbvdl0obttw8NhZemQj0V4muEvePX5uff4jaVXSJUkTkh6N\nkHfw753ifWBaUpds8N8ARMRr4DqwIGkVuFHSPwCuSnoh6eh6phG/gA45NNIl7z5vt8457PyNW8CF\ncq4p4GfJd54cunguaQW4UtLPAhdL+h65Vvxm5ftNLkcwV46ZJ3sVbbV10b62DnCzlLH/mhmQflA9\nDDpmGThE9kAgh4e6rf3XyLmQZ015i3Y9TDK8vjYr61lJvXJNJ4B7EbFC9naWyd/n3YhohqwCICLe\nkS9j7ZFzFC9bed4BHvc/Nj5KviOU1/6DX4xoZmbV3PMwM7NqDh5mZlbNwcPMzKo5eJiZWTUHDzMz\nq+bgYWZm1Rw8zMysmoOHmZlV+ws92v6jnNxHLAAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x78da7f0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical Stages: \n",
+ "7.6\n",
+ "Weight of the extract: 23000.0 kg/h\n",
+ "\n",
+ "Weight of the raffinate 5000.0 kg/h\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.4: Page 506"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.4\n",
+ "# Page: 506\n",
+ "\n",
+ "print'Illustration 10.4 - Page: 506\\n\\n'\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "import numpy\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:kerosene c:Nicotine\n",
+ "F = 1000.0;# [kg/h]\n",
+ "xF = 0.01;# [wt. fraction acetic acid]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data:\n",
+ "# x_prime = kg nicotine/kg water\n",
+ "# y_prime = kg nicotine/kg kerosene\n",
+ "# Eqb = [x_prime y_prme]\n",
+ "Eqb = numpy.array([[0 ,0],[0.001011, 0.000807],[0.00246, 0.001961],[0.00502 ,0.00456],[0.00751, 0.00686],[0.00998, 0.00913],[0.0204 ,0.01870]]);\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "A = 1000*(1-xF);# [kg water/h]\n",
+ "yS = 0;\n",
+ "yS_prime = 0;\n",
+ "y1_prime = 0;\n",
+ "xF_prime = xF/(1-xF);# [kg nicotine/kg water]\n",
+ "# For xF_prime = 0.0101:\n",
+ "yk = 0.0093;\n",
+ "xNp = 0.001;# [wt. fraction acetic acid]\n",
+ "xNp_prime = xNp/(1-xNp);# [kg nicotine/kg water]\n",
+ "# For infinite stages:\n",
+ "# Operating Line should pass through (xNp_prime,y1_prime) & (xF_prime,yk)\n",
+ "Operat = numpy.array([[xNp_prime, y1_prime],[xF_prime ,yk]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1],label=\"equilibrium Line\")\n",
+ "plt.plot(Operat[:,0],Operat[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine / kg water\");\n",
+ "plt.ylabel(\"kg nicotine / kg kerosene\");\n",
+ "plt.title(\"Solution 10.4(a)\")\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0,0.01))\n",
+ "plt.show()\n",
+ "AbyBm = (yk-y1_prime)/(xF_prime-xNp_prime);\n",
+ "Bm = A/AbyBm;# [kg kerosene/h];\n",
+ "print\"Mininmum kerosene rate: \",round(Bm,2),\" kg kerosene/h \\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "B = 1150.0;# [kg/h]\n",
+ "AbyB = A/B;\n",
+ "# From Eqn. 10.36:\n",
+ "y2_prime = ((xF_prime-xNp_prime)*AbyB)+yS_prime;# [kg nicotine/kg kerosene]\n",
+ "# Operating Line should pass through (xNp_prime,y1_prime) & (xF_prime,y2_prime)\n",
+ "Operat = numpy.array([[xNp_prime, y1_prime],[xF_prime, y2_prime]]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1],label=\"equilibrium Line\")\n",
+ "plt.plot(Operat[:,0],Operat[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"kg nicotine/kg water\");\n",
+ "plt.ylabel(\"kg nicotine/kg kerosene\");\n",
+ "plt.title(\"Solution 10.4(b)\")\n",
+ "plt.xlim((0,0.012))\n",
+ "plt.ylim((0,0.01))\n",
+ "plt.show()\n",
+ "# From Figure:\n",
+ "Np = 8.3;\n",
+ "print\"Number of theoretical stages: \\n\",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.4 - Page: 506\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3MyhjTGg4dgx69XIeUV640OmHAViwdQGdpnfijSZv0OWGLsEN0oQ0v+YuSy0s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\nAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x78b2e48>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Mininmum kerosene rate: 968.71 kg kerosene/h \n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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7ygTgHQgG8G6cTGfgWaAE8BdQ2bNt/CiU24VDuW4Q2PU7etS5ehkyBL7//vIS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\nua7xbQHGGNcFW59MCeDPRNu7gaTT6Sa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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c37160>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical stages: \n",
+ "8.3\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.5: Page 510"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.5\n",
+ "# Page: 510\n",
+ "\n",
+ "print'Illustration 10.5 - Page: 510\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import pylab\n",
+ "import numpy.linalg as lin\n",
+ "import numpy\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:ethylbenzne b:diethylene glycol c:styrene\n",
+ "F = 1000;# [kg/h]\n",
+ "xF = 0.5;# [Wt. fraction styrene]\n",
+ "xPE = 0.9;# [kg styrene/kg hydrocarbon]\n",
+ "xRNp = 0.1;# [kg styrene/kg hydrocarbon]\n",
+ "#******#\n",
+ "\n",
+ "# X: kg styrene/kg hydrocarbon\n",
+ "# Y: kg styrene/kg hydrocarbon\n",
+ "# N:kg glycol/kg hydrocarbon\n",
+ "# Equilibrium data:\n",
+ "# Hydrocarbon rich solutions:\n",
+ "# Eqb1 = [X N]\n",
+ "Eqb1 = numpy.array([[0 ,0.00675],[0.0870 ,0.00817],[0.1833, 0.00938],[0.288 ,0.01010],[0.384 ,0.01101],[0.458, 0.01215],[0.464 ,0.01215],[0.561 ,0.01410],[0.573, 0.01405],[0.781 ,0.01833],[1 ,0.0256]]);\n",
+ "# Solvent rich solutions:\n",
+ "# Eqb2 = [Y_star N]\n",
+ "Eqb2 = numpy.array([[0 ,8.62],[0.1429 ,7.71],[0.273, 6.81],[0.386, 6.04],[0.480, 5.44],[0.557, 5.02],[0.565, 4.95],[0.655, 4.46],[0.674, 4.37],[0.833, 3.47],[1 ,2.69]]);\n",
+ "\n",
+ "plt.plot(Eqb1[:,0],Eqb1[:,1],label=\"X Vs N\")\n",
+ "plt.plot(Eqb2[:,0],Eqb2[:,1],label=\"Y Vs N\")\n",
+ "plt.grid('on');\n",
+ "legend(loc='upper right');\n",
+ "plt.xlabel(\"kg styrene / kg hydrocarbon\");\n",
+ "plt.ylabel(\"kg diethylene glycol / kg hydrocarbon\");\n",
+ "plt.title(\"Equilibrium Data\")\n",
+ "# In Fig. 10.31 (Pg 512):\n",
+ "# Point E1 is located.\n",
+ "NE1 = 3.10;\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "# From Fig. 10.30 (Pg 511):\n",
+ "Np = 9.5;\n",
+ "print\"Minimum number of theoretical stages:\\n\",Np\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "# The tie line when extended passes through F provides the minimum reflux ratio.\n",
+ "# From the plot:\n",
+ "N_deltaEm = 20.76;\n",
+ "# From Eqn. 10.48:\n",
+ "Ratiom = (N_deltaEm-NE1)/NE1;# [kg reflux/kg extract product]\n",
+ "print\"Minimum extract reflux ratio: \",round(Ratiom,3),\"kg reflux/kg extract product\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (c)\n",
+ "\n",
+ "Ratio = 1.5*Ratiom;# [kg reflux/kg extract product]\n",
+ "# From Eqn. 10.48;\n",
+ "N_deltaE = (Ratio*NE1)+NE1;\n",
+ "# Point deltaE is plotted.\n",
+ "# A straight line from deltaE through F intersects line X = 0.10 at deltaR.\n",
+ "N_deltaR = -29.6;\n",
+ "# In Fig. 10.31 (Pg 512):\n",
+ "# Random lines are drawn from deltaE for the concentrations to the right of F, and from deltaR for those to the left,and intersection of these with the solubility curves provide the coordinates of the opeating curve.\n",
+ "# The tie line data are plotted directly to provide the equilibrium curve.\n",
+ "# From Fig. 10.32 (Pg 513):\n",
+ "Np = 15.5;\n",
+ "# Feed is to be introduced in the seventh from the extract product end of cascade.\n",
+ "# From Fig. 10.31 (Pg 512):\n",
+ "XRNp = 0.10;\n",
+ "NRNp = 0.0082;\n",
+ "# Basis:1 hour.\n",
+ "# Overall plant balance:\n",
+ "# (1): PE_prime+RNp_prime = F\n",
+ "# C Balance\n",
+ "# (2): PE_prime*(1-XRNp)+RNp_prime*XRNp = F*xF\n",
+ "# Solving (1) & (2) simultaneously:\n",
+ "a = numpy.array([[1 ,1],[(1-XRNp), XRNp]]);\n",
+ "b = numpy.array([F,F*xF]);\n",
+ "soln =lin.solve(a,b)\n",
+ "PE_prime = soln[0];# [kg/h]\n",
+ "RNp_prime = soln[1];# [kg/h]\n",
+ "RO_prime = Ratio*PE_prime;# [kg/h]\n",
+ "# From Eqn 10.39:\n",
+ "E1_prime = RO_prime+PE_prime;# [kg/h]\n",
+ "BE = E1_prime*NE1;# [kg/h]\n",
+ "E1 = BE+E1_prime;# [kg/h]\n",
+ "RNp = RNp_prime*(1+NRNp);# [kg/h]\n",
+ "S = BE+(RNp_prime*NRNp);# [kg/h]\n",
+ "print\"Number of theoretical stages: \\n\",Np\n",
+ "print\"Extract Flow Rate: \",round(E1,2),\" kg/h\\n\"\n",
+ "print\"solvent Flow Rate: \",S,\" kg/h\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.5 - Page: 510\n",
+ "\n",
+ "\n",
+ "Minimum number of theoretical stages:\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "9.5\n",
+ "\n",
+ "\n",
+ "Minimum extract reflux ratio: 5.697 kg reflux/kg extract product\n",
+ "\n",
+ "\n",
+ "\n",
+ "Number of theoretical stages: \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "15.5\n",
+ "Extract Flow Rate: 19567.58 kg/h\n",
+ "\n",
+ "solvent Flow Rate: 14799.1 kg/h\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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idIF0+NepajdVna+qH6hqd6CRqg7AGRzNmByhyRVNmNdlHl/0/IJNhzZx5egr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\nH2OMyU4uyn8R7Wu0p32N9hw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3fqyIfCAiC4EvcD5ciovIh24Mr4l7O3ca7R0Tkf9z21su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\nxuQR1uEbY0weYR2+McbkEf8PM26glcYuDusAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7e76ac8>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.6: Page 516"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.6\n",
+ "# Page: 516\n",
+ "\n",
+ "print'Illustration 10.6 - Page: 516\\n\\n'\n",
+ "\n",
+ "import numpy.linalg as lin\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:heptane b:p-chloronitrobenzene c:o-chloronitrobenzene d:aq. methanol\n",
+ "xb = 0.4;# [Wt fraction]\n",
+ "xC = 0.60;# [Wt fraction]\n",
+ "F = 100;# [kg]\n",
+ "# The para isomer(b) favours the heptane(a) and the ortho isomer(c) favours the methanol(d).\n",
+ "# Basis: 1 hour.\n",
+ "A = 2400;# [kg/h]\n",
+ "D = 2760;# [kg/h]\n",
+ "xbW = 0.8;# [Wt fraction]\n",
+ "xbZ = 0.15;# [Wt fraction]\n",
+ "kb=1.35;\n",
+ "kc=0.835;\n",
+ "#*******#\n",
+ "\n",
+ "B = xb*F;# [kg]\n",
+ "C = F-B;# [kg]\n",
+ "# W = kg A rich product, after solvent removal\n",
+ "# Z = kg D rich product, after solvent removal\n",
+ "# B balance:\n",
+ "# (1): (0.80*W)+(0.15*Z) = B\n",
+ "# C balance:\n",
+ "# (2): (0.20*W)+(0.85*Z) = C\n",
+ "# Solving (1) & (2) simultaneously:\n",
+ "a = numpy.array([[0.80, 0.15],[0.20, 0.85]]);\n",
+ "b = [B,C];\n",
+ "soln = lin.solve(a,b)\n",
+ "W = soln[0];\n",
+ "Z = soln[1];\n",
+ "Wb = xbW*W;# [kg]\n",
+ "Wc = W-Wb;# [kg]\n",
+ "Zb = xbZ*Z;# [kg]\n",
+ "Zc = Z-Zb;# [kg]\n",
+ "xB1_prime = Zb/D;\n",
+ "xC1_prime = Zc/D;\n",
+ "yB1_prime = Wb/D;\n",
+ "yC1_prime = Wc/D;\n",
+ "DbyA = D/A;\n",
+ "# Equilibrium curve:\n",
+ "# First distribution coeffecient: yB_star/xB_prime = 1.35\n",
+ "def f68(x1):\n",
+ " return kb*x1\n",
+ "x1 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Second distribution coeffecient: yC_star/xC_prime = 0.835\n",
+ "def f69(x2):\n",
+ " return kc*x2\n",
+ "x2 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Operating Line, corresponding to First distribution coeffecient:\n",
+ "def f70(x3):\n",
+ " return (DbyA*x3)+yB1_prime\n",
+ "x3 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "def f71(x4):\n",
+ " return DbyA*(x4-xB1_prime)\n",
+ "x4 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "# Operating Line, corresponding to Second distribution coeffecient:\n",
+ "def f72(x5):\n",
+ " return (DbyA*x5)+yC1_prime\n",
+ "x5 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "def f73(x6):\n",
+ " return (DbyA)*(x6-xC1_prime);\n",
+ "x6 = numpy.arange(0,0.06+0.01,0.01)\n",
+ "\n",
+ "\n",
+ "plot(x1,f68(x1),label=\"Equilibrium curve\")\n",
+ "plt.plot(x3,f70(x3),label=\"Operating curve\")\n",
+ "plt.plot(x4,f71(x4),label=\"Operating curve\");\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"xB_prime\");\n",
+ "plt.ylabel(\"yB_prime\");\n",
+ "plt.title(\"yB_star/xB_prime = 1.35\");\n",
+ "plt.xlim((0,0.05))\n",
+ "plt.ylim((0,0.07))\n",
+ "plt.show()\n",
+ "\n",
+ "plot(x2,f69(x2),label=\"Equilibrium curve\")\n",
+ "plt.plot(x5,f72(x5),label=\"Operating curve\")\n",
+ "plt.plot(x6,f73(x6),label=\"Operating curve\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.xlabel(\"xC_prime\");\n",
+ "plt.ylabel(\"yC_prime\");\n",
+ "plt.title(\"yC_star/xC_prime = 0.835\");\n",
+ "plt.xlim((0,0.06))\n",
+ "plt.ylim((0,0.07))\n",
+ "plt.show()\n",
+ "# The stages are constructed.\n",
+ "# The feed matching is shown on Fig. 10.37 (Pg 518):\n",
+ "f_prime = 6.6;\n",
+ "fstage = 4.6;\n",
+ "print\"Number of ideal stage is \\n\",fstage+f_prime-1\n",
+ "print\"The feed stage is \",fstage,\"th from the solvent-D inlet\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.6 - Page: 516\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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aoCslS5T0ttJxgs3CigBmaWuWtkbwBOMQmOU1Xv/5+kxdPTXba7z7xd0teBQC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\n0jwM9TGCwOb75xCOtkg/nJ7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EMAzDKBI2hGUYhmEUCQsghmEYRpGwAGIYhmEU\nCQsghmEYRpGwAGIYhmEUif8fHq0g8rWtPKEAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7e76a20>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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FpCpwNnBviu01DCMNsfk5/ogqTJvmAsfmzXDHHXDZZbDPPkm9SMoHO+wKfA2s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\nFy4MlD3lwb89uXtYUmUJQz8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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c385c0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of ideal stage is \n",
+ "10.2\n",
+ "The feed stage is 4.6 th from the solvent-D inlet\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.7: Page 525"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.7\n",
+ "# Page: 525\n",
+ "\n",
+ "print'Illustration 10.7 - Page: 525\\n\\n'\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# c:Water d:Toulene \n",
+ "Density_c = 998;# [kg/cubic m]\n",
+ "viscosity_c = 0.95*10**(-3);# [kg/m.s]\n",
+ "Dc = 2.2*10**(-9);# [square m/s]\n",
+ "Density_d = 865;# [kg/cubic m]\n",
+ "viscosity_d = 0.59*10**(-3);# [kg/m.s]\n",
+ "Dd = 1.5*10**(-9);# [square m/s]\n",
+ "sigma = 0.022;# [N/m]\n",
+ "Dist = 20.8;# [Distribution Coeffecient]\n",
+ "d = 0.5;# [m]\n",
+ "h = 0.5;# [m]\n",
+ "di = 0.15;# [m]\n",
+ "N = 13.3;# [r/s]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "qC = 3*10**(-3);# [cubic m/s]\n",
+ "qD = 3*10**(-4);# [cubic m/s]\n",
+ "#********#\n",
+ "\n",
+ "V = math.pi*h*d**2/4;# [Vessel volume,cubic m]\n",
+ "phi_DF = qD/(qD+qC);# [Volume fraction toulene]\n",
+ "# Assume:\n",
+ "phi_Dbyphi_DF = 0.9;\n",
+ "phi_D = phi_Dbyphi_DF*phi_DF;\n",
+ "phi_W = 1-phi_D;\n",
+ "# From Eqn. 10.56:\n",
+ "Density_M = (Density_c*phi_W)+(Density_d*phi_D);# [kg/cubic m]\n",
+ "if phi_W>0.4:\n",
+ " viscosity_M = (viscosity_c/phi_W)*(1+(6*viscosity_d*phi_D/(viscosity_d+viscosity_c)));# [kg/m s]\n",
+ "else:\n",
+ " viscosity_M = (viscosity_c/phi_D)*(1-(1.5*viscosity_c*phi_W/(viscosity_d+viscosity_c)));# [kg/m s]\n",
+ "\n",
+ "# Impeller Reynold's Number:\n",
+ "IRe = (di**2*N*Density_M/viscosity_M);\n",
+ "# From Fig 6.5 (Pg 152), curve g:\n",
+ "Po = 0.72;\n",
+ "P = Po*Density_M*N**3*di**5;# [W]\n",
+ "# From Eqn. 10.61:\n",
+ "Value1 = P*qD*viscosity_c**2/(V*sigma**3);\n",
+ "Value2 = viscosity_c**3/(qD*Density_c**2*sigma);\n",
+ "Value3 = Density_c/(Density_c-Density_d);\n",
+ "Value4 = sigma**3*Density_c/(viscosity_c**4*g);\n",
+ "Value5 = viscosity_d/viscosity_c;\n",
+ "phi_Dbyphi_DF = 3.39*Value1**0.247*Value2**0.427*Value3**0.430*Value4**0.401*Value5**0.0987;\n",
+ "# The value of phi_Dbyphi_DF is sufficiently close to the value 0.90 assumed earlier.\n",
+ "phi_D = phi_Dbyphi_DF*phi_DF;\n",
+ "# From Eqn. 10.6:\n",
+ "Value6 = viscosity_c/Density_c;# [square m/s]\n",
+ "Value7 = P/(V*Density_M);\n",
+ "Value8 = sigma/Density_c;\n",
+ "dp = 10**(-2.066+(0.732*phi_D))*Value6**0.0473*Value7**(-0.204)*Value8**(0.274);# [m]\n",
+ "a = 6*phi_D/dp;# [square m/cubic m]\n",
+ "Sca = viscosity_c/(Density_c*Dc);\n",
+ "# From Eqn. 10.65:\n",
+ "Shc = 65.3;\n",
+ "kLc = Shc*Dc/dp;# [kmol/square m s (kmol/cubic m)]\n",
+ "thetha = V/(qD+qC);# [s]\n",
+ "# From Table 10.1 (Pg 524):\n",
+ "# lambda = [lambda1 lambda2 lambda3]\n",
+ "Lambda = [1.359 ,7.23, 17.9];\n",
+ "# B = [B1 B2 B3]\n",
+ "B = [1.42 ,0.603 ,0.317];\n",
+ "Val = numpy.zeros(3);\n",
+ "Sum = 0;\n",
+ "for n in range(0,3):\n",
+ " Val[n] = (B[n]**2)*exp((-Lambda[n])*64*Dd*thetha/dp**2);\n",
+ " Sum = Sum+Val[n];\n",
+ "\n",
+ "# From Eqn. 10.66:\n",
+ "kLd = -(dp/(6*thetha))*math.log((3.0/8)*Sum);\n",
+ "mCD = 1.0/Dist;\n",
+ "# From Eqn. 10.67:\n",
+ "KLd = 1/((1/kLd)+(1/(mCD*kLc)));# [kmol/square m s (kmol/cubic m)]\n",
+ "Z = 0.5;# [m]\n",
+ "Vd = qD/(math.pi*Z**2.0/4);# [m/s]\n",
+ "# From Eqn.10.70:\n",
+ "NtoD = Z/(Vd/(KLd*a));\n",
+ "# From Eqn. 10.71:\n",
+ "EMD = NtoD/(NtoD+1);\n",
+ "print\"Expected stage efficiency: \\n\",round(EMD,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.7 - Page: 525\n",
+ "\n",
+ "\n",
+ "Expected stage efficiency: \n",
+ "0.93\n"
+ ]
+ }
+ ],
+ "prompt_number": 41
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.8: Pg-539"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.8\n",
+ "# Page: 539\n",
+ "\n",
+ "print'Illustration 10.8 - Page: 539\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:acetic acid c:Water d:Isopropylether layer\n",
+ "# Water solution (continuous):\n",
+ "C = 8000.0;# [kg/h]\n",
+ "xCn = 0.175;# [mass fraction]\n",
+ "Density_c = 1009.0;# [kg/cubic m]\n",
+ "viscosity_c = 3.1*10**(-3);# [kg/m.s]\n",
+ "Dc = 1.24*10**(-9);# [square m/s]\n",
+ "\n",
+ "# Isopropyl Ethr Layer:\n",
+ "D = 20000.0;# [kg/h]\n",
+ "xDnPlus1 = 0.05;# [mass fraction]\n",
+ "Density_d = 730;# [kg/cubic m]\n",
+ "viscosity_d = 0.9*10**(-3);# [kg/m.s]\n",
+ "Dd = 1.96*10**(-9);# [square m/s]\n",
+ "\n",
+ "sigma = 0.013;# [/N/m]\n",
+ "m = 2.68;# [Distributon coeffecient]\n",
+ "#*******#\n",
+ "\n",
+ "Ma = 60.1;\n",
+ "g = 9.81;# [m/square s]\n",
+ "cCn = xCn*Density_c/Ma;# [kmol/cubic m]\n",
+ "cDnPlus1 = xDnPlus1*Density_d/Ma;# [kmol/cubic m]\n",
+ "mCD = m*(Density_c/Density_d);# [(kmol/cubic min ether)/(kmol/cubic m in water)]\n",
+ "\n",
+ "# Perforations:\n",
+ "Do = 0.006;# [m]\n",
+ "pitch = 0.015;# [m]\n",
+ "qD = D/(3600.0*Density_d);# [cubic m/s]\n",
+ "delta_Density = Density_c-Density_d;# [kg/cubic m]\n",
+ "Value1 = Do/(sigma/(delta_Density*g))**0.5;\n",
+ "if Value1<0.1785:\n",
+ " # From Eqn. 10.74(a):\n",
+ " doBydj = (0.485*Value1**2)+1;\n",
+ "else:\n",
+ " # From Eqn. 10.74(b)\n",
+ " doBydj = (1.51*Value1)+0.12;\n",
+ "\n",
+ "dj = Do/doBydj;# [m]\n",
+ "Vomax = 2.69*((dj/Do)**2)*(sigma/(dj*((0.5137*Density_d)+(0.4719*Density_c))))**0.5;# [m/s]\n",
+ "# Since Vomax is less than 0.1:\n",
+ "Vo = 0.1;# [m/s]\n",
+ "Ao = qD/Vo;# [square m]\n",
+ "No = Ao/(math.pi*Do**2.0/4);# [square m]\n",
+ "# From Eqn. 6.30:\n",
+ "# Plate area for perforation:\n",
+ "Aa = Ao/(0.907*(Do/pitch)**2);# [square m]\n",
+ "\n",
+ "# Downspout:\n",
+ "dp = 0.0007;# [m]\n",
+ "# From Eqn. 10.75:\n",
+ "U = Density_c**2*sigma**3/(g*viscosity_c**4*delta_Density);\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 1.515;\n",
+ "abcissa = 0.62;\n",
+ "def f74(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vt = fsolve(f74,7);# [m/s]\n",
+ "Vd = Vt[0];# [m/s]\n",
+ "qC = C/(Density_c*3600);# [cubic m/s]\n",
+ "Ad = qC/Vd;# [square m]\n",
+ "# From Table 6.2 (Pg 169):\n",
+ "# Allowing for supports and unperforated area:\n",
+ "At = Aa/0.65;# [square m]\n",
+ "T = (At*4/math.pi)**0.5;# [m]\n",
+ "An = At-Ad;# [square m]\n",
+ "\n",
+ "\n",
+ "# Drop Size:\n",
+ "alpha1 = 10.76;\n",
+ "alpha2 = 52560;\n",
+ "alpha3 = 1.24*10**6;\n",
+ "alpha4 = 3.281;\n",
+ "abcissa = (alpha2*sigma*Do/delta_Density)+(alpha3*Do**1.12*Vo**0.547*viscosity_c**0.279/delta_Density**1.5);\n",
+ "Parameter = alpha1*Density_d*Vo**2/(delta_Density);\n",
+ "ordinate = 0.024;\n",
+ "dp = ordinate/alpha4;\n",
+ "\n",
+ "# Coalesced layer:\n",
+ "Vn = qD/An;# [m/s]\n",
+ "# From Eqn. 10.80:\n",
+ "ho = (Vo**2-Vn**2)*Density_d/(2*g*0.67**2*delta_Density);# [m]\n",
+ "hD = ho;\n",
+ "# From Eqn. 10.82:\n",
+ "hC = 4.5*Vd**2*Density_c/(2*g*delta_Density);# [m]\n",
+ "# From Eqn. 10.78:\n",
+ "h = hC+hD;\n",
+ "# Since this is very shallow, increase it by placing an orifice at the bottom of the downspout.\n",
+ "# VR: Velocity through the restriction.\n",
+ "# hR: Corresponding depth of the coalesced layer.\n",
+ "# Assume:\n",
+ "Vr = 0.332;# [m/s]\n",
+ "hr = (Vr**2-Vd**2)*Density_c/(2*0.67**2*delta_Density);\n",
+ "Ar = qC/Vr;# [square m]\n",
+ "dr = (4*Ar/math.pi)**0.5;# [m]\n",
+ "h = h+hr;# [m]\n",
+ "# The above results are satisfacyory.\n",
+ "Z = 0.35;# [m]\n",
+ "# Lead the downspout apron to within 0.1 m of the tray below.\n",
+ "\n",
+ "# Dispersed-phase holdup:\n",
+ "# From Eqn. 10.48:\n",
+ "Vsphi_D = Vn;\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 165.2;\n",
+ "abcissa = 30.0;\n",
+ "def f75(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vtl = fsolve(f75,7);# [m/s]\n",
+ "# For solids:\n",
+ "# From Fig. 10.48 (Pg 536):\n",
+ "abcissa = dp/(3*viscosity_c**2/(4*Density_c*delta_Density*g))**(1.0/3);\n",
+ "phi_D = [0, 0.1 ,0.2 ,0.3];\n",
+ "# Corresponding ordinates, from Fig. 10.48 (Pg 536):\n",
+ "ordinate1 = [8.8, 5.9 ,4.3 ,3.0];\n",
+ "Value1 = 1.0/(4*viscosity_c*delta_Density*g/(3*Density_c**2))**(1.0/3);\n",
+ "Val = numpy.zeros((4,7));\n",
+ "# Val = [phi_D ordinate Vs(1-phi_D) (Vs for solids) Vs/Vt (Vs for liquids) (Vs*phi_D (for liquids))]\n",
+ "for i in range(0,4):\n",
+ " Val[i,0] = phi_D[i];\n",
+ " Val[i,1] = ordinate1[i];\n",
+ " Val[i,2] = Val[i,1]/Value1;\n",
+ " Val[i,3] = Val[i,2]/(1-Val[i,0]);\n",
+ " Val[i,4] = Val[i,3]/Val[0,3];\n",
+ " Val[i,5] = Vtl*Val[i,4];\n",
+ " Val[i,6] = Val[i,5]*Val[i,0];\n",
+ "\n",
+ "\n",
+ "# By Interpolation:\n",
+ "Phi_D = 0.1;\n",
+ "\n",
+ "thetha_f =0.2498 # s\n",
+ "# From Eqn. 10.87:\n",
+ "const = 1.5;\n",
+ "kLDf = const*(Dd/(math.pi*thetha_f))**0.5;# [m/s]\n",
+ "# From Eqn. 10.86\n",
+ "KLDf = 1.0/((1.0/kLDf)*(1+((1.0/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# The ordinate of Fig. 10.47 for the drops larger than 70. Hence mass transfer coeffecient during drop rise is given by Eqn. 10.89:\n",
+ "# From Eqn. 10.91:\n",
+ "b = 1.052*dp**0.225;\n",
+ "# From Eqn. 10.90:\n",
+ "omega = (1.0/(2*math.pi))*math.sqrt(192*sigma*b/(dp**3*((3*Density_d)+(2*Density_c))));# [1/s]\n",
+ "Del = 0.2;\n",
+ "kLDr = math.sqrt((4.0*Dd*omega/math.pi)*(1+Del+(1.0/2)*Del**2));\n",
+ "KLDr = 1.0/1/((1/kLDr)*(1+((1/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# From Eqn. 10.98:\n",
+ "EMD = ((4.4*KLDf/Vo)*(dp/Do)**2)+(6*KLDr*Phi_D*(Z-h)/(dp*Vn))/(1+((0.4*KLDf/Vo)*(dp*1.0/Do)**2)+(3*KLDr*Phi_D*(Z-h)/(dp*Vn)));\n",
+ "print\"Stage Efficiency: \",round(-EMD,3)\n",
+ "# The solution in the textbook is incorrect\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.8 - Page: 539\n",
+ "\n",
+ "\n",
+ "Stage Efficiency: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 0.057\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.9: Pg-551"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.9\n",
+ "# Page: 551\n",
+ "\n",
+ "print('Illustration 10.9 - Page: 551\\n\\n');\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#****Data****#\n",
+ "B = 20000;# [kg/h]\n",
+ "#******#\n",
+ "\n",
+ "# x and y are taken in weight fraction acetic acid.\n",
+ "x1 = 0.30;# [Wt fraction]\n",
+ "xF = 0.30;# [Wt fraction]\n",
+ "y2 = 0;# [Wt fraction]\n",
+ "x2 = 0.02;# [Wt fraction]\n",
+ "y1 = 0.10;# [Wt fraction]\n",
+ "# The operating diagram is plotted in Fig. 10.23:\n",
+ "# Data = [x x_star]\n",
+ "# From Fig. 10.23 (Pg 503):\n",
+ "Data = numpy.array([[0.30 ,0.230],[0.25 ,0.192],[0.20 ,0.154],[0.15, 0.114],[0.10, 0.075],[0.05, 0.030],[0.02, 0]]);\n",
+ "Val = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " Val[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x\");\n",
+ "plt.ylabel(\"1/(x-x*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# From Area Under the curve:\n",
+ "Area = 8.40;\n",
+ "# The mutual solubility of water and isopropyl ether is very small.\n",
+ "Ma = 18.0;# [kg/kmol water]\n",
+ "Mb = 60.0;# [kg/kmol isopropyl ether]\n",
+ "r = Ma/Mb;\n",
+ "# From Eqn. 10.110:\n",
+ "NtoR = Area+(1.0/2)*math.log(1-x2/(1-x1))+(1.0/2)*math.log(x2*(r-1)+1.0/(x1*(r-1)+1));\n",
+ "# Since the operating line and equilibrium line are parallel:\n",
+ "Np = NtoR;\n",
+ "print\"Number of theoretical Units: \\n\",round(NtoR,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.9 - Page: 551\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3Tpd0PNDm5rNZa3j99eSS17POgqeeSm6Y+8xnfMNco5ShxzAB+DTwQUlz069J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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x78c3a58>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number of theoretical Units: \n",
+ "8.5\n"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex10.10:pg-552"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.10\n",
+ "# Page: 552\n",
+ "\n",
+ "print('Illustration 10.10 - Page: 552\\n\\n');\n",
+ "\n",
+ "# Solution\n",
+ "import math\n",
+ "#****Data****#\n",
+ "B = 1150;# [kg/h]\n",
+ "#*******#\n",
+ "\n",
+ "# x and y are taken in weight ratio.\n",
+ "x1_prime = 0.0101;# [Wt. fraction]\n",
+ "xF_prime = 0.0101;# [Wt. fraction]\n",
+ "y2_prime = 0;# [Wt. fraction]\n",
+ "x2_prime = 0.001001;# [Wt. fraction]\n",
+ "y1_prime = 0.0782;# [Wt. fraction]\n",
+ "# From Illustration 10.4:\n",
+ "A = 990.0;# [kg/h]\n",
+ "# At the dilute end:\n",
+ "m1_prime = 0.798;\n",
+ "Value1 = m1_prime*B/A;\n",
+ "# At the concentrated end:\n",
+ "m2_prime = 0.953;\n",
+ "Value2 = m2_prime*B/A;\n",
+ "ValueAv = (Value1*Value2)**0.5;\n",
+ "# From Eqn. 10.116:\n",
+ "# Since y2_prime = 0\n",
+ "Value3 = x2_prime/x1_prime;\n",
+ "NtoR = (math.log((1.0/Value3)*(1-(1/ValueAv))+(1/ValueAv)))/(1-(1/ValueAv));\n",
+ "print\"Number of theoretical Unit : \",round(NtoR,1),\"\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.10 - Page: 552\n",
+ "\n",
+ "\n",
+ "Number of theoretical Unit : 8.6 \n"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter11.ipynb b/Mass_-_Transfer_Operations/Chapter11.ipynb
new file mode 100755
index 00000000..061424be
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter11.ipynb
@@ -0,0 +1,1207 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:8fb22aace15e10e38e42ea742c8ed08180c4384c86b8595f798fa8d94958571f"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 11: Absorption And Ion Exchange"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.1: Page 575"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.1\n",
+ "# Page: 575\n",
+ "\n",
+ "print'Illustration 11.1 - Page: 575\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "#*****Data*****#\n",
+ "Temp = 30.0;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# From Fig. 11.5 (Pg 572)\n",
+ "# The isosteres for various concentrations are straight and their slopes are measured with the help of milimeter rule.\n",
+ "# Data = [X(kg acetone/kg carbon) lambda(slope of isostere)]\n",
+ "Data = numpy.array([[0.05 ,1.170],[0.10, 1.245],[0.15 ,1.3],[0.20 ,1.310],[0.25 ,1.340],[0.30 ,1.327]]);# [kg acetone/kg carbon]\n",
+ "lambdar = 551.0;# [reference at 30 OC,kJ/kg]\n",
+ "Val = numpy.zeros(shape=(6,5));\n",
+ "for i in range(0,6):\n",
+ " Val[i,0] = Data[i,0];# [kg acetone/kg carbon]\n",
+ " Val[i,1] = Data[i,1];# [slope of isostere]\n",
+ " Val[i,2] = -Data[i,1]*lambdar;# [kJ/kg acetone]\n",
+ "\n",
+ "\n",
+ "plt.plot(Val[:,0],Val[:,2])\n",
+ "plt.grid();\n",
+ "xlabel(\"X (kg carbon / kg acetone)\");\n",
+ "ylabel(\"Differential heat of adsorption (kJ / kg acetone)\");\n",
+ "title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area: The area under the curve between X = 0 to X = X\n",
+ "# Corresponding to Data(:,1):\n",
+ "Area = numpy.array([-29.8 ,-63.0, -97.9 ,-134.0, -170.5, -207.5]);\n",
+ "for i in range(0,6):\n",
+ " Val[i,3] = Area[i];\n",
+ " Val[i,4] = Area[i]+(lambdar*Val[i,0]);\n",
+ "print \" (1) = X(kg acetone/kg carbon) \\n (2)= Slope of isostere \\n (3)= Differential heat of adsorption(kJ/kg acetone) \\n (4)=deltaH_prime(vapour(kJ/kg carbon)) \\n (5)=deltaH(liquid(kJ/kg carbon)\"\n",
+ "print\"(1) \\t \\t \\t \\t (2) \\t \\t \\t \\t (3) \\t \\t \\t \\t \\t \\t \\t \\t (4) \\t \\t \\t \\t \\t \\t (5) \" \n",
+ "for i in range(0,6):\n",
+ " print Val[i,0],\" \\t \\t \\t \",Val[i,1],\" \\t \\t \",Val[i,2],\" \\t \\t \\t \\t \\t \",Val[i,3],\" \\t \\t \\t \\t\",Val[i,4]\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.1 - Page: 575\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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JJbfmzoWddoJ994ULLyx0NM65bMnXXF4/mdk1ZrZv9Li2WJOJW14u2ofXXBOe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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7df0da0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (1) = X(kg acetone/kg carbon) \n",
+ " (2)= Slope of isostere \n",
+ " (3)= Differential heat of adsorption(kJ/kg acetone) \n",
+ " (4)=deltaH_prime(vapour(kJ/kg carbon)) \n",
+ " (5)=deltaH(liquid(kJ/kg carbon)\n",
+ "(1) \t \t \t \t (2) \t \t \t \t (3) \t \t \t \t \t \t \t \t (4) \t \t \t \t \t \t (5) \n",
+ "0.05 \t \t \t 1.17 \t \t -644.67 \t \t \t \t \t -29.8 \t \t \t \t-2.25\n",
+ "0.1 \t \t \t 1.245 \t \t -685.995 \t \t \t \t \t -63.0 \t \t \t \t-7.9\n",
+ "0.15 \t \t \t 1.3 \t \t -716.3 \t \t \t \t \t -97.9 \t \t \t \t-15.25\n",
+ "0.2 \t \t \t 1.31 \t \t -721.81 \t \t \t \t \t -134.0 \t \t \t \t-23.8\n",
+ "0.25 \t \t \t 1.34 \t \t -738.34 \t \t \t \t \t -170.5 \t \t \t \t-32.75\n",
+ "0.3 \t \t \t 1.327 \t \t -731.177 \t \t \t \t \t -207.5 \t \t \t \t-42.2\n"
+ ]
+ }
+ ],
+ "prompt_number": 66
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.2: Page 596"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.2\n",
+ "# Page: 596\n",
+ "\n",
+ "print'Illustration 11.2 - Page: 596\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "#*****Data*****#\n",
+ "# x:kg carbon/kg soln\n",
+ "# y_star: Equilibrium colour, units/kg soln.\n",
+ "# X:adsorbate concentration, units/kg carbon\n",
+ "# Data = [x Y_star]\n",
+ "Data =numpy.array([[0, 9.6],[0.001, 8.6],[0.004 ,6.3],[0.008, 4.3],[0.02 ,1.7],[0.04, 0.7]]);\n",
+ "Yo = 9.6;# [units of colour/kg soln]\n",
+ "Y1 = 0.1*Yo;# [units of colour/kg soln]\n",
+ "Ls = 1000.0;# [kg soln]\n",
+ "#****************#\n",
+ "\n",
+ "\n",
+ "n = 1.66;# [slope of line]\n",
+ "# At X = 663, Y_star = 4.3\n",
+ "# From eqn. 11.5\n",
+ "X = 663;\n",
+ "Y_star = 4.3;\n",
+ "m = Y_star/X**n;\n",
+ "# Freundlich Equation:\n",
+ "def f76(X):\n",
+ " return m*X**n\n",
+ "X = numpy.arange(0,1000,1);\n",
+ "\n",
+ "plt.plot(X,f76(X));\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "title(\"Equilibium Data(on arithmetic scale)\");\n",
+ "plt.show()\n",
+ "# Single Stage Operation:\n",
+ "# Since fresh carbn is used:\n",
+ "Xo = 0;# [units/kg carbon]\n",
+ "# From scf(30):\n",
+ "X1 = 270;# [units/kg carbon]\n",
+ "Data2 =numpy.array([[Xo, Yo],[X1, Y1]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data2[:,0],Data2[:,1],label=\"Operating line curve\")\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Single stage operation\");\n",
+ "plt.show()\n",
+ "# From Eqn. 11.4:\n",
+ "Ss = Ls*((Yo-Y1)/(X1-Xo));# [kg carbon/kg soln]\n",
+ "print\"Quantity of fresh carbon recquired for single stage operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",
+ "\n",
+ "# Two stage cross current operation:\n",
+ "# For the minimumamount of carbon:\n",
+ "X1 = 565;# [units/kg carbon]\n",
+ "Y1 = 3.30;# [units of colour/kg soln]\n",
+ "X2 = 270;# [units/kg carbon]\n",
+ "Y2 = 0.96;# [units of colour/kg soln]\n",
+ "Data3 = numpy.array([[Xo ,Yo],[X1 ,Y1]]);\n",
+ "Data4 = numpy.array([[0 ,Y1],[X2 ,Y2]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data3[:,0],Data3[:,1],label=\"First of two Cocurrent\")\n",
+ "plt.plot(Data4[:,0],Data4[:,1],label=\"Second of two Cocurrent\")\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Two stage Cross current operation\");\n",
+ "plt.show()\n",
+ "# From Eqn. 11.8:\n",
+ "Ss1 = Ls*(Yo-Y1)/(X1-Xo);# [kg]\n",
+ "Ss2 = Ls*(Y1-Y2)/(X2-Xo);# [kg]\n",
+ "Ss = Ss1+Ss2;# [kg]\n",
+ "print\"Quantity of fresh carbon recquired for two stage crosscurrent operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",
+ "\n",
+ "# Two Stage counter current operation:\n",
+ "Yo = 9.6;\n",
+ "Y2 = 0.96;\n",
+ "# By trial and error:\n",
+ "XNpPlus1 = 0;\n",
+ "X1 = 675;\n",
+ "Data5 = numpy.array([[X1 ,Yo],[XNpPlus1 ,Y2]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data5[:,0],Data5[:,1],label=\"Two stage Counter Current\");\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Two stage Counter Current operation\");\n",
+ "# By eqn 11.14:\n",
+ "Ss = Ls*(Yo-Y2)/(X1-XNpPlus1);\n",
+ "print\"Quantity of fresh carbon recquired for two stage Counter Current operation: \",Ss,\" kg carbon/1000 kg solution\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.2 - Page: 596\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xa1a8c88>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Tviud53s/H9NyjTHx5+ef3bzMxx/v+hQSZSC8qPcxiEgfcg6el4OqRu0GAD8S\nw/Z92znm0WNYdfUq6lWuF9OyjTHx47//hb/9DS67zA2GJ8X+mo2dWPQx9CxkKVFqVKjB4FaDGf/F\neL9DycHakoOsLoKsLoIiWRcffQRnnOGGubjjjsRKCpGUbx+Dqg6OYRxxYUS7EZz45In8+/R/U/2w\n6n6HY4yJoSeecGMezZgBnTv7HY2/wrmPoSowGjjd25QG3K2qu6MWlA9NSQGXzbqMxlUbc3vn230p\n3xgTWxkZMGKEO1t4911o0sTviA5dzO5jEJGZwNfAVNxAegOBE1X1wuIWXkCZviWG1b+upvOUzvw4\n7EcOL3u4LzEYY2Jj9264+GLIyoLXXkv8K49idh8D0ERVR6vqOlX9wRs/KYFzasGa12xOxwYdmbR8\nkt+hANaWHMrqIsjqIuhQ62LdOje8RZMmMHt24ieFSAonMewXkU6BFRHpCOyLXkj+G9lhJA998RAH\nMw/6HYoxJgo++8wNhHf11fD441A6nDu6kkg4TUmtgGlAFW/TTuBSVV0ZtaB8bEoK6Dq1K0NaDWFg\ny4G+xmGMiaxp0+DGG93fc87xO5rIivlYSSJSBVBV3VPcQsMoy/fE8OEPHzJizghWXb2KUhLOiZUx\nJp5lZcG//w2vvuo6mY87zu+IIi+WYyUNF5HKuAl6xovIVyJydnELjnfdUrtRNqUs7333nq9xWFty\nkNVFkNVFUDh18fvvcNFFrglp0aKSmRQiKZyfwpd5ZwndgerAIGBsVKOKA4GJfMZ8NiauJ/IxxhRs\n82Y4/XQ3oc7cuVCzpt8Rxb9w+hi+VtUWIjIRSFPVmSKyXFVPOuRC3b0RzwHH44bduExVvwx53vem\nJIDMrEyaPdaMyb0n06lhp8JfYIyJK0uWwAUXwL/+BSNHlvw7mWN5ueoyEfkQOA/4wGtWyipmuY8A\ns1W1OXAisLqYx4uKlFIp3NzhZsYuLPEnSMaUOC++COedB48+CqNGlfykEEnhJIahwC1Aa1XdB5QB\nhhxqgV4ndidVnQSgqhnRvIu6uAa1HMTyLctZtXWVL+VbW3KQ1UWQ1UVQ7rrIzHRnB7ffDh9/7M4Y\nTNEUmhhUNVNVl6nqLm99u6oW51uyMfCriEz2OrKfFZEKxTheVJUvXZ7hbYfH7UQ+xpigXbugZ0/X\nhLR4MbRo4XdEiSnm8zF4c0h/AbRX1SUiMgHYo6p3hOwTF30MAXv+2EPqI6ksvmIxqdVS/Q7HGJOH\n776DXr2VHRL3AAAgAElEQVSgWzd4+GEoU8bviGIvUn0MftzvtwnYpKpLvPXXgVG5dxo8eDCNGjUC\noGrVqrRq1YouXboAwVPHWK1/9cVXnJ1yNuM+H8fjf3s85uXbuq3besHrixfDQw914b774Jhj0li4\nML7ii9Z6WloaU6ZMAcj+voyEcK5Kymv86d9U9ZDHixCRBcDlqvqdiNwJHKaqI0Oej6szBoCte7fS\n/PHmrL5mNbUr1o5ZuWlpadkfiGRndRFkdeGowv/9XxqzZnXhtdegY0e/I/JXLK9K+grYBnzvLduA\ndK9/4JRDLPda4EURWYm7Kun+QzxOzNSuWJt+J/Rj4qKJfodijAEOHIBBg9y9CV9+aUkhksI5Y3gW\neF1V53jr3YG/A5OBR1S1TcSDisMzBoB1O9fR5tk2rBu2jsrlKvsdjjFJ66ef3NVGjRvDpElQIW4v\nX4mtWJ4xtAskBQBV/dDb9gWQIFNkR0ZqtVS6N+nO00uf9jsUY5LWokXQpg307g0vv2xJIRrCSQxb\nRGSkiDQUkUYicjOwVURSKP6NbglnZIeRjP9yPAcyDsSkvEBHk7G6CJWsdTFlirsc9ckn4dZb3U1r\nyVoX0RROYrgEqA+8BbwJNAD6AylA3+iFFp9a1mlJqzqtmL5yut+hGJM0/vwTrrkGxoyBtDSXHEz0\nhNPH0FhVf8y17dSQy00jH1Sc9jEELEhfwNC3h7LmmjWklErxOxxjSrQtW9zIqDVquDkUqlQp/DXJ\nKpZ9DG+ISL2QgjvjOp6TVqcGnahZoSYzV8/0OxRjSrTPP4dTT4Wzz4Y337SkECvhJIargLdEpI6I\nnAdMBM6NbljxLTAk99iFY6M+JLe1nwZZXQSV9LpQdf0IF1wAzzzjxj0qlc+3VUmvCz+EM1bSEuA6\n4CPgTqCbqm6Mclxxr0fTHhzIOMDcdXP9DsWYEuXAARg6FJ54AhYudCOkmtjKt49BRN7Jtak5sAXY\nhZvis1fUgorzPoaAaSunMXXlVOYNmud3KMaUCBs2QJ8+kJoKzz/vJtcx4Yv6nM9eXwJAaCHqrauq\nzi9u4fkGlSCJ4WDmQY5+9GhmXDSDNkdF/D4/Y5LKJ5/AJZfADTe4xeZPKLpYdD7fCpwM/Kyqad4y\nP/C3uAWXBGVSynBDuxuiOiS3tZ8GWV0ElaS6UHWjofbvDy+8ADfeWLSkUJLqIl4UlBgG45qN7hSR\n5SLylIj0FpHDYxNaYhh60lA+Tf+UNdvW+B2KMQnn999hwACXEBYtgjPP9DsiA2HOx+Dd5Xwa7mqk\nrsABYI6qPhiVoBKkKSng7vl3k74rned7P+93KMYkjDVrXH9Cmzauo/mww/yOKPFFvY/BKyQFuE5V\nx+faXhPorqovFjeAfMpNqMSwfd92jnn0GFZdvYp6lesV/gJjktxrrwXvZB461PoTIiUmN7ipaiZu\nSIzc23+NVlJIRDUq1GBwq8GM/2J84TsXkbWfBlldBCVqXfz5JwwbBrfcAnPmwOWXFz8pJGpdxLNw\nbnD7TEQeE5FOInKyiJwiIidHPbIEM6LdCCavmMyO/Tv8DsWYuLRpE3TpAuvXw9KlcLJ9i8StcMZK\nSsNdppqDqp4RpZgSrikp4LJZl9G4amNu73y736EYE1c++shNqjN8ONx0U/53MZviiUkfg18SNTGs\n/nU1nad05sdhP3J4Wbt4y5isLLjvPje8xYsvwhlR+zlpIIaD6InIaBG5I+TvHSJyR3ELLoma12xO\nxwYdmbR8UsSOae2nQVYXQYlQF9u3Q48e7mxh6dLoJYVEqItEE84J3e/eshc3Mc95QKMoxpTQRnYY\nyUNfPMTBzIN+h2KMb5YsgVNOgeOPh3nzoG5dvyMyRVHkpiQRKQd8qKqdC935ECVqU1JA16ldGdJq\nCANbDvQ7FGNiShWeegpGj4ann3ajo5rYieV8DLkdDhxV3IJLslEdR/HAwgfI0qSb+dQksd274eKL\nXUJYuNCSQiILp4/h65Dlv8D/gEeiH1ri6pbajbIpZXnvu/eKfSxrPw2yugiKt7oIXH5asyZ8+SUc\nc0zsyo63uigJSoexT2B2VQUygF9U1RrQCxCYyGfMZ2Po0bQHYrd1mhJKFR59FO69Fx5/3E3BaRJf\nuGMltQI64ZLDp6q6MqpBJXgfA0BmVibNHmvG5N6T6dSwk9/hGBNxO3e64Sw2bIBXX4UmTfyOyMTy\nctVhwAtATaA28IKIXFfcgku6lFIp3NzhZsYuHOt3KMZE3KJFrumoQQPXn2BJoWQJp/P5cuA0Vb1D\nVW8H2gJXRDeskmFQy0Es37KcVVtXHfIxrP00yOoiyK+6UIVx46BXLxg/HiZMgHLlfAklm30uIi/c\nq5Ky8nlsClC+dHmGtx0e1Yl8jImV7dtdQpgxw50xnH++3xGZaAlnrKQRuEl7ZuKm9TwfmJJ7KO6I\nBlUC+hgC9vyxh9RHUll8xWJSq6X6HY4xh2ThQjft5kUXwf33Q9myfkdk8hLTsZJE5BSgI8HO5+XF\nLbiQ8kpMYgC4dd6t7D6wm8f/9rjfoRhTJJmZMHasu/LouefcEBcmfkW981lEqgcW4EdcB/SLQLq3\nzYRp2GnDePmbl9m6d2uRX2vtp0FWF0GxqIuNG91Um3PnuvsU4jUp2Oci8grqY/gKWBayLPWWwGMT\nptoVa9PvhH5MXDTR71CMCcvMmdC6NZx9tksM9WxiwqRiw27HyLqd62jzbBvWDVtH5XKV/Q7HmDzt\n2wfXX++SwUsvwWmn+R2RKYqYjpUkIr1FZJyIPCQiPQt/hckttVoq3Zt05+mlT/sdijF5WrnSjYi6\nbx8sX25JIZmFc4PbWOA64L/AauA6ERkT7cBKopEdRjL+y/EcyDgQ9mus/TTI6iIoknWhCo88Amed\nBf/+N0yfDpUT6KTWPheRF85YSX8DWqlqJoCITAFWALdEMa4SqWWdlrSq04rpK6dzxSl2j6Dx3y+/\nwODB7h6FL7+0O5iNE859DKuAM1R1u7deA/hEVU+MWlAlsI8hYEH6Aoa+PZQ116whpVSK3+GYJDZn\nDgwZ4hLDXXdBmTJ+R2SKK1J9DOGcMYwBvhKRT3A3uHUGRhW3YBFJwV3dtElVk6bfolODTtSsUJOZ\nq2dy0fE2FKWJvQMH4NZb3R3ML7wAXbv6HZGJN4X2Majqy0A74E3gDaCtqr4SgbKHAd/ibppLGoEh\nuccuHEs4Z0XWfhpkdRF0qHWxYoW7DHXDBve4JCQF+1xEXjidzxcA+1R1lqq+DRwQkWKNkiIi9XBz\nRz+HOwtJKj2a9uBAxgHmrpvrdygmSWRmwgMPQLduMHKkO1uoUcPvqEy8CqePYaWqtsy1bYWqtjrk\nQkVmAPcDlYEbczclleQ+hoBpK6cxdeVU5g2a53copoRbvx4GDQIRmDYNGjb0OyITLbG8jyGvQg65\n11REeuBmgVuez7GTQv8T+rN2x1oWb17sdyimhFKFqVPh1FOhZ0/4+GNLCiY84XQ+LxORh4HHcV/k\n1+CGxThU7YFeInIeUB6oLCLTVHVQ6E6DBw+mUaNGAFStWpVWrVrRpUsXINimmOjrN7S7gQcWPsC1\nta7Nd//Q9lO/4/V7PbAtXuLxc33FihUMHz483+d374bp07vwv//BmDFpHH00pKTET/yRXJ8wYUKJ\n/H4IZz0tLY0pU6YAZH9fRoSqFrgAFYEHCI6VNAY4vLDXhbPgrnB6J4/tmgz2/rFXaz5YU1f/ujrf\nfT755JPYBRTnrC6CCqqL999XrVtX9YYbVPfvj11MfrHPRZD33Vns72Zfx0oSkc7ADaraK9d29TOu\nWLp7/t2k70rn+d7P+x2KSXD79sFNN8G778KUKXDGGX5HZGItpmMlRYuqzs+dFJLNNadew5tr3mTT\nnk1+h2IS2OefQ6tWsHu3G/PIkoIpDl8Tg4EaFWowuNVgxn+R94R4oe3ryc7qIihQF/v3u7OEPn3c\nhDovvABVq/obW6zZ5yLyCpqo5wHvb9/YhZOcRrQbweQVk9mxf4ffoZgEsmgRnHwypKfDqlVw4YV+\nR2RKinz7GETkG6AF8JWqnhTToJKojyHgslmX0bhqY27vfLvfoZg498cfbmyjSZNg4kToaz/djCcW\nfQzvAzuBFiLyW65lT3ELNjnd1P4mHl38KL//+bvfoZg49tVXbkiL1atdX4IlBRMN+SYGVb1JVasC\ns1W1Uq4lgUZrTwzNazanY4OOTFo+Kcd2az8NSua6+PNPGD0azj0XRo2C665Lo3Ztv6OKD8n8uYiW\ncAbR6yUitUWkh7fUikVgyWhkh5E89MVDHMw86HcoJo6sXOlmU1u2zM2sNmCAG97CmGgJZ6ykvsB/\ngPm4O587ATep6oyoBZWEfQwBXad2ZUirIQxsOdDvUIzP/vzTDXz36KPw4INw6aWWEEzBItXHEO5E\nPWep6i/eek1gntpEPVHx4Q8fMmLOCFZdvYpSYlcTJ6slS2DoUGjQAJ58EurX9zsikwhiPYjeryHr\n20niwe+irVtqN8qmlOW9794DrP00VDLUReDu5Z49XV/CO+/knRSSoS7CZXUReeEkhg+AOSIyWESG\nALNxVyyZKAhM5DPmszFhTeRjSo60NDjxRNi8Gb7+Gi65xJqOjD/CGitJRPoAHbzVT1X1zagGlcRN\nSQCZWZk0e6wZk3tPplPDTn6HY6Js9264+WaYPRueeMKdLRhzKGI6VpKqvqGqI7wlqknBQEqpFG7u\ncDNjF471OxQTZe+8Ayec4M4MvvnGkoKJD9a7GacGtRzE8i3LeX6mjboaUJLakn/91TUVXX+9m1Xt\nqaegSpXwX1+S6qK4rC4izxJDnCpfujzD2w7n5W9e9jsUE0GqMH06tGgBRx3lxjiykVBNvCnSfAwi\nUh2op6qroheS9TEE7PljD6mPpLL4isWkVkv1OxxTTN99B1dfDTt3wtNPuyk3jYmkmPUxiMh8Eans\nJYVlwHMikvcY0SaiKperzJWnXMm4z8f5HYophj/+gHvugfbtoUcPWLzYkoKJb+E0JVVR1T3AhcA0\nVW0DnBXdsExA6z9b8/I3L7N171a/Q/FdIrYlL1jgJtBZssQNgHf99VA6nJnWC5GIdREtVheRF05i\nSBGRI4G+wHveNmvniZHqh1Wn3wn9mLhoot+hmCLYvt3duTxgANx/P8ya5e5iNiYRhDMkxkXA7cBC\nVb1aRJoAD6pqn6gFZX0MOazbuY42z7Zh3bB1VC5nA9vGs0Dn8s03Q79+rgmpUiW/ozLJIpZjJXVU\n1c8K2xZJlhj+6pI3LuGkOidxU4eb/A7F5CPQubxrl+tcbt3a74hMsonlDW6P5rHN2jViJNB+OrLD\nSMZ/OZ4DGQf8DchH8dqWvG8f3HGH61zu2dNNuRntpBCvdeEHq4vIy7cbTETaAe2BmiIyguDAeZWA\nlBjEZkK0rNOSVnVaMX3ldK445Qq/wzG4ZqO334bhw918CStWQL16fkdlTPEVNOdzZ+AM4CrgqZCn\nfgPeUdXvoxaUNSXlaUH6Aoa+PZQ116whpZTlZj+tXQvDhsGPP7r5Es480++IjIltH0NDVU0vbkFF\nYYkhb6pKh0kduL7t9Vx0/EV+h5OU9u2DsWPdYHcjR7rkULas31EZ40S9j0FEHvEePiYi7+Ra3i5u\nwSY8oe2ngSG5xy4cm5RDcvvZlqzqLjk9/njXybxihZs3wa+kYO3qQVYXkVfQrTbTvL92220c6dG0\nB7fMu4W56+bSrUk3v8NJCoFmo3Xr4LnnrNnIlHxFGispVqwpqWDTVk5j6sqpzBs0z+9QSrS9e2HM\nGHfpqTUbmUQQy7GSOorIRyLyvYj86C3riluwOXT9T+jP2h1rWbx5sd+hlEhZWW4o7GOPhQ0b/G82\nMibWwrmP4XngYaAjcKq3tIlmUCYor/bTMilluKHdDTyw8IHYB+SjWLQlf/EFtGsHjz8Or7/u7mKO\nx0tQrV09yOoi8sJJDLtU9X1V3aqq2wJL1CMzBRp60lA+Tf+UNdvW+B1KibBpE/zjH3DRRfCvf7kE\n0bat31EZ449wLlcdi7uhbSbwR2C7qn4VtaCsjyEsd8+/m/Rd6Tzf22Z5O1T79sFDD8Ejj7jhLEaN\ngooV/Y7KmEMTy/sY0shjNFVVjdq8U5YYwrN933aOefQYVl29inqV47C9I46pwmuvucHuTjsNHnwQ\nGjXyOypjiidmnc+q2kVVz8i9FLdgE56C2k9rVKjB4FaDGf9FcsybFKm25C+/hE6d3I1q06e7BJFo\nScHa1YOsLiKv0ClDRGQ07oxBCDlzUNW7oxiXCdOIdiM48ckT+ffp/6b6YdX9Dieu/fAD3HILfP65\nGw570CBIsZFFjPmLcJqSbiSYEA4DegDfquplUQvKmpKK5LJZl9G4amNu73y736HEpe3bXSJ44QU3\ng9r110OFCn5HZUzkxayPIY+CywEfqmrn4hZeQBmWGIpg9a+r6TylMz8O+5HDyx7udzhx48ABmDgR\n/vMf6NsXRo+GWrX8jsqY6InlfAy5HQ4cdagFikh9EflERP4rIt+IyHWHeqxkEE77afOazenYoCOT\nlk+KfkA+CrctOSvLnR00a+YuO/3sM3dfQklKCtauHmR1EXnh9DF8HbJaCqgFFKd/4SBwvaquEJGK\nwDIR+UhVVxfjmElvZIeR9H29L/9s/U/KpJTxOxzffPyxu0u5TBmXHDp18jsiYxJPOH0MjUJWM4Ct\nqnowYgGIvAU8qqrzQrZZU9Ih6Dq1K0NaDWFgy4F+hxJzS5bArbe6+RHuv9/dqCbFPqE2JrHE8nLV\n9SHLpggnhUbAScCiSB0zmY3qOIoHFj5Almb5HUrMfPst9OkDF1wAf/87rF7t+hMsKRhz6AptSooW\nrxnpdWCYqu7N/fzgwYNp5F1cXrVqVVq1akWXLl2AYJtiMqyHtp8Wtn+3zt0om1KWsdPH0r5B+7iI\nP5LrgW1paWn8/DN88EEXZs+GPn3SeP55OPvs+Io3musrVqxg+PDhcROPn+sTJkxI6u+HKVOmAGR/\nX0aCL8Nui0gZ4F3gfVWdkMfz1pTkSUtLy/5AhOO1/77GhC8nsPCyhUgJ+9mclpbGscd24b774KWX\n4Jpr4IYboEoVvyOLvaJ+Lkoyq4sg3y5XLXaB7ttqKrBdVa/PZx9LDIcoMyuTZo81Y3LvyXRqWHJ6\nXnfudJedPv20uzHtlltK1lVGxkSCn5erFlcH4B/AGSKy3FvO8SGOEimlVAo3d7iZsQvH+h1KROze\nDXffDU2bwi+/wPLlMH68JQVjoinmiUFVP1PVUqraSlVP8pYPYh1HoghtXw/XoJaDWL5lOau2rop8\nQDESSAhHH+2m1Pz8c/jHP9Jo0MDvyOLDoXwuSiqri8jz44zBRFn50uUZ3nZ4Qk7ks3u3G77i6KPd\n2Eaffw5TpsAxx/gdmTHJw+Z8LqH2/LGH1EdSWXzFYlKrpfodTqF273bDV0ycCOedB7fdZsnAmKJK\n5D4GEwOVy1XmylOuZNzn4/wOpUC7d8O997ozhO+/h4ULYepUSwrG+MkSQ5wrTvvpsNOG8fI3L7N1\n79bIBRQhv/7qzgqaNIH//c8lhGnTXCdzfqwtOcjqIsjqIvIsMZRgtSvWpt8J/Zi4aKLfoWTbuBGG\nDXMD3G3bBosWuclyCkoIxpjYsj6GEm7dznW0ebYN64ato3K5yr7F8d138MAD8OabMHSomxOhbl3f\nwjGmRLI+BhOW1GqpdG/SnaeXPu1L+cuXu7GLOnSABg1cP8J//mNJwZh4ZokhzkWi/XRkh5GM/3I8\nBzIOFD+gMKhCWpq7uuhvf4PTTnP3IoweDTVqHPpxrS05yOoiyOoi8iwxJIGWdVrSqk4rpq+cHtVy\nDh6El1+G1q3hqqugd2+XEG64ASpVimrRxpgIsj6GJLEgfQFD3x7KmmvWkFIqJaLH3rMHnnsOJkyA\nxo1dIujRA0rZzw5jYsr6GEyRdGrQiZoVajJz9cyIHXPjRrjxRpcMliyBmTNh/nzo1cuSgjGJzP77\nxrlItZ+KCKM6jmLswrEU92xs2TIYMABatXLzK3/1VbAJKZqsLTnI6iLI6iLyLDEkkR5Ne3Ag4wBz\n180t8mv//NPNgdC+PVx4IZx0kus/ePhhaNgwCsEaY3xjfQxJZtrKaUxdOZV5g+YVvjPw00/wzDNu\nad4crr0WevaElMh2UxhjIsD6GMwh6X9Cf9buWMvizYvz3UfVDVHRvz8cf7ybB2HuXJg3D84/35KC\nMSWdJYY4F+n20zIpZbih3Q15Dsm9bx9MngynnAKDB0PbtvDjj/DEE3DccREN45BYW3KQ1UWQ1UXk\nWWJIQkNPGsqn6Z+yZtsaAFatgn/9C+rXh9dfh/vucwPbDRsGVav6HKwxJuasjyFJ3fbR3cxfmc7B\n159n82Y3ftFll2EzpBmTwCLVx2CJIcksX+46kl+etR0d2JUpHRbT87xylC7td2TGmOKyzuckEYn2\n0x07XD/Bqae6zuO6deGbxTXYNXYFF/RKnKRgbclBVhdBVheRlyBfCaaoDh6EDz5ws6F99BGcey7c\nfTd07x56VVGxf1gYY0oga0oqYVaudMngxRfddJmXXuqGvbZOZGNKvkg1JdkZQwmweTO89ppLCDt2\nwKBB8OmnNiuaMebQWB9DnMuv/XTbNnjqKejSBVq0cJecPvwwrF8P995bMpOCtSUHWV0EWV1Enp0x\nJJA9e+Ctt9yAdZ9/7voNhg+Hc86B8uX9js4YU1JYH0Oc++03mD3bNRXNnevOEPr1c+MVVazod3TG\nmHhi9zGUYL/+Cm+/DW++CQsWQMeO0KePG9W0WjW/ozPGxCu7j6GE2bABHnnEnREcfTTMmePmPHjp\npTRmz3Z3Jid7UrC25CCriyCri8izPgafZGW5Wc9mz4b33nOdxj17umkxzzoLDjvM7WefeWNMrFlT\nUgzt3OnOBGbPdjef1aoF553nlo4dSZg7kI0x8cn6GBJAZiasWOHuPH7vPXfzWefOwWRgM58ZYyLJ\n+hjikKobrvqJJ1xnca1aMHCgmwXt3/92E9688w5cfXX4ScHaT4OsLoKsLoKsLiLPGi+KQdV1Gs+f\n72Y3mzcPSpWCM8+ECy6ARx91A9YZY0wisaakIsjMdHcYL1wIn33mlowM6NTJJYMzz3RXFImNTWeM\n8YH1McTAL7/A0qXu6qGFC2HRIjjqKNdR3KGD+5uaaonAGBMfErqPQUTOEZE1IvK9iIz0I4bctm+H\nDz+E++93N5I1aADNmrnxh/bvh2uvhXXr4Ntv3UQ3l14KTZpEPylY+2mQ1UWQ1UWQ1UXkxbyPQURS\ngMeAs4DNwBIReVtVV8ei/P37YfVq+Oab4PL117B7N5x8MrRu7YapfvDB2HzxF2bFihV06dLF3yDi\nhNVFkNVFkNVF5PnR+dwGWKuq6wFE5BWgNxCxxJCR4TqF167NuaxZAxs3wjHHwAknuOWf/3R/GzVy\nHcfxZteuXX6HEDesLoKsLoKsLiLPj8RwFLAxZH0TcFo4L8zKcr/sd+xww05v3hxcNm1yfzdudMuR\nR7qO4MBy+ukuITRtCmXKROV9GWNMieBHYgirV/mMM+DAAbfs3euSwe7dbkTRGjWgenXXERxYund3\nf+vVc7/+y5WL8ruIkfXr1/sdQtywugiyugiyuoi8mF+VJCJtgTtV9Rxv/RYgS1UfCNnH/0uSjDEm\nASXk5aoiUhr4H3Am8BOwGOgfq85nY4wxBYt5U5KqZojIv4A5QArwvCUFY4yJH3F5g5sxxhj/xN0F\nmvF481u0iEh9EflERP4rIt+IyHXe9uoi8pGIfCciH4pI1ZDX3OLVzRoR6e5f9NEhIikislxE3vHW\nk7IuRKSqiLwuIqtF5FsROS2J6+IW7//I1yLykoiUS5a6EJFJIrJVRL4O2Vbk9y4ip3j1972IPFJo\nwaoaNwuuaWkt0AgoA6wAmvsdVxTfbx2glfe4Iq7vpTnwIHCzt30kMNZ7fJxXJ2W8OloLlPL7fUS4\nTkYALwJve+tJWRfAVOAy73FpoEoy1oX3ftYB5bz1V4FLk6UugE7AScDXIduK8t4DrUKLgTbe49nA\nOQWVG29nDNk3v6nqQSBw81uJpKo/q+oK7/Fe3E1+RwG9cF8MeH/P9x73Bl5W1YPqbhBci6uzEkFE\n6gHnAc8BgSsrkq4uRKQK0ElVJ4Hrl1PV3SRhXQB7gINABe/ClQq4i1aSoi5U9VNgZ67NRXnvp4nI\nkUAlVV3s7Tct5DV5irfEkNfNb0f5FEtMiUgj3C+DRUBtVd3qPbUVqO09rourk4CSVj/jgZuArJBt\nyVgXjYFfRWSyiHwlIs+KyOEkYV2o6g5gHLABlxB2qepHJGFdhCjqe8+9fTOF1Em8JYak7AkXkYrA\nG8AwVf0t9Dl1534F1UuJqDMR6QH8oqrLCZ4t5JAsdYFrOjoZeEJVTwZ+B0aF7pAsdSEiTYDhuKaR\nukBFEflH6D7JUhd5CeO9H5J4Swybgfoh6/XJmelKHBEpg0sK01X1LW/zVhGp4z1/JPCLtz13/dTz\ntpUE7YFeIvIj8DLQVUSmk5x1sQnYpKpLvPXXcYni5ySsi9bA56q6XVUzgJlAO5KzLgKK8n9ik7e9\nXq7tBdZJvCWGpcAxItJIRMoCFwNv+xxT1IiIAM8D36rqhJCn3sZ1sOH9fStkez8RKSsijYFjcJ1K\nCU9Vb1XV+qraGOgHfKyqA0nOuvgZ2CgiTb1NZwH/Bd4hyeoCWAO0FZHDvP8vZwHfkpx1EVCk/xPe\n52mPd2WbAANDXpM3v3vd8+iFPxd3dc5a4Ba/44nye+2Ia09fASz3lnOA6sBc4DvgQ6BqyGtu9epm\nDXC23+8hSvXSmeBVSUlZF0BLYAmwEvcruUoS18XNuMT4Na6ztUyy1AXu7Pkn4E9c/+uQQ3nvwCle\n/a0FJhZWrt3gZowxJod4a0oyxhjjM0sMxhhjcrDEYIwxJgdLDMYYY3KwxGCMMSYHSwzGGGNysMRg\nYsGZ6gUAAAS7SURBVMob/vcR73FnEWkXoeP+xxu6/IHC9y7wOOtFpHokYvKOd6SIzPHe6zuROm4R\nY+jiV9kmMcV8BjeT3FR1GbDMWz0D+A34IgKHvgKopsW/MSciN/aISIqqZuJuWPwgEsc8xDjs/7gp\nMjtjMIfMG7okdAKRG0VktPc4TUTGisgiEfmfiHT0tncRkXdEpCFwFXC9uIl5OorIRd5kIitEZH4+\nZf7H22eViPT1tr2Nm8/iq8C2kP0reqOUrhKRlSJygbe9v7ftaxEZm09ZI7znvxaRYWG+5/EisgS4\nztvlbOB9QgYGFJFTvVFTG4tITW/SlW+8UVTzPGMRN4HVMq9uPvK2tRGRz71jLQwMoSEig0XkbRGZ\nh7tDVoEqIvKuuAlcnvSGRsi3HkRkr4jc65X3hYjUyquOTMlkvyZMJIWO9KhAiqqeJiLnAqOBbtk7\nqqaLyFPAb6r6MICIrAK6q+oWEamc++Ai0gc3VMSJQE1giYjMV9VeIvKbqp6UR0y3AztV9UTvGFVF\npC4wFjcw3S7gQxHpraqzQso6BRiMG8u/FLDIS1a7CnnPZVT1VO8YKUAzVV0jwUHP2gMTgV6quklE\nHgPmquoDInI2MDSP910TeAY3R0O6BGfsWu1tyxSRs4D7gb97z50EtFDVXSLSBTgVNwnUBtwZzIUi\n8kUB9VAB+EJVb/Oa564A7sujfk0JZGcMJtJCh8ye6f39CjdscmH7LwSmisjl5P2jpQPwkjq/APNx\nX3gFORN4PLCiqru813yibsTOTNyMcafniqkjMFNV96vq79576UTeTU2h7+HVkMen4ebXCGgOPA30\nUNXAqMEdcBNSoapz+OukLABtgfmqmh7yHgCqAq97ZzAP42bwCvgwZD9wg6mtV9Us3Pg7HXEjl6bl\nUw9/qup73uNl5P/vZ0ogSwymODLI+Rk6jJxfnH94fzMJ4+xUVa8GbsMNHbwsn05gyedxQXLvp3kc\nJ/cXfn77FPaefw95fC6uGSlwvC3Aftwv9ILiyy13LAH3APNUtQXQ04slYF8exwgtL78EF9h+MGR7\nFta6kFQsMZji2ArUEjc5eTmgRxFf/xtQKbAiIk1UdbGqjgZ+JecY8gCfAheLSCmveaUThQ+p/BFw\nTUgZVb3XdBaRGl5zTz/c2UeAemWdL26458NxUyF+ihv7vqD3HPoF3hXXxh/Yvsvbf4yIdPa2LwQC\nfSXdgWp5vIdFwOniZvlDRAL7VMaNvAlu1M2CtPH6R0p55X0aRj2YJGWJwRwydfNy3437gvkQN05+\nvrvn8fgd4AKv87Qj8GCgIxRYqKqrcpX3JrAKNxT1POAmr0kp9/FD3QtUC3RqA13UjU8/CvgEN+T5\nUlV9J/Q46maSm+K9ty+BZ1V1ZRjvWSG7X+CA1wwV2B5oAusBPC4ipwJ3Ad299/x34Gdcwgx9378C\nVwIzvffwivfUg7gk8xWQQs6+jtz1vQR4zIt3naq+GU495HM8U8LZsNvGRIGIDACOUtUHC9mvLJDp\ndSC3Ax5XN52nMb6xxGCMj0TkaOA13Nn7n8DV3r0exvjGEoMxxpgcrI/BGGNMDpYYjDHG5GCJwRhj\nTA6WGIwxxuRgicEYY0wOlhiMMcbk8P+dWPmzYQm/hQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7d412e8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Quantity of fresh carbon recquired for single stage operation: 32.0 kg carbon/1000 kg solution\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Ly0t2796d4mdIq+TojBkz4suoVqpUSfbv3y8iiavsiehKe8OHD0+k53HjxknR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\nr4QZo2DBk+4LT8GWJHptlVJFlFKtLUt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+ "text": [
+ "<matplotlib.figure.Figure at 0x997fbe0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Quantity of fresh carbon recquired for two stage crosscurrent operation: 19.8171091445 kg carbon/1000 kg solution\n",
+ "\n",
+ "Quantity of fresh carbon recquired for two stage Counter Current operation: 12.8 kg carbon/1000 kg solution\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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AASpVquRnpYoSGiS9USc9PENtPWlboOjxxvpPP8GoUZHMmgXnzsUQE5P6/jEx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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7928278>"
+ ]
+ }
+ ],
+ "prompt_number": 63
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.3: Page 602"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.3\n",
+ "# Page: 602\n",
+ "\n",
+ "print'Illustration 11.3 - Page: 602\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#***Data***#\n",
+ "T = 1.0; #[m]\n",
+ "di = 0.203;# [m]\n",
+ "n = 1;# [for one impeller]\n",
+ "Density_S = 2300.0;# [kg/cubic m]\n",
+ "Density_p = 2300.0;# [kg/cubic m]\n",
+ "C = 0.150;# [m]\n",
+ "S = 50.0;# [kg]\n",
+ "g = 9.807;# [m/s]\n",
+ "dp = 8*10**(-4);# [m]\n",
+ "N = 8.33; #[r/s]\n",
+ "Temp=25;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# Assume:\n",
+ "Po = 5;\n",
+ "viscosity_L = 8.94*10**(-4);# [kg/m.s]\n",
+ "Density_L = 998.0;# [kg/cubic m]\n",
+ "delta_Density = Density_S-Density_L;# [kg/cubic m]\n",
+ "# By Eqn. 11.23:\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_L);# [m/s]\n",
+ "# By defn. of power number:\n",
+ "# P = Po*Density_m*di**5*Ni**3\n",
+ "# vm = math.pi*T**2*(Z+C)/4\n",
+ "# Solid Volume = S/Density_p;\n",
+ "# If these are substituted in Eqn. 11.22\n",
+ "def f(Z):\n",
+ " return (((Z+C)**(1.3/3))*math.exp(4.35*Z/(T-0.1)))-((1.0839*Po*di**(11.0/2)*N**3*Density_p**(2.0/3))/(g*Vts*T**(7.0/6)*S**(2.0/3)))\n",
+ "Z = fsolve(f,7);# [m]\n",
+ "phi_Sm = 4*S/(math.pi*T**2*(Z+C)*Density_p);\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_L);# [kg/cubic m]\n",
+ "phi_Ss = 0.6;\n",
+ "viscosity_m = viscosity_L/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",
+ "Re = di**2*N*Density_m/viscosity_m;\n",
+ "P = Po*Density_m*N**3*di**5;# [W]\n",
+ "print \"Agitator Power required: \",round(P),\" W\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.3 - Page: 602\n",
+ "\n",
+ "\n",
+ "Agitator Power required: 1113.0 W\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 65
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.4: Page 604"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.4\n",
+ "# Page: 604\n",
+ "\n",
+ "print'Illustration 11.4 - Page: 604\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "#****Data*****#\n",
+ "# b: kerosene c:water\n",
+ "# c:kg water/cubic m liquid\n",
+ "Density_l = 783;# [kg/cubic m]\n",
+ "viscosity_l = 1.7*10**(-3);# [kg/m.s]\n",
+ "Mb = 200;# [kg/kmol]\n",
+ "Density_p = 881;# [kg/cubic m]\n",
+ "m = 0.522;# [(kg water/cubic m kerosene)/(kg water/kg gel)]\n",
+ "Xo = 0;# [kg H2O/kg gel]\n",
+ "#**************#\n",
+ "\n",
+ "# Solution (a)\n",
+ "co = Density_l*4*10**(-5);# [kg water/cubic m]\n",
+ "c1 = Density_l*5*10**(-6);# [kg water/cubic m]\n",
+ "# For Ss minimum:\n",
+ "X1 = c1/m;# [kg H2O/kg gel]\n",
+ "# By Water Balance:\n",
+ "SsminByVl = (co-c1)/(X1-Xo);# [kg gel/cubic m kerosene]\n",
+ "print\"Minimum Solid/Liquid ratio used:\",SsminByVl,\" kg gel/cubic m kerosene\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "# Basis: 1 batch,1.7 cubic m kerosene\n",
+ "Vl = 1.7;# [cubic m]\n",
+ "Ss = 16*1.7;# [kg gel]\n",
+ "V = Ss/Density_p;# [Xol. solid, cubic m]\n",
+ "Vt = 1.7+V;# [Total batch volume, cubic m]\n",
+ "# Take Z = T\n",
+ "T = (Vt*4/math.pi)**(1.0/3);# [m]\n",
+ "# To allow for the adequate free board:\n",
+ "h = 1.75;# [Vessel height,m]\n",
+ "# Use a six-blade disk impeller.\n",
+ "# From Fig. 11.26:\n",
+ "# dp corresponding to 14 mesh:\n",
+ "dp = 1.4/1000;# [m]\n",
+ "TBydi1 = 2.0;\n",
+ "Value1 = (Density_p-Density_l)/Density_l;\n",
+ "# From Fig. 11.26:\n",
+ "TBydi2 = 4.4;\n",
+ "TBydiAv = (TBydi1+TBydi2)/2.0;\n",
+ "di = T/TBydiAv;# [m]\n",
+ "fr = 0.6;# [settled volume fraction of solids]\n",
+ "Vs = V/fr;# [cubic m]\n",
+ "depth = Vs/((math.pi*(T**2))/4);# [m]\n",
+ "# The depth of settled solid is negligible.\n",
+ "# Locate the turbine 150mm from the bottom of the tank.\n",
+ "C = 0.150;# [m]\n",
+ "\n",
+ "# Power:\n",
+ "# Use the sufficient agitator power to lift the solids to 0.6 m above the bottom of the vessel.\n",
+ "Z_prime = 0.6-C;# [m]\n",
+ "# The properties of the slurry in 0.6 m above the bottom of the vessel.\n",
+ "Vm = 0.6*math.pi*T**2.0/4;# [square m]\n",
+ "phi_Sm = V/Vm;# [vol fraction solid]\n",
+ "# From Eqn. 11.24:\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",
+ "# From Eqn. 11.25:\n",
+ "phi_Ss = 0.8;\n",
+ "viscosity_m = viscosity_l/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "# From Eqn. 11.23:\n",
+ "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_l);# [m/s]\n",
+ "# From Eqn. 11.22:\n",
+ "n = 1.0;\n",
+ "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*(TBydiAv**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",
+ "# Assume:\n",
+ "Po = 5.0;\n",
+ "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",
+ "# Use:\n",
+ "N1 = 2.0;# [r/s]\n",
+ "Re = di**2.0*N1*Density_m/viscosity_m;\n",
+ "# From fig. 6.5: Po = 5\n",
+ "# Hence our assumption was right.\n",
+ "print\"Power delivered to the slurry: \",round((P*(N1/N)**3),2),\" W\\n\",\n",
+ "print\"Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\\n\"\n",
+ "\n",
+ "# Mass transfer: \n",
+ "# From Eqn. 11.28:\n",
+ "Rep = (dp**(4.0/3))*(P/Vl)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",
+ "# From Eqn. 2.44:\n",
+ "Temp = 298;# [K]\n",
+ "phi = 1.0;\n",
+ "Va = 0.0756;# [Chapter 2 notation]\n",
+ "Dl = ((117.3*10**(-18))*((phi*Mb)**0.5)*Temp)/(viscosity_l*(Va**(0.6)));\n",
+ "ScL = viscosity_l/(Density_l*Dl);\n",
+ "if dp<(2.0/1000):\n",
+ " # From Eqn. 11.29:\n",
+ " ShL = 2+(0.47*Rep**0.62*(1/TBydiAv**0.17)*ScL**0.36);\n",
+ "else:\n",
+ " # From Eqn. 11.30:\n",
+ " ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",
+ "\n",
+ "kL = ShL*Dl/dp;# [m/s]\n",
+ "apS = (math.pi*dp**2)/(math.pi*dp**3*Density_p/6.0);\n",
+ "apL = apS*16;# [square m/cubic m liquid]\n",
+ "Ratio = Ss/(Vl*m);\n",
+ "# From Eqn. 11.40:\n",
+ "thetha = math.log((co/c1)/(1+(1/Ratio)-(1/Ratio)*(co/c1)))/((1+(1/Ratio))*kL*apL);\n",
+ "print\"Contacting Time required: \",round(thetha/60,2),\" min\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.4 - Page: 604\n",
+ "\n",
+ "\n",
+ "Minimum Solid/Liquid ratio used: 3.654 kg gel/cubic m kerosene\n",
+ "\n",
+ "\n",
+ "Power delivered to the slurry: 350.05 W\n",
+ "Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\n",
+ "\n",
+ "Contacting Time required: 8.3 min\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 69
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.5: Page 606"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.5\n",
+ "# Page: 606\n",
+ "\n",
+ "print'Illustration 11.5 - Page: 606\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "import numpy.linalg as lin\n",
+ "#*****Data******#\n",
+ "Vl = 1.1*10**(-4);# [cubic m/s]\n",
+ "Ss = 0.0012;# [kg/s]\n",
+ "Density_p = 1120;# [kg/cubic m]\n",
+ "dp = 8*10**(-4);# [m]\n",
+ "Ds = 2*10**(-11);# [square m/s]\n",
+ "Dl = 7.3*10**(-10);# [square m/s]\n",
+ "m = 0.2;# [(kg Cu2+/cubic m soln)/(kg Cu2+/kg resin)]\n",
+ "T = 1;# [m]\n",
+ "#********************#\n",
+ "\n",
+ "Z = T;# [m]\n",
+ "# The particles will be lifted to the top of the vessel.\n",
+ "Z_prime = 0.5;# [m]\n",
+ "viscosity_l = 8.94*10**(-4);# [kg/m.s]\n",
+ "Density_l = 998;# [kg/cubic m]\n",
+ "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",
+ "g = 9.80;# [m/square s]\n",
+ "# From Eqn. 11.23:\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_l);\n",
+ "Vm = math.pi*T**2*Z/4.0;# [cubic m]\n",
+ "Vs = Ss/Density_p;# [cubic m/s]\n",
+ "phi_Sm = Vs/(Vs+Vl);# [vol fraction]\n",
+ "# From eqn. 11.24:\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",
+ "# From Eqn. 11.22:\n",
+ "n = 1.0;\n",
+ "di = 0.3;# [m]\n",
+ "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*((T/di)**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",
+ "# To estimate the impeller speed:\n",
+ "# Assume:\n",
+ "Po = 5;\n",
+ "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",
+ "Re = di**2*N*Density_m/viscosity_l;\n",
+ "# From fig. 6.5: Assumption of Po was correct.\n",
+ "print\"Speed of the impeller:\",round(N,2),\" r/s\\n\"\n",
+ "vT = (math.pi/4.0)*T**2*Z;# [cubic m]\n",
+ "vL = vT*(1-phi_Sm);\n",
+ "# From Eqn. 11.28:\n",
+ "Rep = (dp**(4.0/3))*(P/vL)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",
+ "ScL = viscosity_l/(Density_l*Dl);\n",
+ "if dp<(2.0/1000):\n",
+ " # From Eqn. 11.29:\n",
+ " ShL = 2+(0.47*Rep**0.62*((di/T)**0.17)*ScL**0.36);\n",
+ "else:\n",
+ " # From Eqn. 11.30:\n",
+ " ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",
+ "\n",
+ "ShL = 130.3;# Value wrong in book\n",
+ "kL = ShL*Dl/dp;# [m/s]\n",
+ "# Since the dispersion is uniform throughout the vessel, the residence time for both liquid and solid is same.\n",
+ "thetha = vL*(1-phi_Sm)/Vl;# [s]\n",
+ "# From Fig. 11.27:\n",
+ "abcissa = m*kL*dp/(2*Ds*Density_p);\n",
+ "Parameter = 2*m*kL*thetha/(dp*Density_p);\n",
+ "co = 100*Density_l/10.0**6;# [kg/cubic m]\n",
+ "EMS = 0.63;\n",
+ "Xo = 0;\n",
+ "# From Eqn. 11.44:\n",
+ "# (1): X1-(EMS/m)*c1 = 0\n",
+ "# Solute balance:\n",
+ "# (2): (Ss*X1)+(vL*c1) = (vL*co)+(Xo*Ss)\n",
+ "a = [[1 ,-(EMS/m)],[Ss ,Vl]];\n",
+ "b = [0,(Vl*co)+(Xo*Ss)];\n",
+ "soln =lin.solve(a,b);\n",
+ "X1 = soln[0];\n",
+ "c1 = soln[1];\n",
+ "print\"Effluent Cu2+ conc. \",round(c1*10**(6)/Density_l,2),\" ppm\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.5 - Page: 606\n",
+ "\n",
+ "\n",
+ "Speed of the impeller: 2.71 r/s\n",
+ "\n",
+ "Effluent Cu2+ conc. 2.83 ppm\n"
+ ]
+ }
+ ],
+ "prompt_number": 78
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.6: Page 616"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.6\n",
+ "# Page: 616\n",
+ "\n",
+ "print'Illustration 11.6 - Page: 616\\n\\n'\n",
+ "from scipy.optimize import fsolve\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data*****#\n",
+ "# a: air b:silica\n",
+ "Density_a = 1.181;# [kg/cubic m]\n",
+ "Density_b = 671.2;# [kg/cubic m]\n",
+ "kSap = 0.965;# [kg H2O/square m s]\n",
+ "Y1 = 0.005;# [kg H2O/kg dry air]\n",
+ "Y2 = 0.0001;# [kg H2O/kg dry air]\n",
+ "Ss = 0.680;# [square m/s]\n",
+ "Gs = 1.36;# [kg/square m.s]\n",
+ "X2 = 0;# [kg H2O/kg dry air]\n",
+ "# Equilibrium function:\n",
+ "m = 0.0185;\n",
+ "#************#\n",
+ "X1 = (Gs*(Y1-Y2)/Ss)+X2;# [kg H2O/kg dry air]\n",
+ "def f77(X):\n",
+ " return m*X \n",
+ "Y2_star = f77(X2);# [kg H2O/kg dry gel]\n",
+ "Y1_star = f77(X1);# [kg H2O/kg dry gel]\n",
+ "deltaY = ((Y1-Y1_star)-(Y2-Y2_star))/math.log((Y1-Y1_star)/(Y2-Y2_star));\n",
+ "NtoG = (Y1-Y2)/deltaY;\n",
+ "# If the fixed bed data are to be used for estimating the mass transfer coeffecient for a moving bed of solids\n",
+ "va = Ss/Density_b;# [m/s]\n",
+ "vb = Gs/Density_a;# [m/s]\n",
+ "rel_v = va+vb;# [relative velocity,m/s]\n",
+ "G_prime = rel_v*Density_a;# [relative mass velocity of air,kg/square m s]\n",
+ "HtG = Gs/(31.6*G_prime**0.55);# [m]\n",
+ "HtS = Ss/kSap;# [m]\n",
+ "# By Eqn. 11.52:\n",
+ "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",
+ "Z = NtoG*HtoG;# [m]\n",
+ "print\"Height of continuous countercurrent isothermal absorber for drying: \",round(Z,4),\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.6 - Page: 616\n",
+ "\n",
+ "\n",
+ "Height of continuous countercurrent isothermal absorber for drying: 0.2511 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 81
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.7: Page 619"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.7\n",
+ "# Page: 619\n",
+ "\n",
+ "print'Illustration 11.7 - Page: 619\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import numpy.linalg as lin\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#*****Data*****#\n",
+ "# a: C2H4 b:C3H8\n",
+ "# The equlibrium curve is plotted in Fig.11.33 (Pg 620)\n",
+ "# C3H8 is more strongly adsorbed component and composition in the gas and adsorbate are expressed as weight fraction C3H8.\n",
+ "Ma = 28;# [kg/kmol]\n",
+ "Mb = 44.1;# [kg/kmol]\n",
+ "xaF = 0.6;# [mole fraction]\n",
+ "xbF = 0.4;# [mole fraction]\n",
+ "xa1 = 0.05;# [mole fraction]\n",
+ "xa2 = 0.95;# [mole fraction]\n",
+ "#***************#\n",
+ "\n",
+ "xF = xbF*Mb/((xbF*Mb)+(xaF*Ma));# [wt. fraction C3H8]\n",
+ "xb1 = 1-xa1;# [mole fraction]\n",
+ "x1 = xb1*Mb/((xb1*Mb)+xa1*Ma);# [wt. fraction C3H8]\n",
+ "xb2 = 1-xa2;# [mole fraction]\n",
+ "x2 = xb2*Mb/((xb2*Mb)+(xa2*Ma));# [wt. fraction C3H8]\n",
+ "# Basis: 100 kg feed gas\n",
+ "F = 100.0;# [kg]\n",
+ "# (1): R2+PE = F [From Eqn. 11.63]\n",
+ "# (2): (R2*x2)+(PE*x1) = (F*xF) [From Eqn. 11.64]\n",
+ "# Solving simultaneously:\n",
+ "a = [[1, 1],[x2 ,x1]];\n",
+ "b = [F,(F*xF)];\n",
+ "soln = lin.solve(a,b);\n",
+ "R2 = soln[0];# [kg]\n",
+ "PE = soln[1];# [kg]\n",
+ "# Point F at xF and point E1 at x1 are located on the diagram.\n",
+ "# From the diagram:\n",
+ "N1 = 4.57;# [kg carbon/kg adsorbate]\n",
+ "# The minimum reflux ratio is found as it is for the extraction.\n",
+ "delta_Em = 5.80;\n",
+ "Ratio = (delta_Em/N1)-1;# [kg reflux gas/kg product]\n",
+ "R1_m = Ratio*PE;# [kg]\n",
+ "E1_m = R1_m+PE;# [kg]\n",
+ "B_m = N1*E1_m;# [kg carbon/100 kg feed]\n",
+ "Ratio1 = 2*Ratio;\n",
+ "# From Eqn. 11.58:\n",
+ "N_deltaE = (Ratio1+1.0)*N1;# [kg carbon/kg adsorbate]\n",
+ "# Point deltaE is located on the diagram:\n",
+ "R1 = Ratio1*PE;# [kg]\n",
+ "E1 = R1+PE;# [kg]\n",
+ "B = N1*E1;# [kg]\n",
+ "N_deltaR = -(B/R2);# [kg carbon/kg adsorbate]\n",
+ "# Random lines such as the delta_RK are drawn from detaR, and the intersection of equilibrium curves are projected downward in the manner shown to provide the adsorption section operating curve.\n",
+ "# Similarly random lines such as delta_EJ are drawn from deltaE, and the intersections are projected downwards to provide the enriching section operating curve.\n",
+ "# Data = [x x_star]\n",
+ "Data = numpy.array([[0.967 ,0.825],[0.90, 0.710],[0.80 ,0.60],[0.70, 0.50],[0.60 ,0.43],[0.512 ,0.39],[0.40 ,0.193],[0.30, 0.090],[0.20, 0.041],[0.0763, 0.003]]);\n",
+ "Val = zeros(10);\n",
+ "for i in range(0,10):\n",
+ " Val[i] = 1/((Data[i,0])-Data[i,1]);\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x\");\n",
+ "plt.ylabel(\"1 / (x-x*)\");\n",
+ "plt.title(\"Graphical Integraion\");\n",
+ "# The area under the curve between x1 & xF, for the enriching section:\n",
+ "Area1 = 2.65;\n",
+ "# The area under the curve between xF & x2, for the adsorption section:\n",
+ "Area2 = 2.67;\n",
+ "r = Ma/Mb;\n",
+ "# From Eqn.11.66:\n",
+ "# For the enriching section:\n",
+ "NtoG1 = Area1-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",
+ "# For the adsortion section:\n",
+ "NtoG2 = Area2-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",
+ "NtoG = NtoG1+NtoG2;\n",
+ "print\"Number of transfer units: \",NtoG"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.7 - Page: 619\n",
+ "\n",
+ "\n",
+ "Number of transfer units: 5.77763695068\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x9e2fda0>"
+ ]
+ }
+ ],
+ "prompt_number": 85
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.8: Page 627"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.8\n",
+ "# Page: 627\n",
+ "\n",
+ "print'Illustration 11.8 - Page: 627\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#******Data******#\n",
+ "rate = 0.1;# [kg/s]\n",
+ "conc = 3.0;# [kg vapour/100cubic m]\n",
+ "Density_p = 720.0;# [kg/cubic m]\n",
+ "Density_bed = 480.0;# [kg/cubic m]\n",
+ "capablity = 0.45;# [kg vapour/kg carbon]\n",
+ "dp = 0.0028;# [m]\n",
+ "time = 3.0;# [h]\n",
+ "#********************#\n",
+ "\n",
+ "Vap_adsorbed = time*3600.0*rate;# [kg]\n",
+ "C_required = Vap_adsorbed*1.0/capablity;\n",
+ "# Two beds will be needed: one adsorbing and another regenerated.\n",
+ "totC_required = 2*C_required;# [kg]\n",
+ "print\"Amount of carbon required: \",totC_required,\" kg\\n\",\n",
+ "Vol = (C_required/Density_bed);\n",
+ "# Assume:\n",
+ "Z = 0.5;# [m]\n",
+ "Area = Vol/Z;# [square m]\n",
+ "# From Eqn. 6.66:\n",
+ "T = 35.0;# [OC]\n",
+ "viscosity_air = 1.82*10**(-5);# [kg/m.s]\n",
+ "Density_air = (29/22.41)*(273.0/(T+273));\n",
+ "e = 1-(Density_bed/Density_p);\n",
+ "G = rate*(100.0/conc)*(Density_air/(Area));# [kg/square m.s]\n",
+ "Re = dp*G/viscosity_air;\n",
+ "Z = 0.5;# [m]\n",
+ "def f78(delta_p):\n",
+ " return ((delta_p/Z)*(e**3*dp*Density_air)/((1-e)*G**2))-(150*(1-e)/Re)-1.75\n",
+ "delta_p = fsolve(f78,7);\n",
+ "print\"The pressure drop is:\",round(delta_p,2),\" N/square m\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.8 - Page: 627\n",
+ "\n",
+ "\n",
+ "Amount of caron required: 4800.0 kg\n",
+ "The pressure drop is: 1413.31 N/square m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 88
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.9: Page 636"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.9\n",
+ "# Page: 636\n",
+ "\n",
+ "print'Illustration 11.9 - Page: 636\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data******#\n",
+ "Yo = 0.00267;# [kg H2O/kg dry air]\n",
+ "Yb = 0.0001;# [kg H2O/kg dry air]\n",
+ "Ye = 0.024;# [kg H2O/kg dry air]\n",
+ "Z = 0.61;# [m]\n",
+ "G_prime = 0.1295;# [kg/square m.s]\n",
+ "#******************#\n",
+ "\n",
+ "# The equilicrium data is plotted in Fig. 11.45 (Pg 637)\n",
+ "# The gel is initially \"dry\" and the effluent air initially of so low a humidity asto be substantially dry, so that the operating line passes through the origin of the figure\n",
+ "# The operating line is then drawn to intersect the equilibrium curve.\n",
+ "# Data = [Y[kg H2O/kg dry air] Y_star[kg H2O/kg dry air]]\n",
+ "Data =numpy.array([[0.0001, 0.00003],[0.0002, 0.00007],[0.0004 ,0.00016],[0.0006, 0.00027],[0.0008, 0.00041],[0.0010, 0.00057],[0.0012 ,0.000765],[0.0014, 0.000995],[0.0016, 0.00123],[0.0018 ,0.00148],[0.0020 ,0.00175],[0.0022 ,0.00203],[0.0024 ,0.00230]])\n",
+ "Val1 = zeros(13);\n",
+ "# Val1 = [1/(Y-Y_star)]\n",
+ "for i in range(0,13):\n",
+ " Val1[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "# Graphical Integration:\n",
+ "plt.plot(Data[:,0],Val1);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"Y(kg H20 / kg dry air)\");\n",
+ "plt.ylabel(\"1 / (Y-Y_star)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area under The curve between Y = Yb and Y = Y:\n",
+ "Area = [0 ,0.100 ,2.219 ,2.930 ,3.487 ,3.976 ,4.438 ,4.915, 5.432, 6.015, 6.728 ,7.716 ,9.304];\n",
+ "# The total number of transfer unit corresponding to adsorption zone:\n",
+ "NtoG = 9.304;\n",
+ "Val2 = zeros(13);\n",
+ "Val3 = zeros(13);\n",
+ "# Val2 = [(w-wb)/wo]\n",
+ "# Val3 = [Y/Yo]\n",
+ "for i in range(0,13):\n",
+ " Val2[i] = Area[i]/NtoG;\n",
+ " Val3[i] = Data[i,0]/Yo;\n",
+ "\n",
+ "# Eqn. 11.74 can be arranged as follows:\n",
+ "# f = integrate((1-(Y/Yo)),(w-wb)/wa,0,1)\n",
+ "\n",
+ "plt.plot(Val2,Val3);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"(w-wb) / wo\");\n",
+ "plt.ylabel(\"Y / Yo\");\n",
+ "plt.title(\"Break through curve\");\n",
+ "plt.show()\n",
+ "# From area above the curve of scf(2):\n",
+ "f = 0.530;\n",
+ "\n",
+ "Gs = G_prime;# [kg/square m.s]\n",
+ "# From Illustration: 11.6\n",
+ "kYap = 31.6*G_prime**0.55;# [kg H2O/cubic m s delta_Y]\n",
+ "kSap = 0.965;# [kg H2O/cubic m s delta_X]\n",
+ "# From Fig. 11.48:\n",
+ "Xt = 0.0858;# [kg H2O/kg gel]\n",
+ "# From Eqn. 11.76:\n",
+ "Ss = Yo*Gs/Xt;# [kg/square m.s]\n",
+ "m = 0.0185;# [average slope of equilibrium curve]\n",
+ "# From Eqn. 11.51 & Eqn. 11.52:\n",
+ "HtG = Gs/kYap;# [m]\n",
+ "HtS = Ss/kSap;# [m]\n",
+ "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",
+ "# From Eqn. 11.79:\n",
+ "Za = NtoG*HtoG;# [m]\n",
+ "# From Eqn. 11.74:\n",
+ "Degree = (Z-(f*Za))/Z;\n",
+ "Density_bed = 671.2;# [Illustration 11.6, kg/cubic m]\n",
+ "mass_gel = Z*Density_bed;# [kg/square m]\n",
+ "# At saturation point the gel contins:\n",
+ "Y1 = mass_gel*Degree*Xt;# [kg H2O/square m cross section]\n",
+ "# The air introduces:\n",
+ "Y2 = Gs*Yo;# [kg/square m s]\n",
+ "print\"Time to reach breakpoint is: \",round((Y1/(Y2*3600)),4),\" h\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.9 - Page: 636\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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R81jEii0Wn34K48bBkCFJ1yS3khrtVrZ+8Qv4f/8PZs9OuibOuXIwcmS4VfaG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\nDTe3c64xy5aFlTYmTQrJ5vn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+ "text": [
+ "<matplotlib.figure.Figure at 0x9e39c88>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F8ZIsDJsCs/O234qea8XMDjezaYR7Q5+WYDxSRu66C84/Hx59FL75zbSjEaku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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x99973c8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Time to reach breakpoint is: 24.7778 h\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 91
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.10: Page 640"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.10\n",
+ "# Page: 640\n",
+ "\n",
+ "print'Illustration 11.10 - Page: 640\\n\\n'\n",
+ "\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data******#\n",
+ "# a:N2 b:H2O\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "Ma = 29;# [kg/kmol]\n",
+ "Z = 0.268;# [m]\n",
+ "Xo_solid = 0.01;# [kg H20/kg solid]\n",
+ "Density_bed = 712.8;# [kg/cubic m]\n",
+ "T = 28.3;# [OC]\n",
+ "P = 593;# [kN/square m]\n",
+ "Gs = 4052;# [kg/square m.h]\n",
+ "Xo_gas = 1440*10**(-6);# [mole fraction]\n",
+ "#********************#\n",
+ "\n",
+ "# Yo_star is in equilibrium with Xo:\n",
+ "Xo = 0;# [kg H20/kg solid]\n",
+ "Yo_star = 0;# [kg H20/kg N2]\n",
+ "thetha_t = 12.8;# [h]\n",
+ "thetha_b = 9;# [h]\n",
+ "# The breakthrough data are plotted in the manner of Fig. 11.47 (Pg 639) and thetha_s is dtermined:\n",
+ "thetha_s = 10.9;# [h]\n",
+ "Xt = 0.21;# [kg H20/kg solid]\n",
+ "# From Eqn. 11.81:\n",
+ "LUB = (Z/thetha_s)*(thetha_s-thetha_b);\n",
+ "# For thetha_b = 15 h\n",
+ "thetha_b = 15;# [h]\n",
+ "Yo = (Xo_gas/(1-Xo_gas))*(Mb/Ma);# [kg H20/kg N2]\n",
+ "# From Eq. 11.82:\n",
+ "Zs = Gs*(Yo-Yo_star)*thetha_b/(Density_bed*(Xt-Xo_solid));# [m]\n",
+ "# From Eqn. 11.85:\n",
+ "Z = LUB+Zs;\n",
+ "print\"Height of adsorbent column:\",round(Z,4),\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.10 - Page: 640\n",
+ "\n",
+ "\n",
+ "Height of adsorbent column: 0.0467 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 93
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.11: Page 654"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.11\n",
+ "# Page: 645\n",
+ "\n",
+ "print'Illustration 11.11 - Page: 645\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#****Data****#\n",
+ "# For collection of Cu2+:\n",
+ "V = 37850.0;# [l/h]\n",
+ "c1 = 20.0;# [meq Cu2+/l]\n",
+ "c2 = 0.01*c1;# [meq Cu2+/l]\n",
+ "Mass_rate = 2.0;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",
+ "exchanged = V*(c1-c2);# [meq/h]\n",
+ "X2 = 0.30;# [meq Cu2+/g]\n",
+ "#************#/\n",
+ "\n",
+ "# The point(c2,X2) is plotted in Fig. 11.48(a), Pg 645:\n",
+ "# For the minimum resin/solution ratio and an infinitely tall tower, the operating line pass though point P.\n",
+ "X = 4.9;# [meq Cu2+/g]\n",
+ "MinRate = exchanged/(X-X2);# [g/h]\n",
+ "Rate = 1.2*MinRate;# [g/h]\n",
+ "# Copper balance:\n",
+ "X1 = (exchanged/Rate)+X2;# [meq Cu2+/g resin]\n",
+ "# The point (c1,x1) is ploted in Fig. 11.48(a) and operating line drawn can be straight line at this low conc.\n",
+ "# Adapting Eqn. 11.48 and rearranging:\n",
+ "# S*Z*Density_s = (V/Mass_rate)*integrate(1/(c-c_star),c,c1,c2)\n",
+ "# Mass_rate = KL_prime*ap/Density_s\n",
+ "# From the equilibrium curve:\n",
+ "# Data = [c c_star]\n",
+ "Data = numpy.array([[20 ,2.4],[16 ,1.9],[12, 0.5],[8 ,0.25],[4 ,0.10],[2 ,0.05],[1 ,0.02],[0.2, 0]]);\n",
+ "Val = zeros(8);\n",
+ "for i in range(0,8):\n",
+ " Val[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"c\");\n",
+ "plt.ylabel(\"1 / (c-c*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "# From Graphical Integration:\n",
+ "Area = 5.72;\n",
+ "# holdup = S*Z*Density_s\n",
+ "holdup = V*Area/(Mass_rate);\n",
+ "print\"Resin Holdup: \",holdup,\"g\\n\"\n",
+ "\n",
+ "# Regeneration of resin:\n",
+ "# For 70% utilisation of 2N acid, feed must contain:\n",
+ "V = exchanged;\n",
+ "F = V/(0.70*2000);# [l/h]\n",
+ "c1 = 0;# [meq Cu2+/l]\n",
+ "c2 = V*1.0/F;# [meq Cu2+/l]\n",
+ "X1 = 0.30;# [meq Cu2+/g resin]\n",
+ "X2 = 4.12;# [meq cu2+/g resin]\n",
+ "# The points (c1,X1) and (c2,X2) are plotted on Fig 11.48(b), Pg 645\n",
+ "c1_star = 120.0;# [meq Cu2+/l]\n",
+ "c2_star = 1700.0;# [meq Cu2+/l]\n",
+ "logmean = ((c1_star-c1)-(c2_star-c2))/math.log((c1_star-c1)/(c2_star-c2));\n",
+ "Mass_rate = 0.018;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",
+ "# Substituting in equation:\n",
+ "def f79(holdup):\n",
+ " return (V*(c2-c1))-(Mass_rate*holdup*logmean)\n",
+ "holdup = fsolve(f79,7);\n",
+ "print\"Resin Holdup in the regeneration Tower is \",round(holdup,3),\" g\\n\"\n",
+ "#the answers are in textbook is wrong"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.11 - Page: 645\n",
+ "\n",
+ "\n",
+ "Resin Holdup: 108251.0 g\n",
+ "\n",
+ "Resin Holdup in the regeneration Tower is 296720391.501 g\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x9e3b4a8>"
+ ]
+ }
+ ],
+ "prompt_number": 126
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter11_1.ipynb b/Mass_-_Transfer_Operations/Chapter11_1.ipynb
new file mode 100755
index 00000000..429b975a
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter11_1.ipynb
@@ -0,0 +1,1235 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:b2d866884e32978f30135a4e666314d83655be533eae1227e5f3d0fa8ae8f5d8"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 11: Absorption And Ion Exchange"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.1: Page 575"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.1\n",
+ "# Page: 575\n",
+ "\n",
+ "print'Illustration 11.1 - Page: 575\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#*****Data*****#\n",
+ "Temp = 30.0;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# From Fig. 11.5 (Pg 572)\n",
+ "# The isosteres for various concentrations are straight and their slopes are measured with the help of milimeter rule.\n",
+ "# Data = [X(kg acetone/kg carbon) lambda(slope of isostere)]\n",
+ "Data = numpy.array([[0.05 ,1.170],[0.10, 1.245],[0.15 ,1.3],[0.20 ,1.310],[0.25 ,1.340],[0.30 ,1.327]]);# [kg acetone/kg carbon]\n",
+ "lambdar = 551.0;# [reference at 30 OC,kJ/kg]\n",
+ "Val = numpy.zeros(shape=(6,5));\n",
+ "for i in range(0,6):\n",
+ " Val[i,0] = Data[i,0];# [kg acetone/kg carbon]\n",
+ " Val[i,1] = Data[i,1];# [slope of isostere]\n",
+ " Val[i,2] = -Data[i,1]*lambdar;# [kJ/kg acetone]\n",
+ "\n",
+ "\n",
+ "plt.plot(Val[:,0],Val[:,2])\n",
+ "plt.grid();\n",
+ "xlabel(\"X (kg carbon / kg acetone)\");\n",
+ "ylabel(\"Differential heat of adsorption (kJ / kg acetone)\");\n",
+ "title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area: The area under the curve between X = 0 to X = X\n",
+ "# Corresponding to Data(:,1):\n",
+ "Area = numpy.array([-29.8 ,-63.0, -97.9 ,-134.0, -170.5, -207.5]);\n",
+ "for i in range(0,6):\n",
+ " Val[i,3] = Area[i];\n",
+ " Val[i,4] = Area[i]+(lambdar*Val[i,0]);\n",
+ "print \" (1) = X(kg acetone/kg carbon) \\n (2)= Slope of isostere \\n (3)= Differential heat of adsorption(kJ/kg acetone) \\n (4)=deltaH_prime(vapour(kJ/kg carbon)) \\n (5)=deltaH(liquid(kJ/kg carbon)\"\n",
+ "print\"(1) \\t \\t \\t \\t (2) \\t \\t \\t \\t (3) \\t \\t \\t \\t \\t \\t \\t \\t (4) \\t \\t \\t \\t \\t \\t (5) \" \n",
+ "for i in range(0,6):\n",
+ " print Val[i,0],\" \\t \\t \\t \",Val[i,1],\" \\t \\t \",Val[i,2],\" \\t \\t \\t \\t \\t \",Val[i,3],\" \\t \\t \\t \\t\",Val[i,4]\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.1 - Page: 575\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
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JJbfmzoWddoJ994ULLyx0NM65bMnXXF4/mdk1ZrZv9Li2WJOJW14u2ofXXBOe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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x76f12b0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (1) = X(kg acetone/kg carbon) \n",
+ " (2)= Slope of isostere \n",
+ " (3)= Differential heat of adsorption(kJ/kg acetone) \n",
+ " (4)=deltaH_prime(vapour(kJ/kg carbon)) \n",
+ " (5)=deltaH(liquid(kJ/kg carbon)\n",
+ "(1) \t \t \t \t (2) \t \t \t \t (3) \t \t \t \t \t \t \t \t (4) \t \t \t \t \t \t (5) \n",
+ "0.05 \t \t \t 1.17 \t \t -644.67 \t \t \t \t \t -29.8 \t \t \t \t-2.25\n",
+ "0.1 \t \t \t 1.245 \t \t -685.995 \t \t \t \t \t -63.0 \t \t \t \t-7.9\n",
+ "0.15 \t \t \t 1.3 \t \t -716.3 \t \t \t \t \t -97.9 \t \t \t \t-15.25\n",
+ "0.2 \t \t \t 1.31 \t \t -721.81 \t \t \t \t \t -134.0 \t \t \t \t-23.8\n",
+ "0.25 \t \t \t 1.34 \t \t -738.34 \t \t \t \t \t -170.5 \t \t \t \t-32.75\n",
+ "0.3 \t \t \t 1.327 \t \t -731.177 \t \t \t \t \t -207.5 \t \t \t \t-42.2\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.2: Page 596"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.2\n",
+ "# Page: 596\n",
+ "\n",
+ "print'Illustration 11.2 - Page: 596\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#*****Data*****#\n",
+ "# x:kg carbon/kg soln\n",
+ "# y_star: Equilibrium colour, units/kg soln.\n",
+ "# X:adsorbate concentration, units/kg carbon\n",
+ "# Data = [x Y_star]\n",
+ "Data =numpy.array([[0, 9.6],[0.001, 8.6],[0.004 ,6.3],[0.008, 4.3],[0.02 ,1.7],[0.04, 0.7]]);\n",
+ "Yo = 9.6;# [units of colour/kg soln]\n",
+ "Y1 = 0.1*Yo;# [units of colour/kg soln]\n",
+ "Ls = 1000.0;# [kg soln]\n",
+ "#****************#\n",
+ "\n",
+ "\n",
+ "n = 1.66;# [slope of line]\n",
+ "# At X = 663, Y_star = 4.3\n",
+ "# From eqn. 11.5\n",
+ "X = 663;\n",
+ "Y_star = 4.3;\n",
+ "m = Y_star/X**n;\n",
+ "# Freundlich Equation:\n",
+ "def f76(X):\n",
+ " return m*X**n\n",
+ "X = numpy.arange(0,1000,1);\n",
+ "\n",
+ "plt.plot(X,f76(X));\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "title(\"Equilibium Data(on arithmetic scale)\");\n",
+ "plt.show()\n",
+ "# Single Stage Operation:\n",
+ "# Since fresh carbn is used:\n",
+ "Xo = 0;# [units/kg carbon]\n",
+ "# From scf(30):\n",
+ "X1 = 270;# [units/kg carbon]\n",
+ "Data2 =numpy.array([[Xo, Yo],[X1, Y1]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data2[:,0],Data2[:,1],label=\"Operating line curve\")\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Single stage operation\");\n",
+ "plt.show()\n",
+ "# From Eqn. 11.4:\n",
+ "Ss = Ls*((Yo-Y1)/(X1-Xo));# [kg carbon/kg soln]\n",
+ "print\"Quantity of fresh carbon recquired for single stage operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",
+ "\n",
+ "# Two stage cross current operation:\n",
+ "# For the minimumamount of carbon:\n",
+ "X1 = 565;# [units/kg carbon]\n",
+ "Y1 = 3.30;# [units of colour/kg soln]\n",
+ "X2 = 270;# [units/kg carbon]\n",
+ "Y2 = 0.96;# [units of colour/kg soln]\n",
+ "Data3 = numpy.array([[Xo ,Yo],[X1 ,Y1]]);\n",
+ "Data4 = numpy.array([[0 ,Y1],[X2 ,Y2]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data3[:,0],Data3[:,1],label=\"First of two Cocurrent\")\n",
+ "plt.plot(Data4[:,0],Data4[:,1],label=\"Second of two Cocurrent\")\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Two stage Cross current operation\");\n",
+ "plt.show()\n",
+ "# From Eqn. 11.8:\n",
+ "Ss1 = Ls*(Yo-Y1)/(X1-Xo);# [kg]\n",
+ "Ss2 = Ls*(Y1-Y2)/(X2-Xo);# [kg]\n",
+ "Ss = Ss1+Ss2;# [kg]\n",
+ "print\"Quantity of fresh carbon recquired for two stage crosscurrent operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",
+ "\n",
+ "# Two Stage counter current operation:\n",
+ "Yo = 9.6;\n",
+ "Y2 = 0.96;\n",
+ "# By trial and error:\n",
+ "XNpPlus1 = 0;\n",
+ "X1 = 675;\n",
+ "Data5 = numpy.array([[X1 ,Yo],[XNpPlus1 ,Y2]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data5[:,0],Data5[:,1],label=\"Two stage Counter Current\");\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Two stage Counter Current operation\");\n",
+ "# By eqn 11.14:\n",
+ "Ss = Ls*(Yo-Y2)/(X1-XNpPlus1);\n",
+ "print\"Quantity of fresh carbon recquired for two stage Counter Current operation: \",Ss,\" kg carbon/1000 kg solution\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.2 - Page: 596\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x76f16d8>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Tviud53s/H9NyjTHx5+ef3bzMxx/v+hQSZSC8qPcxiEgfcg6el4OqRu0GAD8S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\nTA6WGIwxxuRgicEYY0wOlhiMMcbk8P+dWPmzYQm/hQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7930c18>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Quantity of fresh carbon recquired for single stage operation: 32.0 kg carbon/1000 kg solution\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Ly0t2796d4mdIq+TojBkz4suoVqpUSfbv3y8iiavsiehKe8OHD0+k53HjxknR\nokWlZ8+eMmrUKOnQoYP06NFD8ufPL7NmzZIrV65Iv379pFixYlKiRAkZPnx4fDW52bNnS8OGDWXo\n0KHi5+cnZcqUia+Ml1R/cXpLirve+4b7Z88ekaJFRb74wv7XxpT2dB+uXbsmhQoVkt69e8t3330n\nly5dSrT/m2++kXLlyslff/0lMTExMmbMGAkODo4/t2jRojJ+/Hi5ffu2REZGys6dO0VEZMSIEdKg\nQQM5f/68nD9/XoKDg2XEiBEioh9Y3t7eMnLkSImOjpZ169ZJnjx55MqVKyIi0rlzZ+ncubNERUXJ\nwYMHpUSJEtK4ceNk5U+tvKaISEhISIqlO5Pbv3HjRqlSpYqIiGzbtk3Kli0r9erVExGRDRs2SPXq\n1VNtN660pjWff/65lC5dOtXvIbWSo0uWLJESJUrInj17RETk6NGj8cY5qWHo06fPPXp+88035c6d\nO3Lz5k0ZOXKkZM+eXVatWiUiukRru3bt5Pnnn5eoqCg5d+6c1K1bV6ZPny4i2jBkz55dZs6cKbGx\nsfL5559L8eLFU9RfcrjrvW+4P777TuSBB0RWrnTM9Z1mGABfYAKw17J8ChSwR+OptJnah05NK/ZZ\n0sGff/4pffr0kZIlS4q3t7e0bdtW/vvvPxEReeyxxxL98GNiYiRPnjwSHh4uX331ldSsWTPZa5Yt\nWzb+zVJE5Icffoh/OG7atEly584d/1YqIlK4cGHZuXOnREdHS/bs2eXw4cPx+4YNG5Zij2H06NHS\nuXPn+PXY2FgpUaKEbN68WUT0g2vmzJkpfvak+6OioiRXrlxy8eJF+fDDD+WDDz6QkiVLyvXr1+Wd\nd96R0NDQVNsNCwu7p40xY8ZI/fr1U5QhOjpacuTIkajHMX36dAkJCRERkZYtW8rkyZOTPTc5w2Dd\nY8iRI0d8T01EZOTIkdK0adP49bNnz0rOnDnl5s2b8du++uoradasmYhow1CuXLn4fTdu3BClVPz9\nkZZ+Re6997NyneOkeIouZs8WKVJExFK23CHYyzDY4tn6Ep34riPQCYgEZmfMgeUg7GUa0kGFChWY\nPXs2J06c4ODBg5w+fZpBgwYB2kcfGhqKn58ffn5+FLIMUj516hQnT54kKCj5kb+nT5++p4SmdWnJ\nQoUKJSp7mSdPHq5fv8758+eJjo5OVMbSunxmUlIrr2m9LTWs9+fOnZvatWuzefNmtmzZQtOmTQkO\nDmbbtm168N8hAAAgAElEQVTx66m1m1z5zEKFCnHmzJkU20+r1OnJkycpW7Zsqp8hJfz9/cmRJDJo\nXbEuPDycu3fvUqxYsfjv+Pnnn+f8+fPxx1iXCM2TJw9AooJAJs6QeRGBMWPg3XchLAwsZcvdGlsM\nQ1kRGSki/4jIMREZBaTvF5ZFeOihh+jdu3f8aJnAwEBmzJiRqITkjRs3aNCgAQEBAfzzzz/JXqd4\n8eL3lNAsXrx4mu37+/vj7e1NREREonNTIqXymiVKlEizLUj+oda0aVM2bNjA/v37qVOnDk2bNuX7\n779n165d8UV/7qfd5s2bc/LkSfbu3ZusDGmVHA0ICODo0aPJnpsnT55EZULPnDlzT4GlpJ/XeltA\nQAA5c+bk4sWL8d/v1atX+f3335NtLynpMQpZsc5xSrizLqKj4fnnYcUKnSm1QgVXS2QbthiGm0qp\n+AHwlglvUakcn+U4fPgw48ePj387PXHiBF9//TUNGjQA4Pnnn+eDDz7g0CGd+ePq1assXboUgNat\nW3PmzBkmTZrE7du3iYyMZNeuXQB07dqVMWPGcOHCBS5cuMDo0aPp2bNnmvJ4eXnRvn17Ro0axc2b\nNzl06BBz585N8QGUWnnNOCSVnlSRIkU4duxYom1NmzZl3rx5VK5cmezZsxMSEsLMmTMJCgqK7zHZ\n0m4cDz74IC+++CJdu3Zl8+bN3Llzh1u3brFo0SLGjRuXZsnRAQMG8Mknn7Bv3z5EhKNHj8Yby+rV\nq7Nw4UJiYmL4/vvv2bJlS6r6TaqLYsWK0bJlS1599VUiIyOJjY3l2LFjaV4nNf0ZPJ8bN6B9ezh+\nHDZvhmLFXC3RfZCWrwmoDvwGhFuWA0A1e/ixUmkzNf+Z23Hq1Cnp1KmTlChRQvLmzSslSpSQ559/\nXiIjI+OPmT9/vlSpUkXy588vAQEB0r9///h9Bw8elObNm4ufn58ULVpUxo0bJyJ6VNIrr7wixYoV\nk2LFikloaGiiUUkBAQGJ5ChdurRs2LBBRETOnz8vrVu3lvz580u9evVkxIgRKQafRURWrlwplSpV\nkgIFCkhISIgcOnQofl9awdFffvlFypcvL35+fvHxg8jISMmePbuMHj1aRHT8oHDhwvLiiy/a3G5y\nTJo0SSpXrix58uSREiVKSJcuXeLPuXz5svTo0UP8/f0lICBA3nvvvUSjkqZNmyYPPfSQ5MuXT6pU\nqSIHDhwQEZE9e/ZI5cqVxcfHR3r27CndunVLFHxOqudRo0ZJz549E227evWqvPDCC1KyZEkpUKCA\n1KhRQxYvXiwiInPmzLlH99myZYuPaySnv6Qkvfc9xa/uDNxRF+fOidSrJ9Krl4hVeMrhYKcYg83Z\nVZVS+S1PbIdXPzcpMQyGxCS9963TTGd13E0Xx47B449Dx446tuDM8JG9UmKkaBiUUj1FZL5Saghg\nfZBCW6XxGW08RaGMYTAYEmHufc9gzx5o2xZGjIAXXnB++/YyDKllV81j+etDYsNgMBgMhiR89x30\n6qXTZrdr52ppMoZN2VVFZGta2+wqlOkxGAyJMK6klHEHXcyeDW+9pUcfuXI4qjOzq05JZtvkjDZs\nMBgMno4IvPcejB6tRx55whwFW0gtxtAACAYGo9Nux1khH+ApEanmMKFMj8FgSIS5992PO3fguefg\nt99gzRr3GI7qjBhDDrQR8LL8jeMa8HRGGzYYDAZP5coV6NBB10/YvFnXU8hM2BJjKCUi4akeZGdS\n6zEYDFkVE2NIHmfrIjwcWrWC5s11ymwvL6c1nSbOjDHMUUptSrJszGjD6cEeEzc8bdm0aZNNx8XE\nxvDJtk/w/8iflX+udLncrtRFZl0MrmfPHh1HePZZmDzZvYyCPbGlx1DbajUX0AGIFpHXHCZUCj0G\nQ9rsPLmTLsu70LZ8Wz565CNyemeOYkEGg6tZtQoGDHDv4agOn+CWRuO7RaRORhtP5frGMGSAyzcv\n0//b/oRfDWfx04spV7Ccq0UyGDyaSZNg3DhtHOo47MmXcZzmSlJKFbRaHlBKPQbkz2jDBttITz1b\nv9x+LO+0nL7V+9JgVgMWH1xsf8FcgKfW9nUERhcJOFIXMTEQGgrTp+vsqO5sFOxJaqOS4thHwszn\naOA40N9RAhnsg1KKgXUHEhwQTOdlndn470YmPjaR3Nlzu1o0g8EjuHEDunWDyEhtFHx9XS2R80iX\nK8nRGFeSfbl2+xrPrn6WQ+cPsaTjEio84CFJ4Q0GF3H2LLRuDZUr65hCkjpNboszkuh1IJUcSSKy\nIqONpyiUMQx2R0SYuW8mwzYOY3zL8fSslnZdB4MhK/LHH/DEE9Cvn06G50mj5J0RY2iTxmJwAvby\nnyqleKbWM2zstZEPtn5A31V9uXHnhl2u7SyMXz0Bo4sE7KmLn36CZs10mot33vEso2BPUowxiEgf\nJ8phcBJVilRh9zO7eWndS9T5og5LOi7h/wr/n6vFMhhczmef6ZxHS5eCpSx5lsWWeQy+wEigiWVT\nGDBaRK46TCjjSnIKcw/MZehPQxnbfCz9a/Q3M8sNWZLoaHj1Vd1bWLMGynpwRXunzWNQSq0Afgfm\nohPp9QSqikj7jDaeSpvGMDiJP8//SadlnahSuArTW0/HJ6dP2icZDJmEq1ehc2eIjYUlSzx/5JEz\nU2KUFZGRIvKPiBwTkVGAB9tUz8LRvuSK/hXZNWAXPjl8qDWjFvvP7HdoexnB+NUTMLpIIL26+Ocf\nnd6ibFlYt87zjYI9scUw3FRKNY5bUUo1AqIcJ5LB2eTOnpvpbaYzutloWi5oydRdU01uHkOmZutW\naNhQl9+cOhW8bZnRlYWwxZVUHZgHFLBsugz0FpFfHSaUcSW5jL8v/k3nZZ0J8gtiZtuZ+OYyr1GG\nzMW8eTB0qP772GOulsa+OD1XklKqACAici2jjdrQljEMLuR29G1e++k11hxZw6KnF1G3RF1Xi2Qw\nZJjYWHj7bVi8WAeZK1VytUT2x5m5kgYppfKjC/RMUErtU0o9mtGGDbbhCl9yTu+cTH58Mp+0/ITW\nX7Vm/C/j3cK1ZPzqCRhdJGCLLm7cgI4dtQtp587MaRTsiS0xhn6WXkJLoCDQC/jQoVIZ3IL2Fduz\nc8BOFv+xmLaL2nIx6qKrRTIY7ptTp6BJE11lbf168Pd3tUTujy0xht9FpIpSajIQJiIrlFL7RaRG\nuhvVcyNmApXRaTf6icgOq/3GleRG3Im5w7ANw1jyxxK+7vA1DQMbulokg8Emdu+Gp56CgQPhjTcy\n/0xmZ85jmAMUB4KAqujZ0ptEpFa6G1VqLrBZRL5USnkDea0nzBnD4J6sObKGAd8OILReKG80eoNs\nypYOp8HgGhYuhEGDYMYMbRyyAs6cx9AfeAuoLSJRQHagb3obtASxG4vIlwAiEu3IWdSejjv5kluX\nb83uZ3az7ug6Hl/4OOdunHNq++6kC1djdJFAUl3ExOjewYgRsHFj1jEK9iRNwyAiMSKyV0SuWNYv\nishvGWizDHBeKTXbEsj+QimVJwPXMziRgAIBbOq9idrFalNjeg02/bvJ1SIZDPFcuQJt2mgX0q5d\nUKWKqyXyTJxej8FSQ/oXIFhEdiulJgLXROQdq2OMK8kD+PHYj/T5pg/P1XqO4U2G45Utk1ZGN3gE\nR45A27bwyCMwfjxkz+5qiZyPS2s+Z6hBpYoCv4hIGct6I+BNEWltdYz07t2b0qVLA+Dr60v16tUJ\nCQkBErqOZt3162ciz9Dq/VYArH17LcV9iruVfGY9a6zv2gWffBLC++/Dgw+6Xh5nrYeFhTFnzhwA\nSpcuzbvvvuu04HPBZDZHisjddDeq1BZggIgcUUqNAnKLyBtW+02PwUJYWFj8DeGuxMTG8P7P7/P5\nns+Z224uLcu2dEg7nqALZ2F0oRGBF18MY9WqEJYsgUaNXC2Ra7FXj8HWms+B6FQYAH7AWaXUWeAZ\nEdmbjnZfBhYqpXIAx8hAMNvgeryyefFO03doUqoJPVb0oFe1XoxuNhrvbCYBjcFx3LoFzzwDO3bo\nJTDQ1RJlHmzpMXwBLBORHyzrLYGngdnAJBGxe74E02PwXM7dOEevlb24fuc6X3f4moACAa4WyZAJ\nOX1ajzYqUwa+/BLymOErgHOHqzaIMwoAIvKjZdsvgIeUyDY4i8J5C7Ou+zpal29N7S9qs+bIGleL\nZMhk7NwJdevCk0/C118bo+AIbDEMZ5RSbyilSimlSiulXgf+U0p5AbEOli/LExdo8iSyqWy82ehN\nVnRawUvrXmLID0O4E3Mnw9f1RF04iqyqizlz9HDUzz+HYcP0TOasqgtHYoth6AYEAN8AK9Hxhq6A\nF9DJcaIZPJ2GgQ3Z9+w+/r70N41nN+bfy/+6WiSDh3LnDrz0EowdC2Fh2jgYHIctMYYyIvJvkm11\nRGS3w4QyMYZMhYgwccdExm4dy7TW02hf0WFVYQ2ZkDNndGbUQoV0DYUCBdI+J6vizBjDcqVUSauG\nm6IDzwaDTSilGNxgMGu6rWHoj0N5ed3L3Iq+5WqxDB7A9u1Qpw48+iisXGmMgrOwxTA8B3yjlCqq\nlGoFTAYed6xYhjgyk/+0bom67HtuH2eunyF4VjB/X/z7vs7PTLrIKJldFyI6jvDUUzoJ3ogRkC2F\np1Vm14UrsCVX0m7gFeAnYBTwiIiccLBchkyKby5flnZcyoCaAwj+Mpivf//a1SIZ3Ixbt6B/f/js\nM9i2DVq1crVEWY8UYwxKqdVJNlUEzgBX0CU+2zpMKBNjyBLsP7Ofzss6E1I6hImPTSRPdjPuMKsT\nEQEdOkBQEMyapYvrGGzH4bmSLLEEAOtGxLIuIrI5o42nKJQxDFmGyNuRPL/2eX777zeWPL2Eiv4V\nXS2SwUVs2gTdusGQIXrJ7EV1HIEzgs/DgJrAWREJsyyb4/5mtGGDbWR2/6lPTh8WPLWAQfUG0WRO\nE+YemJvisZldF/dDZtKFiM6G2rUrLFgAQ4fen1HITLpwF1JLZtMHeAwYpZR6CNgJfAesF5EbTpDN\nkEVQStG/Zn/qlaxHp6Wd2Hh8I1NbTSVfDuNHyOzcuKHzHf31l57RXKqUqyUygI1pty2znOuhRyM9\nDNwCfhCRjxwilHElZVlu3LnBy9+9zC8nf2Hx04upWqSqq0UyOIi//tLxhLp1daA5d25XS+T5OGUe\ng1LKSyk12FLFbbuIjBCRhkAX4FRGGzcYkpI3R16+fPJLhjUaRvN5zZmxdwbmJSHzsWQJNG4Mgwfr\nJHjGKLgXqRoGEYlBp8RIuv28iCx0mFSGeLKq/7RntZ783Pdnpu6eStflXbl2+1qW1UVyeKou7tyB\n0FB46y344QcYMCDjQWZP1YU7Y8sEt61Kqf8ppRorpWoqpWoppWo6XDJDlqfCAxXY0X8Hvrl8qTm9\nJkcuHnG1SIYMcPIkhITA8eOwZw/UNE8Rt8WWXElh6GGqiRCRZg6SycQYDPew+OBiXv7uZUY0GcHA\nugNRZiyjR/HTT9CrFwwaBK+9lvIsZkPG8Niaz7ZgDIMhOY5dOkbnZZ0JLBDIrLaz8Mvt52qRDGkQ\nGwvvv6/TWyxcCM0c9jppACcm0VNKjVRKvWP19x2l1DsZbdhgG8Z/msCJ306wrd82AgsEUnNGTXae\n3OlqkVyGJ9wXFy9C69a6t7Bnj+OMgifowtOwpUN3w7JcRxfmaQWUdqBMBkOK5PTOycTHJjLh0Qm0\nXdSWT7Z/QqyYelHuxu7dUKsWVK4MGzZA8eKulshwP9y3K0kplRP4UUSapnlwOjGuJIMthF8Jp8vy\nLhTKXYg57ebwQJ4HXC1SlkcEpk2DkSNh+nSdHdXgPJxZjyEpeYESGW3YYMgopXxLsaXPFir5V6Lm\n9Jr8HP6zq0XK0ly9Cp07a4OwbZsxCp6MLTGG362WP4DDwCTHi2YA4z+1JjldZPfKzkePfMS01tPo\nuLQj7295P0u4ltztvogbfurvDzt2wIMPOq9td9NFZiC1XElxxFVXFSAaOCcidx0nksFw/7R6sBV7\nnt1Dt+Xd2By+mflPzadIviKuFivTIwJTpsCYMTB1qi7BafB8bM2VVB1ojDYOP4vIrw4VysQYDOkk\nOjaad8Pe5csDXzKv3TyaBzV3tUiZlsuXdUGdiAhYvBjKlnW1RAZnDlcNBRYA/kARYIFS6pWMNmww\nOALvbN689/B7zG03l54rezJy00hiYmNcLVamY+dO7ToKDNTxBGMUMhe2BJ8HAPVE5B0RGQHUB55x\nrFiGOIz/NIH70UWLoBbse24f205so/m85pyOPO04wVyAq+4LEfj0U2jbFiZMgIkTIWdOl4gSj/mN\n2B9bRyXFpvC/weC2FM1XlB96/ECLoBbUmlGL749+72qRPJqLF7VBWLpU9xjatXO1RAZHYUuupFfR\nRXtWoMt6tgPmiMgEhwllYgwGO7P5+Ga6r+hOj6o9eK/Ze2T3yu5qkTyKbdt02c2OHeGDDyBHDldL\nZEgOp+ZKUkrVAhqREHzen9GG02jPGAaD3Tl/4zy9v+nNlVtXWPT0IgILBLpaJLcnJgY+/FCPPJo5\nU6e4MLgvDg8+K6UKxi3Av+gA9EIg3LLN4ASM/zSBjOrCP68/a7qt4akKT1Hnizp8e/hb+wjmApxx\nX5w4Ac2bw/r1ep6CuxoF8xuxP6nNY9hHMum2LQgQZH9xDAbHkk1l47WGr9EosBFdl3dl07+bGPfI\nOHJ4Gd+INStWwAsv6DTZr78OXl6ulsjgTEzabUOW5dLNS/Rb1Y9TkadY/PRigvzMu05UlC63uX49\nfPUV1KvnaokM94NTcyUppZ5USn2qlPpEKdUm7TMMBvenYO6CrOy8kh5VelB/Zn2WHVrmapFcyq+/\n6oyoUVGwf78xClkZWya4fQi8AvwB/Am8opQa62jBDBrjP03AEbpQShFaP5R13dfxxvo3eHHti9yK\nvmX3duyNPXUhApMmQYsW8PbbMH8+5M9vt8s7HPMbsT+29BieAFqKyJciMgt4DHDTMJTBkD5qF6/N\nvmf3cSHqAvVn1s8y9aXPnYMnntBuox07oEcPV0tkcAdsmcfwG9BMRC5a1gsBm0SkqsOEMjEGg4sQ\nEabvnc6ITSOY+OhEulft7mqRHMYPP0DfvtCnD7z7LmQ3Uzs8HqfNY1BKdQU+BDahJ7g1Bd4UkUUZ\nalgpL2APcFJE2iTZZwyDwaX8evZXOi3rROPAxkx+fDJ5sudxtUh249YtGDZMz2CeOxceftjVEhns\nhdOCzyLyNdAAWAksB+pn1ChYCAUOkfKQWAPGf2qNM3VRrWg19j67l9sxt6nzRR3+OPeH09q2hfTq\n4sABqF1bZ0Q9cCBzGAXzG7E/tgSfnwKiRGSViHwL3FJKZShLilKqJLp29Ex0L8RgcDvy5cjHvHbz\nGNpgKCFzQ5i9fzae2pONiYFx4+CRR+CNN3RvoVAhV0tlcFdscSX9KiLVkmw7ICLV092oUkuBD4D8\nwFDjSjK4O4fOH6LT0k7UKFaDz1p9hk9OH1eLZDPHj0OvXqAUzJsHpUq5WiKDo3DmPIbkGkn3PEil\nVGt0Fbj9KVzbYHA7KvlXYtczu8jplZPaX9Tm17MOrVVlF0R0DKFOHWjTBjZuNEbBYBu2lPbcq5Qa\nD0xFP8hfAvZmoM1goK1SqhWQC8ivlJonIr2sD+rTpw+lS5cGwNfXl+rVqxMSEgIk+BSzwrq1/9Qd\n5HHletw2V8ozs+1M3p71Nk1GNWHcgHE8V+s5Nm/e7HR5Dhw4wKBBg1Lcf/UqzJ8fwuHDMHZsGOXK\ngZeX8/XljPWJEydm6efDnDlzAOKfl3ZBRFJdgHzAOPQIoj3AWCBvWufZsqBHOK1OZrsYNJs2bXK1\nCG6DO+ni8IXDUu3zatJxSUe5cvOK09tPTRfffSdSvLjIkCEiN286TyZX4U73hauxPDsz/Gx2aa4k\npVRTYIiItE2yXVwpl8FgC7eibzHkhyF8f+x7Fj+9mNrFa7tUnqgoeO01WLMG5syBZs1cKo7BBTg1\nV5KjEJHNSY2CweAp5PLOxdQnpjKuxThaLWzFpB2TXDZqaft2qF4drl7VOY+MUTBkBJcaBkPaWPvX\nszruqounKz3NjgE7WPD7Atotbselm5cc3macLm7e1L2EDh10QZ0FC8DX1+HNuxXuel94MqkV6hln\n+dvJeeIYDJ5JkF8Q2/pto6xfWWpOr8kvJ35xeJs7d0LNmhAeDr/9Bu3bO7xJQxYhxRiDUuogUAXY\nJyI1nCqUiTEYPJhvD3/LM6ufYUiDIQwNHko2Zd+O+e3bOrfRl1/C5MnQyby6GSw4PFeSUupj4Bn0\nqKSbSXaLiDgsMa8xDAZPJ+JqBF2WdcE3ly9z283FP6+/Xa67bx/07g3lysG0aVCkiF0ua8gkODz4\nLCKviYgvsE5EfJIsHpSt3bMx/tMEPEkXgQUC2dxnM9WKVKPmjJpsCd+SoevduQMjR8Ljj8Obb8Ir\nr4QZo2DBk+4LT8GWJHptlVJFlFKtLUthZwhmMHg62b2yM7bFWL5o8wWdl3VmzJYxxMTG3Pd1fv1V\nV1Pbu1dXVuveXae3MBgchS25kjoBHwOb0TOfGwOvichShwllXEmGTMbpyNN0W94N72zeLGi/gKL5\niqZ5zp07OvHdlCnw0UfahWQMgiE1nFmP4TeghYics6z7AxvEFOoxGO6L6Nho3tv8Hl/s+4J5T82j\nRVCLFI/dvRv694fAQPj8cwgIcKKgBo/F2Un0zlutX8Qkv3Maxn+agKfrwjubN+82e5cF7RfQ+5ve\nDN84nOjY6ETHxM1ebtNGxxJWr07eKHi6LuyJ0YX9scUwfA/8oJTqo5TqC6wDvnOsWAZD5uXhMg+z\n79l97Dq1i4fnPszJaycBCAuDqlXh1Cn4/Xfo1s24jgyuwaZcSUqpDkBDy+rPIrLSoUIZV5IhCxAr\nsXy49UMm7ZhMzRNfcnBlKz77TPcWDIb04NRcSSKyXERetSwONQrx9O8PZ886pSmDwRVkU9mocmUY\nsngp53Ls4OBBYxQM7oH75kry84P/+z/4+GM91TOLYvynCWQmXZw/r11FgwfD4o8bs/eT0RQoYPv5\nmUkXGcXowv64r2H45BOdMnLLFm0gVq/WJakMBg9GBObPhypVoEQJnePIZEI1uBv3VY9BKVUQKCki\nvzlOpGRiDN9/r1+tAgNhwgSoVMmRzRsMDuHIEXjhBbh8GaZP1yU3DQZ74rQYg1Jqs1Iqv8Uo7AVm\nKqUmZLTh++Kxx/SrVatW0LQphIbqX5fB4AHcvg3vvQfBwdC6NezaZYyCwb2xxZVUQESuAe2BeSJS\nF0h5Zo6jyJ5dG4RDh/SU0AoV9Myf6Oi0z/VgjP80AU/UxZYtuoDO7t06Ad7gweBtS6X1NPBEXTgK\nowv7Y4th8FJKFQM6AWst21zn7Pf31wbhxx9hyRKdkH7jRpeJYzAkx8WLemBd9+7wwQewapX2hBoM\nnoAtKTE6AiOAbSLyglKqLPCRiHRwmFC2zmMQgRUrYOhQbSA+/hiCghwllsGQJnHB5ddfhy5dtAvJ\nx8fVUhmyCs7MldRIRLamtc2e3PcEt5s3Yfx4vTz/PLz1FuTL5yjxDIZkiQsuX7mig8u1a7taIkNW\nw5kT3KYks21yRhu2K7lzw9tv6wD1iRPw0EMwbx7Exrpasgxj/KcJuKsuoqLgnXd0cLlNG11y09FG\nwV114QqMLuxPimEwpVQDIBjwV0q9SkLiPB/Aywmy3T8lSmiDsGOHDlRPnaprH9ar52rJDJkQEfj2\nWxg0SN9iBw5AyZKulspgyDiplfZsCjQDngOmWe2KBFaLyN8OE8oeuZJiY2HBAu1Wat4cPvwQihe3\nj4CGLM/Ro/rd499/db2E5s1dLZHB4NwYQykRCc9oQ/eDXZPoRUbC2LEwYwa8+qpecuWyz7UNWY6o\nKP2O8dln8MYb2jjkyOFqqQwGjcNjDEqpSZZ//6eUWp1k+TajDTsNHx89XnDXLl0bsVIlPZLJQ9Jr\nGP9pAq7UhYgeclq5sg4yHzig6ya4yiiY+yIBowv7k9pUm3mWv586QxCHExQEy5frOQ+hofC//8HE\niToBvsGQCnFuo3/+gZkzjdvIkPm5r1xJzsLh9Riio+GLL2DUKGjfXg82f+ABx7Vn8EiuX9deyOnT\njdvI4Bk4M1dSI6XUT0qpv5VS/1qWfzLasEvx9tYDzv/8U//SK1aESZPg7l1XS2ZwA2Jj9eC2ChUg\nIsL1biODwdnYMo9hFjAeaATUsSx1HSmU0yhYUBuEzZth7VrtVvrhB1dLlQjjP03AGbr45Rdo0ECP\ndF62TM9idschqOa+SMDowv7Yks7riohk7hrPlSppg7BmDQwcqF8VP/0Uypd3tWQGJ3HyJLz5pq67\nPHasznGUzX2rlRgMDsWW4aofoie0rQDiS6mJyD6HCeXKms+3b+tJcePGQd++MHw491Vay+BRREXp\nmlCTJmnv4ptvmmwqBs/FmfMYwkgmm6qIOKzulEsNQxxnz+o0G+vWwZgx0KcPeLnnhG/D/SOik/O+\n/rqetfzRR1C6tKulMhgyhtOCzyISIiLNki4ZbdjtKVoUZs3SJUW//BLq1oWtDssbmCLGf5qAvXSx\nYwc0bqwnqs2frw2EpxkFc18kYHRhf9KMMSilRqJ7DAqrnoOIjHagXO5D7draICxapKu3Bwfr10uT\nXN/jOHZMZ0jZvl2PUO7Vy3QCDYbksMWVNJQEg5AbaA0cEpF+DhPKHVxJyXHjhq75MGUKvPyy9kPk\nyeNqqQxpcPGiNgQLFugKaoMHm6/NkDlxWowhmYZzAj+KSNOMNp5KG+5pGOKIiNBGYft23Xvo3BlU\nhr8Lg525dUuPI/j4Y+jUCUaOhMKFXS2VweA4nFmPISl5gRLpbVApFaCU2qSU+kMpdVAp9Up6r+Uy\nAgO1a2nhQm0YGjfWeZgcgPGfJmCrLuIS6z70kJ6XsHWrnpeQmYyCuS8SMLqwP7bEGH63Ws0GFAYy\nEuhZUxsAABH5SURBVF+4CwwWkQNKqXzAXqXUTyLyZwau6RoaN9ZV3mfPhtat4Ykn4P33oUgRV0uW\nZdm4Uc9Szp5dG4fGjV0tkcHgedgSYyhttRoN/CcidssdoZT6BpgiIhustrm3Kyk5rl7Vw1pnz9aD\n4V95xeRQcCK7d8OwYbo+wgcfQMeOxrtnyHq4LMZgTyxGZzNQWUSuW233PMMQx5EjMGQIHD6sa1A/\n8YR5QjmQQ4dgxAhdTnPECOjXT/cWDIasiL0Mgy0pMRyCxY20DAi1Ngpx9OnTh9KWweW+vr5Ur16d\nkJAQIMGn6Jbr5csTNmQI7NpFyGuvwf/+R1jXrlCqVLquZ+0/dYvP58L1uG1hYWGcPQvffx/CunXQ\noUMYs2bBo4+6l7yOXD9w4ACDBg1yG3lcuT5x4kTPeT7YeT0sLIw5c+YAxD8v7YKIOH0BsgM/AINS\n2C+Zgjt3RCZOFHngAZHQUJFLl+77Eps2bbK/XB7Kpk2b5MwZkYEDRQoWFBkxQuTKFVdL5RrMfZGA\n0UUClmdnhp/RTnclKaUUMBe4KCKDUzhGnC2XQzl/Ht55R1eOGzUKnnlGp/422Mzly3rY6fTpemLa\nW29lrlFGBoM9cOVw1YzSEOgBNFNK7bcsj7lADufh7w+ffw4//qjzL9SsqYfPGNLk6lUYPVonuj13\nDvbvhwkTjFEwGByJ0w2DiGwVkWwiUl1EaliW750th0uoVk0bhJEjoX9/6NBB14tMBWv/elYiziCU\nK6dVtH079OgRZjKRWMiq90VyGF3YH5Nx3tkopQ3CoUO651C3rs7iev2e+HuW5OpVnb6iXDmd22j7\ndpgzBx580NWSGQxZh6xZ89mdOHVKO8w3bNAVYnr0yJIVYq5e1ekrJk+GVq10GQxjDAyG+yNTzGNI\niSxlGOLYsUNXmwf9dKxXz7XyOImrV3VOwkmT4PHHtUEwhfMMhvThycFnQ3LUr68T+7z0ErRvr4fe\nnD6daf2n589rI1C2rJ4LuG0bzJuXulHIrLpID0YXCRhd2B9jGNyJbNm0QfjrL12BvmpVnfDn1i1X\nS2Y3TpzQHaOHHoILF/SM5fnzTS/BYHAnjCvJnfnnH50Rbv9+XZj4qac8Nr3GkSO6jPbKlXpA1uDB\nULy4q6UyGDIXJsaQldi4Ub9m+/vDxIm6J+Eh7N+vY+qbNsHAgXopVMjVUhkMmRMTY8gihIWFwcMP\n6ydsx47wyCPw4ovaD+OmiEBYmB5d9MQTOo7+zz96+kZGjILxJSdgdJGA0YX9MYbBU/D2hhdegD//\n1OlDK1XSo5fu2i0Deoa5exe+/lqXyX7uOXjySW0QhgwBHx9XS2cwGGzFuJI8lUOHYNAgOHlS54h4\n9FGXiXLtGsycqb1cZcpoQ9C6dZacjmEwuBQTYzBon82aNfDqq1Chgq7/4MRZYSdO6PkHs2dDy5ba\nINSu7bTmDQZDEkyMIYuQqv9UKWjTBg4ehKZNoUEDPYrp6lWHyrR3L3TvDtWr6/rK+/YluJAcifEl\nJ2B0kYDRhf0xhiEzkDMnDB0Kf/yh81NXqACzZkFMjN2auHMHvvoKgoP1/LsaNXT8YPx4KFXKbs0Y\nDAY3wLiSMiN79+rhrTdval9Po0bpvtTp0zBjhl4qVoSXX9adFC8vO8prMBjsgnElGVKmVi34+Wft\nVurWDbp2hYgIm08X0SkqunaFypV1HYT163Wev3btjFEwGDI7xjC4Oen2nyoFXbro9BoPPaR9P6NG\nQVRUiqdERelAcq1a0KePTt/077/w2Wd6dKyrMb7kBIwuEjC6sD/GMGR28uTRBmH/fm0kKlSARYt0\nt8DCb7/pGckBAbBsGbz/vk5sFxoKvr6uE91gMLgGE2PIavz8M4SGEpMrD2taTGLsj7U4dUrnL+rX\nD1MhzWDwYMw8BkO62L8fvpgWQ/YFsxnkPYU/5+yiZZuceHu7WjKDwZBRTPA5i2AP/+mlSzpOUKeO\nDh4XK+nFa4cHUObKAVo95TlGwfiSEzC6SMDowv54yCPBcL/cvQvffw9z58JPP+nqaKNH6xnKCaOK\nPDOFt8FgcCzGlZTJ+PVXbQwWLoRy5aB3b+jUyQSRDYasgL1cSabHkAk4dQqWLNEG4dIlXQTu559N\nVTSDwZA+TIzBzUnJf3rhAkybBiEhUKWKHnI6fjwcPw5jxmROo2B8yQkYXSRgdGF/TI/Bg7h2Db75\nRies275dxw0GDYLHHoNcuVwtncFgyCyYGIObExkJ69ZpV9H69f/f3rkHWVFccfj7gUihKA9LlJcB\nCaZMqYkooLLAKgTRQpRoIlZCxTxMpBI1seIDNWUexihWMD6TaDQxVlBTBsxuIAEkirgSWMCVRUDd\nIshDeZSyCAYj7J780X29czf7hLt7L3vPVzU1Mz0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+ "text": [
+ "<matplotlib.figure.Figure at 0x7938c88>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Quantity of fresh carbon recquired for two stage crosscurrent operation: 19.8171091445 kg carbon/1000 kg solution\n",
+ "\n",
+ "Quantity of fresh carbon recquired for two stage Counter Current operation: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 12.8 kg carbon/1000 kg solution\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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AASpVquRnpYoSGiS9USc9PENtPWlboOjxxvpPP8GoUZHMmgXnzsUQE5P6/jEx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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7d9ad68>"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.3: Page 602"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.3\n",
+ "# Page: 602\n",
+ "\n",
+ "print'Illustration 11.3 - Page: 602\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#***Data***#\n",
+ "T = 1.0; #[m]\n",
+ "di = 0.203;# [m]\n",
+ "n = 1;# [for one impeller]\n",
+ "Density_S = 2300.0;# [kg/cubic m]\n",
+ "Density_p = 2300.0;# [kg/cubic m]\n",
+ "C = 0.150;# [m]\n",
+ "S = 50.0;# [kg]\n",
+ "g = 9.807;# [m/s]\n",
+ "dp = 8*10**(-4);# [m]\n",
+ "N = 8.33; #[r/s]\n",
+ "Temp=25;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# Assume:\n",
+ "Po = 5;\n",
+ "viscosity_L = 8.94*10**(-4);# [kg/m.s]\n",
+ "Density_L = 998.0;# [kg/cubic m]\n",
+ "delta_Density = Density_S-Density_L;# [kg/cubic m]\n",
+ "# By Eqn. 11.23:\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_L);# [m/s]\n",
+ "# By defn. of power number:\n",
+ "# P = Po*Density_m*di**5*Ni**3\n",
+ "# vm = math.pi*T**2*(Z+C)/4\n",
+ "# Solid Volume = S/Density_p;\n",
+ "# If these are substituted in Eqn. 11.22\n",
+ "def f(Z):\n",
+ " return (((Z+C)**(1.3/3))*math.exp(4.35*Z/(T-0.1)))-((1.0839*Po*di**(11.0/2)*N**3*Density_p**(2.0/3))/(g*Vts*T**(7.0/6)*S**(2.0/3)))\n",
+ "Z = fsolve(f,7);# [m]\n",
+ "phi_Sm = 4*S/(math.pi*T**2*(Z+C)*Density_p);\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_L);# [kg/cubic m]\n",
+ "phi_Ss = 0.6;\n",
+ "viscosity_m = viscosity_L/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",
+ "Re = di**2*N*Density_m/viscosity_m;\n",
+ "P = Po*Density_m*N**3*di**5;# [W]\n",
+ "print \"Agitator Power required: \",round(P),\" W\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.3 - Page: 602\n",
+ "\n",
+ "\n",
+ "Agitator Power required: 1113.0 W\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 65
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.4: Page 604"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.4\n",
+ "# Page: 604\n",
+ "\n",
+ "print'Illustration 11.4 - Page: 604\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "#****Data*****#\n",
+ "# b: kerosene c:water\n",
+ "# c:kg water/cubic m liquid\n",
+ "Density_l = 783;# [kg/cubic m]\n",
+ "viscosity_l = 1.7*10**(-3);# [kg/m.s]\n",
+ "Mb = 200;# [kg/kmol]\n",
+ "Density_p = 881;# [kg/cubic m]\n",
+ "m = 0.522;# [(kg water/cubic m kerosene)/(kg water/kg gel)]\n",
+ "Xo = 0;# [kg H2O/kg gel]\n",
+ "#**************#\n",
+ "\n",
+ "# Solution (a)\n",
+ "co = Density_l*4*10**(-5);# [kg water/cubic m]\n",
+ "c1 = Density_l*5*10**(-6);# [kg water/cubic m]\n",
+ "# For Ss minimum:\n",
+ "X1 = c1/m;# [kg H2O/kg gel]\n",
+ "# By Water Balance:\n",
+ "SsminByVl = (co-c1)/(X1-Xo);# [kg gel/cubic m kerosene]\n",
+ "print\"Minimum Solid/Liquid ratio used:\",SsminByVl,\" kg gel/cubic m kerosene\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "# Basis: 1 batch,1.7 cubic m kerosene\n",
+ "Vl = 1.7;# [cubic m]\n",
+ "Ss = 16*1.7;# [kg gel]\n",
+ "V = Ss/Density_p;# [Xol. solid, cubic m]\n",
+ "Vt = 1.7+V;# [Total batch volume, cubic m]\n",
+ "# Take Z = T\n",
+ "T = (Vt*4/math.pi)**(1.0/3);# [m]\n",
+ "# To allow for the adequate free board:\n",
+ "h = 1.75;# [Vessel height,m]\n",
+ "# Use a six-blade disk impeller.\n",
+ "# From Fig. 11.26:\n",
+ "# dp corresponding to 14 mesh:\n",
+ "dp = 1.4/1000;# [m]\n",
+ "TBydi1 = 2.0;\n",
+ "Value1 = (Density_p-Density_l)/Density_l;\n",
+ "# From Fig. 11.26:\n",
+ "TBydi2 = 4.4;\n",
+ "TBydiAv = (TBydi1+TBydi2)/2.0;\n",
+ "di = T/TBydiAv;# [m]\n",
+ "fr = 0.6;# [settled volume fraction of solids]\n",
+ "Vs = V/fr;# [cubic m]\n",
+ "depth = Vs/((math.pi*(T**2))/4);# [m]\n",
+ "# The depth of settled solid is negligible.\n",
+ "# Locate the turbine 150mm from the bottom of the tank.\n",
+ "C = 0.150;# [m]\n",
+ "\n",
+ "# Power:\n",
+ "# Use the sufficient agitator power to lift the solids to 0.6 m above the bottom of the vessel.\n",
+ "Z_prime = 0.6-C;# [m]\n",
+ "# The properties of the slurry in 0.6 m above the bottom of the vessel.\n",
+ "Vm = 0.6*math.pi*T**2.0/4;# [square m]\n",
+ "phi_Sm = V/Vm;# [vol fraction solid]\n",
+ "# From Eqn. 11.24:\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",
+ "# From Eqn. 11.25:\n",
+ "phi_Ss = 0.8;\n",
+ "viscosity_m = viscosity_l/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "# From Eqn. 11.23:\n",
+ "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_l);# [m/s]\n",
+ "# From Eqn. 11.22:\n",
+ "n = 1.0;\n",
+ "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*(TBydiAv**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",
+ "# Assume:\n",
+ "Po = 5.0;\n",
+ "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",
+ "# Use:\n",
+ "N1 = 2.0;# [r/s]\n",
+ "Re = di**2.0*N1*Density_m/viscosity_m;\n",
+ "# From fig. 6.5: Po = 5\n",
+ "# Hence our assumption was right.\n",
+ "print\"Power delivered to the slurry: \",round((P*(N1/N)**3),2),\" W\\n\",\n",
+ "print\"Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\\n\"\n",
+ "\n",
+ "# Mass transfer: \n",
+ "# From Eqn. 11.28:\n",
+ "Rep = (dp**(4.0/3))*(P/Vl)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",
+ "# From Eqn. 2.44:\n",
+ "Temp = 298;# [K]\n",
+ "phi = 1.0;\n",
+ "Va = 0.0756;# [Chapter 2 notation]\n",
+ "Dl = ((117.3*10**(-18))*((phi*Mb)**0.5)*Temp)/(viscosity_l*(Va**(0.6)));\n",
+ "ScL = viscosity_l/(Density_l*Dl);\n",
+ "if dp<(2.0/1000):\n",
+ " # From Eqn. 11.29:\n",
+ " ShL = 2+(0.47*Rep**0.62*(1/TBydiAv**0.17)*ScL**0.36);\n",
+ "else:\n",
+ " # From Eqn. 11.30:\n",
+ " ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",
+ "\n",
+ "kL = ShL*Dl/dp;# [m/s]\n",
+ "apS = (math.pi*dp**2)/(math.pi*dp**3*Density_p/6.0);\n",
+ "apL = apS*16;# [square m/cubic m liquid]\n",
+ "Ratio = Ss/(Vl*m);\n",
+ "# From Eqn. 11.40:\n",
+ "thetha = math.log((co/c1)/(1+(1/Ratio)-(1/Ratio)*(co/c1)))/((1+(1/Ratio))*kL*apL);\n",
+ "print\"Contacting Time required: \",round(thetha/60,2),\" min\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.4 - Page: 604\n",
+ "\n",
+ "\n",
+ "Minimum Solid/Liquid ratio used: 3.654 kg gel/cubic m kerosene\n",
+ "\n",
+ "\n",
+ "Power delivered to the slurry: 350.05 W\n",
+ "Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\n",
+ "\n",
+ "Contacting Time required: 8.3 min\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 69
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.5: Page 606"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.5\n",
+ "# Page: 606\n",
+ "\n",
+ "print'Illustration 11.5 - Page: 606\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "import numpy.linalg as lin\n",
+ "#*****Data******#\n",
+ "Vl = 1.1*10**(-4);# [cubic m/s]\n",
+ "Ss = 0.0012;# [kg/s]\n",
+ "Density_p = 1120;# [kg/cubic m]\n",
+ "dp = 8*10**(-4);# [m]\n",
+ "Ds = 2*10**(-11);# [square m/s]\n",
+ "Dl = 7.3*10**(-10);# [square m/s]\n",
+ "m = 0.2;# [(kg Cu2+/cubic m soln)/(kg Cu2+/kg resin)]\n",
+ "T = 1;# [m]\n",
+ "#********************#\n",
+ "\n",
+ "Z = T;# [m]\n",
+ "# The particles will be lifted to the top of the vessel.\n",
+ "Z_prime = 0.5;# [m]\n",
+ "viscosity_l = 8.94*10**(-4);# [kg/m.s]\n",
+ "Density_l = 998;# [kg/cubic m]\n",
+ "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",
+ "g = 9.80;# [m/square s]\n",
+ "# From Eqn. 11.23:\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_l);\n",
+ "Vm = math.pi*T**2*Z/4.0;# [cubic m]\n",
+ "Vs = Ss/Density_p;# [cubic m/s]\n",
+ "phi_Sm = Vs/(Vs+Vl);# [vol fraction]\n",
+ "# From eqn. 11.24:\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",
+ "# From Eqn. 11.22:\n",
+ "n = 1.0;\n",
+ "di = 0.3;# [m]\n",
+ "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*((T/di)**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",
+ "# To estimate the impeller speed:\n",
+ "# Assume:\n",
+ "Po = 5;\n",
+ "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",
+ "Re = di**2*N*Density_m/viscosity_l;\n",
+ "# From fig. 6.5: Assumption of Po was correct.\n",
+ "print\"Speed of the impeller:\",round(N,2),\" r/s\\n\"\n",
+ "vT = (math.pi/4.0)*T**2*Z;# [cubic m]\n",
+ "vL = vT*(1-phi_Sm);\n",
+ "# From Eqn. 11.28:\n",
+ "Rep = (dp**(4.0/3))*(P/vL)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",
+ "ScL = viscosity_l/(Density_l*Dl);\n",
+ "if dp<(2.0/1000):\n",
+ " # From Eqn. 11.29:\n",
+ " ShL = 2+(0.47*Rep**0.62*((di/T)**0.17)*ScL**0.36);\n",
+ "else:\n",
+ " # From Eqn. 11.30:\n",
+ " ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",
+ "\n",
+ "ShL = 130.3;# Value wrong in book\n",
+ "kL = ShL*Dl/dp;# [m/s]\n",
+ "# Since the dispersion is uniform throughout the vessel, the residence time for both liquid and solid is same.\n",
+ "thetha = vL*(1-phi_Sm)/Vl;# [s]\n",
+ "# From Fig. 11.27:\n",
+ "abcissa = m*kL*dp/(2*Ds*Density_p);\n",
+ "Parameter = 2*m*kL*thetha/(dp*Density_p);\n",
+ "co = 100*Density_l/10.0**6;# [kg/cubic m]\n",
+ "EMS = 0.63;\n",
+ "Xo = 0;\n",
+ "# From Eqn. 11.44:\n",
+ "# (1): X1-(EMS/m)*c1 = 0\n",
+ "# Solute balance:\n",
+ "# (2): (Ss*X1)+(vL*c1) = (vL*co)+(Xo*Ss)\n",
+ "a = [[1 ,-(EMS/m)],[Ss ,Vl]];\n",
+ "b = [0,(Vl*co)+(Xo*Ss)];\n",
+ "soln =lin.solve(a,b);\n",
+ "X1 = soln[0];\n",
+ "c1 = soln[1];\n",
+ "print\"Effluent Cu2+ conc. \",round(c1*10**(6)/Density_l,2),\" ppm\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.5 - Page: 606\n",
+ "\n",
+ "\n",
+ "Speed of the impeller: 2.71 r/s\n",
+ "\n",
+ "Effluent Cu2+ conc. 2.83 ppm\n"
+ ]
+ }
+ ],
+ "prompt_number": 78
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.6: Page 616"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.6\n",
+ "# Page: 616\n",
+ "\n",
+ "print'Illustration 11.6 - Page: 616\\n\\n'\n",
+ "from scipy.optimize import fsolve\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data*****#\n",
+ "# a: air b:silica\n",
+ "Density_a = 1.181;# [kg/cubic m]\n",
+ "Density_b = 671.2;# [kg/cubic m]\n",
+ "kSap = 0.965;# [kg H2O/square m s]\n",
+ "Y1 = 0.005;# [kg H2O/kg dry air]\n",
+ "Y2 = 0.0001;# [kg H2O/kg dry air]\n",
+ "Ss = 0.680;# [square m/s]\n",
+ "Gs = 1.36;# [kg/square m.s]\n",
+ "X2 = 0;# [kg H2O/kg dry air]\n",
+ "# Equilibrium function:\n",
+ "m = 0.0185;\n",
+ "#************#\n",
+ "X1 = (Gs*(Y1-Y2)/Ss)+X2;# [kg H2O/kg dry air]\n",
+ "def f77(X):\n",
+ " return m*X \n",
+ "Y2_star = f77(X2);# [kg H2O/kg dry gel]\n",
+ "Y1_star = f77(X1);# [kg H2O/kg dry gel]\n",
+ "deltaY = ((Y1-Y1_star)-(Y2-Y2_star))/math.log((Y1-Y1_star)/(Y2-Y2_star));\n",
+ "NtoG = (Y1-Y2)/deltaY;\n",
+ "# If the fixed bed data are to be used for estimating the mass transfer coeffecient for a moving bed of solids\n",
+ "va = Ss/Density_b;# [m/s]\n",
+ "vb = Gs/Density_a;# [m/s]\n",
+ "rel_v = va+vb;# [relative velocity,m/s]\n",
+ "G_prime = rel_v*Density_a;# [relative mass velocity of air,kg/square m s]\n",
+ "HtG = Gs/(31.6*G_prime**0.55);# [m]\n",
+ "HtS = Ss/kSap;# [m]\n",
+ "# By Eqn. 11.52:\n",
+ "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",
+ "Z = NtoG*HtoG;# [m]\n",
+ "print\"Height of continuous countercurrent isothermal absorber for drying: \",round(Z,4),\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.6 - Page: 616\n",
+ "\n",
+ "\n",
+ "Height of continuous countercurrent isothermal absorber for drying: 0.2511 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 81
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.7: Page 619"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.7\n",
+ "# Page: 619\n",
+ "\n",
+ "print'Illustration 11.7 - Page: 619\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import numpy.linalg as lin\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#*****Data*****#\n",
+ "# a: C2H4 b:C3H8\n",
+ "# The equlibrium curve is plotted in Fig.11.33 (Pg 620)\n",
+ "# C3H8 is more strongly adsorbed component and composition in the gas and adsorbate are expressed as weight fraction C3H8.\n",
+ "Ma = 28;# [kg/kmol]\n",
+ "Mb = 44.1;# [kg/kmol]\n",
+ "xaF = 0.6;# [mole fraction]\n",
+ "xbF = 0.4;# [mole fraction]\n",
+ "xa1 = 0.05;# [mole fraction]\n",
+ "xa2 = 0.95;# [mole fraction]\n",
+ "#***************#\n",
+ "\n",
+ "xF = xbF*Mb/((xbF*Mb)+(xaF*Ma));# [wt. fraction C3H8]\n",
+ "xb1 = 1-xa1;# [mole fraction]\n",
+ "x1 = xb1*Mb/((xb1*Mb)+xa1*Ma);# [wt. fraction C3H8]\n",
+ "xb2 = 1-xa2;# [mole fraction]\n",
+ "x2 = xb2*Mb/((xb2*Mb)+(xa2*Ma));# [wt. fraction C3H8]\n",
+ "# Basis: 100 kg feed gas\n",
+ "F = 100.0;# [kg]\n",
+ "# (1): R2+PE = F [From Eqn. 11.63]\n",
+ "# (2): (R2*x2)+(PE*x1) = (F*xF) [From Eqn. 11.64]\n",
+ "# Solving simultaneously:\n",
+ "a = [[1, 1],[x2 ,x1]];\n",
+ "b = [F,(F*xF)];\n",
+ "soln = lin.solve(a,b);\n",
+ "R2 = soln[0];# [kg]\n",
+ "PE = soln[1];# [kg]\n",
+ "# Point F at xF and point E1 at x1 are located on the diagram.\n",
+ "# From the diagram:\n",
+ "N1 = 4.57;# [kg carbon/kg adsorbate]\n",
+ "# The minimum reflux ratio is found as it is for the extraction.\n",
+ "delta_Em = 5.80;\n",
+ "Ratio = (delta_Em/N1)-1;# [kg reflux gas/kg product]\n",
+ "R1_m = Ratio*PE;# [kg]\n",
+ "E1_m = R1_m+PE;# [kg]\n",
+ "B_m = N1*E1_m;# [kg carbon/100 kg feed]\n",
+ "Ratio1 = 2*Ratio;\n",
+ "# From Eqn. 11.58:\n",
+ "N_deltaE = (Ratio1+1.0)*N1;# [kg carbon/kg adsorbate]\n",
+ "# Point deltaE is located on the diagram:\n",
+ "R1 = Ratio1*PE;# [kg]\n",
+ "E1 = R1+PE;# [kg]\n",
+ "B = N1*E1;# [kg]\n",
+ "N_deltaR = -(B/R2);# [kg carbon/kg adsorbate]\n",
+ "# Random lines such as the delta_RK are drawn from detaR, and the intersection of equilibrium curves are projected downward in the manner shown to provide the adsorption section operating curve.\n",
+ "# Similarly random lines such as delta_EJ are drawn from deltaE, and the intersections are projected downwards to provide the enriching section operating curve.\n",
+ "# Data = [x x_star]\n",
+ "Data = numpy.array([[0.967 ,0.825],[0.90, 0.710],[0.80 ,0.60],[0.70, 0.50],[0.60 ,0.43],[0.512 ,0.39],[0.40 ,0.193],[0.30, 0.090],[0.20, 0.041],[0.0763, 0.003]]);\n",
+ "Val = zeros(10);\n",
+ "for i in range(0,10):\n",
+ " Val[i] = 1/((Data[i,0])-Data[i,1]);\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x\");\n",
+ "plt.ylabel(\"1 / (x-x*)\");\n",
+ "plt.title(\"Graphical Integraion\");\n",
+ "# The area under the curve between x1 & xF, for the enriching section:\n",
+ "Area1 = 2.65;\n",
+ "# The area under the curve between xF & x2, for the adsorption section:\n",
+ "Area2 = 2.67;\n",
+ "r = Ma/Mb;\n",
+ "# From Eqn.11.66:\n",
+ "# For the enriching section:\n",
+ "NtoG1 = Area1-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",
+ "# For the adsortion section:\n",
+ "NtoG2 = Area2-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",
+ "NtoG = NtoG1+NtoG2;\n",
+ "print\"Number of transfer units: \",NtoG"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.7 - Page: 619\n",
+ "\n",
+ "\n",
+ "Number of transfer units: 5.77763695068\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x7dc14a8>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.8: Page 627"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.8\n",
+ "# Page: 627\n",
+ "\n",
+ "print'Illustration 11.8 - Page: 627\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#******Data******#\n",
+ "rate = 0.1;# [kg/s]\n",
+ "conc = 3.0;# [kg vapour/100cubic m]\n",
+ "Density_p = 720.0;# [kg/cubic m]\n",
+ "Density_bed = 480.0;# [kg/cubic m]\n",
+ "capablity = 0.45;# [kg vapour/kg carbon]\n",
+ "dp = 0.0028;# [m]\n",
+ "time = 3.0;# [h]\n",
+ "#********************#\n",
+ "\n",
+ "Vap_adsorbed = time*3600.0*rate;# [kg]\n",
+ "C_required = Vap_adsorbed*1.0/capablity;\n",
+ "# Two beds will be needed: one adsorbing and another regenerated.\n",
+ "totC_required = 2*C_required;# [kg]\n",
+ "print\"Amount of carbon required: \",totC_required,\" kg\\n\",\n",
+ "Vol = (C_required/Density_bed);\n",
+ "# Assume:\n",
+ "Z = 0.5;# [m]\n",
+ "Area = Vol/Z;# [square m]\n",
+ "# From Eqn. 6.66:\n",
+ "T = 35.0;# [OC]\n",
+ "viscosity_air = 1.82*10**(-5);# [kg/m.s]\n",
+ "Density_air = (29/22.41)*(273.0/(T+273));\n",
+ "e = 1-(Density_bed/Density_p);\n",
+ "G = rate*(100.0/conc)*(Density_air/(Area));# [kg/square m.s]\n",
+ "Re = dp*G/viscosity_air;\n",
+ "Z = 0.5;# [m]\n",
+ "def f78(delta_p):\n",
+ " return ((delta_p/Z)*(e**3*dp*Density_air)/((1-e)*G**2))-(150*(1-e)/Re)-1.75\n",
+ "delta_p = fsolve(f78,7);\n",
+ "print\"The pressure drop is:\",round(delta_p,2),\" N/square m\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.8 - Page: 627\n",
+ "\n",
+ "\n",
+ "Amount of caron required: 4800.0 kg\n",
+ "The pressure drop is: 1413.31 N/square m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 88
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.9: Page 636"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.9\n",
+ "# Page: 636\n",
+ "\n",
+ "print'Illustration 11.9 - Page: 636\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data******#\n",
+ "Yo = 0.00267;# [kg H2O/kg dry air]\n",
+ "Yb = 0.0001;# [kg H2O/kg dry air]\n",
+ "Ye = 0.024;# [kg H2O/kg dry air]\n",
+ "Z = 0.61;# [m]\n",
+ "G_prime = 0.1295;# [kg/square m.s]\n",
+ "#******************#\n",
+ "\n",
+ "# The equilicrium data is plotted in Fig. 11.45 (Pg 637)\n",
+ "# The gel is initially \"dry\" and the effluent air initially of so low a humidity asto be substantially dry, so that the operating line passes through the origin of the figure\n",
+ "# The operating line is then drawn to intersect the equilibrium curve.\n",
+ "# Data = [Y[kg H2O/kg dry air] Y_star[kg H2O/kg dry air]]\n",
+ "Data =numpy.array([[0.0001, 0.00003],[0.0002, 0.00007],[0.0004 ,0.00016],[0.0006, 0.00027],[0.0008, 0.00041],[0.0010, 0.00057],[0.0012 ,0.000765],[0.0014, 0.000995],[0.0016, 0.00123],[0.0018 ,0.00148],[0.0020 ,0.00175],[0.0022 ,0.00203],[0.0024 ,0.00230]])\n",
+ "Val1 = zeros(13);\n",
+ "# Val1 = [1/(Y-Y_star)]\n",
+ "for i in range(0,13):\n",
+ " Val1[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "# Graphical Integration:\n",
+ "plt.plot(Data[:,0],Val1);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"Y(kg H20 / kg dry air)\");\n",
+ "plt.ylabel(\"1 / (Y-Y_star)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area under The curve between Y = Yb and Y = Y:\n",
+ "Area = [0 ,0.100 ,2.219 ,2.930 ,3.487 ,3.976 ,4.438 ,4.915, 5.432, 6.015, 6.728 ,7.716 ,9.304];\n",
+ "# The total number of transfer unit corresponding to adsorption zone:\n",
+ "NtoG = 9.304;\n",
+ "Val2 = zeros(13);\n",
+ "Val3 = zeros(13);\n",
+ "# Val2 = [(w-wb)/wo]\n",
+ "# Val3 = [Y/Yo]\n",
+ "for i in range(0,13):\n",
+ " Val2[i] = Area[i]/NtoG;\n",
+ " Val3[i] = Data[i,0]/Yo;\n",
+ "\n",
+ "# Eqn. 11.74 can be arranged as follows:\n",
+ "# f = integrate((1-(Y/Yo)),(w-wb)/wa,0,1)\n",
+ "\n",
+ "plt.plot(Val2,Val3);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"(w-wb) / wo\");\n",
+ "plt.ylabel(\"Y / Yo\");\n",
+ "plt.title(\"Break through curve\");\n",
+ "plt.show()\n",
+ "# From area above the curve of scf(2):\n",
+ "f = 0.530;\n",
+ "\n",
+ "Gs = G_prime;# [kg/square m.s]\n",
+ "# From Illustration: 11.6\n",
+ "kYap = 31.6*G_prime**0.55;# [kg H2O/cubic m s delta_Y]\n",
+ "kSap = 0.965;# [kg H2O/cubic m s delta_X]\n",
+ "# From Fig. 11.48:\n",
+ "Xt = 0.0858;# [kg H2O/kg gel]\n",
+ "# From Eqn. 11.76:\n",
+ "Ss = Yo*Gs/Xt;# [kg/square m.s]\n",
+ "m = 0.0185;# [average slope of equilibrium curve]\n",
+ "# From Eqn. 11.51 & Eqn. 11.52:\n",
+ "HtG = Gs/kYap;# [m]\n",
+ "HtS = Ss/kSap;# [m]\n",
+ "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",
+ "# From Eqn. 11.79:\n",
+ "Za = NtoG*HtoG;# [m]\n",
+ "# From Eqn. 11.74:\n",
+ "Degree = (Z-(f*Za))/Z;\n",
+ "Density_bed = 671.2;# [Illustration 11.6, kg/cubic m]\n",
+ "mass_gel = Z*Density_bed;# [kg/square m]\n",
+ "# At saturation point the gel contins:\n",
+ "Y1 = mass_gel*Degree*Xt;# [kg H2O/square m cross section]\n",
+ "# The air introduces:\n",
+ "Y2 = Gs*Yo;# [kg/square m s]\n",
+ "print\"Time to reach breakpoint is: \",round((Y1/(Y2*3600)),4),\" h\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.9 - Page: 636\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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R81jEii0Wn34K48bBkCFJ1yS3khrtVrZ+8Qv4f/8PZs9OuibOuXIwcmS4VfaG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\nDTe3c64xy5aFlTYmTQrJ5vn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+ "text": [
+ "<matplotlib.figure.Figure at 0x7d96080>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F8ZIsDJsCs/O234qea8XMDjezaYR7Q5+WYDxSRu66C84/Hx59FL75zbSjEaku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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x781dda0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Time to reach breakpoint is: 24.7778 h\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.10: Page 640"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.10\n",
+ "# Page: 640\n",
+ "\n",
+ "print'Illustration 11.10 - Page: 640\\n\\n'\n",
+ "\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data******#\n",
+ "# a:N2 b:H2O\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "Ma = 29;# [kg/kmol]\n",
+ "Z = 0.268;# [m]\n",
+ "Xo_solid = 0.01;# [kg H20/kg solid]\n",
+ "Density_bed = 712.8;# [kg/cubic m]\n",
+ "T = 28.3;# [OC]\n",
+ "P = 593;# [kN/square m]\n",
+ "Gs = 4052;# [kg/square m.h]\n",
+ "Xo_gas = 1440*10**(-6);# [mole fraction]\n",
+ "#********************#\n",
+ "\n",
+ "# Yo_star is in equilibrium with Xo:\n",
+ "Xo = 0;# [kg H20/kg solid]\n",
+ "Yo_star = 0;# [kg H20/kg N2]\n",
+ "thetha_t = 12.8;# [h]\n",
+ "thetha_b = 9;# [h]\n",
+ "# The breakthrough data are plotted in the manner of Fig. 11.47 (Pg 639) and thetha_s is dtermined:\n",
+ "thetha_s = 10.9;# [h]\n",
+ "Xt = 0.21;# [kg H20/kg solid]\n",
+ "# From Eqn. 11.81:\n",
+ "LUB = (Z/thetha_s)*(thetha_s-thetha_b);\n",
+ "# For thetha_b = 15 h\n",
+ "thetha_b = 15;# [h]\n",
+ "Yo = (Xo_gas/(1-Xo_gas))*(Mb/Ma);# [kg H20/kg N2]\n",
+ "# From Eq. 11.82:\n",
+ "Zs = Gs*(Yo-Yo_star)*thetha_b/(Density_bed*(Xt-Xo_solid));# [m]\n",
+ "# From Eqn. 11.85:\n",
+ "Z = LUB+Zs;\n",
+ "print\"Height of adsorbent column:\",round(Z,4),\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.10 - Page: 640\n",
+ "\n",
+ "\n",
+ "Height of adsorbent column: 0.0467 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 93
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.11: Page 654"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.11\n",
+ "# Page: 645\n",
+ "\n",
+ "print'Illustration 11.11 - Page: 645\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#****Data****#\n",
+ "# For collection of Cu2+:\n",
+ "V = 37850.0;# [l/h]\n",
+ "c1 = 20.0;# [meq Cu2+/l]\n",
+ "c2 = 0.01*c1;# [meq Cu2+/l]\n",
+ "Mass_rate = 2.0;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",
+ "exchanged = V*(c1-c2);# [meq/h]\n",
+ "X2 = 0.30;# [meq Cu2+/g]\n",
+ "#************#/\n",
+ "\n",
+ "# The point(c2,X2) is plotted in Fig. 11.48(a), Pg 645:\n",
+ "# For the minimum resin/solution ratio and an infinitely tall tower, the operating line pass though point P.\n",
+ "X = 4.9;# [meq Cu2+/g]\n",
+ "MinRate = exchanged/(X-X2);# [g/h]\n",
+ "Rate = 1.2*MinRate;# [g/h]\n",
+ "# Copper balance:\n",
+ "X1 = (exchanged/Rate)+X2;# [meq Cu2+/g resin]\n",
+ "# The point (c1,x1) is ploted in Fig. 11.48(a) and operating line drawn can be straight line at this low conc.\n",
+ "# Adapting Eqn. 11.48 and rearranging:\n",
+ "# S*Z*Density_s = (V/Mass_rate)*integrate(1/(c-c_star),c,c1,c2)\n",
+ "# Mass_rate = KL_prime*ap/Density_s\n",
+ "# From the equilibrium curve:\n",
+ "# Data = [c c_star]\n",
+ "Data = numpy.array([[20 ,2.4],[16 ,1.9],[12, 0.5],[8 ,0.25],[4 ,0.10],[2 ,0.05],[1 ,0.02],[0.2, 0]]);\n",
+ "Val = zeros(8);\n",
+ "for i in range(0,8):\n",
+ " Val[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"c\");\n",
+ "plt.ylabel(\"1 / (c-c*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "# From Graphical Integration:\n",
+ "Area = 5.72;\n",
+ "# holdup = S*Z*Density_s\n",
+ "holdup = V*Area/(Mass_rate);\n",
+ "print\"Resin Holdup: \",holdup,\"g\\n\"\n",
+ "\n",
+ "# Regeneration of resin:\n",
+ "# For 70% utilisation of 2N acid, feed must contain:\n",
+ "V = exchanged;\n",
+ "F = V/(0.70*2000);# [l/h]\n",
+ "c1 = 0;# [meq Cu2+/l]\n",
+ "c2 = V*1.0/F;# [meq Cu2+/l]\n",
+ "X1 = 0.30;# [meq Cu2+/g resin]\n",
+ "X2 = 4.12;# [meq cu2+/g resin]\n",
+ "# The points (c1,X1) and (c2,X2) are plotted on Fig 11.48(b), Pg 645\n",
+ "c1_star = 120.0;# [meq Cu2+/l]\n",
+ "c2_star = 1700.0;# [meq Cu2+/l]\n",
+ "logmean = ((c1_star-c1)-(c2_star-c2))/math.log((c1_star-c1)/(c2_star-c2));\n",
+ "Mass_rate = 0.018;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",
+ "# Substituting in equation:\n",
+ "def f79(holdup):\n",
+ " return (V*(c2-c1))-(Mass_rate*holdup*logmean)\n",
+ "holdup = fsolve(f79,7);\n",
+ "print\"Resin Holdup in the regeneration Tower is \",round(holdup,3),\" g\\n\"\n",
+ "#the answers are in textbook is wrong"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.11 - Page: 645\n",
+ "\n",
+ "\n",
+ "Resin Holdup: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 108251.0 g\n",
+ "\n",
+ "Resin Holdup in the regeneration Tower is 296720391.501 g\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x783b160>"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter11_2.ipynb b/Mass_-_Transfer_Operations/Chapter11_2.ipynb
new file mode 100755
index 00000000..429b975a
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter11_2.ipynb
@@ -0,0 +1,1235 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:b2d866884e32978f30135a4e666314d83655be533eae1227e5f3d0fa8ae8f5d8"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 11: Absorption And Ion Exchange"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.1: Page 575"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.1\n",
+ "# Page: 575\n",
+ "\n",
+ "print'Illustration 11.1 - Page: 575\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#*****Data*****#\n",
+ "Temp = 30.0;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# From Fig. 11.5 (Pg 572)\n",
+ "# The isosteres for various concentrations are straight and their slopes are measured with the help of milimeter rule.\n",
+ "# Data = [X(kg acetone/kg carbon) lambda(slope of isostere)]\n",
+ "Data = numpy.array([[0.05 ,1.170],[0.10, 1.245],[0.15 ,1.3],[0.20 ,1.310],[0.25 ,1.340],[0.30 ,1.327]]);# [kg acetone/kg carbon]\n",
+ "lambdar = 551.0;# [reference at 30 OC,kJ/kg]\n",
+ "Val = numpy.zeros(shape=(6,5));\n",
+ "for i in range(0,6):\n",
+ " Val[i,0] = Data[i,0];# [kg acetone/kg carbon]\n",
+ " Val[i,1] = Data[i,1];# [slope of isostere]\n",
+ " Val[i,2] = -Data[i,1]*lambdar;# [kJ/kg acetone]\n",
+ "\n",
+ "\n",
+ "plt.plot(Val[:,0],Val[:,2])\n",
+ "plt.grid();\n",
+ "xlabel(\"X (kg carbon / kg acetone)\");\n",
+ "ylabel(\"Differential heat of adsorption (kJ / kg acetone)\");\n",
+ "title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area: The area under the curve between X = 0 to X = X\n",
+ "# Corresponding to Data(:,1):\n",
+ "Area = numpy.array([-29.8 ,-63.0, -97.9 ,-134.0, -170.5, -207.5]);\n",
+ "for i in range(0,6):\n",
+ " Val[i,3] = Area[i];\n",
+ " Val[i,4] = Area[i]+(lambdar*Val[i,0]);\n",
+ "print \" (1) = X(kg acetone/kg carbon) \\n (2)= Slope of isostere \\n (3)= Differential heat of adsorption(kJ/kg acetone) \\n (4)=deltaH_prime(vapour(kJ/kg carbon)) \\n (5)=deltaH(liquid(kJ/kg carbon)\"\n",
+ "print\"(1) \\t \\t \\t \\t (2) \\t \\t \\t \\t (3) \\t \\t \\t \\t \\t \\t \\t \\t (4) \\t \\t \\t \\t \\t \\t (5) \" \n",
+ "for i in range(0,6):\n",
+ " print Val[i,0],\" \\t \\t \\t \",Val[i,1],\" \\t \\t \",Val[i,2],\" \\t \\t \\t \\t \\t \",Val[i,3],\" \\t \\t \\t \\t\",Val[i,4]\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.1 - Page: 575\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
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JJbfmzoWddoJ994ULLyx0NM65bMnXXF4/mdk1ZrZv9Li2WJOJW14u2ofXXBOe\nfx7GjIELLsj66XPG28pjXhcxr4vsqnURWEmbAZcAWxLu+AIwM9s4l4G54rXWWiGp9O8fpmc555xC\nR+ScKwbpNHm9DJwHXAMMAoYDjc2sKL9GvMkrf776KiSVgw6Cs84qdDTOuUzkay6vlc3sOULymROt\n2Pj7TAqV9KCkadFjtqRpSe+dIekDSf+RtGvS9u6SZkTvXZdJ+S472raFF16Ae++Fyy4rdDTOuUJL\nJ6H8LKkx8KGk4yX9AWiRSaFmNsTMukYzFv8reiBpS+BPhOa1gcDN0m/3Et0CHG5mmwKbShqYSQzl\nIB/tw+usE5LKnXfCFVfkvLh687bymNdFzOsiu2rtQwFOAlYBTgQuAlYFDslG4VGy+CPQP9q0N/CA\nmS0B5kj6EOgl6WOglZlNifa7B9gHGJ+NOFxm1l03DHysqAjLCJ96aqEjcs4VQq19KDktXOoHXG1m\nPaLXNwCTzez+6PXtwFPAHOAyM9sl2t4XON3M9qrmnN6HUiCffRaSyp//DCefXOhonHN1kY0+lHSu\nUOpF0rNA22reOtPMxkbPhwKjcxWDy6927ULzV//+4UrlxBMLHZFzLp9yllASVxOpSGoC7At0S9r8\nObB+0ut2wGfR9nZVtn+e6tzDhw+nffv2ALRu3ZouXbpQUVEBxG2m5fA6uX04X+V/9FElF18MZ5xR\nQaNGsPXWxVEfiW3F9O9TqNfTp0/npJNOKpp4Cvn6b3/7W1l/P4waNQrgt+/LjJlZtQ/g8ujnH1Pt\nk8mD0Ok+ocq2LYHpQFNgI2AWcbPca0AvwvQv44CBKc5rLpgwYULByp4922zDDc1uuqlgISynkHVR\nbLwuYl4Xsei7M6Pv9Zqmr58JbAO8aTlYP17SXcCrZvaPKtvPJCzq9SswwsyejrZ3B0YRBleOM7Nq\nG1S8D6V4zJ4d+lTOPBOOPrrQ0TjnapLr9VCuBI4EWgI/VXnbzGzVTArOFU8oxWXWrNCncs45cOSR\nhY7GOZdKrtdDOc3MWhOuBlpVeRRlMnHLS+4/KJRNNgkd9RdeCHfcUbg4iqEuioXXRczrIrtq7ZQ3\ns0GS1gZ6RJummNn/chuWa0g6dFj+7q/hwwsdkXMuF9KZy+uPwJXAREKHeF/gNDN7OPfh1Z03eRWv\n998P66lccgkcfHCho3HOJcvXOJSzgR6JqxJJawLPA0WZUFzx6tgRnnsuJJVGjcKkks65hiOdubwE\nzE16/U0MILaSAAAaI0lEQVS0zRW5Ymwf3nzzsJzw6afD6DwOaS3GuigUr4uY10V2pXOFMh54WtJo\nQiL5E2E6FOfqZcstQ1LZeedwpTJkSKEjcs5lQ1pzeUkaDOwQvZxkZo/mNKoMeB9K6ZgxA3bdFa6/\nHvbfv9DROFfe8rKmfKnxhFJa3noLdtsNbroJBg8udDTOla98LbDlSlQptA937gzjx4cZih/N4XVv\nKdRFvnhdxLwusitnk0M6l64uXWDcONh999CnsvfehY7IOVcf6YxDGWFm19W2rVh4k1fpmjoV9tgD\nbr8d9lphpRvnXC7lq8lreDXbDs2kUOeq0707PPEEHHFE+OmcKy0pE4qkoZLGAhtJGpv0qCSMRXFF\nrhTbh3v0gLFj4bDDQjNYtpRiXeSK10XM6yK7aupDeQX4ElgTuIp4MONC4K0cx+XKWM+e8PjjMGgQ\n3HtvuAvMOVf8/LZhV7ReeQX22Qfuuy+MV3HO5U5e+lAkbSfpdUmLJC2RtEzS95kU6lw6tt8exoyB\nAw8Mc4A554pbOp3yNwIHAB8AzYHDgZtzGZTLjobQPtynT0gqBxwQpsCvr4ZQF9nidRHzusiutAY2\nmtkHQGMzW2pmdxHWg3cuL/r2hYcfhj/9Cfz/v3PFK51xKC8CuwC3EzrpvwIOMbPOuQ+v7rwPpeGa\nMCEklUcegX79Ch2Ncw1LvsahHBztdzzwI9AO8FmXXN717w8PPBDm/Jo0qdDROOeqqjWhmNkcwi3D\nbc3sfDM7xcw+zHlkLmMNsX14wICwjsrgwfDyy+kf1xDror68LmJeF9mVzl1eg4BpwNPR666SHs91\nYM6lsssuYXzKvvvCq68WOhrnXEI6fShvAjsBE8ysa7RtppltnYf46sz7UMrH+PFhbfqxY6FXr0JH\n41xpy1cfyhIzW1Bl27JMCnUuGwYOhFGjwkSSU6YUOhrnXDoJ5R1JBwJNJG0q6QbCtCyuyJVD+/Ae\ne8Cdd4ak8sYbqfcrh7pIl9dFzOsiu9JJKCcAWwG/AA8A3wMn5TIo5+pizz3httvg97+HN98sdDTO\nlS+fy8s1GI89BkcfHfpWunYtdDTOlZZs9KHUumKjpI7AX4D2Sfubme2UScHOZds++8DSpWHlx6ef\nDssLO+fyJ50mr4eBN4GzgdOSHq7IlWP78ODBcMMNocP+7bfj7eVYF6l4XcS8LrIrnTXll5jZLTmP\nxLks2X9/WLYsrKPy7LOwdVHe4O5cw5OyD0XSGoQR8icAc4ExhI55AMzs23wEWFfeh+ISHngATj01\nJJWttip0NM4Vt2z0odSUUOYAqb6Zzcw2zqTgXPGE4pLdfz+cdhocf3zoU+ncGdZbD5TRfxvnGp6c\nJpRS5QklVllZSUVFRaHDKLgXXoBbb63k228reOut0BzWqVOcYDp3hi23hGbNCh1pfvjnIuZ1EcvL\nXV7OlbqddoJGjaCiAszgq6/grbfC45ln4Mor4aOPYNNNl08ynTvDWmsVOnrnSodfoTgH/PwzvPNO\nnGgSj+bNV0wyHTtCE/9TzDUw3uRVDU8oLlvM4NNPV0wyn30GW2yxYqJZffVCR+xc/eW6U747qTvl\nMbN6T3Ih6UGgY/SyNbDAzLpK2gW4FGgKLAZOM7MJSfGMIqxrP87MRqQ4tyeUiLcPx7JZF4sWwcyZ\nyyeZGTOgdesVk8wmm0DjxlkpNmv8cxHzuojlug/lampIKED/+hZqZkMSzyVdBSRmM54L7GlmX0na\nirAGS7vovVuAw81siqRxkgaa2fj6xuBcfbVsCb17h0fCsmUwe3acYO6/H04/HebODeNgkpNMp07Q\nqlXh4ncuVwra5CVJwMdAfzObVc1784C2QBvgBTPbInpvCFBhZsdUc06/QnFF47vvwoj95KuZd96B\ntm1XvJpp395vZ3aFk7e7vCRtA2xBaG4CwMzuyaTgSF/g66rJJDIYmGpmSyStB3yW9N7nwHpZKN+5\nnFptNejbNzwSli6FDz6IE8xtt4WfCxeueDvz1lvDKqsULn7n6iKdySHPB3YkTGH/JLA78BJQY0KR\n9Czh6qKqM81sbPR8KDC6mmO3Ai4DdqktvuoMHz6c9u3bA9C6dWu6dOnyWztpYu6ecnidPE9RMcRT\nyNeJbcUST0VFBZtvDmuvXcmuu4bX8+bBPfdUMmsWvPxyBTfdBO++W0nbtrDddhV07gxSJR06wH77\nVSDVr/zp06dz0kknFfz3L4bXf/vb38r6+2HUqFEAv31fZiqdJYBnAp2BN82ss6S1gfvNbOeMCpaa\nEK46upnZF0nb2wHPA8PN7NVo2zos3+Q1FNjRm7xqVukdjr8p1bpYvBj+858V7zRbunTFJrN0B2eW\nal3kgtdFLC+3DUt63cx6SJpKWFv+e+A/ZtaxxgNrK1gaCIw0s/5J21oDE4HzzOyxKvu/BpwITCFc\nKV1fXae8JxTX0FUdnJl4fPQRdOiwYqJZe+1CR+xKQb4Sys3AWcCfgFOBH4BpZnZoRgVLdwGvmtk/\nkradDfwf8EHSrruY2byk24ZXJtw2fGKK83pCcWUp1eDMZs3i5NKlC+y9N7RoUehoXbHJ+8BGSRsB\nq5rZW5kUmkueUGJ+OR8r17qoOjhz8mSYPLmSSy+t4NBDfcR/uX4uqpPTu7wkbWFm70nqVs173TIZ\n2Oicyw8JNtggPPbaK2z7+9/DOJnrroMrrggrXPrtyi4bahopf5uZHSmpkmoGOCb3fRQTv0JxrnZm\n8MQTYfDluuuGCTK7rfCnoysn+epDaW5mP9e2rVh4QnEufb/+CnfcARdcAAMGwF//ChtuWOioXCFk\nI6Gks6b8K2luc0UmeQxGufO6iCXXRZMmcPTR8P77sPHG4Spl5EhYsCD18Q2Jfy6yK2VCkbROdGfV\nKpK6Seoe/awAfOyucw1Iq1bhKmXGDPj22zBF/3XXhXEwzqWrpj6UQ4DhwLbAG0lvLQRGmdmYnEdX\nD97k5VzmZs4M/Sv//S9ceinst5933Dd0+epD2c/MHsmkkHzyhOJc9jz/PPzlL2Ghsauugh12KHRE\nLlfy1YfyhKQDJZ0l6VxJ50k6N5NCXX54+3DM6yJWl7oYMACmToXjjoMDDoA//CFctTQU/rnIrnQS\nyr+BQcASwij5RdFP51wZaNQIhg0Lc4r16hWuUo4/Pqz14lyytCaHNLOt8xRPxrzJy7ncmjcPLroo\nDI485RQ46SSfYr8hyNttw5I6ZVKIc67haNMm3AE2eTJMmxbuCBs1KsyA7MpbOgmlLzBV0n8lzYge\nb+c6MJc5bx+OeV3EslUXHTrAww/DQw+FRcK6d4dnn83KqfPGPxfZlc7UcLvnPArnXMnabjt46SUY\nMyZ03m+ySZgjrJO3a5SdtGYbltQX6GBmd0laE2hpZrNzHl09eB+Kc4WzZAncemuYwmWPPUJfy3q+\nWHdJyEsfSrQE8OnAGdGmpsB9mRTqnGuYVlop3AH2/vthYa9OneDss+H77wsdmcuHdPpQ9gX2JrpV\n2Mw+B1rlMiiXHd4+HPO6iOWjLlZbLYywnz49rMfSsSPcfHO4gikm/rnIrnQSyi9mtizxQpKv9eac\nS8v668Pdd8NTT8Gjj8LWW8Njj4Xp813Dk844lNOADsCuwKXAYcBoM7s+9+HVnfehOFeczODpp+G0\n06B16zCVS69ehY7KJeR8Li9JAtYHNickFICnzaxobw70hOJccVu6NFy1nHtuGHV/6aVh6nxXWPka\n2DjOzJ4xs79Ej6JNJm553j4c87qIFbouGjeGww4LHffbbAM9e8LJJ8M33+Q/lkLXRaGZwaxZYWBq\nNtSYUKI/9adK6pmd4pxzLmjRItwB9s478MsvsPnmYSnin4tyLdiGYelSeOstuPFG+NOfwi3d/frB\n+PHZOX86fSjvE/pQPiaeFNLMrCiHLXmTl3Ol6T//gf/7v3Bn2MUXw9ChYWJKV3+LF8Mbb8CLL8Kk\nSfDKK7DWWtC3b/zYaKOw1k2+1kNpX912M5uTScG54gnFudL24othDZZly8IVS//+hY6odCxcCK++\nGpLHpEkhmWy2WbgK6dsX+vQJ44Oqk5c+lChxrA/0j57/APjabSWg3NuHk3ldxIq9Lvr1CxNP/uUv\ncPjhsNde8O67uSmr2OuiNnPnhtuxTz4Ztt0W1lknzFKwbFm42vviC3jzTfjb32Dw4NTJJFtqncsr\nGinfHegI3EU8Ut7XbnPO5USjRjBkCOy7L9x0E1RUhOfnnx++NMuRGXz8cXz1MWkSfPllmEutX7+Q\nNLbdNqyuWSjpNHm9BXQFpppZ12jb296H4pzLl/nzQ7/KXXfBiSfCqadCy5aFjiq3li2D996L+z8m\nTQozDST3f3TqFO6ay4Z89aFMMbOekqaZWddopPyrnlCcc/k2ezacdRZMnBiuVg49FJqkM2d6CViy\nJDRPJZLHSy+FAaB9+8Z9IB06hA70XMjXOJSHJd0KtJZ0FPA8cHsmhbr8KPX24WzyuoiVcl1stBGM\nHh2mb7n/fujcGZ58sv5TuRSyLn78EV54AS64AAYMgDXWgKOOgjlz4IADYMaMeIzIYYfBppvmLplk\nS8rcLqm5mf1sZldK2hVYCGwGnOODG51zhdSjB0yYAE88ETrvr746TOXSrVuhI0vt22/DVUfiCmTG\njJAQ+/YNSylvvz2svnqho8xMyiYvSW+aWTdJ95rZsDzHVW/e5OVcefn1V7jjjtAENmBA6GvZcMNC\nRxVmWU7uQP/kE+jdO+7/6NkTVlml0FHGctqHIukd4BLgIuAvhFuFLfHTzMZkUnCueEJxrjwtXBiu\nUm68MdxufOaZoQ8iH8zCVDLJCWTRojDuI9EH0qVLcff35LoP5RjCevKrAXsBe1b56YpcKbeVZ5vX\nRayh1kWrVqE/YsaMcFfYZpuFW2kXL059TH3r4tdfw6DBa6+FP/whjO8YODDckdWnD4wbB//7Xxgj\ncsop4XbeYk4m2VLTr9jWzI6Jmr7+kbeInHMuA+uuC7fdBiNGwOmnww03hBmN99+//p3aP/0EU6bE\nVx+TJ0O7duHqY/DgkLg22CC7v0cpqqnJK3Gb8LTE+JNS4E1ezrlkzz8fOu6bNQtNYn361H7MggVh\n3qvEGJDp08PiYIn+jx12gDZtch97PuW6D+U5Qp9JD2BSlbfNzAZlUnCueEJxzlW1bFm4zfiss6B7\nd7j88tAklvDll8v3f8yaFe4kSySQ3r0b/kDKXCeUZoQR8vcBh7P8/F1mZhPrXaj0IGEqF4DWwILk\nqyBJGwDvAueZ2dXRtu7AKKA5YY2WESnO7QklUllZSUVFRaHDKApeF7FyrouffoLrrw+TTu67L3z6\naSUffljBt9+Gq47EAMJu3aBp00JHm1857ZQ3s1/MbDKwnZlNNLPKpEe9k0l07iFm1jVKIv+KHsmu\nAZ6ssu0W4HAz2xTYVNLATGIoB9OnTy90CEXD6yJWznWx8sowcmSYKr9dO2jZcjqPPgrz5sHYsWF5\n4t69yy+ZZEtNAxuvi64C7tSKPVlZafKKlhj+I9A/ads+wEfEa68gaR2glZlNiTbdA+wDZGlZmIZp\nwYIFhQ6haHhdxLwuQv/HeefB+ecvYJttCh1Nw1HTXV73RD+vrua9bLUp9QW+NrNZAJJaAqcDOwOn\nJe23HvBZ0uvPo23OOeeKRMqEYmZTo5+VktaMns9N98SSngXaVvPWmWY2Nno+FBid9N75wLVm9qOq\nuSxydTNnzpxCh1A0vC5iXhcxr4vsqqlTXsB5wPFAYoLkpcANZnZBxgVLTQhXHd3M7Ito24uExbwg\ndNYvA84BxgATzGyLaL+hwI5mdkw15/Ueeeecq4dMO+VravI6mbCIVg8zmw0gaWPg75JOMbNrMimY\n0Kz1XiKZAJhZv8RzSecBC83s5uj195J6AVOAYcD11Z000wpxzjlXPzVNvXIwcEAimQCY2UfAgdF7\nmfoT8EAd9j+OMG3+B8CHZuYd8s45V0RqavKaaWZb1/U955xz5ammK5Ql9XwvJyQNlPQfSR9IGpli\nn+uj99+SlDxQco6ktyVNkzSlumNLSW11IWlzSa9K+lnSqXU5ttRkWBfl9rk4MPq/8baklyV1SvfY\nUpNhXZTb52LvqC6mSZoqaad0j12BmVX7IHTAL0zx+DXVcbl4EG4K+BBoD6wETAe2qLLPHoQR9AC9\ngMlJ780G1shnzAWuizWBbYG/AqfW5dhSemRSF2X6udgOWC16PjDxf6RMPxfV1kWZfi5aJD3fhtCl\nUK/PRU0j5RubWasUj3xPxNyT8EvOMbMlwIPA3lX2GQTcHcX+GmHJ4rWT3m8onfW11oWZzTWzN1jx\nSjKdeiwlmdRFQjl9Ll41s++il68B7dI9tsRkUhcJ5fS5+CHpZUtgXrrHVpXOmvLFYD3g06TXn7Hi\nwMaa9jHgOUlvSDoyZ1HmRzp1kYtji1Gmv085fy4OB8bV89hil0ldQBl+LiTtI+k94CngxLocm6xU\nlnxJd2xJqr8q+pjZF9EAzWcl/cfMqs6gXCoyGWfT0MboZPr77GBmX5bb50JSf+AwwrCAOh1bIjKp\nCyjDz4WZPQY8JqkvcK+kzetTWKlcoXxOPOCR6PlntezTLtqGRWNdLIz0f5RwKVeq0qmLXBxbjDL6\nfczsy+hn2Xwuos7n24BBZja/LseWkEzqoiw/FwlR4mwCrBHtV6fPRakklDcIMwy3l9SUMIbl8Sr7\nPE40PkZSb8KU+F9LWkVSq2h7C2BXYEb+Qs+6dOoioeoVW12OLQX1roty/FwoLAsxBjjIzD6sy7El\npt51Uaafi02kMNWVpG4AZvZNOseuoNB3IdThboXdgfcJdx2cEW07Gjg6aZ8bo/ffIkzpArAx4e6E\n6cDMxLGl/KitLghzqH0KfAfMBz4BWqY6tpQf9a2LMv1c3A58A0yLHlNqOraUH/WtizL9XJwe/a7T\nCIsp9qjv5yLlwEbnnHOuLkqlycs551yR84TinHMuKzyhOOecywpPKM4557LCE4pzzrms8ITinHMu\nKzyhuJyTtL6kjyStHr1ePXq9QTX7NpM0UVIjSRWSxuY/Yshl2ZJWkjS1mu2LclFeXUg6M0fnPVHS\nsFyc2xUPTygu58zsU+AW4LJo02XArWb2STW7Hwg8YWbL8hVfVZJyPcddH+ClarYXw6CwM3J03ruA\nE3J0blckPKG4fLkW6C3pJGB74KoU+w0F/l11o6Qekt6UtJGkNSU9K2mmpNuiBZHWqOaYgdGCQdMl\nPRtt6ynplehcL0vaLNo+XNLjkp4HniN8ua8m6YlogaFbkqanGBotwDRD0mVJ5S2S9NeovFclrZXi\ndxxImNW1WpLaRDHuruBmSe9JekbSk5IGV3PMkZKmRGU/ImnlaPvakh6Ntk+PpiVC0kGSXlNYVOnv\n0RXhZcDK0bZ7o/1OiX7PGZJGRNvaR/H8I/o3eFpS8+i9TSQ9pTBT74uSOgKY2ULgG0lbpfq9XQNQ\n6GkB/FE+D2A3YBkwIMX7jYEvk15XAGMJCegNoF20/UZgZJVzrlHlXGsSplnZMHrdOvrZCmgcPd8Z\neCR6PpwwRUvrpLJ/Iiwu1Ah4BhgMrAt8DPwuivd5YO/omGXA76PnlwNnpfg9XwOaV7N9IbAWMDlR\nR8B+wJPR87WBb4E/VHPsGknPLwKOj57/Ezgxei5gVWALwpxMiXq4GRiWiCHpPN2Bt4GVgRaE6Tm6\nRHWyBOiUVMaB0fPngQ7R817A80nnuwA4ttCfQ3/k7lEq09e7hmF34AvCqnDPV/N+G8KXarItgFuB\nXczsq2jbDsA+AGb2tKT5rKg3MNHMPo72WxBtbw3cI6kD4Sok+f/AM0n7QZjfaQ6ApAcITVVLgEoL\nk+ch6X6gH+GqarGZPRkdOxXYpWpQktYDvjWzn6uJuSmhXo6zeLr0HYCHot/ha0kTqjkOYBtJfwVW\nI8xVNj7a3h84KDregO8lHUxIFm9EF10rA1+tcMbw+44xs5+i2McAfQnJaLaZvZ30u7aPJlPcHng4\nOm/id0r4gjBXlmugPKG4vJDUhXBFsB3wkqQHkxLEcrsmPTfgS6AZ0I3lF0GqbUU9S7HPRYS/mveV\ntCFQmfTej9WcI7m86vo4krcnrwq5jOr/fw0k/rKvagnhSmwgYZK+5DJqM4owDfsMSYcAO9Zy/N1m\nVlsHfNU6TP5df0navhRoTriSm29mXVOcL1UdugbC+1BczkV9D7cAIyx00F9J9X0o8wh/Xf92KLAA\n2BO4VFLiS/Jl4I/RuXcFVq/mXK8B/SS1j/ZL7LMq4S9lgENrCb1n1F/QKCpvEjAF2FHS7yQ1BoYA\nE2s5T7LdSN1/YoTFnjaXdHq07WVgcNSXsjahKa46LYGvJK1EdEUSeR44FkBSY0mrRtv2U1hACklr\nKL7jbknSTQmTgH0krRxdfewTbasuQclCP8lsSftF55Wkzkn7rAPMSRG/awA8obh8OBKYY2aJZq6b\ngS0UVof7jZktBWYmOnIJX7BmZv8jJJWbJPUgtMXvKmkGoY/hK6o0lVlYHOkoYIyk6YT1sAGuICSn\nNwl9IIm/mI3l/3o24HVCf827wEdm9mh0VfV/wATCFOdvmNnYpGNIcT6iBNTBzP6bop4sapYaCuwk\n6RjgX4RFjd4F7gXeJEzFX9U5hCT6EvBe0vYRQH9JbxOufrYws/eAs4FnJL1F6B9qG+3/D+BtSfea\n2TTClc8UQr/ObWb2VjW/a/LrA4HDozqfCeyVtE9Plr/ycg2MT1/vioqk4cDaZnZ5Dfs0BZaa2VJJ\n2wE3mVm3fMVYX5J2IHReH1fH41qY2Q+SfkdIGttHSbZkJK6MzKxHoWNxueMJxRWVKFk8B+xoKT6c\nUYf6Q4Qr7MWEO4dWGCjYUEQd8a0JHdyXm9k9BQ6pziSdSLgZ4b5Cx+JyxxOKc865rPA+FOecc1nh\nCcU551xWeEJxzjmXFZ5QnHPOZYUnFOecc1nhCcU551xW/D+xKBVCe8dDPQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x76f12b0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (1) = X(kg acetone/kg carbon) \n",
+ " (2)= Slope of isostere \n",
+ " (3)= Differential heat of adsorption(kJ/kg acetone) \n",
+ " (4)=deltaH_prime(vapour(kJ/kg carbon)) \n",
+ " (5)=deltaH(liquid(kJ/kg carbon)\n",
+ "(1) \t \t \t \t (2) \t \t \t \t (3) \t \t \t \t \t \t \t \t (4) \t \t \t \t \t \t (5) \n",
+ "0.05 \t \t \t 1.17 \t \t -644.67 \t \t \t \t \t -29.8 \t \t \t \t-2.25\n",
+ "0.1 \t \t \t 1.245 \t \t -685.995 \t \t \t \t \t -63.0 \t \t \t \t-7.9\n",
+ "0.15 \t \t \t 1.3 \t \t -716.3 \t \t \t \t \t -97.9 \t \t \t \t-15.25\n",
+ "0.2 \t \t \t 1.31 \t \t -721.81 \t \t \t \t \t -134.0 \t \t \t \t-23.8\n",
+ "0.25 \t \t \t 1.34 \t \t -738.34 \t \t \t \t \t -170.5 \t \t \t \t-32.75\n",
+ "0.3 \t \t \t 1.327 \t \t -731.177 \t \t \t \t \t -207.5 \t \t \t \t-42.2\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.2: Page 596"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.2\n",
+ "# Page: 596\n",
+ "\n",
+ "print'Illustration 11.2 - Page: 596\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#*****Data*****#\n",
+ "# x:kg carbon/kg soln\n",
+ "# y_star: Equilibrium colour, units/kg soln.\n",
+ "# X:adsorbate concentration, units/kg carbon\n",
+ "# Data = [x Y_star]\n",
+ "Data =numpy.array([[0, 9.6],[0.001, 8.6],[0.004 ,6.3],[0.008, 4.3],[0.02 ,1.7],[0.04, 0.7]]);\n",
+ "Yo = 9.6;# [units of colour/kg soln]\n",
+ "Y1 = 0.1*Yo;# [units of colour/kg soln]\n",
+ "Ls = 1000.0;# [kg soln]\n",
+ "#****************#\n",
+ "\n",
+ "\n",
+ "n = 1.66;# [slope of line]\n",
+ "# At X = 663, Y_star = 4.3\n",
+ "# From eqn. 11.5\n",
+ "X = 663;\n",
+ "Y_star = 4.3;\n",
+ "m = Y_star/X**n;\n",
+ "# Freundlich Equation:\n",
+ "def f76(X):\n",
+ " return m*X**n\n",
+ "X = numpy.arange(0,1000,1);\n",
+ "\n",
+ "plt.plot(X,f76(X));\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "title(\"Equilibium Data(on arithmetic scale)\");\n",
+ "plt.show()\n",
+ "# Single Stage Operation:\n",
+ "# Since fresh carbn is used:\n",
+ "Xo = 0;# [units/kg carbon]\n",
+ "# From scf(30):\n",
+ "X1 = 270;# [units/kg carbon]\n",
+ "Data2 =numpy.array([[Xo, Yo],[X1, Y1]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data2[:,0],Data2[:,1],label=\"Operating line curve\")\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Single stage operation\");\n",
+ "plt.show()\n",
+ "# From Eqn. 11.4:\n",
+ "Ss = Ls*((Yo-Y1)/(X1-Xo));# [kg carbon/kg soln]\n",
+ "print\"Quantity of fresh carbon recquired for single stage operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",
+ "\n",
+ "# Two stage cross current operation:\n",
+ "# For the minimumamount of carbon:\n",
+ "X1 = 565;# [units/kg carbon]\n",
+ "Y1 = 3.30;# [units of colour/kg soln]\n",
+ "X2 = 270;# [units/kg carbon]\n",
+ "Y2 = 0.96;# [units of colour/kg soln]\n",
+ "Data3 = numpy.array([[Xo ,Yo],[X1 ,Y1]]);\n",
+ "Data4 = numpy.array([[0 ,Y1],[X2 ,Y2]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data3[:,0],Data3[:,1],label=\"First of two Cocurrent\")\n",
+ "plt.plot(Data4[:,0],Data4[:,1],label=\"Second of two Cocurrent\")\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Two stage Cross current operation\");\n",
+ "plt.show()\n",
+ "# From Eqn. 11.8:\n",
+ "Ss1 = Ls*(Yo-Y1)/(X1-Xo);# [kg]\n",
+ "Ss2 = Ls*(Y1-Y2)/(X2-Xo);# [kg]\n",
+ "Ss = Ss1+Ss2;# [kg]\n",
+ "print\"Quantity of fresh carbon recquired for two stage crosscurrent operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",
+ "\n",
+ "# Two Stage counter current operation:\n",
+ "Yo = 9.6;\n",
+ "Y2 = 0.96;\n",
+ "# By trial and error:\n",
+ "XNpPlus1 = 0;\n",
+ "X1 = 675;\n",
+ "Data5 = numpy.array([[X1 ,Yo],[XNpPlus1 ,Y2]]);\n",
+ "\n",
+ "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",
+ "plt.plot(Data5[:,0],Data5[:,1],label=\"Two stage Counter Current\");\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"units of colour/kg carbon\");\n",
+ "plt.ylabel(\"units of colour/kg solution\");\n",
+ "plt.legend(loc='upper left');\n",
+ "plt.title(\"Two stage Counter Current operation\");\n",
+ "# By eqn 11.14:\n",
+ "Ss = Ls*(Yo-Y2)/(X1-XNpPlus1);\n",
+ "print\"Quantity of fresh carbon recquired for two stage Counter Current operation: \",Ss,\" kg carbon/1000 kg solution\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.2 - Page: 596\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x76f16d8>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Tviud53s/H9NyjTHx5+ef3bzMxx/v+hQSZSC8qPcxiEgfcg6el4OqRu0GAD8S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\nTA6WGIwxxuRgicEYY0wOlhiMMcbk8P+dWPmzYQm/hQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7930c18>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Quantity of fresh carbon recquired for single stage operation: 32.0 kg carbon/1000 kg solution\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Ly0t2796d4mdIq+TojBkz4suoVqpUSfbv3y8iiavsiehKe8OHD0+k53HjxknR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\nIshDeZSyCAYj7J780X29czf7hLt7L3vPVzU1Mz0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+ "text": [
+ "<matplotlib.figure.Figure at 0x7938c88>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Quantity of fresh carbon recquired for two stage crosscurrent operation: 19.8171091445 kg carbon/1000 kg solution\n",
+ "\n",
+ "Quantity of fresh carbon recquired for two stage Counter Current operation: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 12.8 kg carbon/1000 kg solution\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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AASpVquRnpYoSGiS9USc9PENtPWlboOjxxvpPP8GoUZHMmgXnzsUQE5P6/jEx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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7d9ad68>"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.3: Page 602"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.3\n",
+ "# Page: 602\n",
+ "\n",
+ "print'Illustration 11.3 - Page: 602\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#***Data***#\n",
+ "T = 1.0; #[m]\n",
+ "di = 0.203;# [m]\n",
+ "n = 1;# [for one impeller]\n",
+ "Density_S = 2300.0;# [kg/cubic m]\n",
+ "Density_p = 2300.0;# [kg/cubic m]\n",
+ "C = 0.150;# [m]\n",
+ "S = 50.0;# [kg]\n",
+ "g = 9.807;# [m/s]\n",
+ "dp = 8*10**(-4);# [m]\n",
+ "N = 8.33; #[r/s]\n",
+ "Temp=25;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# Assume:\n",
+ "Po = 5;\n",
+ "viscosity_L = 8.94*10**(-4);# [kg/m.s]\n",
+ "Density_L = 998.0;# [kg/cubic m]\n",
+ "delta_Density = Density_S-Density_L;# [kg/cubic m]\n",
+ "# By Eqn. 11.23:\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_L);# [m/s]\n",
+ "# By defn. of power number:\n",
+ "# P = Po*Density_m*di**5*Ni**3\n",
+ "# vm = math.pi*T**2*(Z+C)/4\n",
+ "# Solid Volume = S/Density_p;\n",
+ "# If these are substituted in Eqn. 11.22\n",
+ "def f(Z):\n",
+ " return (((Z+C)**(1.3/3))*math.exp(4.35*Z/(T-0.1)))-((1.0839*Po*di**(11.0/2)*N**3*Density_p**(2.0/3))/(g*Vts*T**(7.0/6)*S**(2.0/3)))\n",
+ "Z = fsolve(f,7);# [m]\n",
+ "phi_Sm = 4*S/(math.pi*T**2*(Z+C)*Density_p);\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_L);# [kg/cubic m]\n",
+ "phi_Ss = 0.6;\n",
+ "viscosity_m = viscosity_L/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",
+ "Re = di**2*N*Density_m/viscosity_m;\n",
+ "P = Po*Density_m*N**3*di**5;# [W]\n",
+ "print \"Agitator Power required: \",round(P),\" W\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.3 - Page: 602\n",
+ "\n",
+ "\n",
+ "Agitator Power required: 1113.0 W\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 65
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.4: Page 604"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.4\n",
+ "# Page: 604\n",
+ "\n",
+ "print'Illustration 11.4 - Page: 604\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "#****Data*****#\n",
+ "# b: kerosene c:water\n",
+ "# c:kg water/cubic m liquid\n",
+ "Density_l = 783;# [kg/cubic m]\n",
+ "viscosity_l = 1.7*10**(-3);# [kg/m.s]\n",
+ "Mb = 200;# [kg/kmol]\n",
+ "Density_p = 881;# [kg/cubic m]\n",
+ "m = 0.522;# [(kg water/cubic m kerosene)/(kg water/kg gel)]\n",
+ "Xo = 0;# [kg H2O/kg gel]\n",
+ "#**************#\n",
+ "\n",
+ "# Solution (a)\n",
+ "co = Density_l*4*10**(-5);# [kg water/cubic m]\n",
+ "c1 = Density_l*5*10**(-6);# [kg water/cubic m]\n",
+ "# For Ss minimum:\n",
+ "X1 = c1/m;# [kg H2O/kg gel]\n",
+ "# By Water Balance:\n",
+ "SsminByVl = (co-c1)/(X1-Xo);# [kg gel/cubic m kerosene]\n",
+ "print\"Minimum Solid/Liquid ratio used:\",SsminByVl,\" kg gel/cubic m kerosene\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "# Basis: 1 batch,1.7 cubic m kerosene\n",
+ "Vl = 1.7;# [cubic m]\n",
+ "Ss = 16*1.7;# [kg gel]\n",
+ "V = Ss/Density_p;# [Xol. solid, cubic m]\n",
+ "Vt = 1.7+V;# [Total batch volume, cubic m]\n",
+ "# Take Z = T\n",
+ "T = (Vt*4/math.pi)**(1.0/3);# [m]\n",
+ "# To allow for the adequate free board:\n",
+ "h = 1.75;# [Vessel height,m]\n",
+ "# Use a six-blade disk impeller.\n",
+ "# From Fig. 11.26:\n",
+ "# dp corresponding to 14 mesh:\n",
+ "dp = 1.4/1000;# [m]\n",
+ "TBydi1 = 2.0;\n",
+ "Value1 = (Density_p-Density_l)/Density_l;\n",
+ "# From Fig. 11.26:\n",
+ "TBydi2 = 4.4;\n",
+ "TBydiAv = (TBydi1+TBydi2)/2.0;\n",
+ "di = T/TBydiAv;# [m]\n",
+ "fr = 0.6;# [settled volume fraction of solids]\n",
+ "Vs = V/fr;# [cubic m]\n",
+ "depth = Vs/((math.pi*(T**2))/4);# [m]\n",
+ "# The depth of settled solid is negligible.\n",
+ "# Locate the turbine 150mm from the bottom of the tank.\n",
+ "C = 0.150;# [m]\n",
+ "\n",
+ "# Power:\n",
+ "# Use the sufficient agitator power to lift the solids to 0.6 m above the bottom of the vessel.\n",
+ "Z_prime = 0.6-C;# [m]\n",
+ "# The properties of the slurry in 0.6 m above the bottom of the vessel.\n",
+ "Vm = 0.6*math.pi*T**2.0/4;# [square m]\n",
+ "phi_Sm = V/Vm;# [vol fraction solid]\n",
+ "# From Eqn. 11.24:\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",
+ "# From Eqn. 11.25:\n",
+ "phi_Ss = 0.8;\n",
+ "viscosity_m = viscosity_l/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "# From Eqn. 11.23:\n",
+ "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_l);# [m/s]\n",
+ "# From Eqn. 11.22:\n",
+ "n = 1.0;\n",
+ "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*(TBydiAv**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",
+ "# Assume:\n",
+ "Po = 5.0;\n",
+ "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",
+ "# Use:\n",
+ "N1 = 2.0;# [r/s]\n",
+ "Re = di**2.0*N1*Density_m/viscosity_m;\n",
+ "# From fig. 6.5: Po = 5\n",
+ "# Hence our assumption was right.\n",
+ "print\"Power delivered to the slurry: \",round((P*(N1/N)**3),2),\" W\\n\",\n",
+ "print\"Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\\n\"\n",
+ "\n",
+ "# Mass transfer: \n",
+ "# From Eqn. 11.28:\n",
+ "Rep = (dp**(4.0/3))*(P/Vl)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",
+ "# From Eqn. 2.44:\n",
+ "Temp = 298;# [K]\n",
+ "phi = 1.0;\n",
+ "Va = 0.0756;# [Chapter 2 notation]\n",
+ "Dl = ((117.3*10**(-18))*((phi*Mb)**0.5)*Temp)/(viscosity_l*(Va**(0.6)));\n",
+ "ScL = viscosity_l/(Density_l*Dl);\n",
+ "if dp<(2.0/1000):\n",
+ " # From Eqn. 11.29:\n",
+ " ShL = 2+(0.47*Rep**0.62*(1/TBydiAv**0.17)*ScL**0.36);\n",
+ "else:\n",
+ " # From Eqn. 11.30:\n",
+ " ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",
+ "\n",
+ "kL = ShL*Dl/dp;# [m/s]\n",
+ "apS = (math.pi*dp**2)/(math.pi*dp**3*Density_p/6.0);\n",
+ "apL = apS*16;# [square m/cubic m liquid]\n",
+ "Ratio = Ss/(Vl*m);\n",
+ "# From Eqn. 11.40:\n",
+ "thetha = math.log((co/c1)/(1+(1/Ratio)-(1/Ratio)*(co/c1)))/((1+(1/Ratio))*kL*apL);\n",
+ "print\"Contacting Time required: \",round(thetha/60,2),\" min\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.4 - Page: 604\n",
+ "\n",
+ "\n",
+ "Minimum Solid/Liquid ratio used: 3.654 kg gel/cubic m kerosene\n",
+ "\n",
+ "\n",
+ "Power delivered to the slurry: 350.05 W\n",
+ "Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\n",
+ "\n",
+ "Contacting Time required: 8.3 min\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 69
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.5: Page 606"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.5\n",
+ "# Page: 606\n",
+ "\n",
+ "print'Illustration 11.5 - Page: 606\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "import numpy.linalg as lin\n",
+ "#*****Data******#\n",
+ "Vl = 1.1*10**(-4);# [cubic m/s]\n",
+ "Ss = 0.0012;# [kg/s]\n",
+ "Density_p = 1120;# [kg/cubic m]\n",
+ "dp = 8*10**(-4);# [m]\n",
+ "Ds = 2*10**(-11);# [square m/s]\n",
+ "Dl = 7.3*10**(-10);# [square m/s]\n",
+ "m = 0.2;# [(kg Cu2+/cubic m soln)/(kg Cu2+/kg resin)]\n",
+ "T = 1;# [m]\n",
+ "#********************#\n",
+ "\n",
+ "Z = T;# [m]\n",
+ "# The particles will be lifted to the top of the vessel.\n",
+ "Z_prime = 0.5;# [m]\n",
+ "viscosity_l = 8.94*10**(-4);# [kg/m.s]\n",
+ "Density_l = 998;# [kg/cubic m]\n",
+ "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",
+ "g = 9.80;# [m/square s]\n",
+ "# From Eqn. 11.23:\n",
+ "Vts = g*dp**2*delta_Density/(18*viscosity_l);\n",
+ "Vm = math.pi*T**2*Z/4.0;# [cubic m]\n",
+ "Vs = Ss/Density_p;# [cubic m/s]\n",
+ "phi_Sm = Vs/(Vs+Vl);# [vol fraction]\n",
+ "# From eqn. 11.24:\n",
+ "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",
+ "# From Eqn. 11.22:\n",
+ "n = 1.0;\n",
+ "di = 0.3;# [m]\n",
+ "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*((T/di)**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",
+ "# To estimate the impeller speed:\n",
+ "# Assume:\n",
+ "Po = 5;\n",
+ "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",
+ "Re = di**2*N*Density_m/viscosity_l;\n",
+ "# From fig. 6.5: Assumption of Po was correct.\n",
+ "print\"Speed of the impeller:\",round(N,2),\" r/s\\n\"\n",
+ "vT = (math.pi/4.0)*T**2*Z;# [cubic m]\n",
+ "vL = vT*(1-phi_Sm);\n",
+ "# From Eqn. 11.28:\n",
+ "Rep = (dp**(4.0/3))*(P/vL)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",
+ "ScL = viscosity_l/(Density_l*Dl);\n",
+ "if dp<(2.0/1000):\n",
+ " # From Eqn. 11.29:\n",
+ " ShL = 2+(0.47*Rep**0.62*((di/T)**0.17)*ScL**0.36);\n",
+ "else:\n",
+ " # From Eqn. 11.30:\n",
+ " ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",
+ "\n",
+ "ShL = 130.3;# Value wrong in book\n",
+ "kL = ShL*Dl/dp;# [m/s]\n",
+ "# Since the dispersion is uniform throughout the vessel, the residence time for both liquid and solid is same.\n",
+ "thetha = vL*(1-phi_Sm)/Vl;# [s]\n",
+ "# From Fig. 11.27:\n",
+ "abcissa = m*kL*dp/(2*Ds*Density_p);\n",
+ "Parameter = 2*m*kL*thetha/(dp*Density_p);\n",
+ "co = 100*Density_l/10.0**6;# [kg/cubic m]\n",
+ "EMS = 0.63;\n",
+ "Xo = 0;\n",
+ "# From Eqn. 11.44:\n",
+ "# (1): X1-(EMS/m)*c1 = 0\n",
+ "# Solute balance:\n",
+ "# (2): (Ss*X1)+(vL*c1) = (vL*co)+(Xo*Ss)\n",
+ "a = [[1 ,-(EMS/m)],[Ss ,Vl]];\n",
+ "b = [0,(Vl*co)+(Xo*Ss)];\n",
+ "soln =lin.solve(a,b);\n",
+ "X1 = soln[0];\n",
+ "c1 = soln[1];\n",
+ "print\"Effluent Cu2+ conc. \",round(c1*10**(6)/Density_l,2),\" ppm\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.5 - Page: 606\n",
+ "\n",
+ "\n",
+ "Speed of the impeller: 2.71 r/s\n",
+ "\n",
+ "Effluent Cu2+ conc. 2.83 ppm\n"
+ ]
+ }
+ ],
+ "prompt_number": 78
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.6: Page 616"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.6\n",
+ "# Page: 616\n",
+ "\n",
+ "print'Illustration 11.6 - Page: 616\\n\\n'\n",
+ "from scipy.optimize import fsolve\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data*****#\n",
+ "# a: air b:silica\n",
+ "Density_a = 1.181;# [kg/cubic m]\n",
+ "Density_b = 671.2;# [kg/cubic m]\n",
+ "kSap = 0.965;# [kg H2O/square m s]\n",
+ "Y1 = 0.005;# [kg H2O/kg dry air]\n",
+ "Y2 = 0.0001;# [kg H2O/kg dry air]\n",
+ "Ss = 0.680;# [square m/s]\n",
+ "Gs = 1.36;# [kg/square m.s]\n",
+ "X2 = 0;# [kg H2O/kg dry air]\n",
+ "# Equilibrium function:\n",
+ "m = 0.0185;\n",
+ "#************#\n",
+ "X1 = (Gs*(Y1-Y2)/Ss)+X2;# [kg H2O/kg dry air]\n",
+ "def f77(X):\n",
+ " return m*X \n",
+ "Y2_star = f77(X2);# [kg H2O/kg dry gel]\n",
+ "Y1_star = f77(X1);# [kg H2O/kg dry gel]\n",
+ "deltaY = ((Y1-Y1_star)-(Y2-Y2_star))/math.log((Y1-Y1_star)/(Y2-Y2_star));\n",
+ "NtoG = (Y1-Y2)/deltaY;\n",
+ "# If the fixed bed data are to be used for estimating the mass transfer coeffecient for a moving bed of solids\n",
+ "va = Ss/Density_b;# [m/s]\n",
+ "vb = Gs/Density_a;# [m/s]\n",
+ "rel_v = va+vb;# [relative velocity,m/s]\n",
+ "G_prime = rel_v*Density_a;# [relative mass velocity of air,kg/square m s]\n",
+ "HtG = Gs/(31.6*G_prime**0.55);# [m]\n",
+ "HtS = Ss/kSap;# [m]\n",
+ "# By Eqn. 11.52:\n",
+ "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",
+ "Z = NtoG*HtoG;# [m]\n",
+ "print\"Height of continuous countercurrent isothermal absorber for drying: \",round(Z,4),\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.6 - Page: 616\n",
+ "\n",
+ "\n",
+ "Height of continuous countercurrent isothermal absorber for drying: 0.2511 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 81
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.7: Page 619"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.7\n",
+ "# Page: 619\n",
+ "\n",
+ "print'Illustration 11.7 - Page: 619\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import numpy.linalg as lin\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#*****Data*****#\n",
+ "# a: C2H4 b:C3H8\n",
+ "# The equlibrium curve is plotted in Fig.11.33 (Pg 620)\n",
+ "# C3H8 is more strongly adsorbed component and composition in the gas and adsorbate are expressed as weight fraction C3H8.\n",
+ "Ma = 28;# [kg/kmol]\n",
+ "Mb = 44.1;# [kg/kmol]\n",
+ "xaF = 0.6;# [mole fraction]\n",
+ "xbF = 0.4;# [mole fraction]\n",
+ "xa1 = 0.05;# [mole fraction]\n",
+ "xa2 = 0.95;# [mole fraction]\n",
+ "#***************#\n",
+ "\n",
+ "xF = xbF*Mb/((xbF*Mb)+(xaF*Ma));# [wt. fraction C3H8]\n",
+ "xb1 = 1-xa1;# [mole fraction]\n",
+ "x1 = xb1*Mb/((xb1*Mb)+xa1*Ma);# [wt. fraction C3H8]\n",
+ "xb2 = 1-xa2;# [mole fraction]\n",
+ "x2 = xb2*Mb/((xb2*Mb)+(xa2*Ma));# [wt. fraction C3H8]\n",
+ "# Basis: 100 kg feed gas\n",
+ "F = 100.0;# [kg]\n",
+ "# (1): R2+PE = F [From Eqn. 11.63]\n",
+ "# (2): (R2*x2)+(PE*x1) = (F*xF) [From Eqn. 11.64]\n",
+ "# Solving simultaneously:\n",
+ "a = [[1, 1],[x2 ,x1]];\n",
+ "b = [F,(F*xF)];\n",
+ "soln = lin.solve(a,b);\n",
+ "R2 = soln[0];# [kg]\n",
+ "PE = soln[1];# [kg]\n",
+ "# Point F at xF and point E1 at x1 are located on the diagram.\n",
+ "# From the diagram:\n",
+ "N1 = 4.57;# [kg carbon/kg adsorbate]\n",
+ "# The minimum reflux ratio is found as it is for the extraction.\n",
+ "delta_Em = 5.80;\n",
+ "Ratio = (delta_Em/N1)-1;# [kg reflux gas/kg product]\n",
+ "R1_m = Ratio*PE;# [kg]\n",
+ "E1_m = R1_m+PE;# [kg]\n",
+ "B_m = N1*E1_m;# [kg carbon/100 kg feed]\n",
+ "Ratio1 = 2*Ratio;\n",
+ "# From Eqn. 11.58:\n",
+ "N_deltaE = (Ratio1+1.0)*N1;# [kg carbon/kg adsorbate]\n",
+ "# Point deltaE is located on the diagram:\n",
+ "R1 = Ratio1*PE;# [kg]\n",
+ "E1 = R1+PE;# [kg]\n",
+ "B = N1*E1;# [kg]\n",
+ "N_deltaR = -(B/R2);# [kg carbon/kg adsorbate]\n",
+ "# Random lines such as the delta_RK are drawn from detaR, and the intersection of equilibrium curves are projected downward in the manner shown to provide the adsorption section operating curve.\n",
+ "# Similarly random lines such as delta_EJ are drawn from deltaE, and the intersections are projected downwards to provide the enriching section operating curve.\n",
+ "# Data = [x x_star]\n",
+ "Data = numpy.array([[0.967 ,0.825],[0.90, 0.710],[0.80 ,0.60],[0.70, 0.50],[0.60 ,0.43],[0.512 ,0.39],[0.40 ,0.193],[0.30, 0.090],[0.20, 0.041],[0.0763, 0.003]]);\n",
+ "Val = zeros(10);\n",
+ "for i in range(0,10):\n",
+ " Val[i] = 1/((Data[i,0])-Data[i,1]);\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x\");\n",
+ "plt.ylabel(\"1 / (x-x*)\");\n",
+ "plt.title(\"Graphical Integraion\");\n",
+ "# The area under the curve between x1 & xF, for the enriching section:\n",
+ "Area1 = 2.65;\n",
+ "# The area under the curve between xF & x2, for the adsorption section:\n",
+ "Area2 = 2.67;\n",
+ "r = Ma/Mb;\n",
+ "# From Eqn.11.66:\n",
+ "# For the enriching section:\n",
+ "NtoG1 = Area1-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",
+ "# For the adsortion section:\n",
+ "NtoG2 = Area2-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",
+ "NtoG = NtoG1+NtoG2;\n",
+ "print\"Number of transfer units: \",NtoG"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.7 - Page: 619\n",
+ "\n",
+ "\n",
+ "Number of transfer units: 5.77763695068\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x7dc14a8>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.8: Page 627"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.8\n",
+ "# Page: 627\n",
+ "\n",
+ "print'Illustration 11.8 - Page: 627\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#******Data******#\n",
+ "rate = 0.1;# [kg/s]\n",
+ "conc = 3.0;# [kg vapour/100cubic m]\n",
+ "Density_p = 720.0;# [kg/cubic m]\n",
+ "Density_bed = 480.0;# [kg/cubic m]\n",
+ "capablity = 0.45;# [kg vapour/kg carbon]\n",
+ "dp = 0.0028;# [m]\n",
+ "time = 3.0;# [h]\n",
+ "#********************#\n",
+ "\n",
+ "Vap_adsorbed = time*3600.0*rate;# [kg]\n",
+ "C_required = Vap_adsorbed*1.0/capablity;\n",
+ "# Two beds will be needed: one adsorbing and another regenerated.\n",
+ "totC_required = 2*C_required;# [kg]\n",
+ "print\"Amount of carbon required: \",totC_required,\" kg\\n\",\n",
+ "Vol = (C_required/Density_bed);\n",
+ "# Assume:\n",
+ "Z = 0.5;# [m]\n",
+ "Area = Vol/Z;# [square m]\n",
+ "# From Eqn. 6.66:\n",
+ "T = 35.0;# [OC]\n",
+ "viscosity_air = 1.82*10**(-5);# [kg/m.s]\n",
+ "Density_air = (29/22.41)*(273.0/(T+273));\n",
+ "e = 1-(Density_bed/Density_p);\n",
+ "G = rate*(100.0/conc)*(Density_air/(Area));# [kg/square m.s]\n",
+ "Re = dp*G/viscosity_air;\n",
+ "Z = 0.5;# [m]\n",
+ "def f78(delta_p):\n",
+ " return ((delta_p/Z)*(e**3*dp*Density_air)/((1-e)*G**2))-(150*(1-e)/Re)-1.75\n",
+ "delta_p = fsolve(f78,7);\n",
+ "print\"The pressure drop is:\",round(delta_p,2),\" N/square m\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.8 - Page: 627\n",
+ "\n",
+ "\n",
+ "Amount of caron required: 4800.0 kg\n",
+ "The pressure drop is: 1413.31 N/square m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 88
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.9: Page 636"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.9\n",
+ "# Page: 636\n",
+ "\n",
+ "print'Illustration 11.9 - Page: 636\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data******#\n",
+ "Yo = 0.00267;# [kg H2O/kg dry air]\n",
+ "Yb = 0.0001;# [kg H2O/kg dry air]\n",
+ "Ye = 0.024;# [kg H2O/kg dry air]\n",
+ "Z = 0.61;# [m]\n",
+ "G_prime = 0.1295;# [kg/square m.s]\n",
+ "#******************#\n",
+ "\n",
+ "# The equilicrium data is plotted in Fig. 11.45 (Pg 637)\n",
+ "# The gel is initially \"dry\" and the effluent air initially of so low a humidity asto be substantially dry, so that the operating line passes through the origin of the figure\n",
+ "# The operating line is then drawn to intersect the equilibrium curve.\n",
+ "# Data = [Y[kg H2O/kg dry air] Y_star[kg H2O/kg dry air]]\n",
+ "Data =numpy.array([[0.0001, 0.00003],[0.0002, 0.00007],[0.0004 ,0.00016],[0.0006, 0.00027],[0.0008, 0.00041],[0.0010, 0.00057],[0.0012 ,0.000765],[0.0014, 0.000995],[0.0016, 0.00123],[0.0018 ,0.00148],[0.0020 ,0.00175],[0.0022 ,0.00203],[0.0024 ,0.00230]])\n",
+ "Val1 = zeros(13);\n",
+ "# Val1 = [1/(Y-Y_star)]\n",
+ "for i in range(0,13):\n",
+ " Val1[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "# Graphical Integration:\n",
+ "plt.plot(Data[:,0],Val1);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"Y(kg H20 / kg dry air)\");\n",
+ "plt.ylabel(\"1 / (Y-Y_star)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area under The curve between Y = Yb and Y = Y:\n",
+ "Area = [0 ,0.100 ,2.219 ,2.930 ,3.487 ,3.976 ,4.438 ,4.915, 5.432, 6.015, 6.728 ,7.716 ,9.304];\n",
+ "# The total number of transfer unit corresponding to adsorption zone:\n",
+ "NtoG = 9.304;\n",
+ "Val2 = zeros(13);\n",
+ "Val3 = zeros(13);\n",
+ "# Val2 = [(w-wb)/wo]\n",
+ "# Val3 = [Y/Yo]\n",
+ "for i in range(0,13):\n",
+ " Val2[i] = Area[i]/NtoG;\n",
+ " Val3[i] = Data[i,0]/Yo;\n",
+ "\n",
+ "# Eqn. 11.74 can be arranged as follows:\n",
+ "# f = integrate((1-(Y/Yo)),(w-wb)/wa,0,1)\n",
+ "\n",
+ "plt.plot(Val2,Val3);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"(w-wb) / wo\");\n",
+ "plt.ylabel(\"Y / Yo\");\n",
+ "plt.title(\"Break through curve\");\n",
+ "plt.show()\n",
+ "# From area above the curve of scf(2):\n",
+ "f = 0.530;\n",
+ "\n",
+ "Gs = G_prime;# [kg/square m.s]\n",
+ "# From Illustration: 11.6\n",
+ "kYap = 31.6*G_prime**0.55;# [kg H2O/cubic m s delta_Y]\n",
+ "kSap = 0.965;# [kg H2O/cubic m s delta_X]\n",
+ "# From Fig. 11.48:\n",
+ "Xt = 0.0858;# [kg H2O/kg gel]\n",
+ "# From Eqn. 11.76:\n",
+ "Ss = Yo*Gs/Xt;# [kg/square m.s]\n",
+ "m = 0.0185;# [average slope of equilibrium curve]\n",
+ "# From Eqn. 11.51 & Eqn. 11.52:\n",
+ "HtG = Gs/kYap;# [m]\n",
+ "HtS = Ss/kSap;# [m]\n",
+ "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",
+ "# From Eqn. 11.79:\n",
+ "Za = NtoG*HtoG;# [m]\n",
+ "# From Eqn. 11.74:\n",
+ "Degree = (Z-(f*Za))/Z;\n",
+ "Density_bed = 671.2;# [Illustration 11.6, kg/cubic m]\n",
+ "mass_gel = Z*Density_bed;# [kg/square m]\n",
+ "# At saturation point the gel contins:\n",
+ "Y1 = mass_gel*Degree*Xt;# [kg H2O/square m cross section]\n",
+ "# The air introduces:\n",
+ "Y2 = Gs*Yo;# [kg/square m s]\n",
+ "print\"Time to reach breakpoint is: \",round((Y1/(Y2*3600)),4),\" h\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.9 - Page: 636\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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R81jEii0Wn34K48bBkCFJ1yS3khrtVrZ+8Qv4f/8PZs9OuibOuXIwcmS4VfaG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\nDTe3c64xy5aFlTYmTQrJ5vn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+ "text": [
+ "<matplotlib.figure.Figure at 0x7d96080>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F8ZIsDJsCs/O234qea8XMDjezaYR7Q5+WYDxSRu66C84/Hx59FL75zbSjEaku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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x781dda0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Time to reach breakpoint is: 24.7778 h\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.10: Page 640"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.10\n",
+ "# Page: 640\n",
+ "\n",
+ "print'Illustration 11.10 - Page: 640\\n\\n'\n",
+ "\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#*****Data******#\n",
+ "# a:N2 b:H2O\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "Ma = 29;# [kg/kmol]\n",
+ "Z = 0.268;# [m]\n",
+ "Xo_solid = 0.01;# [kg H20/kg solid]\n",
+ "Density_bed = 712.8;# [kg/cubic m]\n",
+ "T = 28.3;# [OC]\n",
+ "P = 593;# [kN/square m]\n",
+ "Gs = 4052;# [kg/square m.h]\n",
+ "Xo_gas = 1440*10**(-6);# [mole fraction]\n",
+ "#********************#\n",
+ "\n",
+ "# Yo_star is in equilibrium with Xo:\n",
+ "Xo = 0;# [kg H20/kg solid]\n",
+ "Yo_star = 0;# [kg H20/kg N2]\n",
+ "thetha_t = 12.8;# [h]\n",
+ "thetha_b = 9;# [h]\n",
+ "# The breakthrough data are plotted in the manner of Fig. 11.47 (Pg 639) and thetha_s is dtermined:\n",
+ "thetha_s = 10.9;# [h]\n",
+ "Xt = 0.21;# [kg H20/kg solid]\n",
+ "# From Eqn. 11.81:\n",
+ "LUB = (Z/thetha_s)*(thetha_s-thetha_b);\n",
+ "# For thetha_b = 15 h\n",
+ "thetha_b = 15;# [h]\n",
+ "Yo = (Xo_gas/(1-Xo_gas))*(Mb/Ma);# [kg H20/kg N2]\n",
+ "# From Eq. 11.82:\n",
+ "Zs = Gs*(Yo-Yo_star)*thetha_b/(Density_bed*(Xt-Xo_solid));# [m]\n",
+ "# From Eqn. 11.85:\n",
+ "Z = LUB+Zs;\n",
+ "print\"Height of adsorbent column:\",round(Z,4),\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.10 - Page: 640\n",
+ "\n",
+ "\n",
+ "Height of adsorbent column: 0.0467 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 93
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex11.11: Page 654"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 11.11\n",
+ "# Page: 645\n",
+ "\n",
+ "print'Illustration 11.11 - Page: 645\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#****Data****#\n",
+ "# For collection of Cu2+:\n",
+ "V = 37850.0;# [l/h]\n",
+ "c1 = 20.0;# [meq Cu2+/l]\n",
+ "c2 = 0.01*c1;# [meq Cu2+/l]\n",
+ "Mass_rate = 2.0;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",
+ "exchanged = V*(c1-c2);# [meq/h]\n",
+ "X2 = 0.30;# [meq Cu2+/g]\n",
+ "#************#/\n",
+ "\n",
+ "# The point(c2,X2) is plotted in Fig. 11.48(a), Pg 645:\n",
+ "# For the minimum resin/solution ratio and an infinitely tall tower, the operating line pass though point P.\n",
+ "X = 4.9;# [meq Cu2+/g]\n",
+ "MinRate = exchanged/(X-X2);# [g/h]\n",
+ "Rate = 1.2*MinRate;# [g/h]\n",
+ "# Copper balance:\n",
+ "X1 = (exchanged/Rate)+X2;# [meq Cu2+/g resin]\n",
+ "# The point (c1,x1) is ploted in Fig. 11.48(a) and operating line drawn can be straight line at this low conc.\n",
+ "# Adapting Eqn. 11.48 and rearranging:\n",
+ "# S*Z*Density_s = (V/Mass_rate)*integrate(1/(c-c_star),c,c1,c2)\n",
+ "# Mass_rate = KL_prime*ap/Density_s\n",
+ "# From the equilibrium curve:\n",
+ "# Data = [c c_star]\n",
+ "Data = numpy.array([[20 ,2.4],[16 ,1.9],[12, 0.5],[8 ,0.25],[4 ,0.10],[2 ,0.05],[1 ,0.02],[0.2, 0]]);\n",
+ "Val = zeros(8);\n",
+ "for i in range(0,8):\n",
+ " Val[i] = 1/(Data[i,0]-Data[i,1]);\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"c\");\n",
+ "plt.ylabel(\"1 / (c-c*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "# From Graphical Integration:\n",
+ "Area = 5.72;\n",
+ "# holdup = S*Z*Density_s\n",
+ "holdup = V*Area/(Mass_rate);\n",
+ "print\"Resin Holdup: \",holdup,\"g\\n\"\n",
+ "\n",
+ "# Regeneration of resin:\n",
+ "# For 70% utilisation of 2N acid, feed must contain:\n",
+ "V = exchanged;\n",
+ "F = V/(0.70*2000);# [l/h]\n",
+ "c1 = 0;# [meq Cu2+/l]\n",
+ "c2 = V*1.0/F;# [meq Cu2+/l]\n",
+ "X1 = 0.30;# [meq Cu2+/g resin]\n",
+ "X2 = 4.12;# [meq cu2+/g resin]\n",
+ "# The points (c1,X1) and (c2,X2) are plotted on Fig 11.48(b), Pg 645\n",
+ "c1_star = 120.0;# [meq Cu2+/l]\n",
+ "c2_star = 1700.0;# [meq Cu2+/l]\n",
+ "logmean = ((c1_star-c1)-(c2_star-c2))/math.log((c1_star-c1)/(c2_star-c2));\n",
+ "Mass_rate = 0.018;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",
+ "# Substituting in equation:\n",
+ "def f79(holdup):\n",
+ " return (V*(c2-c1))-(Mass_rate*holdup*logmean)\n",
+ "holdup = fsolve(f79,7);\n",
+ "print\"Resin Holdup in the regeneration Tower is \",round(holdup,3),\" g\\n\"\n",
+ "#the answers are in textbook is wrong"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 11.11 - Page: 645\n",
+ "\n",
+ "\n",
+ "Resin Holdup: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 108251.0 g\n",
+ "\n",
+ "Resin Holdup in the regeneration Tower is 296720391.501 g\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x783b160>"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter12.ipynb b/Mass_-_Transfer_Operations/Chapter12.ipynb
new file mode 100755
index 00000000..62994f86
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter12.ipynb
@@ -0,0 +1,922 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:648d6196f9fbdd06162570014332196a676f8de89932a2e6d95cd0329cdf5ac2"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 12: Drying"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.1: Page 660"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.1\n",
+ "# Page: 660\n",
+ "\n",
+ "print'Illustration 12.1 - Page: 660\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#****Data****#\n",
+ "F=1000;# [kg]\n",
+ "Xo=0.8;# [wt. fraction water]\n",
+ "X1=0.05;# [wt. fraction water]\n",
+ "#************#\n",
+ "\n",
+ "Yo=Xo/(1-Xo);# [kg water/kg dry solid]\n",
+ "Y1=X1/(1-X1);# [kg water/kg dry solid]\n",
+ "solid=F*(1-X1);# [kg]\n",
+ "print\"Moisture to be evaporated: \",solid*(Yo-Y1),\" kg\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.1 - Page: 660\n",
+ "\n",
+ "\n",
+ "Moisture to be evaporated: 3750.0 kg\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.2: Page 665"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.2\n",
+ "# Page: 665\n",
+ "\n",
+ "print'Illustration 12.2 - Page: 665\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "# ***Data***#\n",
+ "Y1 = 0.05;# [kg water/kg dry air]\n",
+ "Yair = 0.01;# [kg water/kg dry air]\n",
+ "TempG1 = 95;# [OC]\n",
+ "width = 1;# [m]\n",
+ "apart = 100.0/1000;# [m]\n",
+ "deep = 38.0/1000;# [m]\n",
+ "Rate_evaporation=7.5*10**(-3);# [kg/s]\n",
+ "#*****************#\n",
+ "\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "vH = (0.00283+(0.00456*Y1))*(TempG1+273);# [cubic m/kg dry air]\n",
+ "freeArea = width*(apart-deep)*11;# [square m]\n",
+ "# Rate of air flow at 1:\n",
+ "Rate_air1 = 3*freeArea/vH;# [square m]\n",
+ "Y2 = Y1+(Rate_evaporation/Rate_air1);# [kg water/kg dry air]\n",
+ "# Assuming adiabatic drying:\n",
+ "# From adiabatic saturation curve, Fig 7.5: (Pg 232)\n",
+ "TempG2 = 86.0;# [OC]\n",
+ "# Overall Water Balance:\n",
+ "G = Rate_evaporation/(Y1-Yair);# [kg dry air/s]\n",
+ "# Rate of air flow at 3:\n",
+ "Rate_air3 = Rate_air1+G;# [kg dry air/s]\n",
+ "# Rate of air flow at 4:\n",
+ "Rate_air4 = Rate_air3;# [kg dry air/s]\n",
+ "# Volumetric Rate through fan:\n",
+ "Rate_fan = Rate_air3/vH;# [cubic m/s]\n",
+ "print\"Percentage of air recycled is:\",round((Rate_air1/Rate_air3)*100,2),\"%\\n\",\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# From Fig. 7.5 (page 232):\n",
+ "# Saturated enthalpy at adiabatic saturation temp.\n",
+ "Enthalpy1 = 233.0;# [kJ/kg dry air]\n",
+ "Enthalpy2 = 233.0;# [kJ/kg dry air]\n",
+ "# Enthalpy of fresh air:\n",
+ "Enthalpy_air = 50.0;# [kJ/kg dry air]\n",
+ "# Assuming complete mixing, by Enthalpy mixing:\n",
+ "Enthalpy3 = ((Enthalpy1*Rate_air1)+(Enthalpy_air*G))/Rate_air3;# [kJ/kg dry air]\n",
+ "Enthalpy4 = Enthalpy3;# [kJ/kg dry air]\n",
+ "# From table 7.1: (Pg 234)\n",
+ "Temp_dry = ((Enthalpy3*1000.0)-(2502300.0*Y1))/(1005.0+(1884.0*Y1));\n",
+ "Power = (Enthalpy2-Enthalpy3)*Rate_air3;# [kW]\n",
+ "# From Fig. 7.5, (Pg 232)\n",
+ "DewPoint1 = 40.4;# [OC]\n",
+ "DewPoint2 = 41.8;# [OC]\n",
+ "DewPoint3 = 40.4;# [OC]\n",
+ "DewPoint4 = 40.4;# [OC]\n",
+ "print\"At Point 1\\n\"\n",
+ "print\"Enthalpy of air:\",Enthalpy1,\" kJ/kg dry air\\n\",\n",
+ "print\"Dew Point of air: \",DewPoint1,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 2\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy2,\" kJ/kg dry air\\n\"\n",
+ "print\"Dew Point of air: \",DewPoint2,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 3\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy3,\" kJ/kg dry air\\n\",\n",
+ "print\"Dew Point of air: \",DewPoint3,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 4\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy4,\" kJ/kg dry air\\n\"\n",
+ "print\"Dew Point of air: \",DewPoint4,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"Dry bulb temparature of air: \",Temp_dry,\" OC\\n\"\n",
+ "print\"Power delivered by heater: \",Power,\" kW\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.2 - Page: 665\n",
+ "\n",
+ "\n",
+ "Percentage of air recycled is: 90.65 %\n",
+ "\n",
+ "\n",
+ "At Point 1\n",
+ "\n",
+ "Enthalpy of air: 233.0 kJ/kg dry air\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 2\n",
+ "\n",
+ "Enthalpy of air: 233.0 kJ/kg dry air\n",
+ "\n",
+ "Dew Point of air: 41.8 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 3\n",
+ "\n",
+ "Enthalpy of air: 215.89174489 kJ/kg dry air\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 4\n",
+ "\n",
+ "Enthalpy of air: 215.89174489 kJ/kg dry air\n",
+ "\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "Dry bulb temparature of air: 82.5843748998 OC\n",
+ "\n",
+ "Power delivered by heater: 34.3125 kW\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.3: Page 671"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.3\n",
+ "# Page: 671\n",
+ "\n",
+ "print'Illustration 12.3 - Page: 671\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "# ***Data***#\n",
+ "SsByA = 40;\n",
+ "x1 = 0.25;# [moisture fraction]\n",
+ "x2 = 0.06;# [moisture fraction]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg moisture/kg dry solid]\n",
+ "X2 = x2/(1-x2);# [kg moisture/kg dry solid]\n",
+ "# Fig. 12.10 (Pg 668) indicates that both constant and falling rate periods are involved.\n",
+ "\n",
+ "# Constant Rate period:\n",
+ "# From Fig. 12.10 (Pg 668):\n",
+ "Xc = 0.200;# [kg moisture/kg dry solid]\n",
+ "Nc = 0.3*10**(-3);# [kg/square m.s]\n",
+ "# From Eqn. 12.4:\n",
+ "thetha1 = SsByA*(X1-Xc)/Nc;# [s]\n",
+ "\n",
+ "# Falling Rate Period:\n",
+ "# From Fig. 12.10 (Pg 668):\n",
+ "# Data=[x N*10^3]\n",
+ "Data = numpy.array([[0.2 ,0.3],[0.18 ,0.266],[0.16 ,0.239],[0.14 ,0.208],[0.12, 0.180],[0.10 ,0.150],[0.09 ,0.097],[0.08, 0.070],[0.07 ,0.043],[0.064 ,0.025]]);\n",
+ "Val = zeros(10);\n",
+ "# Val=[(1/N)*10^(-3)]\n",
+ "for i in range(0,10):\n",
+ " Val[i] = 1/Data[i,1];\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid();\n",
+ "plt.xlabel(\"x [kg moisture / kg dry solid]\");\n",
+ "plt.ylabel(\"10^(-3) / N\");\n",
+ "plt.title(\"Graphical Integration Falling Rate Period\");\n",
+ "# Area under the curve:\n",
+ "Area = 1060.0;\n",
+ "# From Eqn. 12.3:\n",
+ "thetha2 = SsByA*Area;# [s]\n",
+ "thetha = thetha1+thetha2;# [s]\n",
+ "print\"Total Drying Time: \",round(thetha/3600,2),\"h\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.3 - Page: 671\n",
+ "\n",
+ "\n",
+ "Total Drying Time: 16.72 h\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2hhqV1jtutRXccQd84xtw003pZCqVl7pRzxmX54wrLzlj8COGOthhB/jzn+HA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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7786b00>"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.4: Page 676"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.4\n",
+ "# Page: 676\n",
+ "\n",
+ "print'Illustration 12.4 - Page: 676\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "#***Data***#\n",
+ "# For rectangular pan:\n",
+ "l = 0.7;# [m]\n",
+ "b = 0.7;# [m]\n",
+ "zS = 0.025;# [m]\n",
+ "zM = 0.0008;# [m]\n",
+ "d = 0.1;# [m]\n",
+ "Y1 = 0.01;# [kg water/kg dry air]\n",
+ "TempG = 65.0;# [OC]\n",
+ "v = 3.0;# [m/s]\n",
+ "TempR = 120.0;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "vH = (0.00283+(0.00456*Y1))*(TempG+273.0);# [cubic m/kg dry air]\n",
+ "Density_G = (1+Y1)/vH;# [kg/cubic m]\n",
+ "G = v*Density_G;# [kg/square m.s]\n",
+ "de = 4*d*l/(2*(l+d));# [m]\n",
+ "# From Eqn. 12.20:\n",
+ "hc = 5.90*G**0.71/de**0.29;# [W/square m.K]\n",
+ "# Assume:\n",
+ "e = 0.94;\n",
+ "# Estimate:\n",
+ "TempS = 38;# [OC]\n",
+ "# From Eqn. 12.14:\n",
+ "hR = e*5.729*10**(-8)*((273+TempR)**4-(273+TempS)**4)/((273.0+TempR)-(273+TempS));\n",
+ "A = l*b;# [square m]\n",
+ "Am = A;# [square m]\n",
+ "As = 4*l*zS;# [square m]\n",
+ "Au = Am+As;# [square m]\n",
+ "# Thermal Coductivities:\n",
+ "kM = 45;# [W/m.K]\n",
+ "kS = 3.5;# [W/m.K]\n",
+ "# By Eqn. 12.16:\n",
+ "Uk = 1/(((1/hc)*(A/Au))+((zM/kM)*(A/Au))+((zS/kS)*(A/Am)));# [W/squre m.K]\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "Cs = 1005+(1884*Y1);# [kJ/kg]\n",
+ "# At estimated 38 OC\n",
+ "lambdaS = 2411.4;# [kJ/kg]\n",
+ "# From Eqn. 12.18:\n",
+ "# (Ys-Y1)*lambdaS*10^3/Cs = ((1+(Uk/hc))*(TempG-Temps))+((hR/hC)*(TempR-TempS))\n",
+ "# On Simplifying:\n",
+ "# Ys = 0.0864-(10.194*10**(-4)*TempS)\n",
+ "# The eqn. is solved simultaneously with the saturated humidity curve of the psychometric chart for the air water mixture.\n",
+ "# From Fig. 12.12: (Pg 677)\n",
+ "Ys = 0.0460;# [kg water/kg dry air]\n",
+ "TempS = 39;# [OC]\n",
+ "# At 39 OC\n",
+ "lambdaS = 2409.7;# [kJ/kg]\n",
+ "# From Eqn. 12.17:\n",
+ "Nc = (((hc+Uk)*(TempG-TempS))+(hR*(TempR-TempS)))/(lambdaS*10**(3));# [kg water evaporated/square m.s]\n",
+ "print\"The Evaporation Rate: \",round(Nc*A,8),\" kg/s\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "# When no radiation or conduction of heat through the solid occurs, the drying surface assumes wet bulb temparature of the air.\n",
+ "# From Fig. 12.12 (Pg 677)\n",
+ "TempS = 28.5;# [OC]\n",
+ "Ys = 0.025;# [kg water/kg dry air]\n",
+ "lambdaS = 2435;# [kJ/kg]\n",
+ "# From Eqn. 12.17:\n",
+ "Nc = hc*(TempG-TempS)/(lambdaS*10**3);# [kg/aquare m.s]\n",
+ "print\"The Evaporation Rate: \",round(Nc*A,8), \"kg/s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.4 - Page: 676\n",
+ "\n",
+ "\n",
+ "The Evaporation Rate: 0.0003851 kg/s\n",
+ "\n",
+ "The Evaporation Rate: 0.00016105 kg/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.5: Page 684"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.5\n",
+ "# Page: 684\n",
+ "\n",
+ "print'Illustration 12.5 - Page: 684\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "from scipy import integrate\n",
+ "import math\n",
+ "#***Data***#\n",
+ "x1 = 0.025;# [moisture fraction]\n",
+ "x2 = 0.001;# [moisture fraction]\n",
+ "zS = 0.018;# [m]\n",
+ "dp = 2*10**(-4);# [m]\n",
+ "Density_S = 1350;# [kg dry solid/cubic m]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg water/kg dry air]\n",
+ "X2 = x2/(1-x2);# [kg water/kg dry air]\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Y1 = 0.0153;# [kg water/kg dry air]\n",
+ "Tempas = 24;# [OC]\n",
+ "Yas = 0.0190;# [kg water/kg dry air]\n",
+ "Gs = 0.24;# [kg dry air/square m.s]\n",
+ "Gav = Gs+(Gs*(Y1+Yas)/2.0);# [kg dry air/square m.s]\n",
+ "# From Eqn. 12.26:\n",
+ "Nmax = Gs*(Yas-Y1);# [kg evaporated/square m.s]\n",
+ "viscosity_air = 1.8*10**(-5);# [kg/m.s]\n",
+ "X3=lambda X : 1/(Nmax*(1-math.exp(-(0.273/dp**0.35)*((dp*Gav/viscosity_air)**0.215)*(Density_S*zS*X)**0.64)));\n",
+ "Value = integrate.quad(X3,X2,X1);\n",
+ "# From Eqn. 12.3:\n",
+ "thetha = Density_S*zS*Value[0];# [s]\n",
+ "print\"The time for drying: \",round(thetha/60,3),\" min\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.5 - Page: 684\n",
+ "\n",
+ "\n",
+ "The time for drying: 12.593 min\n"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.6: Page 685"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.6\n",
+ "# Page: 685\n",
+ "\n",
+ "print'Illustration 12.6 - Page: 685\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "\n",
+ "import math\n",
+ "#***Data***#\n",
+ "Y1 = 0.01;# [kg water/kg dry air]\n",
+ "Gs = 1.1;# [kg dry air/square m.s]\n",
+ "dia = 13.5/1000;# [m]\n",
+ "l = 13.0/1000;# [m]\n",
+ "zS = 50.0/1000;# [m]\n",
+ "Density_S = 600.0;# [kg dry solid/square m.s]\n",
+ "a = 280.0;# [square m/cubic m]\n",
+ "#************#\n",
+ "\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Yas = 0.031;# [kg water/kg dry air]\n",
+ "Gav = Gs+(Gs*(Y1+Yas)/2.0);# [kg/square m.s]\n",
+ "viscosity_air = 1.9*10**(-5);# [kg/m.s]\n",
+ "Area = (2.0*math.pi*dia**2.0/4)+(math.pi*dia*l);# [square m]\n",
+ "dp = (Area/math.pi)**0.5;# [m]\n",
+ "# From Table 3.3 (Pg 74)\n",
+ "Re = dp*Gav/viscosity_air;\n",
+ "e = 1.0-(dp*a/6);# [fraction voids]\n",
+ "jD = (2.06/e)*Re**(-0.575);\n",
+ "# For air water mixture:\n",
+ "Sc = 0.6;\n",
+ "# From Eqn. 12.33:\n",
+ "kY = jD*Gs/Sc**(2.0/3);# [kg H2O/square m.s.deltaX]\n",
+ "# From Eqn. 12.30:\n",
+ "NtG = kY*a*zS/Gs;\n",
+ "# From Eqn. 12.25:\n",
+ "Nmax = Gs*(Yas-Y1);# [kg/square m.s]\n",
+ "# From Eqn. 12.31:\n",
+ "N = Nmax*(1-math.exp(-NtG));# [kg water evaporated/square m.s]\n",
+ "Y2 = (Yas-Y1)*(N/Nmax)+Y1;# [kg water/kg dry air]\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Tempas = 33.0;# [OC]\n",
+ "# From eqn. 12.2:\n",
+ "Rate = N/(Density_S*zS);# [kg H2O/(kg dry solid).s]\n",
+ "print\"Humidity of the exit air: \",round(Y2,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Temparature of exit air: \",Tempas,\" degree C\\n\"\n",
+ "print\"Rate of Drying: \",round(Rate,7),\" kg H2O/(kg dry solid).s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.6 - Page: 685\n",
+ "\n",
+ "\n",
+ "Humidity of the exit air: 0.0302 kg water/kg dry air\n",
+ "\n",
+ "Temparature of exit air: 33.0 degree C\n",
+ "\n",
+ "Rate of Drying: 0.0007409 kg H2O/(kg dry solid).s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 26
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.7: Page 700"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.7\n",
+ "# Page: 700\n",
+ "\n",
+ "print'Illustration 12.7 - Page: 700\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "\n",
+ "import math\n",
+ "from numpy.linalg import inv\n",
+ "#***Data***#\n",
+ "x1 = 3.5;# [percent moisture]\n",
+ "x2 = 0.2;# [percent moisture]\n",
+ "dia = 1.2;# [m]\n",
+ "l = 6.7;# [m]\n",
+ "Rate_prod = 900.0;# [kg/h]\n",
+ "y2 = 0.5;# [Humidity]\n",
+ "TempG2 = 90.0;# [OC]\n",
+ "TempG1 = 32.0;# [OC]\n",
+ "TempS1 = 25.0;# [OC]\n",
+ "TempS2 = 60.0;# [OC]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(100.0-x1);# [kg H2O/kg dry solid]\n",
+ "X2 = x2/(100.0-x2);# [kg H2O/kg dry solid]\n",
+ "Ss = Rate_prod*(1-X2);# [kg dry solid/h]\n",
+ "Rate_drying = Ss*(X1-X2);# [kg water evaporated/h]\n",
+ "Y2 = (y2/(1-y2))/100.0;# [kg water/kg dry air]\n",
+ "Tempo = 0.0;# [Base temp,OC]\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "# Enthalpy of air entering the drier:\n",
+ "HG2 = (1005.0+(1884.0*Y2))*(TempG2-Tempo)+(2502300.0*Y2);# [J/kg dry air]\n",
+ "# For the outlet air:\n",
+ "# HG1 = (1005.0+(1884*Y1))*(TempG1-Tempo)+(2502300*Y1); [J/kg dry air]\n",
+ "# HG1 = (1005.0*TempG1)+((1884+TempG1)+2502300)*Y1; [J/kg dry air]\n",
+ "CsNH4 = 1507.0;# [J/kg.K]\n",
+ "CsH2O = 4187.0;# [J/kg.K]\n",
+ "# From Eqn. 11.45:\n",
+ "HS2 = CsNH4*(TempS2-Tempo)+(X2*CsH2O*(TempS2-Tempo));# [J/kg dry air]\n",
+ "HS1 = CsNH4*(TempS1-Tempo)+(X1*CsH2O*(TempS1-Tempo));# [J/kg dry air]\n",
+ "# The estimated combined natural convection and radiation heat transfer coeffecient from the drier to the surrounding:\n",
+ "h = 12.0;# [W/square m.K]\n",
+ "deltaTemp = ((TempG2-TempS1)+(TempG1-TempS1))/2;# [OC]\n",
+ "Ae = math.pi*dia*l;# [square m]\n",
+ "Q = h*3600.0*Ae*deltaTemp;# [kJ/h]\n",
+ "# Moisture Balance, Eqn. 12.39:\n",
+ "# Ss*(X1-X2) = Gs(Y1-Y2)\n",
+ "# (Gs*Y1)-(Gs*Y2) = (Ss*(X1-X2)) ........(1)\n",
+ "# Enthalapy Balance, Eqn. 12.40:\n",
+ "# (Ss*HS1)+(Gs*HG2) = (Ss*HG2)+(Gs*HG1)+Q \n",
+ "# Gs*(HG2-HG1) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-((1005*TempG1)+((1884+TempG1)+2502300)*Y1)) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-(1005*TempG1))-(Gs*Y1*((1884+TempG1)+2502300)) = (Ss*HS2)+Q-(Ss*HS1)........ (2)\n",
+ "# Solving Simultaneously:\n",
+ "a = numpy.array([[HG2-(1005.0*TempG1),-((1884.0+TempG1)+2502300.0)],[(-Y2), 1.0]]);\n",
+ "b = numpy.array([[((Ss*HS2)+Q-(Ss*HS1))],[(Ss*(X1-X2))]]);\n",
+ "c=inv(a)\n",
+ "soln =np.dot(c, b)\n",
+ "Gs = soln[0];# [kg dry air/h]\n",
+ "Y1 = soln[1]/soln[0];# [kg water/kg dry air]\n",
+ "# From Fig. 7.5 (Pg 232)\n",
+ "Enthalpy_air = 56.0;# [kJ/kg dry air]\n",
+ "HeatLoad = Gs*(HG2-Enthalpy_air*1000);# [W]\n",
+ "print\"Air Flow Rate: \",round(Gs,2),\" kg/h\\n\"\n",
+ "print\"Moisture content of air: \",round(Y1,2),\" kg water/kg dry air \\n\"\n",
+ "print\"Heat Load of drier: \",round(HeatLoad/1000),\" kW\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.7 - Page: 700\n",
+ "\n",
+ "\n",
+ "Air Flow Rate: 2681.03 kg/h\n",
+ "\n",
+ "Moisture content of air: 0.02 kg water/kg dry air \n",
+ "\n",
+ "Heat Load of drier: 163995.0 kW\n"
+ ]
+ }
+ ],
+ "prompt_number": 50
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.8: Page 705"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.8\n",
+ "# Page: 705\n",
+ "\n",
+ "print'Illustration 12.8 - Page: 705\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "from numpy.linalg import inv\n",
+ "import numpy as np\n",
+ "#***Data***#\n",
+ "x1 = 8.0;# [percent moisture]\n",
+ "x2 = 0.5;# [percent moisture]\n",
+ "Rate_prod = 0.63;# [kg/s]\n",
+ "# Drying Gas:\n",
+ "xCO2 = 0.025;# [mole fraction]\n",
+ "xO2 = 0.147;# [mole fraction]\n",
+ "xN2 = 0.760;# [mole fraction]\n",
+ "xH2O = 0.068;# [mole fraction]\n",
+ "TempG2 = 480.0;# [OC]\n",
+ "Cs = 0.837;# [kJ/kg.K]\n",
+ "Temp1 = 27.0;# [OC]\n",
+ "Temp2 = 150.0;# [OC]\n",
+ "dp = 200.0*10**(-6);# [m]\n",
+ "Density_S = 1300.0;# [kg/cubic m]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(100-x1);# [kg water/kg dry solid]\n",
+ "X2 = x2/(100-x2);# [kg water/kg dry solid]\n",
+ "Ss = Rate_prod*(1-X2);# [kg dry solid/s]\n",
+ "Water_evap = Ss*(X1-X2);# [kg/s]\n",
+ "# Basis: 1 kmol of dry gas:\n",
+ "xDry = 1.0-xH2O;# [kmol]\n",
+ "XCO2 = 44.0*xCO2;# [kg]\n",
+ "XO2 = 32.0*xO2;# [kg]\n",
+ "XN2 = 28.0*xN2;# [kg]\n",
+ "Xdry = XCO2+XO2+XN2;# [kg]\n",
+ "cCO2 = 45.6;# [kJ/kmol.K]\n",
+ "cO2 = 29.9;# [kJ/kmol.K]\n",
+ "cN2 = 29.9;# [kJ/kmol.K]\n",
+ "cH2O = 4.187;# [kJ/kg.K]\n",
+ "Mav = Xdry/xDry;# [kg/kmol]\n",
+ "Y2 = xH2O*18.02/(xDry*Mav);# [kg water/kg dry gas]\n",
+ "cav = ((xCO2*cCO2)+(xO2*cO2)+(xN2*cN2))/(xDry*Mav);# [kJ/kmol.K]\n",
+ "# Assume:\n",
+ "TempG1 = 120.0;# [OC]\n",
+ "cDry = 1.005;# [kJ/kmol.K]\n",
+ "Tempo = 0;# [Base Temp,OC]\n",
+ "# By Eqn. 7.13:\n",
+ "HG2 = (cav+(1.97*Y2))*(TempG2-Tempo)+(2502.3*Y2);# [kJ/kg dry air]\n",
+ "# For the outlet air:\n",
+ "# HG1 = (1.005+(1.884*Y1))*(TempG1-Tempo)+(2502.3*Y1); [kJ/kg dry air]\n",
+ "# HG1 = (1.005*TempG1)+((1.884+TempG1)+2502.3)*Y1; [kJ/kg dry air]\n",
+ "# By Eqn. 11.45:\n",
+ "HS1 = (Cs*(Temp1-Tempo))+(cH2O*X1*(Temp1-Tempo));# [kJ/kg dry air]\n",
+ "HS2 = (Cs*(Temp2-Tempo))+(cH2O*X2*(Temp2-Tempo));# [kJ/kg dry air]\n",
+ "# Q = 0.15*HG2*Gs; [kJ/s]\n",
+ "# Moisture Balance, Eqn. 12.39:\n",
+ "# Ss*(X1-X2) = Gs(Y1-Y2)\n",
+ "# (Gs*Y1)-(Gs*Y2) = (Ss*(X1-X2)) ........(1)\n",
+ "# Enthalapy Balance, Eqn. 12.40:\n",
+ "# (Ss*HS1)+(Gs*HG2) = (Ss*HG2)+(Gs*HG1)+Q \n",
+ "# Gs*(HG2-HG1) = (Ss*HS2)+(0.15*HG2*Gs)-(Ss*HS1)\n",
+ "# Gs*(HG2-(0.15*HG2)-((1.005*TempG1)+((1.884+TempG1)+2502.3)*Y1)) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-(0.15*HG2)-(1.005*TempG1))-(Gs*Y1*((1.884+TempG1)+2502.3)) = (Ss*HS2)+Q-(Ss*HS1)........ (2)\n",
+ "a = np.array([[(HG2-(0.15*HG2)-(1.005*TempG1)),-((1.884+TempG1)+2502.3)],[(-Y2), 1.0]]);\n",
+ "b = np.array([(Ss*HS2)-(Ss*HS1),(Ss*(X1-X2))]);\n",
+ "c=inv(a)\n",
+ "soln = np.dot(c, b)\n",
+ "Gs = soln[0];# [kg dry air/s]\n",
+ "Y1 = soln[1]/soln[0];# [kg water/kg dry gas]\n",
+ "HG1 = (1.005+(1.884*Y1))*(TempG1-Tempo)+(2502.3*Y1);# [kJ/kg dry air]\n",
+ "Q = 0.15*HG2*Gs;# [kJ/s]\n",
+ "# Assuming the sychrometric ratio of the gas as same as that of air:\n",
+ "# For Zone II:\n",
+ "Tempw = 65.0;# [OC]\n",
+ "Temp_A = 68.0;# [OC]\n",
+ "# At point A, Fig. 12.28 (Pg 702)\n",
+ "Enthalpy_A = Cs*(Temp_A-Tempo)+(X1*cH2O*(Temp_A-Tempo));# [kJ/kg dry air]\n",
+ "# At point B, Fig. 12.28 (Pg 702)\n",
+ "Temp_B = Temp_A;# [OC]\n",
+ "Enthalpy_B = Cs*(Temp_B-Tempo)+(X2*cH2O*(Temp_B-Tempo));# [kJ/kg dry air]\n",
+ "\n",
+ "# Assuming that the heat losses in the three zones are propotional to the number of transfer units in each zone and to the average temp. difference between the gas and the surrounding air.\n",
+ "# Fractional heat loss in each Zone:\n",
+ "fr1 = 0.14;\n",
+ "fr2 = 0.65;\n",
+ "fr3 = 0.20;\n",
+ "# Calculations for zone III:\n",
+ "Cs3 = cav+(1.97*Y2);# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "def f1(TempGD):\n",
+ " return (Gs*Cs3*(TempG2-TempGD))-(Ss*(HS2-Enthalpy_B)+(fr3*Q))\n",
+ "TempGD = fsolve(f1,7);# [OC]\n",
+ "delta_TempG = Ss*(HS2-Enthalpy_B)/(Gs*Cs3);# [OC]\n",
+ "delta_TempM = ((TempG2-Temp2)+(TempGD-Temp_A))/2;# [OC]\n",
+ "NtoG3 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "# Calculations for zone I:\n",
+ "Cs1 = 1.005+(1.884*Y1);# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "def f2(TempGC):\n",
+ " return (Gs*Cs1*(TempGC-TempG1))-(Ss*(Enthalpy_A-HS1)+(fr1*Q))\n",
+ "TempGC = fsolve(f2,7);# [OC]\n",
+ "delta_TempG = Ss*(Enthalpy_A-HS1)/(Gs*Cs1);# [OC]\n",
+ "delta_TempM = ((TempGC-Temp_A)+(TempG1-Temp1))/2;# [OC]\n",
+ "NtoG1 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "# Calculations for zone II:\n",
+ "Cs2 = (cav+Cs1)/2.0;# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "True_deltaTemp = TempGD-TempGC;# [OC]\n",
+ "delta_Temp = fr2*Q/(Cs1*Gs);# [Change in temp resulting from heat loss,OC]\n",
+ "delta_TempG = True_deltaTemp-delta_Temp;# [OC]\n",
+ "delta_TempM = ((TempGD-Temp_A)-(TempGC-Temp_A))/log((TempGD-Temp_A)/(TempGC-Temp_A));# [OC]\n",
+ "NtoG2 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "NtoG = NtoG1+NtoG2+NtoG3;\n",
+ "\n",
+ "# Standard diameters are availaible at 1, 1.2 & 1.4 m.\n",
+ "Td = 1.2;# [m]\n",
+ "Area = math.pi*Td**2.0/4;# [square m]\n",
+ "Gs = Gs/Area;# [kg/square m.s]\n",
+ "Ss = Ss/Area;# [kg/square m.s]\n",
+ "Gav = Gs*(1+(Y1+Y2)/2.0);# [kg/square m.s]\n",
+ "# From Eqn. 12.47:\n",
+ "Ua = 237.0*Gav**0.417/Td;# [W/square m.K]\n",
+ "HtoG = Gs*Cs2*1000.0/Ua;# [m]\n",
+ "Z = NtoG*HtoG;# [m]\n",
+ "# Assume:\n",
+ "v = 0.35;# [m/s]\n",
+ "N = v/(math.pi*Td);# [1/s]\n",
+ "# From Eqn. 12.37:\n",
+ "K = 0.6085/(Density_S*dp**(1.0/2));\n",
+ "# Take:\n",
+ "phi_D = 0.05;\n",
+ "# From Eqn. 12.35:\n",
+ "phi_DO = phi_D-(K*Gav);\n",
+ "# From Eqn. 12.35:\n",
+ "s = 0.3344*Ss/(phi_DO*Density_S*N**0.9*Td);# [m/s]\n",
+ "print\"Height of the drier: \",round(Z,2),\" m\\n\"\n",
+ "print\"Drier Slope: \",round(s,5),\" m/m \\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.8 - Page: 705\n",
+ "\n",
+ "\n",
+ "Height of the drier: 5.89 m\n",
+ "\n",
+ "Drier Slope: 0.03304 m/m \n"
+ ]
+ }
+ ],
+ "prompt_number": 56
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.9: Page 709"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.9\n",
+ "# Page: 709\n",
+ "\n",
+ "print'Illustration 12.9 - Page: 709\\n\\n'\n",
+ "import numpy as np\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "# Solution \n",
+ "\n",
+ "#***Data***#\n",
+ "x1 = 0.46;# [fraction moisture]\n",
+ "x2 = 0.085;# [fraction moisture]\n",
+ "Y1 = 0.08;# [kg water/kg dry solid]\n",
+ "Y2 = 0.03;# [kg water/kg dry solid]\n",
+ "G = 1.36;# [kg/square m.s]\n",
+ "#**********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg water/kg dry solid]\n",
+ "X2 = x2/(1-x2);# [kg water/kg dry solid]\n",
+ "# By water balance:\n",
+ "SsByGs = (Y1-Y2)/(X1-X2);# [kg dry solid/kg air]\n",
+ "# Since the initial moisture content of the rayon is less than the critical, drying takes place entirely within zone III.\n",
+ "# Comparing with Eqn. 12.22:\n",
+ "# (kY*A/(Ss(Xc-X*)))=0.0137*G**1.47\n",
+ "# thetha=integrate('(1/(0.0137*G**1.47))*(1/((X-X_star)*(Yw-Y)))','X',X2,X1) # [s]\n",
+ "X = np.array([X1, 0.80, 0.60, 0.40, 0.20 ,X2]);# [kg water/kg dry solid]\n",
+ "Y = zeros(6);\n",
+ "for i in range(0,6):\n",
+ " # From Eqn. 12.54:\n",
+ " Y[i] = Y2+((X[i]-X2)*SsByGs);# [kg water/kg dry gas]\n",
+ "\n",
+ "# From Fig. 7.5 (Pg 232):\n",
+ "Yw = np.array([0.0950, 0.0920, 0.0790, 0.0680, 0.0550, 0.0490]);# [kg water/kg dry gas]\n",
+ "X_star = zeros(6);\n",
+ "RH=zeros(6)\n",
+ "Val = zeros(6);\n",
+ "P = 51780.0;# [vapour pressure, kN/square m]\n",
+ "for i in range(0,6):\n",
+ " # From Eqn 7.8:\n",
+ " def f(p):\n",
+ " return Y[i]-((p/(101330.0-p))*(18.0/29))\n",
+ " p = fsolve(f,7);# [kN/square m]\n",
+ " RH[i] = (p/P)*100.0;\n",
+ " X_star[i] = (RH[i]/4)/(100.0-(RH[i]/4));# [kg water/kg dry solid]\n",
+ " Val[i] = 1/((X[i]-X_star[i])*(Yw[i]-Y[i]));\n",
+ "\n",
+ "plt.plot(X,Val);\n",
+ "plt.grid();\n",
+ "plt.xlabel(\"X kg water/kg dry solid\");\n",
+ "plt.ylabel(\"1/((X-X*)*(Yw-Y))\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area Under the curve:\n",
+ "Area = 151.6;\n",
+ "# From Eqn. 12.59:\n",
+ "thetha = Area/(0.0137*G**1.47);\n",
+ "print\"Time required for drying: \",round(thetha/3600,2),\" h\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.9 - Page: 709\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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tyX4amWWeDuTqL+nPyd/gVEnbVFyhmUX3Qyg1zQMagdWAVmDrkmU+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\nc86V5Y2Ec865sryRcM45V9b/B77dKwo9Y8ZFAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7a843c8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Time required for drying: 1.96 h\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 66
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter12_1.ipynb b/Mass_-_Transfer_Operations/Chapter12_1.ipynb
new file mode 100755
index 00000000..9416a787
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter12_1.ipynb
@@ -0,0 +1,932 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:7b3124ef7f3febbf9fbbcfec34e4b1fa9fd03169d0e2aea25265368468350ca6"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 12: Drying"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.1: Page 660"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.1\n",
+ "# Page: 660\n",
+ "\n",
+ "print'Illustration 12.1 - Page: 660\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#****Data****#\n",
+ "F=1000;# [kg]\n",
+ "Xo=0.8;# [wt. fraction water]\n",
+ "X1=0.05;# [wt. fraction water]\n",
+ "#************#\n",
+ "\n",
+ "Yo=Xo/(1-Xo);# [kg water/kg dry solid]\n",
+ "Y1=X1/(1-X1);# [kg water/kg dry solid]\n",
+ "solid=F*(1-X1);# [kg]\n",
+ "print\"Moisture to be evaporated: \",solid*(Yo-Y1),\" kg\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.1 - Page: 660\n",
+ "\n",
+ "\n",
+ "Moisture to be evaporated: 3750.0 kg\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.2: Page 665"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.2\n",
+ "# Page: 665\n",
+ "\n",
+ "print'Illustration 12.2 - Page: 665\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "# ***Data***#\n",
+ "Y1 = 0.05;# [kg water/kg dry air]\n",
+ "Yair = 0.01;# [kg water/kg dry air]\n",
+ "TempG1 = 95;# [OC]\n",
+ "width = 1;# [m]\n",
+ "apart = 100.0/1000;# [m]\n",
+ "deep = 38.0/1000;# [m]\n",
+ "Rate_evaporation=7.5*10**(-3);# [kg/s]\n",
+ "#*****************#\n",
+ "\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "vH = (0.00283+(0.00456*Y1))*(TempG1+273);# [cubic m/kg dry air]\n",
+ "freeArea = width*(apart-deep)*11;# [square m]\n",
+ "# Rate of air flow at 1:\n",
+ "Rate_air1 = 3*freeArea/vH;# [square m]\n",
+ "Y2 = Y1+(Rate_evaporation/Rate_air1);# [kg water/kg dry air]\n",
+ "# Assuming adiabatic drying:\n",
+ "# From adiabatic saturation curve, Fig 7.5: (Pg 232)\n",
+ "TempG2 = 86.0;# [OC]\n",
+ "# Overall Water Balance:\n",
+ "G = Rate_evaporation/(Y1-Yair);# [kg dry air/s]\n",
+ "# Rate of air flow at 3:\n",
+ "Rate_air3 = Rate_air1+G;# [kg dry air/s]\n",
+ "# Rate of air flow at 4:\n",
+ "Rate_air4 = Rate_air3;# [kg dry air/s]\n",
+ "# Volumetric Rate through fan:\n",
+ "Rate_fan = Rate_air3/vH;# [cubic m/s]\n",
+ "print\"Percentage of air recycled is:\",round((Rate_air1/Rate_air3)*100,2),\"%\\n\",\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# From Fig. 7.5 (page 232):\n",
+ "# Saturated enthalpy at adiabatic saturation temp.\n",
+ "Enthalpy1 = 233.0;# [kJ/kg dry air]\n",
+ "Enthalpy2 = 233.0;# [kJ/kg dry air]\n",
+ "# Enthalpy of fresh air:\n",
+ "Enthalpy_air = 50.0;# [kJ/kg dry air]\n",
+ "# Assuming complete mixing, by Enthalpy mixing:\n",
+ "Enthalpy3 = ((Enthalpy1*Rate_air1)+(Enthalpy_air*G))/Rate_air3;# [kJ/kg dry air]\n",
+ "Enthalpy4 = Enthalpy3;# [kJ/kg dry air]\n",
+ "# From table 7.1: (Pg 234)\n",
+ "Temp_dry = ((Enthalpy3*1000.0)-(2502300.0*Y1))/(1005.0+(1884.0*Y1));\n",
+ "Power = (Enthalpy2-Enthalpy3)*Rate_air3;# [kW]\n",
+ "# From Fig. 7.5, (Pg 232)\n",
+ "DewPoint1 = 40.4;# [OC]\n",
+ "DewPoint2 = 41.8;# [OC]\n",
+ "DewPoint3 = 40.4;# [OC]\n",
+ "DewPoint4 = 40.4;# [OC]\n",
+ "print\"At Point 1\\n\"\n",
+ "print\"Enthalpy of air:\",Enthalpy1,\" kJ/kg dry air\\n\",\n",
+ "print\"Dew Point of air: \",DewPoint1,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 2\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy2,\" kJ/kg dry air\\n\"\n",
+ "print\"Dew Point of air: \",DewPoint2,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 3\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy3,\" kJ/kg dry air\\n\",\n",
+ "print\"Dew Point of air: \",DewPoint3,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 4\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy4,\" kJ/kg dry air\\n\"\n",
+ "print\"Dew Point of air: \",DewPoint4,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"Dry bulb temparature of air: \",Temp_dry,\" OC\\n\"\n",
+ "print\"Power delivered by heater: \",Power,\" kW\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.2 - Page: 665\n",
+ "\n",
+ "\n",
+ "Percentage of air recycled is: 90.65 %\n",
+ "\n",
+ "\n",
+ "At Point 1\n",
+ "\n",
+ "Enthalpy of air: 233.0 kJ/kg dry air\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 2\n",
+ "\n",
+ "Enthalpy of air: 233.0 kJ/kg dry air\n",
+ "\n",
+ "Dew Point of air: 41.8 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 3\n",
+ "\n",
+ "Enthalpy of air: 215.89174489 kJ/kg dry air\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 4\n",
+ "\n",
+ "Enthalpy of air: 215.89174489 kJ/kg dry air\n",
+ "\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "Dry bulb temparature of air: 82.5843748998 OC\n",
+ "\n",
+ "Power delivered by heater: 34.3125 kW\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.3: Page 671"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.3\n",
+ "# Page: 671\n",
+ "\n",
+ "print'Illustration 12.3 - Page: 671\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "# ***Data***#\n",
+ "SsByA = 40;\n",
+ "x1 = 0.25;# [moisture fraction]\n",
+ "x2 = 0.06;# [moisture fraction]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg moisture/kg dry solid]\n",
+ "X2 = x2/(1-x2);# [kg moisture/kg dry solid]\n",
+ "# Fig. 12.10 (Pg 668) indicates that both constant and falling rate periods are involved.\n",
+ "\n",
+ "# Constant Rate period:\n",
+ "# From Fig. 12.10 (Pg 668):\n",
+ "Xc = 0.200;# [kg moisture/kg dry solid]\n",
+ "Nc = 0.3*10**(-3);# [kg/square m.s]\n",
+ "# From Eqn. 12.4:\n",
+ "thetha1 = SsByA*(X1-Xc)/Nc;# [s]\n",
+ "\n",
+ "# Falling Rate Period:\n",
+ "# From Fig. 12.10 (Pg 668):\n",
+ "# Data=[x N*10^3]\n",
+ "Data = numpy.array([[0.2 ,0.3],[0.18 ,0.266],[0.16 ,0.239],[0.14 ,0.208],[0.12, 0.180],[0.10 ,0.150],[0.09 ,0.097],[0.08, 0.070],[0.07 ,0.043],[0.064 ,0.025]]);\n",
+ "Val = zeros(10);\n",
+ "# Val=[(1/N)*10^(-3)]\n",
+ "for i in range(0,10):\n",
+ " Val[i] = 1/Data[i,1];\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid();\n",
+ "plt.xlabel(\"x [kg moisture / kg dry solid]\");\n",
+ "plt.ylabel(\"10^(-3) / N\");\n",
+ "plt.title(\"Graphical Integration Falling Rate Period\");\n",
+ "# Area under the curve:\n",
+ "Area = 1060.0;\n",
+ "# From Eqn. 12.3:\n",
+ "thetha2 = SsByA*Area;# [s]\n",
+ "thetha = thetha1+thetha2;# [s]\n",
+ "print\"Total Drying Time: \",round(thetha/3600,2),\"h\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.3 - Page: 671\n",
+ "\n",
+ "\n",
+ "Total Drying Time: 16.72 h\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2hhqV1jtutRXccQd84xtw003pZCqVl7pRzxmX54wrLzlj8COGOthhB/jzn+HA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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x772c470>"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.4: Page 676"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.4\n",
+ "# Page: 676\n",
+ "\n",
+ "print'Illustration 12.4 - Page: 676\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "#***Data***#\n",
+ "# For rectangular pan:\n",
+ "l = 0.7;# [m]\n",
+ "b = 0.7;# [m]\n",
+ "zS = 0.025;# [m]\n",
+ "zM = 0.0008;# [m]\n",
+ "d = 0.1;# [m]\n",
+ "Y1 = 0.01;# [kg water/kg dry air]\n",
+ "TempG = 65.0;# [OC]\n",
+ "v = 3.0;# [m/s]\n",
+ "TempR = 120.0;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "vH = (0.00283+(0.00456*Y1))*(TempG+273.0);# [cubic m/kg dry air]\n",
+ "Density_G = (1+Y1)/vH;# [kg/cubic m]\n",
+ "G = v*Density_G;# [kg/square m.s]\n",
+ "de = 4*d*l/(2*(l+d));# [m]\n",
+ "# From Eqn. 12.20:\n",
+ "hc = 5.90*G**0.71/de**0.29;# [W/square m.K]\n",
+ "# Assume:\n",
+ "e = 0.94;\n",
+ "# Estimate:\n",
+ "TempS = 38;# [OC]\n",
+ "# From Eqn. 12.14:\n",
+ "hR = e*5.729*10**(-8)*((273+TempR)**4-(273+TempS)**4)/((273.0+TempR)-(273+TempS));\n",
+ "A = l*b;# [square m]\n",
+ "Am = A;# [square m]\n",
+ "As = 4*l*zS;# [square m]\n",
+ "Au = Am+As;# [square m]\n",
+ "# Thermal Coductivities:\n",
+ "kM = 45;# [W/m.K]\n",
+ "kS = 3.5;# [W/m.K]\n",
+ "# By Eqn. 12.16:\n",
+ "Uk = 1/(((1/hc)*(A/Au))+((zM/kM)*(A/Au))+((zS/kS)*(A/Am)));# [W/squre m.K]\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "Cs = 1005+(1884*Y1);# [kJ/kg]\n",
+ "# At estimated 38 OC\n",
+ "lambdaS = 2411.4;# [kJ/kg]\n",
+ "# From Eqn. 12.18:\n",
+ "# (Ys-Y1)*lambdaS*10^3/Cs = ((1+(Uk/hc))*(TempG-Temps))+((hR/hC)*(TempR-TempS))\n",
+ "# On Simplifying:\n",
+ "# Ys = 0.0864-(10.194*10**(-4)*TempS)\n",
+ "# The eqn. is solved simultaneously with the saturated humidity curve of the psychometric chart for the air water mixture.\n",
+ "# From Fig. 12.12: (Pg 677)\n",
+ "Ys = 0.0460;# [kg water/kg dry air]\n",
+ "TempS = 39;# [OC]\n",
+ "# At 39 OC\n",
+ "lambdaS = 2409.7;# [kJ/kg]\n",
+ "# From Eqn. 12.17:\n",
+ "Nc = (((hc+Uk)*(TempG-TempS))+(hR*(TempR-TempS)))/(lambdaS*10**(3));# [kg water evaporated/square m.s]\n",
+ "print\"The Evaporation Rate: \",round(Nc*A,8),\" kg/s\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "# When no radiation or conduction of heat through the solid occurs, the drying surface assumes wet bulb temparature of the air.\n",
+ "# From Fig. 12.12 (Pg 677)\n",
+ "TempS = 28.5;# [OC]\n",
+ "Ys = 0.025;# [kg water/kg dry air]\n",
+ "lambdaS = 2435;# [kJ/kg]\n",
+ "# From Eqn. 12.17:\n",
+ "Nc = hc*(TempG-TempS)/(lambdaS*10**3);# [kg/aquare m.s]\n",
+ "print\"The Evaporation Rate: \",round(Nc*A,8), \"kg/s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.4 - Page: 676\n",
+ "\n",
+ "\n",
+ "The Evaporation Rate: 0.0003851 kg/s\n",
+ "\n",
+ "The Evaporation Rate: 0.00016105 kg/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.5: Page 684"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.5\n",
+ "# Page: 684\n",
+ "\n",
+ "print'Illustration 12.5 - Page: 684\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "from scipy import integrate\n",
+ "import math\n",
+ "#***Data***#\n",
+ "x1 = 0.025;# [moisture fraction]\n",
+ "x2 = 0.001;# [moisture fraction]\n",
+ "zS = 0.018;# [m]\n",
+ "dp = 2*10**(-4);# [m]\n",
+ "Density_S = 1350;# [kg dry solid/cubic m]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg water/kg dry air]\n",
+ "X2 = x2/(1-x2);# [kg water/kg dry air]\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Y1 = 0.0153;# [kg water/kg dry air]\n",
+ "Tempas = 24;# [OC]\n",
+ "Yas = 0.0190;# [kg water/kg dry air]\n",
+ "Gs = 0.24;# [kg dry air/square m.s]\n",
+ "Gav = Gs+(Gs*(Y1+Yas)/2.0);# [kg dry air/square m.s]\n",
+ "# From Eqn. 12.26:\n",
+ "Nmax = Gs*(Yas-Y1);# [kg evaporated/square m.s]\n",
+ "viscosity_air = 1.8*10**(-5);# [kg/m.s]\n",
+ "X3=lambda X : 1/(Nmax*(1-math.exp(-(0.273/dp**0.35)*((dp*Gav/viscosity_air)**0.215)*(Density_S*zS*X)**0.64)));\n",
+ "Value = integrate.quad(X3,X2,X1);\n",
+ "# From Eqn. 12.3:\n",
+ "thetha = Density_S*zS*Value[0];# [s]\n",
+ "print\"The time for drying: \",round(thetha/60,3),\" min\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.5 - Page: 684\n",
+ "\n",
+ "\n",
+ "The time for drying: 12.593 min\n"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.6: Page 685"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.6\n",
+ "# Page: 685\n",
+ "\n",
+ "print'Illustration 12.6 - Page: 685\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "\n",
+ "import math\n",
+ "#***Data***#\n",
+ "Y1 = 0.01;# [kg water/kg dry air]\n",
+ "Gs = 1.1;# [kg dry air/square m.s]\n",
+ "dia = 13.5/1000;# [m]\n",
+ "l = 13.0/1000;# [m]\n",
+ "zS = 50.0/1000;# [m]\n",
+ "Density_S = 600.0;# [kg dry solid/square m.s]\n",
+ "a = 280.0;# [square m/cubic m]\n",
+ "#************#\n",
+ "\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Yas = 0.031;# [kg water/kg dry air]\n",
+ "Gav = Gs+(Gs*(Y1+Yas)/2.0);# [kg/square m.s]\n",
+ "viscosity_air = 1.9*10**(-5);# [kg/m.s]\n",
+ "Area = (2.0*math.pi*dia**2.0/4)+(math.pi*dia*l);# [square m]\n",
+ "dp = (Area/math.pi)**0.5;# [m]\n",
+ "# From Table 3.3 (Pg 74)\n",
+ "Re = dp*Gav/viscosity_air;\n",
+ "e = 1.0-(dp*a/6);# [fraction voids]\n",
+ "jD = (2.06/e)*Re**(-0.575);\n",
+ "# For air water mixture:\n",
+ "Sc = 0.6;\n",
+ "# From Eqn. 12.33:\n",
+ "kY = jD*Gs/Sc**(2.0/3);# [kg H2O/square m.s.deltaX]\n",
+ "# From Eqn. 12.30:\n",
+ "NtG = kY*a*zS/Gs;\n",
+ "# From Eqn. 12.25:\n",
+ "Nmax = Gs*(Yas-Y1);# [kg/square m.s]\n",
+ "# From Eqn. 12.31:\n",
+ "N = Nmax*(1-math.exp(-NtG));# [kg water evaporated/square m.s]\n",
+ "Y2 = (Yas-Y1)*(N/Nmax)+Y1;# [kg water/kg dry air]\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Tempas = 33.0;# [OC]\n",
+ "# From eqn. 12.2:\n",
+ "Rate = N/(Density_S*zS);# [kg H2O/(kg dry solid).s]\n",
+ "print\"Humidity of the exit air: \",round(Y2,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Temparature of exit air: \",Tempas,\" degree C\\n\"\n",
+ "print\"Rate of Drying: \",round(Rate,7),\" kg H2O/(kg dry solid).s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.6 - Page: 685\n",
+ "\n",
+ "\n",
+ "Humidity of the exit air: 0.0302 kg water/kg dry air\n",
+ "\n",
+ "Temparature of exit air: 33.0 degree C\n",
+ "\n",
+ "Rate of Drying: 0.0007409 kg H2O/(kg dry solid).s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 26
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.7: Page 700"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.7\n",
+ "# Page: 700\n",
+ "\n",
+ "print'Illustration 12.7 - Page: 700\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "\n",
+ "import math\n",
+ "from numpy.linalg import inv\n",
+ "#***Data***#\n",
+ "x1 = 3.5;# [percent moisture]\n",
+ "x2 = 0.2;# [percent moisture]\n",
+ "dia = 1.2;# [m]\n",
+ "l = 6.7;# [m]\n",
+ "Rate_prod = 900.0;# [kg/h]\n",
+ "y2 = 0.5;# [Humidity]\n",
+ "TempG2 = 90.0;# [OC]\n",
+ "TempG1 = 32.0;# [OC]\n",
+ "TempS1 = 25.0;# [OC]\n",
+ "TempS2 = 60.0;# [OC]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(100.0-x1);# [kg H2O/kg dry solid]\n",
+ "X2 = x2/(100.0-x2);# [kg H2O/kg dry solid]\n",
+ "Ss = Rate_prod*(1-X2);# [kg dry solid/h]\n",
+ "Rate_drying = Ss*(X1-X2);# [kg water evaporated/h]\n",
+ "Y2 = (y2/(1-y2))/100.0;# [kg water/kg dry air]\n",
+ "Tempo = 0.0;# [Base temp,OC]\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "# Enthalpy of air entering the drier:\n",
+ "HG2 = (1005.0+(1884.0*Y2))*(TempG2-Tempo)+(2502300.0*Y2);# [J/kg dry air]\n",
+ "# For the outlet air:\n",
+ "# HG1 = (1005.0+(1884*Y1))*(TempG1-Tempo)+(2502300*Y1); [J/kg dry air]\n",
+ "# HG1 = (1005.0*TempG1)+((1884+TempG1)+2502300)*Y1; [J/kg dry air]\n",
+ "CsNH4 = 1507.0;# [J/kg.K]\n",
+ "CsH2O = 4187.0;# [J/kg.K]\n",
+ "# From Eqn. 11.45:\n",
+ "HS2 = CsNH4*(TempS2-Tempo)+(X2*CsH2O*(TempS2-Tempo));# [J/kg dry air]\n",
+ "HS1 = CsNH4*(TempS1-Tempo)+(X1*CsH2O*(TempS1-Tempo));# [J/kg dry air]\n",
+ "# The estimated combined natural convection and radiation heat transfer coeffecient from the drier to the surrounding:\n",
+ "h = 12.0;# [W/square m.K]\n",
+ "deltaTemp = ((TempG2-TempS1)+(TempG1-TempS1))/2;# [OC]\n",
+ "Ae = math.pi*dia*l;# [square m]\n",
+ "Q = h*3600.0*Ae*deltaTemp;# [kJ/h]\n",
+ "# Moisture Balance, Eqn. 12.39:\n",
+ "# Ss*(X1-X2) = Gs(Y1-Y2)\n",
+ "# (Gs*Y1)-(Gs*Y2) = (Ss*(X1-X2)) ........(1)\n",
+ "# Enthalapy Balance, Eqn. 12.40:\n",
+ "# (Ss*HS1)+(Gs*HG2) = (Ss*HG2)+(Gs*HG1)+Q \n",
+ "# Gs*(HG2-HG1) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-((1005*TempG1)+((1884+TempG1)+2502300)*Y1)) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-(1005*TempG1))-(Gs*Y1*((1884+TempG1)+2502300)) = (Ss*HS2)+Q-(Ss*HS1)........ (2)\n",
+ "# Solving Simultaneously:\n",
+ "a = numpy.array([[HG2-(1005.0*TempG1),-((1884.0+TempG1)+2502300.0)],[(-Y2), 1.0]]);\n",
+ "b = numpy.array([[((Ss*HS2)+Q-(Ss*HS1))],[(Ss*(X1-X2))]]);\n",
+ "c=inv(a)\n",
+ "soln =np.dot(c, b)\n",
+ "Gs = soln[0];# [kg dry air/h]\n",
+ "Y1 = soln[1]/soln[0];# [kg water/kg dry air]\n",
+ "# From Fig. 7.5 (Pg 232)\n",
+ "Enthalpy_air = 56.0;# [kJ/kg dry air]\n",
+ "HeatLoad = Gs*(HG2-Enthalpy_air*1000);# [W]\n",
+ "print\"Air Flow Rate: \",round(Gs,2),\" kg/h\\n\"\n",
+ "print\"Moisture content of air: \",round(Y1,2),\" kg water/kg dry air \\n\"\n",
+ "print\"Heat Load of drier: \",round(HeatLoad/1000),\" kW\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.7 - Page: 700\n",
+ "\n",
+ "\n",
+ "Air Flow Rate: 2681.03 kg/h\n",
+ "\n",
+ "Moisture content of air: 0.02 kg water/kg dry air \n",
+ "\n",
+ "Heat Load of drier: 163995.0 kW\n"
+ ]
+ }
+ ],
+ "prompt_number": 50
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.8: Page 705"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.8\n",
+ "# Page: 705\n",
+ "\n",
+ "print'Illustration 12.8 - Page: 705\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "from numpy.linalg import inv\n",
+ "import numpy as np\n",
+ "#***Data***#\n",
+ "x1 = 8.0;# [percent moisture]\n",
+ "x2 = 0.5;# [percent moisture]\n",
+ "Rate_prod = 0.63;# [kg/s]\n",
+ "# Drying Gas:\n",
+ "xCO2 = 0.025;# [mole fraction]\n",
+ "xO2 = 0.147;# [mole fraction]\n",
+ "xN2 = 0.760;# [mole fraction]\n",
+ "xH2O = 0.068;# [mole fraction]\n",
+ "TempG2 = 480.0;# [OC]\n",
+ "Cs = 0.837;# [kJ/kg.K]\n",
+ "Temp1 = 27.0;# [OC]\n",
+ "Temp2 = 150.0;# [OC]\n",
+ "dp = 200.0*10**(-6);# [m]\n",
+ "Density_S = 1300.0;# [kg/cubic m]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(100-x1);# [kg water/kg dry solid]\n",
+ "X2 = x2/(100-x2);# [kg water/kg dry solid]\n",
+ "Ss = Rate_prod*(1-X2);# [kg dry solid/s]\n",
+ "Water_evap = Ss*(X1-X2);# [kg/s]\n",
+ "# Basis: 1 kmol of dry gas:\n",
+ "xDry = 1.0-xH2O;# [kmol]\n",
+ "XCO2 = 44.0*xCO2;# [kg]\n",
+ "XO2 = 32.0*xO2;# [kg]\n",
+ "XN2 = 28.0*xN2;# [kg]\n",
+ "Xdry = XCO2+XO2+XN2;# [kg]\n",
+ "cCO2 = 45.6;# [kJ/kmol.K]\n",
+ "cO2 = 29.9;# [kJ/kmol.K]\n",
+ "cN2 = 29.9;# [kJ/kmol.K]\n",
+ "cH2O = 4.187;# [kJ/kg.K]\n",
+ "Mav = Xdry/xDry;# [kg/kmol]\n",
+ "Y2 = xH2O*18.02/(xDry*Mav);# [kg water/kg dry gas]\n",
+ "cav = ((xCO2*cCO2)+(xO2*cO2)+(xN2*cN2))/(xDry*Mav);# [kJ/kmol.K]\n",
+ "# Assume:\n",
+ "TempG1 = 120.0;# [OC]\n",
+ "cDry = 1.005;# [kJ/kmol.K]\n",
+ "Tempo = 0;# [Base Temp,OC]\n",
+ "# By Eqn. 7.13:\n",
+ "HG2 = (cav+(1.97*Y2))*(TempG2-Tempo)+(2502.3*Y2);# [kJ/kg dry air]\n",
+ "# For the outlet air:\n",
+ "# HG1 = (1.005+(1.884*Y1))*(TempG1-Tempo)+(2502.3*Y1); [kJ/kg dry air]\n",
+ "# HG1 = (1.005*TempG1)+((1.884+TempG1)+2502.3)*Y1; [kJ/kg dry air]\n",
+ "# By Eqn. 11.45:\n",
+ "HS1 = (Cs*(Temp1-Tempo))+(cH2O*X1*(Temp1-Tempo));# [kJ/kg dry air]\n",
+ "HS2 = (Cs*(Temp2-Tempo))+(cH2O*X2*(Temp2-Tempo));# [kJ/kg dry air]\n",
+ "# Q = 0.15*HG2*Gs; [kJ/s]\n",
+ "# Moisture Balance, Eqn. 12.39:\n",
+ "# Ss*(X1-X2) = Gs(Y1-Y2)\n",
+ "# (Gs*Y1)-(Gs*Y2) = (Ss*(X1-X2)) ........(1)\n",
+ "# Enthalapy Balance, Eqn. 12.40:\n",
+ "# (Ss*HS1)+(Gs*HG2) = (Ss*HG2)+(Gs*HG1)+Q \n",
+ "# Gs*(HG2-HG1) = (Ss*HS2)+(0.15*HG2*Gs)-(Ss*HS1)\n",
+ "# Gs*(HG2-(0.15*HG2)-((1.005*TempG1)+((1.884+TempG1)+2502.3)*Y1)) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-(0.15*HG2)-(1.005*TempG1))-(Gs*Y1*((1.884+TempG1)+2502.3)) = (Ss*HS2)+Q-(Ss*HS1)........ (2)\n",
+ "a = np.array([[(HG2-(0.15*HG2)-(1.005*TempG1)),-((1.884+TempG1)+2502.3)],[(-Y2), 1.0]]);\n",
+ "b = np.array([(Ss*HS2)-(Ss*HS1),(Ss*(X1-X2))]);\n",
+ "c=inv(a)\n",
+ "soln = np.dot(c, b)\n",
+ "Gs = soln[0];# [kg dry air/s]\n",
+ "Y1 = soln[1]/soln[0];# [kg water/kg dry gas]\n",
+ "HG1 = (1.005+(1.884*Y1))*(TempG1-Tempo)+(2502.3*Y1);# [kJ/kg dry air]\n",
+ "Q = 0.15*HG2*Gs;# [kJ/s]\n",
+ "# Assuming the sychrometric ratio of the gas as same as that of air:\n",
+ "# For Zone II:\n",
+ "Tempw = 65.0;# [OC]\n",
+ "Temp_A = 68.0;# [OC]\n",
+ "# At point A, Fig. 12.28 (Pg 702)\n",
+ "Enthalpy_A = Cs*(Temp_A-Tempo)+(X1*cH2O*(Temp_A-Tempo));# [kJ/kg dry air]\n",
+ "# At point B, Fig. 12.28 (Pg 702)\n",
+ "Temp_B = Temp_A;# [OC]\n",
+ "Enthalpy_B = Cs*(Temp_B-Tempo)+(X2*cH2O*(Temp_B-Tempo));# [kJ/kg dry air]\n",
+ "\n",
+ "# Assuming that the heat losses in the three zones are propotional to the number of transfer units in each zone and to the average temp. difference between the gas and the surrounding air.\n",
+ "# Fractional heat loss in each Zone:\n",
+ "fr1 = 0.14;\n",
+ "fr2 = 0.65;\n",
+ "fr3 = 0.20;\n",
+ "# Calculations for zone III:\n",
+ "Cs3 = cav+(1.97*Y2);# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "def f1(TempGD):\n",
+ " return (Gs*Cs3*(TempG2-TempGD))-(Ss*(HS2-Enthalpy_B)+(fr3*Q))\n",
+ "TempGD = fsolve(f1,7);# [OC]\n",
+ "delta_TempG = Ss*(HS2-Enthalpy_B)/(Gs*Cs3);# [OC]\n",
+ "delta_TempM = ((TempG2-Temp2)+(TempGD-Temp_A))/2;# [OC]\n",
+ "NtoG3 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "# Calculations for zone I:\n",
+ "Cs1 = 1.005+(1.884*Y1);# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "def f2(TempGC):\n",
+ " return (Gs*Cs1*(TempGC-TempG1))-(Ss*(Enthalpy_A-HS1)+(fr1*Q))\n",
+ "TempGC = fsolve(f2,7);# [OC]\n",
+ "delta_TempG = Ss*(Enthalpy_A-HS1)/(Gs*Cs1);# [OC]\n",
+ "delta_TempM = ((TempGC-Temp_A)+(TempG1-Temp1))/2;# [OC]\n",
+ "NtoG1 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "# Calculations for zone II:\n",
+ "Cs2 = (cav+Cs1)/2.0;# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "True_deltaTemp = TempGD-TempGC;# [OC]\n",
+ "delta_Temp = fr2*Q/(Cs1*Gs);# [Change in temp resulting from heat loss,OC]\n",
+ "delta_TempG = True_deltaTemp-delta_Temp;# [OC]\n",
+ "delta_TempM = ((TempGD-Temp_A)-(TempGC-Temp_A))/log((TempGD-Temp_A)/(TempGC-Temp_A));# [OC]\n",
+ "NtoG2 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "NtoG = NtoG1+NtoG2+NtoG3;\n",
+ "\n",
+ "# Standard diameters are availaible at 1, 1.2 & 1.4 m.\n",
+ "Td = 1.2;# [m]\n",
+ "Area = math.pi*Td**2.0/4;# [square m]\n",
+ "Gs = Gs/Area;# [kg/square m.s]\n",
+ "Ss = Ss/Area;# [kg/square m.s]\n",
+ "Gav = Gs*(1+(Y1+Y2)/2.0);# [kg/square m.s]\n",
+ "# From Eqn. 12.47:\n",
+ "Ua = 237.0*Gav**0.417/Td;# [W/square m.K]\n",
+ "HtoG = Gs*Cs2*1000.0/Ua;# [m]\n",
+ "Z = NtoG*HtoG;# [m]\n",
+ "# Assume:\n",
+ "v = 0.35;# [m/s]\n",
+ "N = v/(math.pi*Td);# [1/s]\n",
+ "# From Eqn. 12.37:\n",
+ "K = 0.6085/(Density_S*dp**(1.0/2));\n",
+ "# Take:\n",
+ "phi_D = 0.05;\n",
+ "# From Eqn. 12.35:\n",
+ "phi_DO = phi_D-(K*Gav);\n",
+ "# From Eqn. 12.35:\n",
+ "s = 0.3344*Ss/(phi_DO*Density_S*N**0.9*Td);# [m/s]\n",
+ "print\"Height of the drier: \",round(Z,2),\" m\\n\"\n",
+ "print\"Drier Slope: \",round(s,5),\" m/m \\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.8 - Page: 705\n",
+ "\n",
+ "\n",
+ "Height of the drier: 5.89 m\n",
+ "\n",
+ "Drier Slope: 0.03304 m/m \n"
+ ]
+ }
+ ],
+ "prompt_number": 56
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.9: Page 709"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.9\n",
+ "# Page: 709\n",
+ "\n",
+ "print'Illustration 12.9 - Page: 709\\n\\n'\n",
+ "import numpy as np\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "# Solution \n",
+ "\n",
+ "#***Data***#\n",
+ "x1 = 0.46;# [fraction moisture]\n",
+ "x2 = 0.085;# [fraction moisture]\n",
+ "Y1 = 0.08;# [kg water/kg dry solid]\n",
+ "Y2 = 0.03;# [kg water/kg dry solid]\n",
+ "G = 1.36;# [kg/square m.s]\n",
+ "#**********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg water/kg dry solid]\n",
+ "X2 = x2/(1-x2);# [kg water/kg dry solid]\n",
+ "# By water balance:\n",
+ "SsByGs = (Y1-Y2)/(X1-X2);# [kg dry solid/kg air]\n",
+ "# Since the initial moisture content of the rayon is less than the critical, drying takes place entirely within zone III.\n",
+ "# Comparing with Eqn. 12.22:\n",
+ "# (kY*A/(Ss(Xc-X*)))=0.0137*G**1.47\n",
+ "# thetha=integrate('(1/(0.0137*G**1.47))*(1/((X-X_star)*(Yw-Y)))','X',X2,X1) # [s]\n",
+ "X = np.array([X1, 0.80, 0.60, 0.40, 0.20 ,X2]);# [kg water/kg dry solid]\n",
+ "Y = zeros(6);\n",
+ "for i in range(0,6):\n",
+ " # From Eqn. 12.54:\n",
+ " Y[i] = Y2+((X[i]-X2)*SsByGs);# [kg water/kg dry gas]\n",
+ "\n",
+ "# From Fig. 7.5 (Pg 232):\n",
+ "Yw = np.array([0.0950, 0.0920, 0.0790, 0.0680, 0.0550, 0.0490]);# [kg water/kg dry gas]\n",
+ "X_star = zeros(6);\n",
+ "RH=zeros(6)\n",
+ "Val = zeros(6);\n",
+ "P = 51780.0;# [vapour pressure, kN/square m]\n",
+ "for i in range(0,6):\n",
+ " # From Eqn 7.8:\n",
+ " def f(p):\n",
+ " return Y[i]-((p/(101330.0-p))*(18.0/29))\n",
+ " p = fsolve(f,7);# [kN/square m]\n",
+ " RH[i] = (p/P)*100.0;\n",
+ " X_star[i] = (RH[i]/4)/(100.0-(RH[i]/4));# [kg water/kg dry solid]\n",
+ " Val[i] = 1/((X[i]-X_star[i])*(Yw[i]-Y[i]));\n",
+ "\n",
+ "plt.plot(X,Val);\n",
+ "plt.grid();\n",
+ "plt.xlabel(\"X kg water/kg dry solid\");\n",
+ "plt.ylabel(\"1/((X-X*)*(Yw-Y))\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area Under the curve:\n",
+ "Area = 151.6;\n",
+ "# From Eqn. 12.59:\n",
+ "thetha = Area/(0.0137*G**1.47);\n",
+ "print\"Time required for drying: \",round(thetha/3600,2),\" h\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.9 - Page: 709\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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tyX4amWWeDuTqL+nPyd/gVEnbVFyhmUX3Qyg1zQMagdWAVmDrkmU+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\nc86V5Y2Ec865sryRcM45V9b/B77dKwo9Y8ZFAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7885c50>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Time required for drying: 1.96 h\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter12_2.ipynb b/Mass_-_Transfer_Operations/Chapter12_2.ipynb
new file mode 100755
index 00000000..9416a787
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter12_2.ipynb
@@ -0,0 +1,932 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:7b3124ef7f3febbf9fbbcfec34e4b1fa9fd03169d0e2aea25265368468350ca6"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 12: Drying"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.1: Page 660"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.1\n",
+ "# Page: 660\n",
+ "\n",
+ "print'Illustration 12.1 - Page: 660\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "#****Data****#\n",
+ "F=1000;# [kg]\n",
+ "Xo=0.8;# [wt. fraction water]\n",
+ "X1=0.05;# [wt. fraction water]\n",
+ "#************#\n",
+ "\n",
+ "Yo=Xo/(1-Xo);# [kg water/kg dry solid]\n",
+ "Y1=X1/(1-X1);# [kg water/kg dry solid]\n",
+ "solid=F*(1-X1);# [kg]\n",
+ "print\"Moisture to be evaporated: \",solid*(Yo-Y1),\" kg\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.1 - Page: 660\n",
+ "\n",
+ "\n",
+ "Moisture to be evaporated: 3750.0 kg\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.2: Page 665"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.2\n",
+ "# Page: 665\n",
+ "\n",
+ "print'Illustration 12.2 - Page: 665\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "# ***Data***#\n",
+ "Y1 = 0.05;# [kg water/kg dry air]\n",
+ "Yair = 0.01;# [kg water/kg dry air]\n",
+ "TempG1 = 95;# [OC]\n",
+ "width = 1;# [m]\n",
+ "apart = 100.0/1000;# [m]\n",
+ "deep = 38.0/1000;# [m]\n",
+ "Rate_evaporation=7.5*10**(-3);# [kg/s]\n",
+ "#*****************#\n",
+ "\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "vH = (0.00283+(0.00456*Y1))*(TempG1+273);# [cubic m/kg dry air]\n",
+ "freeArea = width*(apart-deep)*11;# [square m]\n",
+ "# Rate of air flow at 1:\n",
+ "Rate_air1 = 3*freeArea/vH;# [square m]\n",
+ "Y2 = Y1+(Rate_evaporation/Rate_air1);# [kg water/kg dry air]\n",
+ "# Assuming adiabatic drying:\n",
+ "# From adiabatic saturation curve, Fig 7.5: (Pg 232)\n",
+ "TempG2 = 86.0;# [OC]\n",
+ "# Overall Water Balance:\n",
+ "G = Rate_evaporation/(Y1-Yair);# [kg dry air/s]\n",
+ "# Rate of air flow at 3:\n",
+ "Rate_air3 = Rate_air1+G;# [kg dry air/s]\n",
+ "# Rate of air flow at 4:\n",
+ "Rate_air4 = Rate_air3;# [kg dry air/s]\n",
+ "# Volumetric Rate through fan:\n",
+ "Rate_fan = Rate_air3/vH;# [cubic m/s]\n",
+ "print\"Percentage of air recycled is:\",round((Rate_air1/Rate_air3)*100,2),\"%\\n\",\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# From Fig. 7.5 (page 232):\n",
+ "# Saturated enthalpy at adiabatic saturation temp.\n",
+ "Enthalpy1 = 233.0;# [kJ/kg dry air]\n",
+ "Enthalpy2 = 233.0;# [kJ/kg dry air]\n",
+ "# Enthalpy of fresh air:\n",
+ "Enthalpy_air = 50.0;# [kJ/kg dry air]\n",
+ "# Assuming complete mixing, by Enthalpy mixing:\n",
+ "Enthalpy3 = ((Enthalpy1*Rate_air1)+(Enthalpy_air*G))/Rate_air3;# [kJ/kg dry air]\n",
+ "Enthalpy4 = Enthalpy3;# [kJ/kg dry air]\n",
+ "# From table 7.1: (Pg 234)\n",
+ "Temp_dry = ((Enthalpy3*1000.0)-(2502300.0*Y1))/(1005.0+(1884.0*Y1));\n",
+ "Power = (Enthalpy2-Enthalpy3)*Rate_air3;# [kW]\n",
+ "# From Fig. 7.5, (Pg 232)\n",
+ "DewPoint1 = 40.4;# [OC]\n",
+ "DewPoint2 = 41.8;# [OC]\n",
+ "DewPoint3 = 40.4;# [OC]\n",
+ "DewPoint4 = 40.4;# [OC]\n",
+ "print\"At Point 1\\n\"\n",
+ "print\"Enthalpy of air:\",Enthalpy1,\" kJ/kg dry air\\n\",\n",
+ "print\"Dew Point of air: \",DewPoint1,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 2\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy2,\" kJ/kg dry air\\n\"\n",
+ "print\"Dew Point of air: \",DewPoint2,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 3\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy3,\" kJ/kg dry air\\n\",\n",
+ "print\"Dew Point of air: \",DewPoint3,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"At Point 4\\n\"\n",
+ "print\"Enthalpy of air: \",Enthalpy4,\" kJ/kg dry air\\n\"\n",
+ "print\"Dew Point of air: \",DewPoint4,\" degree C\\n\"\n",
+ "print\"\\n\"\n",
+ "print\"Dry bulb temparature of air: \",Temp_dry,\" OC\\n\"\n",
+ "print\"Power delivered by heater: \",Power,\" kW\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.2 - Page: 665\n",
+ "\n",
+ "\n",
+ "Percentage of air recycled is: 90.65 %\n",
+ "\n",
+ "\n",
+ "At Point 1\n",
+ "\n",
+ "Enthalpy of air: 233.0 kJ/kg dry air\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 2\n",
+ "\n",
+ "Enthalpy of air: 233.0 kJ/kg dry air\n",
+ "\n",
+ "Dew Point of air: 41.8 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 3\n",
+ "\n",
+ "Enthalpy of air: 215.89174489 kJ/kg dry air\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "At Point 4\n",
+ "\n",
+ "Enthalpy of air: 215.89174489 kJ/kg dry air\n",
+ "\n",
+ "Dew Point of air: 40.4 degree C\n",
+ "\n",
+ "\n",
+ "\n",
+ "Dry bulb temparature of air: 82.5843748998 OC\n",
+ "\n",
+ "Power delivered by heater: 34.3125 kW\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.3: Page 671"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.3\n",
+ "# Page: 671\n",
+ "\n",
+ "print'Illustration 12.3 - Page: 671\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "# ***Data***#\n",
+ "SsByA = 40;\n",
+ "x1 = 0.25;# [moisture fraction]\n",
+ "x2 = 0.06;# [moisture fraction]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg moisture/kg dry solid]\n",
+ "X2 = x2/(1-x2);# [kg moisture/kg dry solid]\n",
+ "# Fig. 12.10 (Pg 668) indicates that both constant and falling rate periods are involved.\n",
+ "\n",
+ "# Constant Rate period:\n",
+ "# From Fig. 12.10 (Pg 668):\n",
+ "Xc = 0.200;# [kg moisture/kg dry solid]\n",
+ "Nc = 0.3*10**(-3);# [kg/square m.s]\n",
+ "# From Eqn. 12.4:\n",
+ "thetha1 = SsByA*(X1-Xc)/Nc;# [s]\n",
+ "\n",
+ "# Falling Rate Period:\n",
+ "# From Fig. 12.10 (Pg 668):\n",
+ "# Data=[x N*10^3]\n",
+ "Data = numpy.array([[0.2 ,0.3],[0.18 ,0.266],[0.16 ,0.239],[0.14 ,0.208],[0.12, 0.180],[0.10 ,0.150],[0.09 ,0.097],[0.08, 0.070],[0.07 ,0.043],[0.064 ,0.025]]);\n",
+ "Val = zeros(10);\n",
+ "# Val=[(1/N)*10^(-3)]\n",
+ "for i in range(0,10):\n",
+ " Val[i] = 1/Data[i,1];\n",
+ "\n",
+ "plt.plot(Data[:,0],Val);\n",
+ "plt.grid();\n",
+ "plt.xlabel(\"x [kg moisture / kg dry solid]\");\n",
+ "plt.ylabel(\"10^(-3) / N\");\n",
+ "plt.title(\"Graphical Integration Falling Rate Period\");\n",
+ "# Area under the curve:\n",
+ "Area = 1060.0;\n",
+ "# From Eqn. 12.3:\n",
+ "thetha2 = SsByA*Area;# [s]\n",
+ "thetha = thetha1+thetha2;# [s]\n",
+ "print\"Total Drying Time: \",round(thetha/3600,2),\"h\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.3 - Page: 671\n",
+ "\n",
+ "\n",
+ "Total Drying Time: 16.72 h\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2hhqV1jtutRXccQd84xtw003pZCqVl7pRzxmX54wrLzlj8COGOthhB/jzn+HA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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x772c470>"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.4: Page 676"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.4\n",
+ "# Page: 676\n",
+ "\n",
+ "print'Illustration 12.4 - Page: 676\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "#***Data***#\n",
+ "# For rectangular pan:\n",
+ "l = 0.7;# [m]\n",
+ "b = 0.7;# [m]\n",
+ "zS = 0.025;# [m]\n",
+ "zM = 0.0008;# [m]\n",
+ "d = 0.1;# [m]\n",
+ "Y1 = 0.01;# [kg water/kg dry air]\n",
+ "TempG = 65.0;# [OC]\n",
+ "v = 3.0;# [m/s]\n",
+ "TempR = 120.0;# [OC]\n",
+ "#*************#\n",
+ "\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "vH = (0.00283+(0.00456*Y1))*(TempG+273.0);# [cubic m/kg dry air]\n",
+ "Density_G = (1+Y1)/vH;# [kg/cubic m]\n",
+ "G = v*Density_G;# [kg/square m.s]\n",
+ "de = 4*d*l/(2*(l+d));# [m]\n",
+ "# From Eqn. 12.20:\n",
+ "hc = 5.90*G**0.71/de**0.29;# [W/square m.K]\n",
+ "# Assume:\n",
+ "e = 0.94;\n",
+ "# Estimate:\n",
+ "TempS = 38;# [OC]\n",
+ "# From Eqn. 12.14:\n",
+ "hR = e*5.729*10**(-8)*((273+TempR)**4-(273+TempS)**4)/((273.0+TempR)-(273+TempS));\n",
+ "A = l*b;# [square m]\n",
+ "Am = A;# [square m]\n",
+ "As = 4*l*zS;# [square m]\n",
+ "Au = Am+As;# [square m]\n",
+ "# Thermal Coductivities:\n",
+ "kM = 45;# [W/m.K]\n",
+ "kS = 3.5;# [W/m.K]\n",
+ "# By Eqn. 12.16:\n",
+ "Uk = 1/(((1/hc)*(A/Au))+((zM/kM)*(A/Au))+((zS/kS)*(A/Am)));# [W/squre m.K]\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "Cs = 1005+(1884*Y1);# [kJ/kg]\n",
+ "# At estimated 38 OC\n",
+ "lambdaS = 2411.4;# [kJ/kg]\n",
+ "# From Eqn. 12.18:\n",
+ "# (Ys-Y1)*lambdaS*10^3/Cs = ((1+(Uk/hc))*(TempG-Temps))+((hR/hC)*(TempR-TempS))\n",
+ "# On Simplifying:\n",
+ "# Ys = 0.0864-(10.194*10**(-4)*TempS)\n",
+ "# The eqn. is solved simultaneously with the saturated humidity curve of the psychometric chart for the air water mixture.\n",
+ "# From Fig. 12.12: (Pg 677)\n",
+ "Ys = 0.0460;# [kg water/kg dry air]\n",
+ "TempS = 39;# [OC]\n",
+ "# At 39 OC\n",
+ "lambdaS = 2409.7;# [kJ/kg]\n",
+ "# From Eqn. 12.17:\n",
+ "Nc = (((hc+Uk)*(TempG-TempS))+(hR*(TempR-TempS)))/(lambdaS*10**(3));# [kg water evaporated/square m.s]\n",
+ "print\"The Evaporation Rate: \",round(Nc*A,8),\" kg/s\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "# When no radiation or conduction of heat through the solid occurs, the drying surface assumes wet bulb temparature of the air.\n",
+ "# From Fig. 12.12 (Pg 677)\n",
+ "TempS = 28.5;# [OC]\n",
+ "Ys = 0.025;# [kg water/kg dry air]\n",
+ "lambdaS = 2435;# [kJ/kg]\n",
+ "# From Eqn. 12.17:\n",
+ "Nc = hc*(TempG-TempS)/(lambdaS*10**3);# [kg/aquare m.s]\n",
+ "print\"The Evaporation Rate: \",round(Nc*A,8), \"kg/s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.4 - Page: 676\n",
+ "\n",
+ "\n",
+ "The Evaporation Rate: 0.0003851 kg/s\n",
+ "\n",
+ "The Evaporation Rate: 0.00016105 kg/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.5: Page 684"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.5\n",
+ "# Page: 684\n",
+ "\n",
+ "print'Illustration 12.5 - Page: 684\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "from scipy import integrate\n",
+ "import math\n",
+ "#***Data***#\n",
+ "x1 = 0.025;# [moisture fraction]\n",
+ "x2 = 0.001;# [moisture fraction]\n",
+ "zS = 0.018;# [m]\n",
+ "dp = 2*10**(-4);# [m]\n",
+ "Density_S = 1350;# [kg dry solid/cubic m]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg water/kg dry air]\n",
+ "X2 = x2/(1-x2);# [kg water/kg dry air]\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Y1 = 0.0153;# [kg water/kg dry air]\n",
+ "Tempas = 24;# [OC]\n",
+ "Yas = 0.0190;# [kg water/kg dry air]\n",
+ "Gs = 0.24;# [kg dry air/square m.s]\n",
+ "Gav = Gs+(Gs*(Y1+Yas)/2.0);# [kg dry air/square m.s]\n",
+ "# From Eqn. 12.26:\n",
+ "Nmax = Gs*(Yas-Y1);# [kg evaporated/square m.s]\n",
+ "viscosity_air = 1.8*10**(-5);# [kg/m.s]\n",
+ "X3=lambda X : 1/(Nmax*(1-math.exp(-(0.273/dp**0.35)*((dp*Gav/viscosity_air)**0.215)*(Density_S*zS*X)**0.64)));\n",
+ "Value = integrate.quad(X3,X2,X1);\n",
+ "# From Eqn. 12.3:\n",
+ "thetha = Density_S*zS*Value[0];# [s]\n",
+ "print\"The time for drying: \",round(thetha/60,3),\" min\\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.5 - Page: 684\n",
+ "\n",
+ "\n",
+ "The time for drying: 12.593 min\n"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.6: Page 685"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.6\n",
+ "# Page: 685\n",
+ "\n",
+ "print'Illustration 12.6 - Page: 685\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "\n",
+ "import math\n",
+ "#***Data***#\n",
+ "Y1 = 0.01;# [kg water/kg dry air]\n",
+ "Gs = 1.1;# [kg dry air/square m.s]\n",
+ "dia = 13.5/1000;# [m]\n",
+ "l = 13.0/1000;# [m]\n",
+ "zS = 50.0/1000;# [m]\n",
+ "Density_S = 600.0;# [kg dry solid/square m.s]\n",
+ "a = 280.0;# [square m/cubic m]\n",
+ "#************#\n",
+ "\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Yas = 0.031;# [kg water/kg dry air]\n",
+ "Gav = Gs+(Gs*(Y1+Yas)/2.0);# [kg/square m.s]\n",
+ "viscosity_air = 1.9*10**(-5);# [kg/m.s]\n",
+ "Area = (2.0*math.pi*dia**2.0/4)+(math.pi*dia*l);# [square m]\n",
+ "dp = (Area/math.pi)**0.5;# [m]\n",
+ "# From Table 3.3 (Pg 74)\n",
+ "Re = dp*Gav/viscosity_air;\n",
+ "e = 1.0-(dp*a/6);# [fraction voids]\n",
+ "jD = (2.06/e)*Re**(-0.575);\n",
+ "# For air water mixture:\n",
+ "Sc = 0.6;\n",
+ "# From Eqn. 12.33:\n",
+ "kY = jD*Gs/Sc**(2.0/3);# [kg H2O/square m.s.deltaX]\n",
+ "# From Eqn. 12.30:\n",
+ "NtG = kY*a*zS/Gs;\n",
+ "# From Eqn. 12.25:\n",
+ "Nmax = Gs*(Yas-Y1);# [kg/square m.s]\n",
+ "# From Eqn. 12.31:\n",
+ "N = Nmax*(1-math.exp(-NtG));# [kg water evaporated/square m.s]\n",
+ "Y2 = (Yas-Y1)*(N/Nmax)+Y1;# [kg water/kg dry air]\n",
+ "# From Fig 7.5 (Pg 232)\n",
+ "Tempas = 33.0;# [OC]\n",
+ "# From eqn. 12.2:\n",
+ "Rate = N/(Density_S*zS);# [kg H2O/(kg dry solid).s]\n",
+ "print\"Humidity of the exit air: \",round(Y2,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Temparature of exit air: \",Tempas,\" degree C\\n\"\n",
+ "print\"Rate of Drying: \",round(Rate,7),\" kg H2O/(kg dry solid).s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.6 - Page: 685\n",
+ "\n",
+ "\n",
+ "Humidity of the exit air: 0.0302 kg water/kg dry air\n",
+ "\n",
+ "Temparature of exit air: 33.0 degree C\n",
+ "\n",
+ "Rate of Drying: 0.0007409 kg H2O/(kg dry solid).s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 26
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.7: Page 700"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.7\n",
+ "# Page: 700\n",
+ "\n",
+ "print'Illustration 12.7 - Page: 700\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "\n",
+ "import math\n",
+ "from numpy.linalg import inv\n",
+ "#***Data***#\n",
+ "x1 = 3.5;# [percent moisture]\n",
+ "x2 = 0.2;# [percent moisture]\n",
+ "dia = 1.2;# [m]\n",
+ "l = 6.7;# [m]\n",
+ "Rate_prod = 900.0;# [kg/h]\n",
+ "y2 = 0.5;# [Humidity]\n",
+ "TempG2 = 90.0;# [OC]\n",
+ "TempG1 = 32.0;# [OC]\n",
+ "TempS1 = 25.0;# [OC]\n",
+ "TempS2 = 60.0;# [OC]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(100.0-x1);# [kg H2O/kg dry solid]\n",
+ "X2 = x2/(100.0-x2);# [kg H2O/kg dry solid]\n",
+ "Ss = Rate_prod*(1-X2);# [kg dry solid/h]\n",
+ "Rate_drying = Ss*(X1-X2);# [kg water evaporated/h]\n",
+ "Y2 = (y2/(1-y2))/100.0;# [kg water/kg dry air]\n",
+ "Tempo = 0.0;# [Base temp,OC]\n",
+ "# From Table 7.1: (Pg 234)\n",
+ "# Enthalpy of air entering the drier:\n",
+ "HG2 = (1005.0+(1884.0*Y2))*(TempG2-Tempo)+(2502300.0*Y2);# [J/kg dry air]\n",
+ "# For the outlet air:\n",
+ "# HG1 = (1005.0+(1884*Y1))*(TempG1-Tempo)+(2502300*Y1); [J/kg dry air]\n",
+ "# HG1 = (1005.0*TempG1)+((1884+TempG1)+2502300)*Y1; [J/kg dry air]\n",
+ "CsNH4 = 1507.0;# [J/kg.K]\n",
+ "CsH2O = 4187.0;# [J/kg.K]\n",
+ "# From Eqn. 11.45:\n",
+ "HS2 = CsNH4*(TempS2-Tempo)+(X2*CsH2O*(TempS2-Tempo));# [J/kg dry air]\n",
+ "HS1 = CsNH4*(TempS1-Tempo)+(X1*CsH2O*(TempS1-Tempo));# [J/kg dry air]\n",
+ "# The estimated combined natural convection and radiation heat transfer coeffecient from the drier to the surrounding:\n",
+ "h = 12.0;# [W/square m.K]\n",
+ "deltaTemp = ((TempG2-TempS1)+(TempG1-TempS1))/2;# [OC]\n",
+ "Ae = math.pi*dia*l;# [square m]\n",
+ "Q = h*3600.0*Ae*deltaTemp;# [kJ/h]\n",
+ "# Moisture Balance, Eqn. 12.39:\n",
+ "# Ss*(X1-X2) = Gs(Y1-Y2)\n",
+ "# (Gs*Y1)-(Gs*Y2) = (Ss*(X1-X2)) ........(1)\n",
+ "# Enthalapy Balance, Eqn. 12.40:\n",
+ "# (Ss*HS1)+(Gs*HG2) = (Ss*HG2)+(Gs*HG1)+Q \n",
+ "# Gs*(HG2-HG1) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-((1005*TempG1)+((1884+TempG1)+2502300)*Y1)) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-(1005*TempG1))-(Gs*Y1*((1884+TempG1)+2502300)) = (Ss*HS2)+Q-(Ss*HS1)........ (2)\n",
+ "# Solving Simultaneously:\n",
+ "a = numpy.array([[HG2-(1005.0*TempG1),-((1884.0+TempG1)+2502300.0)],[(-Y2), 1.0]]);\n",
+ "b = numpy.array([[((Ss*HS2)+Q-(Ss*HS1))],[(Ss*(X1-X2))]]);\n",
+ "c=inv(a)\n",
+ "soln =np.dot(c, b)\n",
+ "Gs = soln[0];# [kg dry air/h]\n",
+ "Y1 = soln[1]/soln[0];# [kg water/kg dry air]\n",
+ "# From Fig. 7.5 (Pg 232)\n",
+ "Enthalpy_air = 56.0;# [kJ/kg dry air]\n",
+ "HeatLoad = Gs*(HG2-Enthalpy_air*1000);# [W]\n",
+ "print\"Air Flow Rate: \",round(Gs,2),\" kg/h\\n\"\n",
+ "print\"Moisture content of air: \",round(Y1,2),\" kg water/kg dry air \\n\"\n",
+ "print\"Heat Load of drier: \",round(HeatLoad/1000),\" kW\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.7 - Page: 700\n",
+ "\n",
+ "\n",
+ "Air Flow Rate: 2681.03 kg/h\n",
+ "\n",
+ "Moisture content of air: 0.02 kg water/kg dry air \n",
+ "\n",
+ "Heat Load of drier: 163995.0 kW\n"
+ ]
+ }
+ ],
+ "prompt_number": 50
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.8: Page 705"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.8\n",
+ "# Page: 705\n",
+ "\n",
+ "print'Illustration 12.8 - Page: 705\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "from numpy.linalg import inv\n",
+ "import numpy as np\n",
+ "#***Data***#\n",
+ "x1 = 8.0;# [percent moisture]\n",
+ "x2 = 0.5;# [percent moisture]\n",
+ "Rate_prod = 0.63;# [kg/s]\n",
+ "# Drying Gas:\n",
+ "xCO2 = 0.025;# [mole fraction]\n",
+ "xO2 = 0.147;# [mole fraction]\n",
+ "xN2 = 0.760;# [mole fraction]\n",
+ "xH2O = 0.068;# [mole fraction]\n",
+ "TempG2 = 480.0;# [OC]\n",
+ "Cs = 0.837;# [kJ/kg.K]\n",
+ "Temp1 = 27.0;# [OC]\n",
+ "Temp2 = 150.0;# [OC]\n",
+ "dp = 200.0*10**(-6);# [m]\n",
+ "Density_S = 1300.0;# [kg/cubic m]\n",
+ "#***********#\n",
+ "\n",
+ "X1 = x1/(100-x1);# [kg water/kg dry solid]\n",
+ "X2 = x2/(100-x2);# [kg water/kg dry solid]\n",
+ "Ss = Rate_prod*(1-X2);# [kg dry solid/s]\n",
+ "Water_evap = Ss*(X1-X2);# [kg/s]\n",
+ "# Basis: 1 kmol of dry gas:\n",
+ "xDry = 1.0-xH2O;# [kmol]\n",
+ "XCO2 = 44.0*xCO2;# [kg]\n",
+ "XO2 = 32.0*xO2;# [kg]\n",
+ "XN2 = 28.0*xN2;# [kg]\n",
+ "Xdry = XCO2+XO2+XN2;# [kg]\n",
+ "cCO2 = 45.6;# [kJ/kmol.K]\n",
+ "cO2 = 29.9;# [kJ/kmol.K]\n",
+ "cN2 = 29.9;# [kJ/kmol.K]\n",
+ "cH2O = 4.187;# [kJ/kg.K]\n",
+ "Mav = Xdry/xDry;# [kg/kmol]\n",
+ "Y2 = xH2O*18.02/(xDry*Mav);# [kg water/kg dry gas]\n",
+ "cav = ((xCO2*cCO2)+(xO2*cO2)+(xN2*cN2))/(xDry*Mav);# [kJ/kmol.K]\n",
+ "# Assume:\n",
+ "TempG1 = 120.0;# [OC]\n",
+ "cDry = 1.005;# [kJ/kmol.K]\n",
+ "Tempo = 0;# [Base Temp,OC]\n",
+ "# By Eqn. 7.13:\n",
+ "HG2 = (cav+(1.97*Y2))*(TempG2-Tempo)+(2502.3*Y2);# [kJ/kg dry air]\n",
+ "# For the outlet air:\n",
+ "# HG1 = (1.005+(1.884*Y1))*(TempG1-Tempo)+(2502.3*Y1); [kJ/kg dry air]\n",
+ "# HG1 = (1.005*TempG1)+((1.884+TempG1)+2502.3)*Y1; [kJ/kg dry air]\n",
+ "# By Eqn. 11.45:\n",
+ "HS1 = (Cs*(Temp1-Tempo))+(cH2O*X1*(Temp1-Tempo));# [kJ/kg dry air]\n",
+ "HS2 = (Cs*(Temp2-Tempo))+(cH2O*X2*(Temp2-Tempo));# [kJ/kg dry air]\n",
+ "# Q = 0.15*HG2*Gs; [kJ/s]\n",
+ "# Moisture Balance, Eqn. 12.39:\n",
+ "# Ss*(X1-X2) = Gs(Y1-Y2)\n",
+ "# (Gs*Y1)-(Gs*Y2) = (Ss*(X1-X2)) ........(1)\n",
+ "# Enthalapy Balance, Eqn. 12.40:\n",
+ "# (Ss*HS1)+(Gs*HG2) = (Ss*HG2)+(Gs*HG1)+Q \n",
+ "# Gs*(HG2-HG1) = (Ss*HS2)+(0.15*HG2*Gs)-(Ss*HS1)\n",
+ "# Gs*(HG2-(0.15*HG2)-((1.005*TempG1)+((1.884+TempG1)+2502.3)*Y1)) = (Ss*HS2)+Q-(Ss*HS1)\n",
+ "# Gs*(HG2-(0.15*HG2)-(1.005*TempG1))-(Gs*Y1*((1.884+TempG1)+2502.3)) = (Ss*HS2)+Q-(Ss*HS1)........ (2)\n",
+ "a = np.array([[(HG2-(0.15*HG2)-(1.005*TempG1)),-((1.884+TempG1)+2502.3)],[(-Y2), 1.0]]);\n",
+ "b = np.array([(Ss*HS2)-(Ss*HS1),(Ss*(X1-X2))]);\n",
+ "c=inv(a)\n",
+ "soln = np.dot(c, b)\n",
+ "Gs = soln[0];# [kg dry air/s]\n",
+ "Y1 = soln[1]/soln[0];# [kg water/kg dry gas]\n",
+ "HG1 = (1.005+(1.884*Y1))*(TempG1-Tempo)+(2502.3*Y1);# [kJ/kg dry air]\n",
+ "Q = 0.15*HG2*Gs;# [kJ/s]\n",
+ "# Assuming the sychrometric ratio of the gas as same as that of air:\n",
+ "# For Zone II:\n",
+ "Tempw = 65.0;# [OC]\n",
+ "Temp_A = 68.0;# [OC]\n",
+ "# At point A, Fig. 12.28 (Pg 702)\n",
+ "Enthalpy_A = Cs*(Temp_A-Tempo)+(X1*cH2O*(Temp_A-Tempo));# [kJ/kg dry air]\n",
+ "# At point B, Fig. 12.28 (Pg 702)\n",
+ "Temp_B = Temp_A;# [OC]\n",
+ "Enthalpy_B = Cs*(Temp_B-Tempo)+(X2*cH2O*(Temp_B-Tempo));# [kJ/kg dry air]\n",
+ "\n",
+ "# Assuming that the heat losses in the three zones are propotional to the number of transfer units in each zone and to the average temp. difference between the gas and the surrounding air.\n",
+ "# Fractional heat loss in each Zone:\n",
+ "fr1 = 0.14;\n",
+ "fr2 = 0.65;\n",
+ "fr3 = 0.20;\n",
+ "# Calculations for zone III:\n",
+ "Cs3 = cav+(1.97*Y2);# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "def f1(TempGD):\n",
+ " return (Gs*Cs3*(TempG2-TempGD))-(Ss*(HS2-Enthalpy_B)+(fr3*Q))\n",
+ "TempGD = fsolve(f1,7);# [OC]\n",
+ "delta_TempG = Ss*(HS2-Enthalpy_B)/(Gs*Cs3);# [OC]\n",
+ "delta_TempM = ((TempG2-Temp2)+(TempGD-Temp_A))/2;# [OC]\n",
+ "NtoG3 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "# Calculations for zone I:\n",
+ "Cs1 = 1.005+(1.884*Y1);# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "def f2(TempGC):\n",
+ " return (Gs*Cs1*(TempGC-TempG1))-(Ss*(Enthalpy_A-HS1)+(fr1*Q))\n",
+ "TempGC = fsolve(f2,7);# [OC]\n",
+ "delta_TempG = Ss*(Enthalpy_A-HS1)/(Gs*Cs1);# [OC]\n",
+ "delta_TempM = ((TempGC-Temp_A)+(TempG1-Temp1))/2;# [OC]\n",
+ "NtoG1 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "# Calculations for zone II:\n",
+ "Cs2 = (cav+Cs1)/2.0;# [kJ/(kg dry gas).K]\n",
+ "# Heat balance:\n",
+ "True_deltaTemp = TempGD-TempGC;# [OC]\n",
+ "delta_Temp = fr2*Q/(Cs1*Gs);# [Change in temp resulting from heat loss,OC]\n",
+ "delta_TempG = True_deltaTemp-delta_Temp;# [OC]\n",
+ "delta_TempM = ((TempGD-Temp_A)-(TempGC-Temp_A))/log((TempGD-Temp_A)/(TempGC-Temp_A));# [OC]\n",
+ "NtoG2 = delta_TempG/delta_TempM;\n",
+ "\n",
+ "NtoG = NtoG1+NtoG2+NtoG3;\n",
+ "\n",
+ "# Standard diameters are availaible at 1, 1.2 & 1.4 m.\n",
+ "Td = 1.2;# [m]\n",
+ "Area = math.pi*Td**2.0/4;# [square m]\n",
+ "Gs = Gs/Area;# [kg/square m.s]\n",
+ "Ss = Ss/Area;# [kg/square m.s]\n",
+ "Gav = Gs*(1+(Y1+Y2)/2.0);# [kg/square m.s]\n",
+ "# From Eqn. 12.47:\n",
+ "Ua = 237.0*Gav**0.417/Td;# [W/square m.K]\n",
+ "HtoG = Gs*Cs2*1000.0/Ua;# [m]\n",
+ "Z = NtoG*HtoG;# [m]\n",
+ "# Assume:\n",
+ "v = 0.35;# [m/s]\n",
+ "N = v/(math.pi*Td);# [1/s]\n",
+ "# From Eqn. 12.37:\n",
+ "K = 0.6085/(Density_S*dp**(1.0/2));\n",
+ "# Take:\n",
+ "phi_D = 0.05;\n",
+ "# From Eqn. 12.35:\n",
+ "phi_DO = phi_D-(K*Gav);\n",
+ "# From Eqn. 12.35:\n",
+ "s = 0.3344*Ss/(phi_DO*Density_S*N**0.9*Td);# [m/s]\n",
+ "print\"Height of the drier: \",round(Z,2),\" m\\n\"\n",
+ "print\"Drier Slope: \",round(s,5),\" m/m \\n\","
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.8 - Page: 705\n",
+ "\n",
+ "\n",
+ "Height of the drier: 5.89 m\n",
+ "\n",
+ "Drier Slope: 0.03304 m/m \n"
+ ]
+ }
+ ],
+ "prompt_number": 56
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex12.9: Page 709"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 12.9\n",
+ "# Page: 709\n",
+ "\n",
+ "print'Illustration 12.9 - Page: 709\\n\\n'\n",
+ "import numpy as np\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "# Solution \n",
+ "\n",
+ "#***Data***#\n",
+ "x1 = 0.46;# [fraction moisture]\n",
+ "x2 = 0.085;# [fraction moisture]\n",
+ "Y1 = 0.08;# [kg water/kg dry solid]\n",
+ "Y2 = 0.03;# [kg water/kg dry solid]\n",
+ "G = 1.36;# [kg/square m.s]\n",
+ "#**********#\n",
+ "\n",
+ "X1 = x1/(1-x1);# [kg water/kg dry solid]\n",
+ "X2 = x2/(1-x2);# [kg water/kg dry solid]\n",
+ "# By water balance:\n",
+ "SsByGs = (Y1-Y2)/(X1-X2);# [kg dry solid/kg air]\n",
+ "# Since the initial moisture content of the rayon is less than the critical, drying takes place entirely within zone III.\n",
+ "# Comparing with Eqn. 12.22:\n",
+ "# (kY*A/(Ss(Xc-X*)))=0.0137*G**1.47\n",
+ "# thetha=integrate('(1/(0.0137*G**1.47))*(1/((X-X_star)*(Yw-Y)))','X',X2,X1) # [s]\n",
+ "X = np.array([X1, 0.80, 0.60, 0.40, 0.20 ,X2]);# [kg water/kg dry solid]\n",
+ "Y = zeros(6);\n",
+ "for i in range(0,6):\n",
+ " # From Eqn. 12.54:\n",
+ " Y[i] = Y2+((X[i]-X2)*SsByGs);# [kg water/kg dry gas]\n",
+ "\n",
+ "# From Fig. 7.5 (Pg 232):\n",
+ "Yw = np.array([0.0950, 0.0920, 0.0790, 0.0680, 0.0550, 0.0490]);# [kg water/kg dry gas]\n",
+ "X_star = zeros(6);\n",
+ "RH=zeros(6)\n",
+ "Val = zeros(6);\n",
+ "P = 51780.0;# [vapour pressure, kN/square m]\n",
+ "for i in range(0,6):\n",
+ " # From Eqn 7.8:\n",
+ " def f(p):\n",
+ " return Y[i]-((p/(101330.0-p))*(18.0/29))\n",
+ " p = fsolve(f,7);# [kN/square m]\n",
+ " RH[i] = (p/P)*100.0;\n",
+ " X_star[i] = (RH[i]/4)/(100.0-(RH[i]/4));# [kg water/kg dry solid]\n",
+ " Val[i] = 1/((X[i]-X_star[i])*(Yw[i]-Y[i]));\n",
+ "\n",
+ "plt.plot(X,Val);\n",
+ "plt.grid();\n",
+ "plt.xlabel(\"X kg water/kg dry solid\");\n",
+ "plt.ylabel(\"1/((X-X*)*(Yw-Y))\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area Under the curve:\n",
+ "Area = 151.6;\n",
+ "# From Eqn. 12.59:\n",
+ "thetha = Area/(0.0137*G**1.47);\n",
+ "print\"Time required for drying: \",round(thetha/3600,2),\" h\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 12.9 - Page: 709\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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tyX4amWWeDuTqL+nPyd/gVEnbVFyhmUX3Qyg1zQMagdWAVmDrkmU+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\nc86V5Y2Ec865sryRcM45V9b/B77dKwo9Y8ZFAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7885c50>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Time required for drying: 1.96 h\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter13.ipynb b/Mass_-_Transfer_Operations/Chapter13.ipynb
new file mode 100755
index 00000000..225bff8b
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter13.ipynb
@@ -0,0 +1,443 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:b841fc17f95d9198998020044b36e625586e7f0a22a266fb56d5482aed19bd4a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 13: Leaching"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.1: Page 722"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.1\n",
+ "# Page: 722\n",
+ "\n",
+ "print'Illustration 13.1 - Page: 722\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import numpy as np\n",
+ "import math\n",
+ "#***Data***#\n",
+ "Density_L = 1137.0;# [kg/cubic m]\n",
+ "Density_S = 960.0;# [kg/cubic m]\n",
+ "Density_p = 1762.0;# [kg/cubic m]\n",
+ "A_prime = 16.4;# [square m/kg]\n",
+ "g = 9.81;# [square m/s]\n",
+ "sigma = 0.066;# [N/m]\n",
+ "Z = 3.0;# [m]\n",
+ "dia = 1.0;# [m]\n",
+ "#**********#\n",
+ "\n",
+ "e = 1-(Density_S/Density_p);# [fraction void]\n",
+ "ap = A_prime*Density_S;# [square m/cubic m]\n",
+ "# By Eqn. 6.67:\n",
+ "dp = 6*(1-e)/ap;# [m]\n",
+ "# By Eqn. 13.6:\n",
+ "K = dp**2*e**3.0*g/(150.0*(1-e)**2);# [cubic m/s]\n",
+ "check = K*Density_L*g/(g*sigma);\n",
+ "if (check<0.02):\n",
+ " # By Eqn. 13.3: \n",
+ " So = 0.075;\n",
+ "else:\n",
+ " # By Eqn. 13.4:\n",
+ " So = 0.0018/(check)\n",
+ "\n",
+ "# By Eqn. 13.2:\n",
+ "ZD = (0.275/g)/((K/g)**0.5*(Density_L/sigma));# [m]\n",
+ "# By Eqn. 13.1:\n",
+ "Sav = ((Z-ZD)*So/Z)+(ZD/Z);\n",
+ "# VolRatio=Vol liquid retained/Vol bed.\n",
+ "VolRatio = Sav*e;\n",
+ "print\"Vol liquid retained/Vol bed : \",round(VolRatio,4),\" cubic m/cubic m\\n\"\n",
+ "Mass = VolRatio*math.pi*dia**2*Z*Density_L/4;# [kg]\n",
+ "# Mass ratio=Mass Liquid/Mass dry solid\n",
+ "MassRatio = VolRatio*Density_L/(Density_S);\n",
+ "print\"Mass liquid/Mass dry solid: \",round(MassRatio,4),\" kg/kg\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.1 - Page: 722\n",
+ "\n",
+ "\n",
+ "Vol liquid retained/Vol bed : 0.058 cubic m/cubic m\n",
+ "\n",
+ "Mass liquid/Mass dry solid: 0.0687 kg/kg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.2: Page 749"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.2\n",
+ "# Page: 749\n",
+ "\n",
+ "print'Illustration 13.2 - Page: 749\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#***Data***#\n",
+ "# Eqb=[x(Wt fraction NaOH in clear solution) N(kg CaCO3/kg soln in settled sludge) y*(wt fraction NaOH in soln of settled sludge)]\n",
+ "# a=H2O b=CaCO3 c=NaOH\n",
+ "Eqb = np.array([[0.090 ,0.495, 0.0917],[0.0700, 0.525, 0.0762],[0.0473, 0.568, 0.0608],[0.0330, 0.600, 0.0452],[0.0208, 0.620, 0.0295],[0.01187 ,0.650, 0.0204],[0.00710, 0.659, 0.01435],[0.00450, 0.666, 0.01015]]);\n",
+ "def f80(x):\n",
+ " return x\n",
+ "x = np.arange(0,0.12,0.01);\n",
+ "Mass_c = 0.1;# [kg]\n",
+ "Mass_b = 0.125;# [kg]\n",
+ "Mass_a = 0.9;# [kg]\n",
+ "#**************#\n",
+ "\n",
+ "\n",
+ "plt.plot(x,f80(x),label=\"N Vs x\")\n",
+ "plt.plot(Eqb[:,2],Eqb[:,1],label=\"N Vs Y\");\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x,y Wt. fraction of NaOH in loquid\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.legend('upper left');\n",
+ "plt.title(\"Equilibrium Plot\")\n",
+ "plt.show()\n",
+ "# Basis: 1 kg soln in original mixture.\n",
+ "# As in Fig. 13.27 (Pg 750)\n",
+ "# The original mixture corresponds to M1:\n",
+ "NM1 = 0.125;# [kg CaCO3/kg soln]\n",
+ "yM1 = 0.1;# [kg NaOH/kg solution]\n",
+ "# The tie line through M1 is drawn. At point E1 representing the settled sludge:\n",
+ "N1 = 0.47;# [kg CaCO3/kg soln]\n",
+ "y1 = 0.100;# [kg NaOH/kg solution]\n",
+ "E1 = Mass_b/N1;# [kg soln. in sludge]\n",
+ "Ro = 1-E1;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 2:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M2 = E1+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM2 = Mass_b/(E1+Ro);# [kg CaCO3/kg soln]\n",
+ "# M2 is located on line RoE1. At this value of N, and the tie line through M2 is drawn. At E2:\n",
+ "N2 = 0.62;# [kg CaCO3/kg soln]\n",
+ "y2 = 0.035;# [kg NaOH/kg solution]\n",
+ "E2 = Mass_b/N2;# [kg soln. in sludge]\n",
+ "Ro = 1-E2;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 3:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M3 = E2+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM3 = Mass_b/M3;# [kg CaCO3/kg soln]\n",
+ "# Tie line E3R3 is located through M3.At E3:\n",
+ "N3 = 0.662;# [kg CaCO3/kg soln]\n",
+ "y3 = 0.012;# [kg NaOH/kg solution]\n",
+ "# By Eqn. 13.8:\n",
+ "E3 = Mass_b/N3;# [kg soln. in sludge]\n",
+ "print\"The fraction of original NaOH in the slurry: \",round(E3*y3/Mass_c,4),\" \\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.2 - Page: 749\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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RwshUk/gN0DPF/B4EA/9l5O57gCuApwjOQP7s7kvNbHLCJbSfBxaZ2YJwm1/K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\ngg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7c610f0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The fraction of original NaOH in the slurry: 0.0227 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.3: Page 754"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.3\n",
+ "# Page: 754\n",
+ "\n",
+ "print'Illustration 13.3 - Page: 754\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "import numpy as np\n",
+ "from scipy import interp\n",
+ "import matplotlib.pyplot as plt\n",
+ "#***Data***#\n",
+ "# a=H2O b=CaCO3 c=NaOH \n",
+ "mass_c = 400;# [kg/h]\n",
+ "x1 = 0.1;# [wt fraction NaOH in overflow]\n",
+ "#**********#\n",
+ "\n",
+ "Mb = 100.0;# [kg/kmol]\n",
+ "Mc = 40.0;# [kg/kmol]\n",
+ "rate_c = mass_c/Mc;# [kmol/h]\n",
+ "rate_b = rate_c/2;# [kmol/h]\n",
+ "mass_b = rate_b*Mb;# [kg/h]\n",
+ "# After trial calculations:\n",
+ "y3 = 0.01;# [kg NaOH/kg solution]\n",
+ "N3 = 0.666;# [kg CaCO3/kg solution]\n",
+ "E3 = mass_b/N3;# [kg/h]\n",
+ "lost_c = E3*y3;# [kg/h]\n",
+ "sludge_a = E3-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg NaOH/kg solution]\n",
+ "R1 = overflow_c/x1;# [kg overflow/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# For purpose of calculation, it may be imagined that agitators are not present in the flowsheet and the first thickner is fed with the dry mixture of the reaction products, CaCO3 and NaOH, together with overflow from the second thickner.\n",
+ "F = 400.0;# [kg NaOH/h]\n",
+ "NF = mass_b/F;# [kg CaCO3/kg NaOH]\n",
+ "yF = 1.0;# [wt fraction NaOH in dry solid, CaCO3 free basis]\n",
+ "# Points R1, E3, RNpPlus1 and F are plotted as in Fig 13.30 (Pg 755) and locate the point deltaR at the intersection of lines FR1 and E3RNpPlus1 extended. The coordinates of point deltaR are NdeltaR=-0.1419, ydeltaR=-0.00213. Further computation must be done on enlarged section of the equilibrium diagram (Fig 13.31 (Pg 755)). Point deltaR is plotted and the stages stepped off in a usual manner. The construction are projected on the xy diagram. Three stages produce a value: y3=0.001\n",
+ "print\"The NaOH lost in sludge: \",round((lost_c/mass_c)*100,2),\"%\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "#*** Data***#\n",
+ "lost_c = 0.001*mass_c;# [kg/h]\n",
+ "#***********#\n",
+ "\n",
+ "NNp_by_yNp = mass_b/lost_c;# [kg CaCO3/kg NaOH in final sludge]\n",
+ "# In order to determine the liquid content of the final sludge:\n",
+ "# Eqb=[N y_star]\n",
+ "Eqb = np.array([[0.659 , 0.01435],[0.666, 0.01015],[0.677, 0.002],[0.679, 0.001],[0.680 ,0.0005]]);\n",
+ "N_by_ystar = zeros(5);\n",
+ "for i in range(0,5):\n",
+ " N_by_ystar[i] = Eqb[i,0]/(Eqb[i,1]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1]);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x Wt fraction of NaOH\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.title(\"Equilibrium plot\")\n",
+ "# By Interpolation, for N_by_ystar=NNp_by_yNp:\n",
+ "NNp = interp(NNp_by_yNp,N_by_ystar,Eqb[:,0]);# [kg CaCO3/kg soln]\n",
+ "yNp = NNp/NNp_by_yNp;# [wt fraction NaOH in the liquid of the final sludge]\n",
+ "ENp = mass_b/NNp;# [kg/h]\n",
+ "ENp_a = ENp-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg/h]\n",
+ "R1 = overflow_c/0.1;# [kg/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# On the operating diagram (Fig 13.32 (Pg 757)) point deltaR is located and stages were constructed. \n",
+ "# Beyond the fourth stage, the ratio of the overflow to the liquid in the sludge become substantially constant.\n",
+ "R_by_E = RNpPlus1/ENp;\n",
+ "# This is the initial slope of the operating line on the lower part of the figure.\n",
+ "# From Illustration 13.2:\n",
+ "m = 0.01015/0.00450;\n",
+ "Value1 = R_by_E/m;\n",
+ "xNpPlus1 = 0;# [kg NaOH/kg solution]\n",
+ "y4 = 0.007;# [wt fraction NaOH in the liquid]\n",
+ "Value2 = (yNp-(m*xNpPlus1))/(y4-(m*xNpPlus1));\n",
+ "# From Fig 5.16: (Pg 129):\n",
+ "# An Additional 2.3 stages beyond 4 are computed graphically are required.\n",
+ "# An additional two stage will make yNp/y4=0.099:\n",
+ "yNp = 0.099*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,2),\"kg NaOH was lost if 6 thickners were used\\n\"\n",
+ "# An additional three stage will make yNp/y4=0.0365:\n",
+ "yNp = 0.0365*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,3),\"kg NaOH was lost if 7 thickners were used\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.3 - Page: 754\n",
+ "\n",
+ "\n",
+ "The NaOH lost in sludge: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 1.88 %\n",
+ "\n",
+ "\n",
+ "\n",
+ "0.51"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " kg NaOH was lost if 6 thickners were used\n",
+ "\n",
+ "0.188 kg NaOH was lost if 7 thickners were used\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2WRg1qpVVVoERI4Zz2mnwsY+lyTfr6ft2tdzW1lZX8VRzubW1lcmTJwMs/n1Z\nSd32yUj6C7Ab8Bvgn8C/gK9FRJcPZEoaBpwSESOy5eOARRFxRlGZXwOtETEtW34M2Ik00KDDfSXd\nAJze/qCopDnA0A7ee+M+GbMqe/xx2Hvv9PDmL38JK6xQ64isp6reJwN8NSs3HngbWAfYp4z9ZgAb\nSRokaQBwAGkG52LXZsdvr5Rei4j53ex7NfDZbJ+NgQEdvffGzKrv4x+H6dPTczTbbQdz5tQ6Iqu1\nTisZSR+WtGnW+f5ORLweEacAvwVe7+7AEbGQVDHdRJq9+fJsdNhYSWOzMtcDT2V3IxOBcV3tmx16\nErCBpIeAqWSVlHWutKmomTkXBXnlYqWV0izOhx2WKpprrsnlNBXl6yI/XfXJnEfHHfyrAyeQBgN0\nKSJuAG4oWTexZHl8uftm698DDuru3GZWOxKMG5cGA+y/P9xzD/zkJ9C/rF5g60u6mrtsZkRs1cm2\nRyJi01wjW0rukzGrDy+9lJ6nWbAApk6FNdaodUTWlWr2yazcxbZlKxWAmfVtH/xgepZmxx1h663h\nrrtqHZFVkyfIbAJuby5wLgqqmYt+/eCHP4Tf/Ab22w/OOQfqqaHB10V+umohPQr4P0n7ATNJz7Ns\nBWwHfKEKsZlZHzNiRBp9tu++qZ9m0iRYZZVaR2V56vI5GUkfIHXwt/e/PAJcFhH/qUJsS8V9Mmb1\n69134aij4Pbb4YorYLPNah2Rtat0n0xuE2TWmisZs/p36aXwne/Az38OX/lKraMxqM3DmNbg3N5c\n4FwU1EMuDjoo3c2cemoa8vzuu7WJox5y0Ve5kjGzmtpsszSb8/z5sMMO8P/+X60jskpyc5mZ1YWI\nNOrszDNh8uQ0SMCqr+p9Mtn0LcGSsyW/TnqR2I/rdd4wVzJmjemuu2D0aPjGN+DEE9PwZ6ueWvTJ\n3Aj8mTTK7MvAdaQJLOcDkysViOXH7c0FzkVBveZihx1gxoz0npqRI9OMAXmr11z0BeVUMrtGxHER\n8VBEPBgRxwM7RcTpwKB8wzOzZrTGGnDrrTB4cJr/7L77ah2R9VY5zWUPAodmr0dG0hDgoojYXNID\nEbFFFeLsMTeXmfUNV1+dZnQ+5RT41rfS5JuWn1r0yWwDXAyslK16g/QK5keAPSLiD5UKppJcyZj1\nHXPmwD77pJFoEyfCiivWOqK+qxZ9Mg9FxKeAwcDgiNgMmBMRb9VrBWNLcntzgXNR0Ei52HBD+Nvf\n0qsChg6EQvQeAAATiUlEQVRNb+CspEbKRaMpp5K5UtKyEfFaRLwmaU3g1rwDMzMrtsIKcPHFcOSR\naXDAFVfUOiIrRznNZYcCI4F9gXVJr0H+bkTcnH94vefmMrO+a+bMNMnml74EZ5wBy/rlIxVTk7nL\nJI0HRgAfBb4ZEXdXKoC8uJIx69teeSVNS/P663D55bD22rWOqG+oWp+MpKOzz3eA5Uh3MbOAYdk6\naxBuby5wLgoaPRcDB8J118Huu8M228Add/T+WI2ei3rW3ZsxVyr69yrgyaJ13ZI0QtJjkp6UdEwn\nZSZk22dJ2qLcfbMKcJGkgeXEYmZ9zzLLwAknwCWXwIEHwumnw6JFtY7KiuU2d5mkfsDjwK7Ac6Rp\naEZHxOyiMiOB8RExUtJQ4NyIGNbdvpLWBS4CPg5sFRGvdHB+N5eZNZFnn4X994cPfximTIGWllpH\n1Jgaaar/IaShznMj4j1gGrBXSZlRwBSA7GHPFklrlLHvOcD3c4zdzBrMuuvCnXfCRz+aZgloa6t1\nRAb5VjJrA88WLc/L1pVTZq3O9pW0FzAvIh6sdMB9ldubC5yLgr6YiwEDYMIE+MlPYLfd0pDncvTF\nXNSL/jkeu9y2qrJvyyQtDxwP7FbO/mPGjGHQoEEAtLS0MHjwYIYPHw4ULiovN9dyu3qJp5bLbW1t\ndRVPJZfXWKOVs86C008fzt13w377tbLccp2Xb8tue+ol/mout7a2MnnyZIDFvy8rqdM+GUknd7JP\nAETEqV0eWBoGnBIRI7Ll44BFEXFGUZlfA60RMS1bfgzYCVi/o31Js0HfBrydHWIdUp/NkIh4oeT8\n7pMxa3JvvAGHHgpPPJEe3txgg1pHVP+q2SfzFvBmySdI85Z1OFKsxAxgI0mDJA0ADiA9yFnsWuCr\nsLhSei0i5ne2b0Q8HBEfiYj1I2J9UjPalqUVjJkZwMorw9SpcPDBMGxYGvJs1dVpJRMRZ0XE2RFx\nNmkk1/LAwaRO+PW7O3BELATGAzcBjwKXR8RsSWMljc3KXA88JWkOMBEY19W+HZ2m7G/axEqbipqZ\nc1HQLLmQ4PDD4ZprYNy4NOT5/feXLNMsuaiFLvtkJK0O/C/pZWWXkO4aXi334BFxA3BDybqJJcvj\ny923gzK++TWzsmy7bZqO5sAD4XOfS3c4H/5wraPq+7rqkzkL+BJwIXBBRLxRzcCWlvtkzKwj778P\nJ5+cnqWZNg0+85laR1RfqjZ3maRFwALgvQ42R0SsUqkg8uBKxsy68uc/wyGHwPHHwxFH+GVo7arW\n8R8Ry0TEByJi5Q4+dV3B2JLc3lzgXBQ0ey722APuvTdNSfPZz7byRkO11TSOPB/GNDOra+uvD3ff\nnd60uc028MgjtY6o78lt7rJac3OZmfXE5Mnwve+lGQNGj651NLVTk/fJNCJXMmbWU7NmwT77pNcH\nnH12mqam2TTSBJlWJ5q97b2Yc1HgXBS052LzzWHGjDSj8447pn9t6biSMTMr0tICV10Fe++d+mlu\nrusXzdc/N5eZmXWitTU9vPmtb6WZApZpgj/L3SdTJlcyZlYJzz8PBxyQ5kG79FJYffVaR5Qv98lY\nj7ntvcC5KHAuCrrKxVprwe23wyabwNZbpz4bK58rGTOzbiy7LJx1VvrsvjtMnAhuKCmPm8vMzHrg\niSfSMOctt4Rf/QpWWKHWEVWWm8vMzGpo443TdDSLFqV31Dz5ZK0jqm+uZJqA294LnIsC56Kgp7lY\nccU059m4cWkW56uuyieuvsCVjJlZL0jwzW+m2Zz/93/TlDQLF9Y6qvrjPhkzs6X08svwla/A22+n\nd9SsuWatI+o998mYmdWZ1VdPdzS77JKGOd95Z60jqh+5VzKSRkh6TNKTko7ppMyEbPssSVt0t6+k\nMyXNzspfKWnVvL9HI3Pbe4FzUeBcFFQiF8ssAyedBJMmpYc3zzzTw5wh50pGUj/gfGAEsAkwWtIn\nS8qMBDaMiI2Aw4BflbHvzcCmEbE58ARwXJ7fw8ysXJ//PNx3H1xxRRrq/PrrtY6otvK+kxkCzImI\nuRHxHjAN2KukzChgCkBETAdaJK3R1b4RcUtELMr2nw6sk/P3aGjDhw+vdQh1w7kocC4KKp2L9daD\nv/wl9c1svTU8+GBFD99Q8q5k1gaKJ8uel60rp8xaZewLcAhw/VJHamZWQcstB7/8JZxySuqrueSS\nWkdUG3lXMuW2SPZqJIOkE4AFEXFZb/ZvFm57L3AuCpyLgjxz8eUvwx13wE9+AoccAv/6V26nqkv9\ncz7+c8C6Rcvrku5IuiqzTlZm2a72lTQGGAns0tnJx4wZw6BBgwBoaWlh8ODBi2+L2y8qLzfXcrt6\niaeWy21tbXUVTy2X29racj3+Sy+1cs45cOutw9lkExgxopX/+R8YNar237+1tZXJkycDLP59WUm5\nPicjqT/wOKkieB64DxgdEbOLyowExkfESEnDgF9ExLCu9pU0Ajgb2CkiXurk3H5Oxszqzrx58KMf\nwZ/+BEcckR7kXHnlWkdV0FDPyUTEQmA8cBPwKHB5VkmMlTQ2K3M98JSkOcBEYFxX+2aHPg9YCbhF\n0gOSLsjze5iZVco666RZnKdPT/OebbghnH02vPNOrSPLh5/4bwKtra2Lb5ObnXNR4FwU1DIXDz+c\nnq+57z74wQ9Sv82AATUJBWiwOxkzM+vapz4FV16ZJtm86ir4xCfSSLT33691ZJXhOxkzszpy551w\nwgnw6qtw6qmw995pMs5qqfSdjCsZM7M6EwE33pgqGykNf/7856tT2bi5zHqsdPhuM3MuCpyLgnrL\nhZRe8zxjBhx3HHznO7DjjmkWgUbjSsbMrE4tswzsuy889BAceiiMGQMjRqTKp1G4uczMrEEsWAC/\n/S38+McwdGh63mbTTSt7DjeXmZk1qQED4Fvfgjlz0mufP/tZOOgg+Mc/ah1Z51zJNIF6a2+uJeei\nwLkoaLRcLL88HH10ephzo43SXc03v5lmE6g3rmTMzBrUKqukBzkffxxWXRU23zwNEnjxxVpHVuA+\nGTOzPuKf/4Sf/hQuuwzGjUt3Oy0tPTuG+2TMzKxDa64J550HM2fCc8+lprTTT4e33qpdTK5kmkCj\ntTfnybkocC4K+louBg2CSZPgrrvggQfSJJznnQfvvlv9WFzJmJn1UZ/4BFx+OdxwA9x0E2y8cRoC\nvXBh9WJwn4yZWZO45540Vc3zz8MPfwj7758e+CzmucvK5ErGzOy/RcBtt6XK5j//SQ92fuELhXnR\n3PFvPdbX2puXhnNR4FwUNFMuJNh1V7j33jRjwPHHw7bbwu2353O+/vkc1szM6pkEo0alu5jLL4ex\nY2G99XI4T19tUnJzmZlZ+d57D265BfbYw30yZXElY2bWcw3VJyNphKTHJD0p6ZhOykzIts+StEV3\n+0oaKOkWSU9IullSD59nbT7N1N7cHeeiwLkocC7yk1slI6kfcD4wAtgEGC3pkyVlRgIbRsRGwGHA\nr8rY91jglojYGLgtW7YutLW11TqEuuFcFDgXBc5FfvK8kxkCzImIuRHxHjAN2KukzChgCkBETAda\nJK3Rzb6L98n+/WKO36FPeO2112odQt1wLgqciwLnIj95VjJrA88WLc/L1pVTZq0u9v1IRMzPfp4P\nfKRSAZuZWWXlWcmU2+teTgeTOjpe1rPv3v1uzJ07t9Yh1A3nosC5KHAu8pPnczLPAesWLa9LuiPp\nqsw6WZllO1j/XPbzfElrRMS/JK0JvNBZAFLFBkg0vClTpnRfqEk4FwXORYFzkY88K5kZwEaSBgHP\nAwcAo0vKXAuMB6ZJGga8FhHzJb3cxb7XAl8Dzsj+vbqjk1dyCJ6ZmfVObpVMRCyUNB64CegH/DYi\nZksam22fGBHXSxopaQ7wFnBwV/tmhz4d+IOkrwNzgf3z+g5mZrZ0+uzDmGZmVnsNMUFmmQ91Dpf0\ngKSHJbUWrZ8r6cFs231F60+RNC9b/4CkEVX4KkttKXPRIukKSbMlPZo1UTbsA64VzsXQbH1TXReS\nPl70XR+Q9LqkI7JtTXVddJOLprousvXHSXpE0kOSLpO0XLa+Z9dFRNT1h9RcNgcYRBoQ0AZ8sqRM\nC/AIsE62/MGibU8DAzs47snAd2r9/aqciynAIdnP/YFVs59/Bnw/+/kY4PRaf9ca5qLprouiMssA\n/wTWbdbrootcNNV1ke3zFLBctnw58LXeXBeNcCdTzkOdBwJ/ioh5ABHxUsn2zgYBNNrggF7nQtKq\nwA4RMSlbvzAiXs/2acQHXPPKBTTRdVFiV+AfEdH+jFpTXRclSnMBzXVd/Bt4D1hBUn9gBQojfHt0\nXTRCJVPOQ50bAQMl3SFphqSDirYFcGu2/tCS/Q5XmjPttw3SFLA0uVgfeFHSxZL+LukiSStk2xrx\nAde8cgHNdV0U+x/gsqLlZrsuipXmAprouoiIV4CzgWdII3xfj4hbs316dF00QiVTzsiEZYEtgZHA\n54ETJW2Ubds+IrYAdge+LWmHbP2vSL9sBpNui8+uaNT5WJpc9M/WXxARW5JG8/3XvG+R7oEbYTRI\nXrlotusCAEkDgD2BP3Z4gua4LoBOc9FU14WkjwFHkZrN1gJWlPTl/zpBGddFI1Qy5TzU+Sxwc0S8\nExEvA38BNgeIiOezf18EriLdQhIRL0QG+E37+jrX21x8Ols/LyLuz8r9iXRxQfaAK4C6ecC1jlQy\nF1eQ5aLJrovNi7bvDszM/j9p10zXRZe5aMLrYivgnoh4OSIWAlcC22X79Oi6aIRKZvFDndlfGAeQ\nHsgsdg2wvaR+WbPHUOBRSStIWhlA0orA54CHsuU1i/b/Uvv6OtfbXMzObm+flbRxVm4XUocfFB5w\nhS4ecK0zlczFrmS5aLLr4tGi7aOBqSX7NNN10WUumvC6eBwYJml5SSL9P9Keo55dF3mObqjUh/SX\nxeOkkRLHZevGAmOLynyX9IviIeCIbN0GpBEVbcDD7ftm2y4BHgRmZUn6SK2/Z565yNZvDtyffecr\nKYyoGgjcCjwB3Ay01Pp71jAXzXhdrAi8BKxccsxmvC46y0UzXhffL1o/BVi2N9eFH8Y0M7PcNEJz\nmZmZNShXMmZmlhtXMmZmlhtXMmZmlhtXMmZmlhtXMmZmlhtXMtbQJP1c0pFFyzdJuqho+WxJ/yvp\no5JK38xafJwzs6nOz6hATEdJWr5o+c+SVlna43Zxvg9Jmi5ppqTPlGxrlXR/0fLWku4o45ibSrpd\naZr4JyT9oGjbGEnndXCerSrxfaxvcSVjje6vZNNdSFoGWB3YpGj7tsDdpHmnDuziOIcCm0XEEu/c\nkNSvFzEdSZq1FoCI2CMi/t2L45RrF+DBiNgqIu7uYPuH1IP3n2QV5DXATyPiE6QHV7eTNC4r0tHD\ndY0yt5lVmSsZq1uStslmvV1O0orZncYmJcX+RqpIADYlzezwhtJLyZYDPgk8QHpt9w5KL2c6svgA\nkq4FVgL+Lml/SZMl/VrSvcAZWRz3ZDM2390+HU02FcdZSi91miVpvKTDSRMK3iHptqzcXEkDs5+/\nk5V/qD2ObNqP2ZIuzL7jTZI+0EE+BmV3F7Mk3SppXUmDgTOAvbLvVrpfAGcBJ3RyvL9kd0AzJbXn\n8UDgr5HNuhsR7wDjKUwi2mhT3lsN9a91AGadiYj7swrgx8DywKUR8WhJmeclLZS0Lqmy+RtpOvNt\nSe/EeCgi3lN6K+B3I2LPDs4zStIbkWbrRtLupIpi24iIbP67HSLifUm7Aj8F9gUOA9YDNo+IRZJW\ni4hXJX0HGB5punTI/sLPmpPGkCZXXAaYLulO4DVgQ+CAiDhM0uXAPsDvS0I9D7g4Ii6VdDAwISK+\nJOkkYKuIOKKTVP4N+JKk4cAbRevnA7tFxLtKsxBfBmxDqqxnluToKUkrSVopW3WApO2LimzYybmt\nybmSsXp3Kmmiv3eAwzspcw+pyWw74BxSJbMd8DqpOQ16/tf3H6Mw51ILcImkDUkVRvv/N7sAv4qI\nRQAR8WoXxxOwPXBldmeApCuBHUgTDj4dEQ9mZWeSplgvNYzCC6J+R3pDYfuxu/t+PwZ+QHqTYbsB\nwPmSNgfeJ71bhOw7dne8acWVWjn9PNac3Fxm9e6DpEkLVyLdzXTkbuAzwGakyfzupVDp3NPL875d\n9POPgNsiYjPSWwGL4+hJ5VX6y1sU+jHeLVr/Pp3/AdibpqqIiDtIcQ8rWv+/wD8j4tPA1sBy2fpH\nSVO9F04qbQC8GRFvLkUc1oRcyVi9m0j6C/wyUt9DR+4BvgC8HMmrpLuPbSlUMv8GVu5lDKuQ3g4I\nqbmr3S3A2PbBAZJWy9a/ke1TLIC7gC9m06evSLoruYvyf2HfQ3pjI8CXSe/+6Ikfk+5k2iu2VYB/\nZT9/lfROeEi53l7SLrB4IMAEOs+/WadcyVjdkvRV4N2ImEbquN8m61co9TBpVNm9ReseBF4r6hd5\nEHhfUltpx3+mdGRU8fLPgNMk/Z30i7h9229Ir6d9UFIb6T0kABcCN7Z3/C8+YMQDwGTgvizWiyJi\nVhnnb3c4cLCkWaRK5siist2O7IqIG1jyBVMXAF/LYv848GZW7h3Su+B/IOkxUu6mR8Qve3I+M8BT\n/ZuZWX58J2NmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrn5/4wM\nQj9yu23UAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7a1c780>"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.4: Page 758"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.4\n",
+ "# Page: 758\n",
+ "\n",
+ "print'Illustration 13.4 - Page: 758\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "from scipy import interp\n",
+ "#***Data***#\n",
+ "# a:oil b:soyabean c:hexane\n",
+ "# Data=[100y*(Wt % oil in soln) 1/N(kg soln retained/kg insoluble solid)]\n",
+ "Data = numpy.array([[0 ,0.58],[20 ,0.66],[30 ,0.70]]);\n",
+ "# Soyabean feed:\n",
+ "percent_b = 20.0;# [soluble]\n",
+ "yF = 1.0;# [mass fraction oil,solid free basis]\n",
+ "# Solvent:\n",
+ "RNpPlus1 = 1.0;# [hexane,kg]\n",
+ "xNpPlus1 = 0;# [mass fraction oil]\n",
+ "# Leached Solids:\n",
+ "leached = 0.005;# [fraction of oil to be leached]\n",
+ "# Miscella:\n",
+ "percent_miscella = 10.0;# [percent of insoluble solid]\n",
+ "#**********#\n",
+ "\n",
+ "N = zeros(3);\n",
+ "ystar_By_N = zeros(3);\n",
+ "for i in range(0,3):\n",
+ " N[i] = 1/Data[i,1];# [kg insoluble solid/kg soln retained]\n",
+ " ystar_By_N[i] = Data[i,0]/(100*N[i]);# [kg oil/kg insoluble solid]\n",
+ "\n",
+ "# Basis: 1 kg flakes introduced\n",
+ "# Soyabean feed:\n",
+ "mass_b = 1-(percent_b/100.0);# [insoluble,kg]\n",
+ "F = 1.0-mass_b;# [kg]\n",
+ "NF = mass_b/F;# [kg insoluble solid/kg oil]\n",
+ "\n",
+ "# Leached Solids:\n",
+ "Ratio = leached/(1-leached);# [kg oil/kg insoluble solid]\n",
+ "# By interpolation:\n",
+ "Np = interp(Ratio,ystar_By_N,N);\n",
+ "miscella_b = (percent_miscella/100.0)*mass_b;# [Insoluble solid lost to miscella,kg]\n",
+ "leached_b = (1-(percent_miscella/100.0))*mass_b;# [Insoluble solid in miscella,kg]\n",
+ "ENp = leached_b/Np;# [kg soln retained]\n",
+ "retained_a = Ratio*leached_b;# [oil retained,kg]\n",
+ "retained_c = ENp-retained_a;# [Hexane retained,kg]\n",
+ "yNp = retained_a/ENp;# [mass fraction of oil in retained liquid]\n",
+ "\n",
+ "# Miscella:\n",
+ "mass_c = 1.0-retained_c;# [kg]\n",
+ "mass_a = F-retained_a;# [kg]\n",
+ "R1 = mass_c+mass_a;# [clear miscella,kg]\n",
+ "x1 = mass_a/R1;# [mass fraction of oil in the liquid]\n",
+ "NR1 = miscella_b/R1;# [kg insoluble solid/kg soln]\n",
+ "\n",
+ "# The operating diagram is shown in Fig 13.33 (Pg 759).\n",
+ "# Point R1 represents the cloudy miscella and is therefore is displaced from the axis of he graph at NR1. Point deltaR is located as usual and the stages determined with the N=0 axis for all the stages but the first.\n",
+ "print\"Between 4 and 5 stages are required\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.4 - Page: 758\n",
+ "\n",
+ "\n",
+ "Between 4 and 5 stages are required\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter13_1.ipynb b/Mass_-_Transfer_Operations/Chapter13_1.ipynb
new file mode 100755
index 00000000..8bfca391
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter13_1.ipynb
@@ -0,0 +1,438 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:b262ce3e37d7d3aade80ecf338d6eca332831fbc19ddab243748a217da7431ac"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 13: Leaching"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.1: Page 722"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.1\n",
+ "# Page: 722\n",
+ "\n",
+ "print'Illustration 13.1 - Page: 722\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import numpy as np\n",
+ "import math\n",
+ "#***Data***#\n",
+ "Density_L = 1137.0;# [kg/cubic m]\n",
+ "Density_S = 960.0;# [kg/cubic m]\n",
+ "Density_p = 1762.0;# [kg/cubic m]\n",
+ "A_prime = 16.4;# [square m/kg]\n",
+ "g = 9.81;# [square m/s]\n",
+ "sigma = 0.066;# [N/m]\n",
+ "Z = 3.0;# [m]\n",
+ "dia = 1.0;# [m]\n",
+ "#**********#\n",
+ "\n",
+ "e = 1-(Density_S/Density_p);# [fraction void]\n",
+ "ap = A_prime*Density_S;# [square m/cubic m]\n",
+ "# By Eqn. 6.67:\n",
+ "dp = 6*(1-e)/ap;# [m]\n",
+ "# By Eqn. 13.6:\n",
+ "K = dp**2*e**3.0*g/(150.0*(1-e)**2);# [cubic m/s]\n",
+ "check = K*Density_L*g/(g*sigma);\n",
+ "if (check<0.02):\n",
+ " # By Eqn. 13.3: \n",
+ " So = 0.075;\n",
+ "else:\n",
+ " # By Eqn. 13.4:\n",
+ " So = 0.0018/(check)\n",
+ "\n",
+ "# By Eqn. 13.2:\n",
+ "ZD = (0.275/g)/((K/g)**0.5*(Density_L/sigma));# [m]\n",
+ "# By Eqn. 13.1:\n",
+ "Sav = ((Z-ZD)*So/Z)+(ZD/Z);\n",
+ "# VolRatio=Vol liquid retained/Vol bed.\n",
+ "VolRatio = Sav*e;\n",
+ "print\"Vol liquid retained/Vol bed : \",round(VolRatio,4),\" cubic m/cubic m\\n\"\n",
+ "Mass = VolRatio*math.pi*dia**2*Z*Density_L/4;# [kg]\n",
+ "# Mass ratio=Mass Liquid/Mass dry solid\n",
+ "MassRatio = VolRatio*Density_L/(Density_S);\n",
+ "print\"Mass liquid/Mass dry solid: \",round(MassRatio,4),\" kg/kg\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.1 - Page: 722\n",
+ "\n",
+ "\n",
+ "Vol liquid retained/Vol bed : 0.058 cubic m/cubic m\n",
+ "\n",
+ "Mass liquid/Mass dry solid: 0.0687 kg/kg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.2: Page 749"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.2\n",
+ "# Page: 749\n",
+ "\n",
+ "print'Illustration 13.2 - Page: 749\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#***Data***#\n",
+ "# Eqb=[x(Wt fraction NaOH in clear solution) N(kg CaCO3/kg soln in settled sludge) y*(wt fraction NaOH in soln of settled sludge)]\n",
+ "# a=H2O b=CaCO3 c=NaOH\n",
+ "Eqb = np.array([[0.090 ,0.495, 0.0917],[0.0700, 0.525, 0.0762],[0.0473, 0.568, 0.0608],[0.0330, 0.600, 0.0452],[0.0208, 0.620, 0.0295],[0.01187 ,0.650, 0.0204],[0.00710, 0.659, 0.01435],[0.00450, 0.666, 0.01015]]);\n",
+ "def f80(x):\n",
+ " return x\n",
+ "x = np.arange(0,0.12,0.01);\n",
+ "Mass_c = 0.1;# [kg]\n",
+ "Mass_b = 0.125;# [kg]\n",
+ "Mass_a = 0.9;# [kg]\n",
+ "#**************#\n",
+ "\n",
+ "\n",
+ "plt.plot(x,f80(x),label=\"N Vs x\")\n",
+ "plt.plot(Eqb[:,2],Eqb[:,1],label=\"N Vs Y\");\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x,y Wt. fraction of NaOH in loquid\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.legend('upper left');\n",
+ "plt.title(\"Equilibrium Plot\")\n",
+ "plt.show()\n",
+ "# Basis: 1 kg soln in original mixture.\n",
+ "# As in Fig. 13.27 (Pg 750)\n",
+ "# The original mixture corresponds to M1:\n",
+ "NM1 = 0.125;# [kg CaCO3/kg soln]\n",
+ "yM1 = 0.1;# [kg NaOH/kg solution]\n",
+ "# The tie line through M1 is drawn. At point E1 representing the settled sludge:\n",
+ "N1 = 0.47;# [kg CaCO3/kg soln]\n",
+ "y1 = 0.100;# [kg NaOH/kg solution]\n",
+ "E1 = Mass_b/N1;# [kg soln. in sludge]\n",
+ "Ro = 1-E1;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 2:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M2 = E1+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM2 = Mass_b/(E1+Ro);# [kg CaCO3/kg soln]\n",
+ "# M2 is located on line RoE1. At this value of N, and the tie line through M2 is drawn. At E2:\n",
+ "N2 = 0.62;# [kg CaCO3/kg soln]\n",
+ "y2 = 0.035;# [kg NaOH/kg solution]\n",
+ "E2 = Mass_b/N2;# [kg soln. in sludge]\n",
+ "Ro = 1-E2;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 3:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M3 = E2+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM3 = Mass_b/M3;# [kg CaCO3/kg soln]\n",
+ "# Tie line E3R3 is located through M3.At E3:\n",
+ "N3 = 0.662;# [kg CaCO3/kg soln]\n",
+ "y3 = 0.012;# [kg NaOH/kg solution]\n",
+ "# By Eqn. 13.8:\n",
+ "E3 = Mass_b/N3;# [kg soln. in sludge]\n",
+ "print\"The fraction of original NaOH in the slurry: \",round(E3*y3/Mass_c,4),\" \\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.2 - Page: 749\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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RwshUk/gN0DPF/B4EA/9l5O57gCuApwjOQP7s7kvNbHLCJbSfBxaZ2YJwm1/K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\ngg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7720908>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The fraction of original NaOH in the slurry: 0.0227 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.3: Page 754"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.3\n",
+ "# Page: 754\n",
+ "\n",
+ "print'Illustration 13.3 - Page: 754\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "import numpy as np\n",
+ "from scipy import interp\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#***Data***#\n",
+ "# a=H2O b=CaCO3 c=NaOH \n",
+ "mass_c = 400;# [kg/h]\n",
+ "x1 = 0.1;# [wt fraction NaOH in overflow]\n",
+ "#**********#\n",
+ "\n",
+ "Mb = 100.0;# [kg/kmol]\n",
+ "Mc = 40.0;# [kg/kmol]\n",
+ "rate_c = mass_c/Mc;# [kmol/h]\n",
+ "rate_b = rate_c/2;# [kmol/h]\n",
+ "mass_b = rate_b*Mb;# [kg/h]\n",
+ "# After trial calculations:\n",
+ "y3 = 0.01;# [kg NaOH/kg solution]\n",
+ "N3 = 0.666;# [kg CaCO3/kg solution]\n",
+ "E3 = mass_b/N3;# [kg/h]\n",
+ "lost_c = E3*y3;# [kg/h]\n",
+ "sludge_a = E3-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg NaOH/kg solution]\n",
+ "R1 = overflow_c/x1;# [kg overflow/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# For purpose of calculation, it may be imagined that agitators are not present in the flowsheet and the first thickner is fed with the dry mixture of the reaction products, CaCO3 and NaOH, together with overflow from the second thickner.\n",
+ "F = 400.0;# [kg NaOH/h]\n",
+ "NF = mass_b/F;# [kg CaCO3/kg NaOH]\n",
+ "yF = 1.0;# [wt fraction NaOH in dry solid, CaCO3 free basis]\n",
+ "# Points R1, E3, RNpPlus1 and F are plotted as in Fig 13.30 (Pg 755) and locate the point deltaR at the intersection of lines FR1 and E3RNpPlus1 extended. The coordinates of point deltaR are NdeltaR=-0.1419, ydeltaR=-0.00213. Further computation must be done on enlarged section of the equilibrium diagram (Fig 13.31 (Pg 755)). Point deltaR is plotted and the stages stepped off in a usual manner. The construction are projected on the xy diagram. Three stages produce a value: y3=0.001\n",
+ "print\"The NaOH lost in sludge: \",round((lost_c/mass_c)*100,2),\"%\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "#*** Data***#\n",
+ "lost_c = 0.001*mass_c;# [kg/h]\n",
+ "#***********#\n",
+ "\n",
+ "NNp_by_yNp = mass_b/lost_c;# [kg CaCO3/kg NaOH in final sludge]\n",
+ "# In order to determine the liquid content of the final sludge:\n",
+ "# Eqb=[N y_star]\n",
+ "Eqb = np.array([[0.659 , 0.01435],[0.666, 0.01015],[0.677, 0.002],[0.679, 0.001],[0.680 ,0.0005]]);\n",
+ "N_by_ystar = zeros(5);\n",
+ "for i in range(0,5):\n",
+ " N_by_ystar[i] = Eqb[i,0]/(Eqb[i,1]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1]);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x Wt fraction of NaOH\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.title(\"Equilibrium plot\")\n",
+ "# By Interpolation, for N_by_ystar=NNp_by_yNp:\n",
+ "NNp = interp(NNp_by_yNp,N_by_ystar,Eqb[:,0]);# [kg CaCO3/kg soln]\n",
+ "yNp = NNp/NNp_by_yNp;# [wt fraction NaOH in the liquid of the final sludge]\n",
+ "ENp = mass_b/NNp;# [kg/h]\n",
+ "ENp_a = ENp-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg/h]\n",
+ "R1 = overflow_c/0.1;# [kg/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# On the operating diagram (Fig 13.32 (Pg 757)) point deltaR is located and stages were constructed. \n",
+ "# Beyond the fourth stage, the ratio of the overflow to the liquid in the sludge become substantially constant.\n",
+ "R_by_E = RNpPlus1/ENp;\n",
+ "# This is the initial slope of the operating line on the lower part of the figure.\n",
+ "# From Illustration 13.2:\n",
+ "m = 0.01015/0.00450;\n",
+ "Value1 = R_by_E/m;\n",
+ "xNpPlus1 = 0;# [kg NaOH/kg solution]\n",
+ "y4 = 0.007;# [wt fraction NaOH in the liquid]\n",
+ "Value2 = (yNp-(m*xNpPlus1))/(y4-(m*xNpPlus1));\n",
+ "# From Fig 5.16: (Pg 129):\n",
+ "# An Additional 2.3 stages beyond 4 are computed graphically are required.\n",
+ "# An additional two stage will make yNp/y4=0.099:\n",
+ "yNp = 0.099*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,2),\"kg NaOH was lost if 6 thickners were used\\n\"\n",
+ "# An additional three stage will make yNp/y4=0.0365:\n",
+ "yNp = 0.0365*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,3),\"kg NaOH was lost if 7 thickners were used\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.3 - Page: 754\n",
+ "\n",
+ "\n",
+ "The NaOH lost in sludge: 1.88 %\n",
+ "\n",
+ "\n",
+ "\n",
+ "0.51"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " kg NaOH was lost if 6 thickners were used\n",
+ "\n",
+ "0.188 kg NaOH was lost if 7 thickners were used\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2WRg1qpVVVoERI4Zz2mnwsY+lyTfr6ft2tdzW1lZX8VRzubW1lcmTJwMs/n1Z\nSd32yUj6C7Ab8Bvgn8C/gK9FRJcPZEoaBpwSESOy5eOARRFxRlGZXwOtETEtW34M2Ik00KDDfSXd\nAJze/qCopDnA0A7ee+M+GbMqe/xx2Hvv9PDmL38JK6xQ64isp6reJwN8NSs3HngbWAfYp4z9ZgAb\nSRokaQBwAGkG52LXZsdvr5Rei4j53ex7NfDZbJ+NgQEdvffGzKrv4x+H6dPTczTbbQdz5tQ6Iqu1\nTisZSR+WtGnW+f5ORLweEacAvwVe7+7AEbGQVDHdRJq9+fJsdNhYSWOzMtcDT2V3IxOBcV3tmx16\nErCBpIeAqWSVlHWutKmomTkXBXnlYqWV0izOhx2WKpprrsnlNBXl6yI/XfXJnEfHHfyrAyeQBgN0\nKSJuAG4oWTexZHl8uftm698DDuru3GZWOxKMG5cGA+y/P9xzD/zkJ9C/rF5g60u6mrtsZkRs1cm2\nRyJi01wjW0rukzGrDy+9lJ6nWbAApk6FNdaodUTWlWr2yazcxbZlKxWAmfVtH/xgepZmxx1h663h\nrrtqHZFVkyfIbAJuby5wLgqqmYt+/eCHP4Tf/Ab22w/OOQfqqaHB10V+umohPQr4P0n7ATNJz7Ns\nBWwHfKEKsZlZHzNiRBp9tu++qZ9m0iRYZZVaR2V56vI5GUkfIHXwt/e/PAJcFhH/qUJsS8V9Mmb1\n69134aij4Pbb4YorYLPNah2Rtat0n0xuE2TWmisZs/p36aXwne/Az38OX/lKraMxqM3DmNbg3N5c\n4FwU1EMuDjoo3c2cemoa8vzuu7WJox5y0Ve5kjGzmtpsszSb8/z5sMMO8P/+X60jskpyc5mZ1YWI\nNOrszDNh8uQ0SMCqr+p9Mtn0LcGSsyW/TnqR2I/rdd4wVzJmjemuu2D0aPjGN+DEE9PwZ6ueWvTJ\n3Aj8mTTK7MvAdaQJLOcDkysViOXH7c0FzkVBveZihx1gxoz0npqRI9OMAXmr11z0BeVUMrtGxHER\n8VBEPBgRxwM7RcTpwKB8wzOzZrTGGnDrrTB4cJr/7L77ah2R9VY5zWUPAodmr0dG0hDgoojYXNID\nEbFFFeLsMTeXmfUNV1+dZnQ+5RT41rfS5JuWn1r0yWwDXAyslK16g/QK5keAPSLiD5UKppJcyZj1\nHXPmwD77pJFoEyfCiivWOqK+qxZ9Mg9FxKeAwcDgiNgMmBMRb9VrBWNLcntzgXNR0Ei52HBD+Nvf\n0qsChg6EQvQeAAATiUlEQVRNb+CspEbKRaMpp5K5UtKyEfFaRLwmaU3g1rwDMzMrtsIKcPHFcOSR\naXDAFVfUOiIrRznNZYcCI4F9gXVJr0H+bkTcnH94vefmMrO+a+bMNMnml74EZ5wBy/rlIxVTk7nL\nJI0HRgAfBb4ZEXdXKoC8uJIx69teeSVNS/P663D55bD22rWOqG+oWp+MpKOzz3eA5Uh3MbOAYdk6\naxBuby5wLgoaPRcDB8J118Huu8M228Add/T+WI2ei3rW3ZsxVyr69yrgyaJ13ZI0QtJjkp6UdEwn\nZSZk22dJ2qLcfbMKcJGkgeXEYmZ9zzLLwAknwCWXwIEHwumnw6JFtY7KiuU2d5mkfsDjwK7Ac6Rp\naEZHxOyiMiOB8RExUtJQ4NyIGNbdvpLWBS4CPg5sFRGvdHB+N5eZNZFnn4X994cPfximTIGWllpH\n1Jgaaar/IaShznMj4j1gGrBXSZlRwBSA7GHPFklrlLHvOcD3c4zdzBrMuuvCnXfCRz+aZgloa6t1\nRAb5VjJrA88WLc/L1pVTZq3O9pW0FzAvIh6sdMB9ldubC5yLgr6YiwEDYMIE+MlPYLfd0pDncvTF\nXNSL/jkeu9y2qrJvyyQtDxwP7FbO/mPGjGHQoEEAtLS0MHjwYIYPHw4ULiovN9dyu3qJp5bLbW1t\ndRVPJZfXWKOVs86C008fzt13w377tbLccp2Xb8tue+ol/mout7a2MnnyZIDFvy8rqdM+GUknd7JP\nAETEqV0eWBoGnBIRI7Ll44BFEXFGUZlfA60RMS1bfgzYCVi/o31Js0HfBrydHWIdUp/NkIh4oeT8\n7pMxa3JvvAGHHgpPPJEe3txgg1pHVP+q2SfzFvBmySdI85Z1OFKsxAxgI0mDJA0ADiA9yFnsWuCr\nsLhSei0i5ne2b0Q8HBEfiYj1I2J9UjPalqUVjJkZwMorw9SpcPDBMGxYGvJs1dVpJRMRZ0XE2RFx\nNmkk1/LAwaRO+PW7O3BELATGAzcBjwKXR8RsSWMljc3KXA88JWkOMBEY19W+HZ2m7G/axEqbipqZ\nc1HQLLmQ4PDD4ZprYNy4NOT5/feXLNMsuaiFLvtkJK0O/C/pZWWXkO4aXi334BFxA3BDybqJJcvj\ny923gzK++TWzsmy7bZqO5sAD4XOfS3c4H/5wraPq+7rqkzkL+BJwIXBBRLxRzcCWlvtkzKwj778P\nJ5+cnqWZNg0+85laR1RfqjZ3maRFwALgvQ42R0SsUqkg8uBKxsy68uc/wyGHwPHHwxFH+GVo7arW\n8R8Ry0TEByJi5Q4+dV3B2JLc3lzgXBQ0ey722APuvTdNSfPZz7byRkO11TSOPB/GNDOra+uvD3ff\nnd60uc028MgjtY6o78lt7rJac3OZmfXE5Mnwve+lGQNGj651NLVTk/fJNCJXMmbWU7NmwT77pNcH\nnH12mqam2TTSBJlWJ5q97b2Yc1HgXBS052LzzWHGjDSj8447pn9t6biSMTMr0tICV10Fe++d+mlu\nrusXzdc/N5eZmXWitTU9vPmtb6WZApZpgj/L3SdTJlcyZlYJzz8PBxyQ5kG79FJYffVaR5Qv98lY\nj7ntvcC5KHAuCrrKxVprwe23wyabwNZbpz4bK58rGTOzbiy7LJx1VvrsvjtMnAhuKCmPm8vMzHrg\niSfSMOctt4Rf/QpWWKHWEVWWm8vMzGpo443TdDSLFqV31Dz5ZK0jqm+uZJqA294LnIsC56Kgp7lY\nccU059m4cWkW56uuyieuvsCVjJlZL0jwzW+m2Zz/93/TlDQLF9Y6qvrjPhkzs6X08svwla/A22+n\nd9SsuWatI+o998mYmdWZ1VdPdzS77JKGOd95Z60jqh+5VzKSRkh6TNKTko7ppMyEbPssSVt0t6+k\nMyXNzspfKWnVvL9HI3Pbe4FzUeBcFFQiF8ssAyedBJMmpYc3zzzTw5wh50pGUj/gfGAEsAkwWtIn\nS8qMBDaMiI2Aw4BflbHvzcCmEbE58ARwXJ7fw8ysXJ//PNx3H1xxRRrq/PrrtY6otvK+kxkCzImI\nuRHxHjAN2KukzChgCkBETAdaJK3R1b4RcUtELMr2nw6sk/P3aGjDhw+vdQh1w7kocC4KKp2L9daD\nv/wl9c1svTU8+GBFD99Q8q5k1gaKJ8uel60rp8xaZewLcAhw/VJHamZWQcstB7/8JZxySuqrueSS\nWkdUG3lXMuW2SPZqJIOkE4AFEXFZb/ZvFm57L3AuCpyLgjxz8eUvwx13wE9+AoccAv/6V26nqkv9\ncz7+c8C6Rcvrku5IuiqzTlZm2a72lTQGGAns0tnJx4wZw6BBgwBoaWlh8ODBi2+L2y8qLzfXcrt6\niaeWy21tbXUVTy2X29racj3+Sy+1cs45cOutw9lkExgxopX/+R8YNar237+1tZXJkycDLP59WUm5\nPicjqT/wOKkieB64DxgdEbOLyowExkfESEnDgF9ExLCu9pU0Ajgb2CkiXurk3H5Oxszqzrx58KMf\nwZ/+BEcckR7kXHnlWkdV0FDPyUTEQmA8cBPwKHB5VkmMlTQ2K3M98JSkOcBEYFxX+2aHPg9YCbhF\n0gOSLsjze5iZVco666RZnKdPT/OebbghnH02vPNOrSPLh5/4bwKtra2Lb5ObnXNR4FwU1DIXDz+c\nnq+57z74wQ9Sv82AATUJBWiwOxkzM+vapz4FV16ZJtm86ir4xCfSSLT33691ZJXhOxkzszpy551w\nwgnw6qtw6qmw995pMs5qqfSdjCsZM7M6EwE33pgqGykNf/7856tT2bi5zHqsdPhuM3MuCpyLgnrL\nhZRe8zxjBhx3HHznO7DjjmkWgUbjSsbMrE4tswzsuy889BAceiiMGQMjRqTKp1G4uczMrEEsWAC/\n/S38+McwdGh63mbTTSt7DjeXmZk1qQED4Fvfgjlz0mufP/tZOOgg+Mc/ah1Z51zJNIF6a2+uJeei\nwLkoaLRcLL88HH10ephzo43SXc03v5lmE6g3rmTMzBrUKqukBzkffxxWXRU23zwNEnjxxVpHVuA+\nGTOzPuKf/4Sf/hQuuwzGjUt3Oy0tPTuG+2TMzKxDa64J550HM2fCc8+lprTTT4e33qpdTK5kmkCj\ntTfnybkocC4K+louBg2CSZPgrrvggQfSJJznnQfvvlv9WFzJmJn1UZ/4BFx+OdxwA9x0E2y8cRoC\nvXBh9WJwn4yZWZO45540Vc3zz8MPfwj7758e+CzmucvK5ErGzOy/RcBtt6XK5j//SQ92fuELhXnR\n3PFvPdbX2puXhnNR4FwUNFMuJNh1V7j33jRjwPHHw7bbwu2353O+/vkc1szM6pkEo0alu5jLL4ex\nY2G99XI4T19tUnJzmZlZ+d57D265BfbYw30yZXElY2bWcw3VJyNphKTHJD0p6ZhOykzIts+StEV3\n+0oaKOkWSU9IullSD59nbT7N1N7cHeeiwLkocC7yk1slI6kfcD4wAtgEGC3pkyVlRgIbRsRGwGHA\nr8rY91jglojYGLgtW7YutLW11TqEuuFcFDgXBc5FfvK8kxkCzImIuRHxHjAN2KukzChgCkBETAda\nJK3Rzb6L98n+/WKO36FPeO2112odQt1wLgqciwLnIj95VjJrA88WLc/L1pVTZq0u9v1IRMzPfp4P\nfKRSAZuZWWXlWcmU2+teTgeTOjpe1rPv3v1uzJ07t9Yh1A3nosC5KHAu8pPnczLPAesWLa9LuiPp\nqsw6WZllO1j/XPbzfElrRMS/JK0JvNBZAFLFBkg0vClTpnRfqEk4FwXORYFzkY88K5kZwEaSBgHP\nAwcAo0vKXAuMB6ZJGga8FhHzJb3cxb7XAl8Dzsj+vbqjk1dyCJ6ZmfVObpVMRCyUNB64CegH/DYi\nZksam22fGBHXSxopaQ7wFnBwV/tmhz4d+IOkrwNzgf3z+g5mZrZ0+uzDmGZmVnsNMUFmmQ91Dpf0\ngKSHJbUWrZ8r6cFs231F60+RNC9b/4CkEVX4KkttKXPRIukKSbMlPZo1UTbsA64VzsXQbH1TXReS\nPl70XR+Q9LqkI7JtTXVddJOLprousvXHSXpE0kOSLpO0XLa+Z9dFRNT1h9RcNgcYRBoQ0AZ8sqRM\nC/AIsE62/MGibU8DAzs47snAd2r9/aqciynAIdnP/YFVs59/Bnw/+/kY4PRaf9ca5qLprouiMssA\n/wTWbdbrootcNNV1ke3zFLBctnw58LXeXBeNcCdTzkOdBwJ/ioh5ABHxUsn2zgYBNNrggF7nQtKq\nwA4RMSlbvzAiXs/2acQHXPPKBTTRdVFiV+AfEdH+jFpTXRclSnMBzXVd/Bt4D1hBUn9gBQojfHt0\nXTRCJVPOQ50bAQMl3SFphqSDirYFcGu2/tCS/Q5XmjPttw3SFLA0uVgfeFHSxZL+LukiSStk2xrx\nAde8cgHNdV0U+x/gsqLlZrsuipXmAprouoiIV4CzgWdII3xfj4hbs316dF00QiVTzsiEZYEtgZHA\n54ETJW2Ubds+IrYAdge+LWmHbP2vSL9sBpNui8+uaNT5WJpc9M/WXxARW5JG8/3XvG+R7oEbYTRI\nXrlotusCAEkDgD2BP3Z4gua4LoBOc9FU14WkjwFHkZrN1gJWlPTl/zpBGddFI1Qy5TzU+Sxwc0S8\nExEvA38BNgeIiOezf18EriLdQhIRL0QG+E37+jrX21x8Ols/LyLuz8r9iXRxQfaAK4C6ecC1jlQy\nF1eQ5aLJrovNi7bvDszM/j9p10zXRZe5aMLrYivgnoh4OSIWAlcC22X79Oi6aIRKZvFDndlfGAeQ\nHsgsdg2wvaR+WbPHUOBRSStIWhlA0orA54CHsuU1i/b/Uvv6OtfbXMzObm+flbRxVm4XUocfFB5w\nhS4ecK0zlczFrmS5aLLr4tGi7aOBqSX7NNN10WUumvC6eBwYJml5SSL9P9Keo55dF3mObqjUh/SX\nxeOkkRLHZevGAmOLynyX9IviIeCIbN0GpBEVbcDD7ftm2y4BHgRmZUn6SK2/Z565yNZvDtyffecr\nKYyoGgjcCjwB3Ay01Pp71jAXzXhdrAi8BKxccsxmvC46y0UzXhffL1o/BVi2N9eFH8Y0M7PcNEJz\nmZmZNShXMmZmlhtXMmZmlhtXMmZmlhtXMmZmlhtXMmZmlhtXMtbQJP1c0pFFyzdJuqho+WxJ/yvp\no5JK38xafJwzs6nOz6hATEdJWr5o+c+SVlna43Zxvg9Jmi5ppqTPlGxrlXR/0fLWku4o45ibSrpd\naZr4JyT9oGjbGEnndXCerSrxfaxvcSVjje6vZNNdSFoGWB3YpGj7tsDdpHmnDuziOIcCm0XEEu/c\nkNSvFzEdSZq1FoCI2CMi/t2L45RrF+DBiNgqIu7uYPuH1IP3n2QV5DXATyPiE6QHV7eTNC4r0tHD\ndY0yt5lVmSsZq1uStslmvV1O0orZncYmJcX+RqpIADYlzezwhtJLyZYDPgk8QHpt9w5KL2c6svgA\nkq4FVgL+Lml/SZMl/VrSvcAZWRz3ZDM2390+HU02FcdZSi91miVpvKTDSRMK3iHptqzcXEkDs5+/\nk5V/qD2ObNqP2ZIuzL7jTZI+0EE+BmV3F7Mk3SppXUmDgTOAvbLvVrpfAGcBJ3RyvL9kd0AzJbXn\n8UDgr5HNuhsR7wDjKUwi2mhT3lsN9a91AGadiYj7swrgx8DywKUR8WhJmeclLZS0Lqmy+RtpOvNt\nSe/EeCgi3lN6K+B3I2LPDs4zStIbkWbrRtLupIpi24iIbP67HSLifUm7Aj8F9gUOA9YDNo+IRZJW\ni4hXJX0HGB5punTI/sLPmpPGkCZXXAaYLulO4DVgQ+CAiDhM0uXAPsDvS0I9D7g4Ii6VdDAwISK+\nJOkkYKuIOKKTVP4N+JKk4cAbRevnA7tFxLtKsxBfBmxDqqxnluToKUkrSVopW3WApO2LimzYybmt\nybmSsXp3Kmmiv3eAwzspcw+pyWw74BxSJbMd8DqpOQ16/tf3H6Mw51ILcImkDUkVRvv/N7sAv4qI\nRQAR8WoXxxOwPXBldmeApCuBHUgTDj4dEQ9mZWeSplgvNYzCC6J+R3pDYfuxu/t+PwZ+QHqTYbsB\nwPmSNgfeJ71bhOw7dne8acWVWjn9PNac3Fxm9e6DpEkLVyLdzXTkbuAzwGakyfzupVDp3NPL875d\n9POPgNsiYjPSWwGL4+hJ5VX6y1sU+jHeLVr/Pp3/AdibpqqIiDtIcQ8rWv+/wD8j4tPA1sBy2fpH\nSVO9F04qbQC8GRFvLkUc1oRcyVi9m0j6C/wyUt9DR+4BvgC8HMmrpLuPbSlUMv8GVu5lDKuQ3g4I\nqbmr3S3A2PbBAZJWy9a/ke1TLIC7gC9m06evSLoruYvyf2HfQ3pjI8CXSe/+6Ikfk+5k2iu2VYB/\nZT9/lfROeEi53l7SLrB4IMAEOs+/WadcyVjdkvRV4N2ImEbquN8m61co9TBpVNm9ReseBF4r6hd5\nEHhfUltpx3+mdGRU8fLPgNMk/Z30i7h9229Ir6d9UFIb6T0kABcCN7Z3/C8+YMQDwGTgvizWiyJi\nVhnnb3c4cLCkWaRK5siist2O7IqIG1jyBVMXAF/LYv848GZW7h3Su+B/IOkxUu6mR8Qve3I+M8BT\n/ZuZWX58J2NmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrn5/4wM\nQj9yu23UAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x77209b0>"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.4: Page 758"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.4\n",
+ "# Page: 758\n",
+ "\n",
+ "print'Illustration 13.4 - Page: 758\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "from scipy import interp\n",
+ "#***Data***#\n",
+ "# a:oil b:soyabean c:hexane\n",
+ "# Data=[100y*(Wt % oil in soln) 1/N(kg soln retained/kg insoluble solid)]\n",
+ "Data = numpy.array([[0 ,0.58],[20 ,0.66],[30 ,0.70]]);\n",
+ "# Soyabean feed:\n",
+ "percent_b = 20.0;# [soluble]\n",
+ "yF = 1.0;# [mass fraction oil,solid free basis]\n",
+ "# Solvent:\n",
+ "RNpPlus1 = 1.0;# [hexane,kg]\n",
+ "xNpPlus1 = 0;# [mass fraction oil]\n",
+ "# Leached Solids:\n",
+ "leached = 0.005;# [fraction of oil to be leached]\n",
+ "# Miscella:\n",
+ "percent_miscella = 10.0;# [percent of insoluble solid]\n",
+ "#**********#\n",
+ "\n",
+ "N = zeros(3);\n",
+ "ystar_By_N = zeros(3);\n",
+ "for i in range(0,3):\n",
+ " N[i] = 1/Data[i,1];# [kg insoluble solid/kg soln retained]\n",
+ " ystar_By_N[i] = Data[i,0]/(100*N[i]);# [kg oil/kg insoluble solid]\n",
+ "\n",
+ "# Basis: 1 kg flakes introduced\n",
+ "# Soyabean feed:\n",
+ "mass_b = 1-(percent_b/100.0);# [insoluble,kg]\n",
+ "F = 1.0-mass_b;# [kg]\n",
+ "NF = mass_b/F;# [kg insoluble solid/kg oil]\n",
+ "\n",
+ "# Leached Solids:\n",
+ "Ratio = leached/(1-leached);# [kg oil/kg insoluble solid]\n",
+ "# By interpolation:\n",
+ "Np = interp(Ratio,ystar_By_N,N);\n",
+ "miscella_b = (percent_miscella/100.0)*mass_b;# [Insoluble solid lost to miscella,kg]\n",
+ "leached_b = (1-(percent_miscella/100.0))*mass_b;# [Insoluble solid in miscella,kg]\n",
+ "ENp = leached_b/Np;# [kg soln retained]\n",
+ "retained_a = Ratio*leached_b;# [oil retained,kg]\n",
+ "retained_c = ENp-retained_a;# [Hexane retained,kg]\n",
+ "yNp = retained_a/ENp;# [mass fraction of oil in retained liquid]\n",
+ "\n",
+ "# Miscella:\n",
+ "mass_c = 1.0-retained_c;# [kg]\n",
+ "mass_a = F-retained_a;# [kg]\n",
+ "R1 = mass_c+mass_a;# [clear miscella,kg]\n",
+ "x1 = mass_a/R1;# [mass fraction of oil in the liquid]\n",
+ "NR1 = miscella_b/R1;# [kg insoluble solid/kg soln]\n",
+ "\n",
+ "# The operating diagram is shown in Fig 13.33 (Pg 759).\n",
+ "# Point R1 represents the cloudy miscella and is therefore is displaced from the axis of he graph at NR1. Point deltaR is located as usual and the stages determined with the N=0 axis for all the stages but the first.\n",
+ "print\"Between 4 and 5 stages are required\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.4 - Page: 758\n",
+ "\n",
+ "\n",
+ "Between 4 and 5 stages are required\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter13_2.ipynb b/Mass_-_Transfer_Operations/Chapter13_2.ipynb
new file mode 100755
index 00000000..8bfca391
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter13_2.ipynb
@@ -0,0 +1,438 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:b262ce3e37d7d3aade80ecf338d6eca332831fbc19ddab243748a217da7431ac"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 13: Leaching"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.1: Page 722"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.1\n",
+ "# Page: 722\n",
+ "\n",
+ "print'Illustration 13.1 - Page: 722\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import numpy as np\n",
+ "import math\n",
+ "#***Data***#\n",
+ "Density_L = 1137.0;# [kg/cubic m]\n",
+ "Density_S = 960.0;# [kg/cubic m]\n",
+ "Density_p = 1762.0;# [kg/cubic m]\n",
+ "A_prime = 16.4;# [square m/kg]\n",
+ "g = 9.81;# [square m/s]\n",
+ "sigma = 0.066;# [N/m]\n",
+ "Z = 3.0;# [m]\n",
+ "dia = 1.0;# [m]\n",
+ "#**********#\n",
+ "\n",
+ "e = 1-(Density_S/Density_p);# [fraction void]\n",
+ "ap = A_prime*Density_S;# [square m/cubic m]\n",
+ "# By Eqn. 6.67:\n",
+ "dp = 6*(1-e)/ap;# [m]\n",
+ "# By Eqn. 13.6:\n",
+ "K = dp**2*e**3.0*g/(150.0*(1-e)**2);# [cubic m/s]\n",
+ "check = K*Density_L*g/(g*sigma);\n",
+ "if (check<0.02):\n",
+ " # By Eqn. 13.3: \n",
+ " So = 0.075;\n",
+ "else:\n",
+ " # By Eqn. 13.4:\n",
+ " So = 0.0018/(check)\n",
+ "\n",
+ "# By Eqn. 13.2:\n",
+ "ZD = (0.275/g)/((K/g)**0.5*(Density_L/sigma));# [m]\n",
+ "# By Eqn. 13.1:\n",
+ "Sav = ((Z-ZD)*So/Z)+(ZD/Z);\n",
+ "# VolRatio=Vol liquid retained/Vol bed.\n",
+ "VolRatio = Sav*e;\n",
+ "print\"Vol liquid retained/Vol bed : \",round(VolRatio,4),\" cubic m/cubic m\\n\"\n",
+ "Mass = VolRatio*math.pi*dia**2*Z*Density_L/4;# [kg]\n",
+ "# Mass ratio=Mass Liquid/Mass dry solid\n",
+ "MassRatio = VolRatio*Density_L/(Density_S);\n",
+ "print\"Mass liquid/Mass dry solid: \",round(MassRatio,4),\" kg/kg\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.1 - Page: 722\n",
+ "\n",
+ "\n",
+ "Vol liquid retained/Vol bed : 0.058 cubic m/cubic m\n",
+ "\n",
+ "Mass liquid/Mass dry solid: 0.0687 kg/kg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.2: Page 749"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.2\n",
+ "# Page: 749\n",
+ "\n",
+ "print'Illustration 13.2 - Page: 749\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#***Data***#\n",
+ "# Eqb=[x(Wt fraction NaOH in clear solution) N(kg CaCO3/kg soln in settled sludge) y*(wt fraction NaOH in soln of settled sludge)]\n",
+ "# a=H2O b=CaCO3 c=NaOH\n",
+ "Eqb = np.array([[0.090 ,0.495, 0.0917],[0.0700, 0.525, 0.0762],[0.0473, 0.568, 0.0608],[0.0330, 0.600, 0.0452],[0.0208, 0.620, 0.0295],[0.01187 ,0.650, 0.0204],[0.00710, 0.659, 0.01435],[0.00450, 0.666, 0.01015]]);\n",
+ "def f80(x):\n",
+ " return x\n",
+ "x = np.arange(0,0.12,0.01);\n",
+ "Mass_c = 0.1;# [kg]\n",
+ "Mass_b = 0.125;# [kg]\n",
+ "Mass_a = 0.9;# [kg]\n",
+ "#**************#\n",
+ "\n",
+ "\n",
+ "plt.plot(x,f80(x),label=\"N Vs x\")\n",
+ "plt.plot(Eqb[:,2],Eqb[:,1],label=\"N Vs Y\");\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x,y Wt. fraction of NaOH in loquid\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.legend('upper left');\n",
+ "plt.title(\"Equilibrium Plot\")\n",
+ "plt.show()\n",
+ "# Basis: 1 kg soln in original mixture.\n",
+ "# As in Fig. 13.27 (Pg 750)\n",
+ "# The original mixture corresponds to M1:\n",
+ "NM1 = 0.125;# [kg CaCO3/kg soln]\n",
+ "yM1 = 0.1;# [kg NaOH/kg solution]\n",
+ "# The tie line through M1 is drawn. At point E1 representing the settled sludge:\n",
+ "N1 = 0.47;# [kg CaCO3/kg soln]\n",
+ "y1 = 0.100;# [kg NaOH/kg solution]\n",
+ "E1 = Mass_b/N1;# [kg soln. in sludge]\n",
+ "Ro = 1-E1;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 2:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M2 = E1+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM2 = Mass_b/(E1+Ro);# [kg CaCO3/kg soln]\n",
+ "# M2 is located on line RoE1. At this value of N, and the tie line through M2 is drawn. At E2:\n",
+ "N2 = 0.62;# [kg CaCO3/kg soln]\n",
+ "y2 = 0.035;# [kg NaOH/kg solution]\n",
+ "E2 = Mass_b/N2;# [kg soln. in sludge]\n",
+ "Ro = 1-E2;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 3:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M3 = E2+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM3 = Mass_b/M3;# [kg CaCO3/kg soln]\n",
+ "# Tie line E3R3 is located through M3.At E3:\n",
+ "N3 = 0.662;# [kg CaCO3/kg soln]\n",
+ "y3 = 0.012;# [kg NaOH/kg solution]\n",
+ "# By Eqn. 13.8:\n",
+ "E3 = Mass_b/N3;# [kg soln. in sludge]\n",
+ "print\"The fraction of original NaOH in the slurry: \",round(E3*y3/Mass_c,4),\" \\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.2 - Page: 749\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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RwshUk/gN0DPF/B4EA/9l5O57gCuApwjOQP7s7kvNbHLCJbSfBxaZ2YJwm1/K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\ngg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7720908>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The fraction of original NaOH in the slurry: 0.0227 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.3: Page 754"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.3\n",
+ "# Page: 754\n",
+ "\n",
+ "print'Illustration 13.3 - Page: 754\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "import numpy as np\n",
+ "from scipy import interp\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#***Data***#\n",
+ "# a=H2O b=CaCO3 c=NaOH \n",
+ "mass_c = 400;# [kg/h]\n",
+ "x1 = 0.1;# [wt fraction NaOH in overflow]\n",
+ "#**********#\n",
+ "\n",
+ "Mb = 100.0;# [kg/kmol]\n",
+ "Mc = 40.0;# [kg/kmol]\n",
+ "rate_c = mass_c/Mc;# [kmol/h]\n",
+ "rate_b = rate_c/2;# [kmol/h]\n",
+ "mass_b = rate_b*Mb;# [kg/h]\n",
+ "# After trial calculations:\n",
+ "y3 = 0.01;# [kg NaOH/kg solution]\n",
+ "N3 = 0.666;# [kg CaCO3/kg solution]\n",
+ "E3 = mass_b/N3;# [kg/h]\n",
+ "lost_c = E3*y3;# [kg/h]\n",
+ "sludge_a = E3-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg NaOH/kg solution]\n",
+ "R1 = overflow_c/x1;# [kg overflow/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# For purpose of calculation, it may be imagined that agitators are not present in the flowsheet and the first thickner is fed with the dry mixture of the reaction products, CaCO3 and NaOH, together with overflow from the second thickner.\n",
+ "F = 400.0;# [kg NaOH/h]\n",
+ "NF = mass_b/F;# [kg CaCO3/kg NaOH]\n",
+ "yF = 1.0;# [wt fraction NaOH in dry solid, CaCO3 free basis]\n",
+ "# Points R1, E3, RNpPlus1 and F are plotted as in Fig 13.30 (Pg 755) and locate the point deltaR at the intersection of lines FR1 and E3RNpPlus1 extended. The coordinates of point deltaR are NdeltaR=-0.1419, ydeltaR=-0.00213. Further computation must be done on enlarged section of the equilibrium diagram (Fig 13.31 (Pg 755)). Point deltaR is plotted and the stages stepped off in a usual manner. The construction are projected on the xy diagram. Three stages produce a value: y3=0.001\n",
+ "print\"The NaOH lost in sludge: \",round((lost_c/mass_c)*100,2),\"%\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "#*** Data***#\n",
+ "lost_c = 0.001*mass_c;# [kg/h]\n",
+ "#***********#\n",
+ "\n",
+ "NNp_by_yNp = mass_b/lost_c;# [kg CaCO3/kg NaOH in final sludge]\n",
+ "# In order to determine the liquid content of the final sludge:\n",
+ "# Eqb=[N y_star]\n",
+ "Eqb = np.array([[0.659 , 0.01435],[0.666, 0.01015],[0.677, 0.002],[0.679, 0.001],[0.680 ,0.0005]]);\n",
+ "N_by_ystar = zeros(5);\n",
+ "for i in range(0,5):\n",
+ " N_by_ystar[i] = Eqb[i,0]/(Eqb[i,1]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1]);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x Wt fraction of NaOH\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.title(\"Equilibrium plot\")\n",
+ "# By Interpolation, for N_by_ystar=NNp_by_yNp:\n",
+ "NNp = interp(NNp_by_yNp,N_by_ystar,Eqb[:,0]);# [kg CaCO3/kg soln]\n",
+ "yNp = NNp/NNp_by_yNp;# [wt fraction NaOH in the liquid of the final sludge]\n",
+ "ENp = mass_b/NNp;# [kg/h]\n",
+ "ENp_a = ENp-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg/h]\n",
+ "R1 = overflow_c/0.1;# [kg/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# On the operating diagram (Fig 13.32 (Pg 757)) point deltaR is located and stages were constructed. \n",
+ "# Beyond the fourth stage, the ratio of the overflow to the liquid in the sludge become substantially constant.\n",
+ "R_by_E = RNpPlus1/ENp;\n",
+ "# This is the initial slope of the operating line on the lower part of the figure.\n",
+ "# From Illustration 13.2:\n",
+ "m = 0.01015/0.00450;\n",
+ "Value1 = R_by_E/m;\n",
+ "xNpPlus1 = 0;# [kg NaOH/kg solution]\n",
+ "y4 = 0.007;# [wt fraction NaOH in the liquid]\n",
+ "Value2 = (yNp-(m*xNpPlus1))/(y4-(m*xNpPlus1));\n",
+ "# From Fig 5.16: (Pg 129):\n",
+ "# An Additional 2.3 stages beyond 4 are computed graphically are required.\n",
+ "# An additional two stage will make yNp/y4=0.099:\n",
+ "yNp = 0.099*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,2),\"kg NaOH was lost if 6 thickners were used\\n\"\n",
+ "# An additional three stage will make yNp/y4=0.0365:\n",
+ "yNp = 0.0365*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,3),\"kg NaOH was lost if 7 thickners were used\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.3 - Page: 754\n",
+ "\n",
+ "\n",
+ "The NaOH lost in sludge: 1.88 %\n",
+ "\n",
+ "\n",
+ "\n",
+ "0.51"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " kg NaOH was lost if 6 thickners were used\n",
+ "\n",
+ "0.188 kg NaOH was lost if 7 thickners were used\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2WRg1qpVVVoERI4Zz2mnwsY+lyTfr6ft2tdzW1lZX8VRzubW1lcmTJwMs/n1Z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\nQj9yu23UAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x77209b0>"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.4: Page 758"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.4\n",
+ "# Page: 758\n",
+ "\n",
+ "print'Illustration 13.4 - Page: 758\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "from scipy import interp\n",
+ "#***Data***#\n",
+ "# a:oil b:soyabean c:hexane\n",
+ "# Data=[100y*(Wt % oil in soln) 1/N(kg soln retained/kg insoluble solid)]\n",
+ "Data = numpy.array([[0 ,0.58],[20 ,0.66],[30 ,0.70]]);\n",
+ "# Soyabean feed:\n",
+ "percent_b = 20.0;# [soluble]\n",
+ "yF = 1.0;# [mass fraction oil,solid free basis]\n",
+ "# Solvent:\n",
+ "RNpPlus1 = 1.0;# [hexane,kg]\n",
+ "xNpPlus1 = 0;# [mass fraction oil]\n",
+ "# Leached Solids:\n",
+ "leached = 0.005;# [fraction of oil to be leached]\n",
+ "# Miscella:\n",
+ "percent_miscella = 10.0;# [percent of insoluble solid]\n",
+ "#**********#\n",
+ "\n",
+ "N = zeros(3);\n",
+ "ystar_By_N = zeros(3);\n",
+ "for i in range(0,3):\n",
+ " N[i] = 1/Data[i,1];# [kg insoluble solid/kg soln retained]\n",
+ " ystar_By_N[i] = Data[i,0]/(100*N[i]);# [kg oil/kg insoluble solid]\n",
+ "\n",
+ "# Basis: 1 kg flakes introduced\n",
+ "# Soyabean feed:\n",
+ "mass_b = 1-(percent_b/100.0);# [insoluble,kg]\n",
+ "F = 1.0-mass_b;# [kg]\n",
+ "NF = mass_b/F;# [kg insoluble solid/kg oil]\n",
+ "\n",
+ "# Leached Solids:\n",
+ "Ratio = leached/(1-leached);# [kg oil/kg insoluble solid]\n",
+ "# By interpolation:\n",
+ "Np = interp(Ratio,ystar_By_N,N);\n",
+ "miscella_b = (percent_miscella/100.0)*mass_b;# [Insoluble solid lost to miscella,kg]\n",
+ "leached_b = (1-(percent_miscella/100.0))*mass_b;# [Insoluble solid in miscella,kg]\n",
+ "ENp = leached_b/Np;# [kg soln retained]\n",
+ "retained_a = Ratio*leached_b;# [oil retained,kg]\n",
+ "retained_c = ENp-retained_a;# [Hexane retained,kg]\n",
+ "yNp = retained_a/ENp;# [mass fraction of oil in retained liquid]\n",
+ "\n",
+ "# Miscella:\n",
+ "mass_c = 1.0-retained_c;# [kg]\n",
+ "mass_a = F-retained_a;# [kg]\n",
+ "R1 = mass_c+mass_a;# [clear miscella,kg]\n",
+ "x1 = mass_a/R1;# [mass fraction of oil in the liquid]\n",
+ "NR1 = miscella_b/R1;# [kg insoluble solid/kg soln]\n",
+ "\n",
+ "# The operating diagram is shown in Fig 13.33 (Pg 759).\n",
+ "# Point R1 represents the cloudy miscella and is therefore is displaced from the axis of he graph at NR1. Point deltaR is located as usual and the stages determined with the N=0 axis for all the stages but the first.\n",
+ "print\"Between 4 and 5 stages are required\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.4 - Page: 758\n",
+ "\n",
+ "\n",
+ "Between 4 and 5 stages are required\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter1_1.ipynb b/Mass_-_Transfer_Operations/Chapter1_1.ipynb
new file mode 100755
index 00000000..3d150815
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter1_1.ipynb
@@ -0,0 +1,76 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:a429839b93fda73811b080ddf18c202362ff24ca9ec207904626c7313297633e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter1 : The Mass-Transfer Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex1.1: Page 17"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 1.1\n",
+ "# Page: 17\n",
+ "\n",
+ "print'Illustration 1.1 - Page: 17\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# Taking conversion factor from table 1.5 (Pg 15)\n",
+ "# viscosity: [(lb/ft.h)]*4.134*10^(-4) [kg/m.s] (Pg 15)\n",
+ "# time: [h] = 3600.0 [s]\n",
+ "# Density: [lb/cubic feet]*16.09 = [kg/cubic m] (Pg 15)\n",
+ "# Length: [ft]*0.3048 = [m]\n",
+ "N = (2.778*10**(-4))*(30600/(1/(0.3048**(3.0/2))))*((1/(4.134*(10**(-4))*16.019))**0.111)*(((1/16.019)/(1/16.019))**0.26);\n",
+ "print'The coeffecient for S.I. Unit is',round(N,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 1.1 - Page: 17\n",
+ "\n",
+ "\n",
+ "The coeffecient for S.I. Unit is 2.5\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter1_2.ipynb b/Mass_-_Transfer_Operations/Chapter1_2.ipynb
new file mode 100755
index 00000000..3d150815
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter1_2.ipynb
@@ -0,0 +1,76 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:a429839b93fda73811b080ddf18c202362ff24ca9ec207904626c7313297633e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter1 : The Mass-Transfer Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex1.1: Page 17"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 1.1\n",
+ "# Page: 17\n",
+ "\n",
+ "print'Illustration 1.1 - Page: 17\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# Taking conversion factor from table 1.5 (Pg 15)\n",
+ "# viscosity: [(lb/ft.h)]*4.134*10^(-4) [kg/m.s] (Pg 15)\n",
+ "# time: [h] = 3600.0 [s]\n",
+ "# Density: [lb/cubic feet]*16.09 = [kg/cubic m] (Pg 15)\n",
+ "# Length: [ft]*0.3048 = [m]\n",
+ "N = (2.778*10**(-4))*(30600/(1/(0.3048**(3.0/2))))*((1/(4.134*(10**(-4))*16.019))**0.111)*(((1/16.019)/(1/16.019))**0.26);\n",
+ "print'The coeffecient for S.I. Unit is',round(N,2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 1.1 - Page: 17\n",
+ "\n",
+ "\n",
+ "The coeffecient for S.I. Unit is 2.5\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter2.ipynb b/Mass_-_Transfer_Operations/Chapter2.ipynb
new file mode 100755
index 00000000..3d3cab36
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter2.ipynb
@@ -0,0 +1,333 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:c59a45d9559284ebbfa4c56e2ad9c90a674f6e02433515bc489ee0db922e92e9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 2:Molecular Diffusion In Fluids"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.1: Pg-30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Illustration 2.1\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = O2 & b = CO\n",
+ "Dab = 1.87*10**(-5);#[square m/s]\n",
+ "Pt = 10**5;#[N/square m]\n",
+ "z = 0.002;#[m]\n",
+ "R = 8314;#[Nm/kmol]\n",
+ "T = 273;#[K]\n",
+ "Pa1 = 13*10.0**(3);#[N/square m]\n",
+ "Pb1 = 10**(5)-13*10**(3);#[N/square m]\n",
+ "Pa2 = 6500;#[N/square m]\n",
+ "Pb2 = 10**(5)-6500.0;#[N/square m]\n",
+ "#********#\n",
+ "\n",
+ "# Calculation from Eqn. 2.30\n",
+ "Pbm = (Pb1-Pb2)/math.log(Pb1/Pb2);#[N/square m]\n",
+ "Na = Dab*Pt*(Pa1-Pa2)/(R*T*z*Pbm);#[kmol/square m.s]\n",
+ "print\" Rate of diffusion of oxygen is\",round(Na,7),\"kmol/square m.sec \""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Rate of diffusion of oxygen is 2.97e-05 kmol/square m.sec \n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.2:Pg-30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration2.2\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "\n",
+ "#***Data***#\n",
+ "Pt = 10**5.0;#[N/square m]\n",
+ "z = 0.002;#[m]\n",
+ "R = 8314.0;#[Nm/kmol]\n",
+ "T = 273;#[K]\n",
+ "#a = O2 b = CH4 c = H2\n",
+ "Pa1 = 13*10**(3);#[N/square m]\n",
+ "Pb1 = 10**(5)-13*10**(3);#[N/square m]\n",
+ "Pa2 = 6500.0;#[N/square m]\n",
+ "Pb2 = 10.0**(5)-6500;#[N/square m]\n",
+ "Dac = 6.99*10**(-5);#[N/square m]\n",
+ "Dab = 1.86*10.0**(-5);#[N/square m]\n",
+ "#*******#\n",
+ "\n",
+ "# Calculation from Eqn. 2.30\n",
+ "Pbm = (Pb1-Pb2)/math.log(Pb1/Pb2);#[N/square m]\n",
+ "Yb_prime = 2.0/(2+1);\n",
+ "Yc_prime = 1-Yb_prime;\n",
+ "Dam = 1.0/((Yb_prime/Dab)+(Yc_prime/Dac));#[square m.s]\n",
+ "Na = Dam*(Pa1-Pa2)*Pt/(R*T*z*Pbm);#[kmol/square m.s]\n",
+ "print \"Rate of diffusion is\",round(Na,7),\"kmol/square m.sec\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Rate of diffusion is 3.91e-05 kmol/square m.sec\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.3:Pg-32"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration2.3\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = C2H5OH b = air\n",
+ "Pt = 101.3*10**(3);#[N/square m]\n",
+ "T = 273.0 ;#[K]\n",
+ "#********#\n",
+ "\n",
+ "Ma = 46.07;# [kg/kmol]\n",
+ "Mb = 29.0;# [kg/kmol]\n",
+ "#For air from Table 2.2 (Pg 33)\n",
+ "Eb_by_k = 78.6;# [K]\n",
+ "rb = 0.3711; # [nm]\n",
+ "# For C2H5OH using Eqn. 2.38 & 2.39\n",
+ "# From Table 2.3\n",
+ "Va = (2*0.0148)+(6*0.0037)+(0.0074);# [cubic m/kmol]\n",
+ "Tba = 351.4;# [K]\n",
+ "ra = 1.18*(Va**(1/3.0));#[nm]\n",
+ "Ea_by_k = 1.21*Tba;# [K]\n",
+ "rab = (ra+rb)/2.0;# [nm]\n",
+ "Eab_by_k = math.sqrt(Ea_by_k*Eb_by_k);# [K]\n",
+ "Collision_value = T/Eab_by_k;\n",
+ "#From Fig. 2.5 (Page: 32) f(collision value)\n",
+ "Collision_func = 0.595;\n",
+ "Dab = (10**(-4)*(1.084-(0.249*math.sqrt((1/Ma)+(1/Mb))))*T**(3.0/2)*math.sqrt((1/Ma)+(1/Mb)))/(Pt*(rab**2)*Collision_func);#[square m/s]\n",
+ "print\" The diffusivity of ethanol through air at 1 atm. & 0 degree C is\",round(Dab,7),\"m^2/s\"\n",
+ "print\" The observed value from (Table 2.1) is 1.02*10^(-5) square m/s'\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " The diffusivity of ethanol through air at 1 atm. & 0 degree C is 1.05e-05 m^2/s\n",
+ " The observed value from (Table 2.1) is 1.02*10^(-5) square m/s'\n"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.4:Pg-34"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.4\n",
+ "import math\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = acetic acid b = H2O\n",
+ "z = 0.001;# [m]\n",
+ "Dab = 0.95*10**(-9);#[square m/s]\n",
+ "#************#\n",
+ "\n",
+ "Ma = 60.03;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "#At 17 C & 9% solution\n",
+ "density1 = 1012; #[kg/cubic m]\n",
+ "Xa1 = (0.09/Ma)/((0.09/Ma)+(0.91/Mb));\n",
+ "Xb1 = 1-Xa1;\n",
+ "M1 = 1/((0.09/Ma)+(0.91/Mb));# [kg/kmol]\n",
+ "#At 17 C & 3% solution\n",
+ "density2 = 1003.2; #[kg/cubic m]\n",
+ "Xa2 = (0.03/Ma)/((0.03/Ma)+(0.97/Mb));\n",
+ "Xb2 = 1-Xa2;\n",
+ "M2 = 1/((0.03/Ma)+(0.97/Mb));# [kg/kmol]\n",
+ "avg_density_by_M = ((density1/M1)+(density2/M2))/2;#[kmol/cubic m]\n",
+ "# From Eqn. 2.42\n",
+ "Xbm = (Xb2-Xb1)/math.log(Xb2/Xb1);\n",
+ "# From Eqn. 2.41\n",
+ "Na = Dab*(avg_density_by_M)*(Xa1-Xa2)/(Xbm*z); #[square m/s]\n",
+ "print\" The rate of diffusion is\",round(Na,9),\"square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " The rate of diffusion is 1.018e-06 square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.5:Pg-37"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.5\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = mannitol b = H2O\n",
+ "T = 293; # [K]\n",
+ "#*****#\n",
+ "\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# From Table 2.3 (Pg 33)\n",
+ "Va = (0.0148*6)+(0.0037*14)+(0.0074*6); # [cubic m/kmol]\n",
+ "viscosity = 0.001005; # [kg/m.s]\n",
+ "association_factor = 2.26; # [water as a solvent]\n",
+ "Dab = (117.3*10**(-18))*((association_factor*Mb)**0.5)*T/(viscosity*Va**0.6); # [square m/s]\n",
+ "print\" Diffusivity of mannitol is\",round(Dab,12),\"square m/s\"\n",
+ "print\" Observed value is 0.56*10^(-9) square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Diffusivity of mannitol is 6.01e-10 square m/s\n",
+ " Observed value is 0.56*10^(-9) square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 48
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.6:Pg-37"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.6\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "T2 = 70+273;# [K]\n",
+ "#**********#\n",
+ "\n",
+ "# a = mannitol b = H2O\n",
+ "# From Illustration 2.5 at 20 C\n",
+ "viscosity1 = 1.005*10**(-3); # [kg/m.s]\n",
+ "Dab1 = 0.56*10**(-9); #[m^2/s]\n",
+ "T1 = 273+20;# [K]\n",
+ "# At 70 C\n",
+ "viscosity2 = 0.4061*10**(-3); # kg/m.s\n",
+ "# Eqn. 2.44 indicates Dab*viscocity/T = constnt\n",
+ "Dab2 = Dab1*(T2)*(viscosity1)/(T1*viscosity2);# [square m/s]\n",
+ "print\" Diffusivity of mannitol at 70 degree C is\",round(Dab2,11),\"square/s \"\n",
+ "print\" Observed value at 70 degree C is 1.56*10^(-9) square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Diffusivity of mannitol at 70 degree C is 1.62e-09 square/s \n",
+ " Observed value at 70 degree C is 1.56*10^(-9) square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 53
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter2_1.ipynb b/Mass_-_Transfer_Operations/Chapter2_1.ipynb
new file mode 100755
index 00000000..3d3cab36
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter2_1.ipynb
@@ -0,0 +1,333 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:c59a45d9559284ebbfa4c56e2ad9c90a674f6e02433515bc489ee0db922e92e9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 2:Molecular Diffusion In Fluids"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.1: Pg-30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Illustration 2.1\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = O2 & b = CO\n",
+ "Dab = 1.87*10**(-5);#[square m/s]\n",
+ "Pt = 10**5;#[N/square m]\n",
+ "z = 0.002;#[m]\n",
+ "R = 8314;#[Nm/kmol]\n",
+ "T = 273;#[K]\n",
+ "Pa1 = 13*10.0**(3);#[N/square m]\n",
+ "Pb1 = 10**(5)-13*10**(3);#[N/square m]\n",
+ "Pa2 = 6500;#[N/square m]\n",
+ "Pb2 = 10**(5)-6500.0;#[N/square m]\n",
+ "#********#\n",
+ "\n",
+ "# Calculation from Eqn. 2.30\n",
+ "Pbm = (Pb1-Pb2)/math.log(Pb1/Pb2);#[N/square m]\n",
+ "Na = Dab*Pt*(Pa1-Pa2)/(R*T*z*Pbm);#[kmol/square m.s]\n",
+ "print\" Rate of diffusion of oxygen is\",round(Na,7),\"kmol/square m.sec \""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Rate of diffusion of oxygen is 2.97e-05 kmol/square m.sec \n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.2:Pg-30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration2.2\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "\n",
+ "#***Data***#\n",
+ "Pt = 10**5.0;#[N/square m]\n",
+ "z = 0.002;#[m]\n",
+ "R = 8314.0;#[Nm/kmol]\n",
+ "T = 273;#[K]\n",
+ "#a = O2 b = CH4 c = H2\n",
+ "Pa1 = 13*10**(3);#[N/square m]\n",
+ "Pb1 = 10**(5)-13*10**(3);#[N/square m]\n",
+ "Pa2 = 6500.0;#[N/square m]\n",
+ "Pb2 = 10.0**(5)-6500;#[N/square m]\n",
+ "Dac = 6.99*10**(-5);#[N/square m]\n",
+ "Dab = 1.86*10.0**(-5);#[N/square m]\n",
+ "#*******#\n",
+ "\n",
+ "# Calculation from Eqn. 2.30\n",
+ "Pbm = (Pb1-Pb2)/math.log(Pb1/Pb2);#[N/square m]\n",
+ "Yb_prime = 2.0/(2+1);\n",
+ "Yc_prime = 1-Yb_prime;\n",
+ "Dam = 1.0/((Yb_prime/Dab)+(Yc_prime/Dac));#[square m.s]\n",
+ "Na = Dam*(Pa1-Pa2)*Pt/(R*T*z*Pbm);#[kmol/square m.s]\n",
+ "print \"Rate of diffusion is\",round(Na,7),\"kmol/square m.sec\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Rate of diffusion is 3.91e-05 kmol/square m.sec\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.3:Pg-32"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration2.3\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = C2H5OH b = air\n",
+ "Pt = 101.3*10**(3);#[N/square m]\n",
+ "T = 273.0 ;#[K]\n",
+ "#********#\n",
+ "\n",
+ "Ma = 46.07;# [kg/kmol]\n",
+ "Mb = 29.0;# [kg/kmol]\n",
+ "#For air from Table 2.2 (Pg 33)\n",
+ "Eb_by_k = 78.6;# [K]\n",
+ "rb = 0.3711; # [nm]\n",
+ "# For C2H5OH using Eqn. 2.38 & 2.39\n",
+ "# From Table 2.3\n",
+ "Va = (2*0.0148)+(6*0.0037)+(0.0074);# [cubic m/kmol]\n",
+ "Tba = 351.4;# [K]\n",
+ "ra = 1.18*(Va**(1/3.0));#[nm]\n",
+ "Ea_by_k = 1.21*Tba;# [K]\n",
+ "rab = (ra+rb)/2.0;# [nm]\n",
+ "Eab_by_k = math.sqrt(Ea_by_k*Eb_by_k);# [K]\n",
+ "Collision_value = T/Eab_by_k;\n",
+ "#From Fig. 2.5 (Page: 32) f(collision value)\n",
+ "Collision_func = 0.595;\n",
+ "Dab = (10**(-4)*(1.084-(0.249*math.sqrt((1/Ma)+(1/Mb))))*T**(3.0/2)*math.sqrt((1/Ma)+(1/Mb)))/(Pt*(rab**2)*Collision_func);#[square m/s]\n",
+ "print\" The diffusivity of ethanol through air at 1 atm. & 0 degree C is\",round(Dab,7),\"m^2/s\"\n",
+ "print\" The observed value from (Table 2.1) is 1.02*10^(-5) square m/s'\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " The diffusivity of ethanol through air at 1 atm. & 0 degree C is 1.05e-05 m^2/s\n",
+ " The observed value from (Table 2.1) is 1.02*10^(-5) square m/s'\n"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.4:Pg-34"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.4\n",
+ "import math\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = acetic acid b = H2O\n",
+ "z = 0.001;# [m]\n",
+ "Dab = 0.95*10**(-9);#[square m/s]\n",
+ "#************#\n",
+ "\n",
+ "Ma = 60.03;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "#At 17 C & 9% solution\n",
+ "density1 = 1012; #[kg/cubic m]\n",
+ "Xa1 = (0.09/Ma)/((0.09/Ma)+(0.91/Mb));\n",
+ "Xb1 = 1-Xa1;\n",
+ "M1 = 1/((0.09/Ma)+(0.91/Mb));# [kg/kmol]\n",
+ "#At 17 C & 3% solution\n",
+ "density2 = 1003.2; #[kg/cubic m]\n",
+ "Xa2 = (0.03/Ma)/((0.03/Ma)+(0.97/Mb));\n",
+ "Xb2 = 1-Xa2;\n",
+ "M2 = 1/((0.03/Ma)+(0.97/Mb));# [kg/kmol]\n",
+ "avg_density_by_M = ((density1/M1)+(density2/M2))/2;#[kmol/cubic m]\n",
+ "# From Eqn. 2.42\n",
+ "Xbm = (Xb2-Xb1)/math.log(Xb2/Xb1);\n",
+ "# From Eqn. 2.41\n",
+ "Na = Dab*(avg_density_by_M)*(Xa1-Xa2)/(Xbm*z); #[square m/s]\n",
+ "print\" The rate of diffusion is\",round(Na,9),\"square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " The rate of diffusion is 1.018e-06 square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.5:Pg-37"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.5\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = mannitol b = H2O\n",
+ "T = 293; # [K]\n",
+ "#*****#\n",
+ "\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# From Table 2.3 (Pg 33)\n",
+ "Va = (0.0148*6)+(0.0037*14)+(0.0074*6); # [cubic m/kmol]\n",
+ "viscosity = 0.001005; # [kg/m.s]\n",
+ "association_factor = 2.26; # [water as a solvent]\n",
+ "Dab = (117.3*10**(-18))*((association_factor*Mb)**0.5)*T/(viscosity*Va**0.6); # [square m/s]\n",
+ "print\" Diffusivity of mannitol is\",round(Dab,12),\"square m/s\"\n",
+ "print\" Observed value is 0.56*10^(-9) square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Diffusivity of mannitol is 6.01e-10 square m/s\n",
+ " Observed value is 0.56*10^(-9) square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 48
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.6:Pg-37"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.6\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "T2 = 70+273;# [K]\n",
+ "#**********#\n",
+ "\n",
+ "# a = mannitol b = H2O\n",
+ "# From Illustration 2.5 at 20 C\n",
+ "viscosity1 = 1.005*10**(-3); # [kg/m.s]\n",
+ "Dab1 = 0.56*10**(-9); #[m^2/s]\n",
+ "T1 = 273+20;# [K]\n",
+ "# At 70 C\n",
+ "viscosity2 = 0.4061*10**(-3); # kg/m.s\n",
+ "# Eqn. 2.44 indicates Dab*viscocity/T = constnt\n",
+ "Dab2 = Dab1*(T2)*(viscosity1)/(T1*viscosity2);# [square m/s]\n",
+ "print\" Diffusivity of mannitol at 70 degree C is\",round(Dab2,11),\"square/s \"\n",
+ "print\" Observed value at 70 degree C is 1.56*10^(-9) square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Diffusivity of mannitol at 70 degree C is 1.62e-09 square/s \n",
+ " Observed value at 70 degree C is 1.56*10^(-9) square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 53
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter2_2.ipynb b/Mass_-_Transfer_Operations/Chapter2_2.ipynb
new file mode 100755
index 00000000..3d3cab36
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter2_2.ipynb
@@ -0,0 +1,333 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:c59a45d9559284ebbfa4c56e2ad9c90a674f6e02433515bc489ee0db922e92e9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 2:Molecular Diffusion In Fluids"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.1: Pg-30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Illustration 2.1\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = O2 & b = CO\n",
+ "Dab = 1.87*10**(-5);#[square m/s]\n",
+ "Pt = 10**5;#[N/square m]\n",
+ "z = 0.002;#[m]\n",
+ "R = 8314;#[Nm/kmol]\n",
+ "T = 273;#[K]\n",
+ "Pa1 = 13*10.0**(3);#[N/square m]\n",
+ "Pb1 = 10**(5)-13*10**(3);#[N/square m]\n",
+ "Pa2 = 6500;#[N/square m]\n",
+ "Pb2 = 10**(5)-6500.0;#[N/square m]\n",
+ "#********#\n",
+ "\n",
+ "# Calculation from Eqn. 2.30\n",
+ "Pbm = (Pb1-Pb2)/math.log(Pb1/Pb2);#[N/square m]\n",
+ "Na = Dab*Pt*(Pa1-Pa2)/(R*T*z*Pbm);#[kmol/square m.s]\n",
+ "print\" Rate of diffusion of oxygen is\",round(Na,7),\"kmol/square m.sec \""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Rate of diffusion of oxygen is 2.97e-05 kmol/square m.sec \n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.2:Pg-30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration2.2\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "\n",
+ "#***Data***#\n",
+ "Pt = 10**5.0;#[N/square m]\n",
+ "z = 0.002;#[m]\n",
+ "R = 8314.0;#[Nm/kmol]\n",
+ "T = 273;#[K]\n",
+ "#a = O2 b = CH4 c = H2\n",
+ "Pa1 = 13*10**(3);#[N/square m]\n",
+ "Pb1 = 10**(5)-13*10**(3);#[N/square m]\n",
+ "Pa2 = 6500.0;#[N/square m]\n",
+ "Pb2 = 10.0**(5)-6500;#[N/square m]\n",
+ "Dac = 6.99*10**(-5);#[N/square m]\n",
+ "Dab = 1.86*10.0**(-5);#[N/square m]\n",
+ "#*******#\n",
+ "\n",
+ "# Calculation from Eqn. 2.30\n",
+ "Pbm = (Pb1-Pb2)/math.log(Pb1/Pb2);#[N/square m]\n",
+ "Yb_prime = 2.0/(2+1);\n",
+ "Yc_prime = 1-Yb_prime;\n",
+ "Dam = 1.0/((Yb_prime/Dab)+(Yc_prime/Dac));#[square m.s]\n",
+ "Na = Dam*(Pa1-Pa2)*Pt/(R*T*z*Pbm);#[kmol/square m.s]\n",
+ "print \"Rate of diffusion is\",round(Na,7),\"kmol/square m.sec\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Rate of diffusion is 3.91e-05 kmol/square m.sec\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.3:Pg-32"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration2.3\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = C2H5OH b = air\n",
+ "Pt = 101.3*10**(3);#[N/square m]\n",
+ "T = 273.0 ;#[K]\n",
+ "#********#\n",
+ "\n",
+ "Ma = 46.07;# [kg/kmol]\n",
+ "Mb = 29.0;# [kg/kmol]\n",
+ "#For air from Table 2.2 (Pg 33)\n",
+ "Eb_by_k = 78.6;# [K]\n",
+ "rb = 0.3711; # [nm]\n",
+ "# For C2H5OH using Eqn. 2.38 & 2.39\n",
+ "# From Table 2.3\n",
+ "Va = (2*0.0148)+(6*0.0037)+(0.0074);# [cubic m/kmol]\n",
+ "Tba = 351.4;# [K]\n",
+ "ra = 1.18*(Va**(1/3.0));#[nm]\n",
+ "Ea_by_k = 1.21*Tba;# [K]\n",
+ "rab = (ra+rb)/2.0;# [nm]\n",
+ "Eab_by_k = math.sqrt(Ea_by_k*Eb_by_k);# [K]\n",
+ "Collision_value = T/Eab_by_k;\n",
+ "#From Fig. 2.5 (Page: 32) f(collision value)\n",
+ "Collision_func = 0.595;\n",
+ "Dab = (10**(-4)*(1.084-(0.249*math.sqrt((1/Ma)+(1/Mb))))*T**(3.0/2)*math.sqrt((1/Ma)+(1/Mb)))/(Pt*(rab**2)*Collision_func);#[square m/s]\n",
+ "print\" The diffusivity of ethanol through air at 1 atm. & 0 degree C is\",round(Dab,7),\"m^2/s\"\n",
+ "print\" The observed value from (Table 2.1) is 1.02*10^(-5) square m/s'\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " The diffusivity of ethanol through air at 1 atm. & 0 degree C is 1.05e-05 m^2/s\n",
+ " The observed value from (Table 2.1) is 1.02*10^(-5) square m/s'\n"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.4:Pg-34"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.4\n",
+ "import math\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = acetic acid b = H2O\n",
+ "z = 0.001;# [m]\n",
+ "Dab = 0.95*10**(-9);#[square m/s]\n",
+ "#************#\n",
+ "\n",
+ "Ma = 60.03;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "#At 17 C & 9% solution\n",
+ "density1 = 1012; #[kg/cubic m]\n",
+ "Xa1 = (0.09/Ma)/((0.09/Ma)+(0.91/Mb));\n",
+ "Xb1 = 1-Xa1;\n",
+ "M1 = 1/((0.09/Ma)+(0.91/Mb));# [kg/kmol]\n",
+ "#At 17 C & 3% solution\n",
+ "density2 = 1003.2; #[kg/cubic m]\n",
+ "Xa2 = (0.03/Ma)/((0.03/Ma)+(0.97/Mb));\n",
+ "Xb2 = 1-Xa2;\n",
+ "M2 = 1/((0.03/Ma)+(0.97/Mb));# [kg/kmol]\n",
+ "avg_density_by_M = ((density1/M1)+(density2/M2))/2;#[kmol/cubic m]\n",
+ "# From Eqn. 2.42\n",
+ "Xbm = (Xb2-Xb1)/math.log(Xb2/Xb1);\n",
+ "# From Eqn. 2.41\n",
+ "Na = Dab*(avg_density_by_M)*(Xa1-Xa2)/(Xbm*z); #[square m/s]\n",
+ "print\" The rate of diffusion is\",round(Na,9),\"square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " The rate of diffusion is 1.018e-06 square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.5:Pg-37"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.5\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = mannitol b = H2O\n",
+ "T = 293; # [K]\n",
+ "#*****#\n",
+ "\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# From Table 2.3 (Pg 33)\n",
+ "Va = (0.0148*6)+(0.0037*14)+(0.0074*6); # [cubic m/kmol]\n",
+ "viscosity = 0.001005; # [kg/m.s]\n",
+ "association_factor = 2.26; # [water as a solvent]\n",
+ "Dab = (117.3*10**(-18))*((association_factor*Mb)**0.5)*T/(viscosity*Va**0.6); # [square m/s]\n",
+ "print\" Diffusivity of mannitol is\",round(Dab,12),\"square m/s\"\n",
+ "print\" Observed value is 0.56*10^(-9) square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Diffusivity of mannitol is 6.01e-10 square m/s\n",
+ " Observed value is 0.56*10^(-9) square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 48
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex2.6:Pg-37"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 2.6\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "T2 = 70+273;# [K]\n",
+ "#**********#\n",
+ "\n",
+ "# a = mannitol b = H2O\n",
+ "# From Illustration 2.5 at 20 C\n",
+ "viscosity1 = 1.005*10**(-3); # [kg/m.s]\n",
+ "Dab1 = 0.56*10**(-9); #[m^2/s]\n",
+ "T1 = 273+20;# [K]\n",
+ "# At 70 C\n",
+ "viscosity2 = 0.4061*10**(-3); # kg/m.s\n",
+ "# Eqn. 2.44 indicates Dab*viscocity/T = constnt\n",
+ "Dab2 = Dab1*(T2)*(viscosity1)/(T1*viscosity2);# [square m/s]\n",
+ "print\" Diffusivity of mannitol at 70 degree C is\",round(Dab2,11),\"square/s \"\n",
+ "print\" Observed value at 70 degree C is 1.56*10^(-9) square m/s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " Diffusivity of mannitol at 70 degree C is 1.62e-09 square/s \n",
+ " Observed value at 70 degree C is 1.56*10^(-9) square m/s\n"
+ ]
+ }
+ ],
+ "prompt_number": 53
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter3.ipynb b/Mass_-_Transfer_Operations/Chapter3.ipynb
new file mode 100755
index 00000000..36f53f4d
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter3.ipynb
@@ -0,0 +1,628 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:64d9a62e17838716bd10cd86f93be8c39dc69462337a3d3adc3d6ea158cbc575"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 3: Mass-Transfer Coefficients"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.1:Page 53"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.1\n",
+ "# Page: 53\n",
+ "\n",
+ "print'Illustration 3.1 - Page: 53\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = CO2 b = H2O\n",
+ "Ca0 = 0;#[kmol/cubic m]\n",
+ "Cai = 0.0336;#[kmol/cubic m]\n",
+ "Dab = 1.96*10**(-9);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "density = 998.0;# [kg/cubic m]\n",
+ "viscosity = 8.94*10**(-4);#[kg/m.s]\n",
+ "rate = 0.05;#[kg/m.s] mass flow rate of liquid\n",
+ "L = 1;#[m]\n",
+ "g = 9.81;#[m/square s]\n",
+ "# From Eqn. 3.10\n",
+ "Del = ((3*viscosity*rate)/((density**2)*g))**(1.0/3);# [m]\n",
+ "Re = 4*rate/viscosity;\n",
+ "# Flow comes out to be laminar\n",
+ "# From Eqn. 3.19\n",
+ "Kl_avg = ((6*Dab*rate)/(3.141*density*Del*L))**(1.0/2);#[kmol/square m.s.(kmol/cubic m)]\n",
+ "bulk_avg_velocity = rate/(density*Del);#[m/s]\n",
+ "# At the top: Cai-Ca = Cai_Ca0 = Cai\n",
+ "#At the bottom: Cai-Cal\n",
+ "# From Eqn. 3.21 & 3.22\n",
+ "Cal = Cai*(1-(1.0/(exp(Kl_avg/(bulk_avg_velocity*Del)))));# [kmol/cubic m]\n",
+ "rate_absorption = bulk_avg_velocity*Del*(Cal-Ca0);# [kmol/s].(m of width)\n",
+ "print'The rate of absorption is ',round(rate_absorption,8),' kmol/sec.(m of width)'\n",
+ "# The actual value may be substantially larger."
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.1 - Page: 53\n",
+ "\n",
+ "\n",
+ "The rate of absorption is 7.2e-07 kmol/sec.(m of width)\n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.2: Page 56"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.2\n",
+ "# Page: 56\n",
+ "\n",
+ "print'Illustration 3.2 - Page: 56\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "d = 0.025;# [m]\n",
+ "avg_velocity = 3;# [m/s]\n",
+ "viscosity = 8.937*10**(-4);# [kg/m.s]\n",
+ "density = 997;# [kg/m**3]\n",
+ "#*********#\n",
+ "\n",
+ "kinematic_viscosity = viscosity/density;# [square m/s]\n",
+ "Re = d*avg_velocity*density/viscosity;\n",
+ "# Reynold's number comes out to be 83670\n",
+ "# At this Reynold's number fanning factor = 0.0047\n",
+ "f = 0.0047;\n",
+ "L = 1;# [m]\n",
+ "press_drop = 2*density*f*L*(avg_velocity**2)/(d);# [N/square m]\n",
+ "P = 3.141*(d**2)*avg_velocity*press_drop/4;# [N.m/s] for 1m pipe\n",
+ "m = 3.141*(d**2)*L*density/4;\n",
+ "# From Eqn. 3.24\n",
+ "Ld = ((kinematic_viscosity**3)*m/P)**(1.0/4);# [m]\n",
+ "# From Eqn. 3.25\n",
+ "Ud = (kinematic_viscosity*P/m)**(1.0/4);# [m/s]\n",
+ "print'Velocity of small eddies is',round(Ud,4),'m/s'\n",
+ "print'Length scale of small eddies is',round(Ld,7),'m'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.2 - Page: 56\n",
+ "\n",
+ "\n",
+ "Velocity of small eddies is 0.0549 m/s\n",
+ "Length scale of small eddies is 1.63e-05 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.3: Page 69"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.3\n",
+ "# Page: 69\n",
+ "\n",
+ "print'Illustration 3.3 - Page: 69\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# Heat transfer analog to Eqn. 3.12\n",
+ "# The Eqn. remains the same with the dimensionless conc. ratio replaced by ((tl-to)/(ti-to))\n",
+ "\n",
+ "# The dimensionless group:\n",
+ "# eta = 2*Dab*L/(3*del**2*velocity);\n",
+ "# eta = (2/3)*(Dab/(del*velocity))*(L/del);\n",
+ "# Ped = Peclet no. for mass transfer\n",
+ "# eta = (2/3)*(1/Ped)*(L/del);\n",
+ "\n",
+ "# For heat transfer is replaced by\n",
+ "# Peh = Peclet no. for heat transfer\n",
+ "# eta = (2/3)*(1/Peh)*(L/del);\n",
+ "# eta = (2/3)*(alpha/(del*velocity))*(L/del);\n",
+ "# eta = (2*alpha*L)/(3*del**2*velocity);\n",
+ "print'Heat transfer analog to Eqn. 3.21 is eta = (2*alpha*L)/(3*del**2*velocity)'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.3 - Page: 69\n",
+ "\n",
+ "\n",
+ "Heat transfer analog to Eqn. 3.21 is eta = (2*alpha*L)/(3*del**2*velocity)\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.4: Page-69"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.4\n",
+ "# Page: 69\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 3.4 - Page: 69\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = UF6 b = air\n",
+ "# The average heat transfer coefficient: Nu_avg = 0.43+0.532(Re^0.5)(Pr^0.31)\n",
+ "# The analogus expression for mass transfer coefficient: Sh_avg = 0.43+0.532(Re^0.5)(Sc^0.31)\n",
+ "d = 0.006;# [m]\n",
+ "velocity = 3.0;# [m/s]\n",
+ "surf_temp = 43.0;# [C]\n",
+ "bulk_temp = 60.0;# [C]\n",
+ "avg_temp = (surf_temp+bulk_temp)/2; #[C]\n",
+ "density = 4.10;# [kg/cubic m]\n",
+ "viscosity = 2.7*10**(-5);# [kg/m.s]\n",
+ "Dab = 9.04*10**(-6);# [square m/s]\n",
+ "press = 53.32;# [kN/square m]\n",
+ "tot_press = 101.33;# [kN/square m]\n",
+ "#******#\n",
+ "\n",
+ "avg_press = press/2.0; # [kN/square m]\n",
+ "Xa = avg_press/tot_press;\n",
+ "Xb = 1-Xa;\n",
+ "Re = d*velocity*density/viscosity;\n",
+ "Sc = viscosity/(density*Dab);\n",
+ "Sh_avg = 0.43+(0.532*(2733**0.5)*(0.728**0.5));\n",
+ "c = 273.2/(22.41*(273.2+avg_temp));# [kmol/cubic m]\n",
+ "F_avg = Sh_avg*c*Dab/d;#[kmol/cubic m]\n",
+ "Nb = 0.0;\n",
+ "Ca1_by_C = press/tot_press;\n",
+ "Ca2_by_C = 0.0;\n",
+ "Flux_a = 1.0;\n",
+ "# Using Eqn. 3.1\n",
+ "Na = Flux_a*F_avg*math.log((Flux_a-Ca2_by_C)/(Flux_a-Ca1_by_C));#[kmol UF6/square m.s]\n",
+ "print'Rate of sublimation is',round(Na,8),' kmol UF6/square m.s'\n",
+ "# the answer is slightly different in textbook due to approximation"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.4 - Page: 69\n",
+ "\n",
+ "\n",
+ "Rate of sublimation is 0.00102088 kmol UF6/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.5: Page 73"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.5\n",
+ "# Page: 73\n",
+ "\n",
+ "print'Illustration 3.5 - Page: 73\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "velocity = 15.0;# [m/s]\n",
+ "G = 21.3;# [kg/square m.s]\n",
+ "#******#\n",
+ "\n",
+ "# Since the experimental data do not include the effects of changing Prandtl number.\n",
+ "\n",
+ "# Jh = (h/(Cp*density*viscosity)) = (h/Cp*G)*(Pr^(2/3)) = Shi(Re);\n",
+ "\n",
+ "# Shi(Re) must be compatible with 21.3*(G**0.6);\n",
+ "# Let Shi(Re) = b*(Re**n);\n",
+ "# Re = (l*G)/viscosity;\n",
+ "\n",
+ "# h = (Cp*G/(Pr**(2/3)))*b*(Re**n);\n",
+ "# h = (Cp*G/(Pr**(2/3)))*b*((l*b/viscosity)**n) = 21.3*(G**0.6);\n",
+ "\n",
+ "n = 0.6-1;\n",
+ "# b = 21.3*((Pr**(2/3))/Cp)*((l/viscosity)**(-n));\n",
+ "\n",
+ "# Using data for air at 38 C & 1 std atm.\n",
+ "Cp1 = 1002;# [kJ/kg.K]\n",
+ "viscosity1 = 1.85*10**(-5);#[kg/m.s]\n",
+ "k1 = 0.0273;#[W/m.K]\n",
+ "Pr1 = (Cp1*viscosity1)/k1;\n",
+ "b_prime = 21.3*(Pr1**(2.0/3)/Cp1)*((1/viscosity1)**0.4);\n",
+ "# b = b_prime*l**(0.4);\n",
+ "# Jh = (h/(Cp*G))*Pr**(2/3) = b_prime*((l/Re)**(0.4)) = Shi(Re);\n",
+ "\n",
+ "# The heat mass transfer analogy will be used to estimate the mass transfer coefficient. (Jd = Jh)\n",
+ "\n",
+ "# Jd = (KG*Pbm*Mav*Sc**(2/3))/(density*viscosity) = Shi(Re) = b_prime*((l/Re)**0.4);\n",
+ "\n",
+ "# KG*Pbm = F = (b_prime*density*viscosity)/(Re^0.4*Mav*Sc**(2/3)) = (b_prime*(density*velocity)**0.6*(viscosity^0.4))/(Mav*Sc**(2/3));\n",
+ "\n",
+ "# For H2-H20, 38 C, 1std atm\n",
+ "viscosity2 = 9*10**(-6);# [kg/m.s]\n",
+ "density2 = 0.0794;# [kg/cubic m]\n",
+ "Dab = 7.75*10**(-5);# [square m/s]\n",
+ "Sc = viscosity2/(density2*Dab);\n",
+ "\n",
+ "# Assuming desity, Molecular weight and viscosity of the gas are essentially those of H2\n",
+ "\n",
+ "Mav = 2.02;# [kg/kmol]\n",
+ "F = (b_prime*(density2*velocity)**0.6*(viscosity2**0.4))/(Mav*Sc**(2.0/3));# [kmol/square m.s]\n",
+ "print'The required mass transfer: ',round(F,5),' kmol/square m.s'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.5 - Page: 73\n",
+ "\n",
+ "\n",
+ "The required mass transfer: 0.00525 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.6:Page 77"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.6\n",
+ "# Page: 77\n",
+ "\n",
+ "print'Illustration 3.6 - Page: 77\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy import integrate\n",
+ "import math \n",
+ "#***Data***#\n",
+ "Dp = 0.0125;# [m]\n",
+ "viscosity = 2.4*10**(-5);# [kg/m.s]\n",
+ "Sc = 2.0;\n",
+ "E = 0.3;\n",
+ "Go = (2*10**(-3))/0.1;# molar superficial mass velocity [kmol/square m.s]\n",
+ "#********#\n",
+ "\n",
+ "# a = CO b = Ni(CO)4\n",
+ "# Nb = -(Na/4);\n",
+ "Flux_a = 4.0/3;\n",
+ "Ca2_by_C = 0;# At the metal interface\n",
+ "# Ca1_by_C = Ya #mole fraction of CO in the bulk\n",
+ "\n",
+ "# Eqn. 3.1 becomes: Na = (4/3)*F*log((4/3)/((4/3)-Ya));\n",
+ "\n",
+ "# Let G = kmol gas/(square m bed cross section).s\n",
+ "# a = specific metal surface\n",
+ "# z = depth \n",
+ "# Therefore, Na = -(diff(Ya*G))/(a*diff(z));# [kmol/((square m metal surface).s)];\n",
+ "# For each kmol of CO consumed, (1/4)kmol Ni(CO)4 forms, representing a loss of (3/4) kmol per kmol of CO consumed.\n",
+ "# The CO consumed through bed depth dz is therefore (Go-G)(4/3) kmol;\n",
+ "# Ya = (Go-(Go-G)*(4/3))/G;\n",
+ "# G = Go/(4-(3*Ya));\n",
+ "# diff(YaG) = ((4*Go)/(4-3*Ya)**2)*diff(Ya);\n",
+ "\n",
+ "# Substituting in Eqn. 3.64\n",
+ "# -(4*Go/((4-3*Ya)**2*a))*(diff(Ya)/diff(z)) = (4/3)*F*log(4/(4-3*Ya));\n",
+ "\n",
+ "# At depth z:\n",
+ "# Mass velocity of CO = (Go-(Go-G)/(4/3))*28;\n",
+ "# Mass velocity of Ni(CO)4 = ((Go-G)*(1/3))*170.7;\n",
+ "# G_prime = 47.6*Go-19.6G; # total mass velocity [kg/square m.s]\n",
+ "# Substituting G leads to:\n",
+ "# G_prime = Go*(47.6-19.6*(4-3*Ya));# [kg/m.s]\n",
+ "# Re = (Dp*G')/viscosity\n",
+ "\n",
+ "# With Go = 0.002 kmol/square m.s & Ya in the range 1-0.005, the range of Re is 292-444;\n",
+ "# From table 3.3:\n",
+ "# Jd = (F/G)*(Sc**(2/3)) = (2.06/E)*Re**(-0.575);\n",
+ "# F = (2.06/E*(Sc)**(2/3))*(Go/(4-3*Ya))*Re**(-0.575);\n",
+ "\n",
+ "a = 6*(1-E)/Dp;\n",
+ "\n",
+ "# Result after arrangement:\n",
+ "\n",
+ "X2=lambda Ya:-((4*Go)/((4-(3*Ya))**2.0*a))*(3.0/4)*(E*(Sc**(2.0/3))*(4-(3*Ya))/(2.06*Go)*(1/math.log(4.0/(4-(3*Ya)))))*(((Dp/viscosity)*(Go*(47.6-(19.6/(4.0-(3*Ya))))))**0.575);# [m]\n",
+ "Z = integrate.quad(X2,1,0.005);\n",
+ "print'The bed depth required to reduce the CO content to 0.005 is',round(Z[0],3),'m'\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.6 - Page: 77\n",
+ "\n",
+ "\n",
+ "The bed depth required to reduce the CO content to 0.005 is 0.132 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.7: Page 80"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.7\n",
+ "# Page: 80\n",
+ "\n",
+ "print'Illustration 3.7 - Page: 80\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = water b = air\n",
+ "out_dia = 0.0254;# [m]\n",
+ "wall_thick = 0.00165;# [m]\n",
+ "avg_velocity = 4.6;# [m/s]\n",
+ "T1 = 66.0;# [C]\n",
+ "P = 1.0;# [atm]\n",
+ "Pa1 = 0.24;# [atm]\n",
+ "k1 = 11400.0;# [W/(square m.K)]\n",
+ "T2 = 24.0;# [C]\n",
+ "k2 = 570.0;# [W/square m.K]\n",
+ "k_Cu = 381.0;# [w/square m.K]\n",
+ "#******#\n",
+ "\n",
+ "# For the metal tube\n",
+ "int_dia = out_dia-(2*wall_thick);# [m]\n",
+ "avg_dia = (out_dia+int_dia)/2;# [mm]\n",
+ "Nb = 0;\n",
+ "Flux_a = 1;\n",
+ "Ya1 = 0.24;\n",
+ "Yb1 = 1-Ya1;\n",
+ "Mav = (Ya1*18.02)+(Yb1*29);# [kg/kmol]\n",
+ "density = (Mav/22.41)*(273/(273+T1));# [kg/cubic m]\n",
+ "viscosity = 1.75*10**(-5);# [kg/m.s]\n",
+ "Cpa = 1880.0;# [J/kg.K]\n",
+ "Cpmix = 1145.0;# [J/kg.K]\n",
+ "Sc = 0.6;\n",
+ "Pr = 0.75;\n",
+ "G_prime = avg_velocity*density;# [kg/square m.s]\n",
+ "G = G_prime/Mav;# [kmol/square m.s]\n",
+ "Re = avg_dia*G_prime/viscosity;\n",
+ "# From Table 3.3:\n",
+ "# Jd = Std*Sc**(2/3) = (F/G)*Sc**(2/3) = 0.023*Re**(-0.17);\n",
+ "Jd = 0.023*Re**(-0.17);\n",
+ "F = (0.023*G)*(Re**(-0.17)/Sc**(2.0/3));\n",
+ "\n",
+ "# The heat transfer coeffecient in the absence of mass transfer will be estimated through Jd = Jh\n",
+ "# Jh = Sth*Pr^(2/3) = (h/Cp*G_prime)*(Pr^(2/3)) = Jd\n",
+ "h = Jd*Cpmix*G_prime/(Pr**(2.0/3));\n",
+ "\n",
+ "U = 1/((1/k1)+((wall_thick/k_Cu)*(int_dia/avg_dia))+((1/k2)*(int_dia/out_dia)));# W/square m.K\n",
+ "\n",
+ "# Using Eqn. 3.70 & 3.71 with Nb = 0\n",
+ "# Qt = (Na*18.02*Cpa/1-exp(-(Na*18.02*Cpa/h)))*(T1-Ti)+(Lambda_a*Na);\n",
+ "# Qt = 618*(Ti-T2);\n",
+ "# Using Eqn. 3.67, with Nb = 0, Cai/C = pai, Ca1/C = Ya1 = 0.24;\n",
+ "# Na = F*log(((Flux_a)-(pai))/((Flux_a)-(Ya1));\n",
+ "\n",
+ "# Solving above three Eqn. simultaneously:\n",
+ "Ti = 42.2;# [C]\n",
+ "pai = 0.0806;# [atm]\n",
+ "Lambda_a = 43.4*10**6;# [J/kmol]\n",
+ "Na = F*log(((Flux_a)-(pai))/((Flux_a)-(Ya1)));# [kmol/square m.s]\n",
+ "Qt1 = 618*(Ti-T2);# [W/square m]\n",
+ "Qt2 = ((Na*18.02*Cpa/(1-exp(-(Na*18.02*Cpa/h))))*(T1-Ti))+(Lambda_a*Na);# [W/square m]\n",
+ "\n",
+ "# since the value of Qt1 & Qt2 are relatively close\n",
+ "print'The local rate of condensation of water is ',round(Na,6),' kmol/square m.s'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.7 - Page: 80\n",
+ "\n",
+ "\n",
+ "The local rate of condensation of water is 0.000232 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.8: Page 81"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.8\n",
+ "# Page: 81\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 3.8 - Page: 81\\n\\n'\n",
+ "print'Illustration 3.8 (a)\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = water b = air\n",
+ "Nb = 0;\n",
+ "h = 1100.0;# [W/square m]\n",
+ "#*****#\n",
+ "\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Cpa = 2090;# [J/kg.K]\n",
+ "T1 = 600.0;# [C]\n",
+ "Ti = 260;# [C]\n",
+ "# The positive dirn. is taken to be from the bulk gas to the surface.\n",
+ "Has = 2.684*(10**6);# enthapy of saturated steam at 1.2 std atm, rel. to the liquid at 0 C in [J/kg]\n",
+ "Hai = 2.994*(10**6);# enthalpy of steam at 1 std atm, 260 C in [J/kg]\n",
+ "\n",
+ "# Radiation contributions to the heat transfer from the gas to the surface are negligible. Eqn. 3.70 reduces to\n",
+ "Na = -((h/(Ma*Cpa))*log(1-((Cpa*(T1-Ti))/(Has-Hai))));# [kmol/square m.s]\n",
+ "print'The rate of steam flow reqd. is',round(Na,4),' kmol/square m.s\\n\\n'\n",
+ "# negative sign indicates that the mass flux is into the gas\n",
+ "\n",
+ "print'Illustration 3.8 (b)\\n\\n'\n",
+ " \n",
+ "# Solution (b)\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = water b = air\n",
+ "h = 572.0;# [W/square m]\n",
+ "T1 = 25.0;# [C]\n",
+ "#******#\n",
+ "\n",
+ "Ti = 260.0;# [C]\n",
+ "# The positive dirn. is taken to be from the bulk gas to the surface.\n",
+ "Has = 1.047*10**(5);# enthapy of saturated steam at 1.2 std atm, rel. to the liquid at 0 C in [J/kg]\n",
+ "Hai = 2.994*(10**6);# enthalpy of steam at 1 std atm, 260 C in [J/kg]\n",
+ "\n",
+ "# Radiation contributions to the heat transfer from the gas to the surface are negligible. Eqn. 3.70 reduces to\n",
+ "Na = -((h/(Ma*Cpa))*math.log(1-((Cpa*(T1-Ti))/(Has-Hai))));# [kmol/square m.s]\n",
+ "print'The rate of steam flow reqd. is',round(Na,4),' kmol/square m.s'\n",
+ "# negative sign indicates that the mass flux is into \n",
+ "# the answer of part B in textbook is incorrect"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.8 - Page: 81\n",
+ "\n",
+ "\n",
+ "Illustration 3.8 (a)\n",
+ "\n",
+ "\n",
+ "The rate of steam flow reqd. is -0.0348 kmol/square m.s\n",
+ "\n",
+ "\n",
+ "Illustration 3.8 (b)\n",
+ "\n",
+ "\n",
+ "The rate of steam flow reqd. is 0.0028 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 42
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter3_1.ipynb b/Mass_-_Transfer_Operations/Chapter3_1.ipynb
new file mode 100755
index 00000000..36f53f4d
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter3_1.ipynb
@@ -0,0 +1,628 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:64d9a62e17838716bd10cd86f93be8c39dc69462337a3d3adc3d6ea158cbc575"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 3: Mass-Transfer Coefficients"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.1:Page 53"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.1\n",
+ "# Page: 53\n",
+ "\n",
+ "print'Illustration 3.1 - Page: 53\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = CO2 b = H2O\n",
+ "Ca0 = 0;#[kmol/cubic m]\n",
+ "Cai = 0.0336;#[kmol/cubic m]\n",
+ "Dab = 1.96*10**(-9);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "density = 998.0;# [kg/cubic m]\n",
+ "viscosity = 8.94*10**(-4);#[kg/m.s]\n",
+ "rate = 0.05;#[kg/m.s] mass flow rate of liquid\n",
+ "L = 1;#[m]\n",
+ "g = 9.81;#[m/square s]\n",
+ "# From Eqn. 3.10\n",
+ "Del = ((3*viscosity*rate)/((density**2)*g))**(1.0/3);# [m]\n",
+ "Re = 4*rate/viscosity;\n",
+ "# Flow comes out to be laminar\n",
+ "# From Eqn. 3.19\n",
+ "Kl_avg = ((6*Dab*rate)/(3.141*density*Del*L))**(1.0/2);#[kmol/square m.s.(kmol/cubic m)]\n",
+ "bulk_avg_velocity = rate/(density*Del);#[m/s]\n",
+ "# At the top: Cai-Ca = Cai_Ca0 = Cai\n",
+ "#At the bottom: Cai-Cal\n",
+ "# From Eqn. 3.21 & 3.22\n",
+ "Cal = Cai*(1-(1.0/(exp(Kl_avg/(bulk_avg_velocity*Del)))));# [kmol/cubic m]\n",
+ "rate_absorption = bulk_avg_velocity*Del*(Cal-Ca0);# [kmol/s].(m of width)\n",
+ "print'The rate of absorption is ',round(rate_absorption,8),' kmol/sec.(m of width)'\n",
+ "# The actual value may be substantially larger."
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.1 - Page: 53\n",
+ "\n",
+ "\n",
+ "The rate of absorption is 7.2e-07 kmol/sec.(m of width)\n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.2: Page 56"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.2\n",
+ "# Page: 56\n",
+ "\n",
+ "print'Illustration 3.2 - Page: 56\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "d = 0.025;# [m]\n",
+ "avg_velocity = 3;# [m/s]\n",
+ "viscosity = 8.937*10**(-4);# [kg/m.s]\n",
+ "density = 997;# [kg/m**3]\n",
+ "#*********#\n",
+ "\n",
+ "kinematic_viscosity = viscosity/density;# [square m/s]\n",
+ "Re = d*avg_velocity*density/viscosity;\n",
+ "# Reynold's number comes out to be 83670\n",
+ "# At this Reynold's number fanning factor = 0.0047\n",
+ "f = 0.0047;\n",
+ "L = 1;# [m]\n",
+ "press_drop = 2*density*f*L*(avg_velocity**2)/(d);# [N/square m]\n",
+ "P = 3.141*(d**2)*avg_velocity*press_drop/4;# [N.m/s] for 1m pipe\n",
+ "m = 3.141*(d**2)*L*density/4;\n",
+ "# From Eqn. 3.24\n",
+ "Ld = ((kinematic_viscosity**3)*m/P)**(1.0/4);# [m]\n",
+ "# From Eqn. 3.25\n",
+ "Ud = (kinematic_viscosity*P/m)**(1.0/4);# [m/s]\n",
+ "print'Velocity of small eddies is',round(Ud,4),'m/s'\n",
+ "print'Length scale of small eddies is',round(Ld,7),'m'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.2 - Page: 56\n",
+ "\n",
+ "\n",
+ "Velocity of small eddies is 0.0549 m/s\n",
+ "Length scale of small eddies is 1.63e-05 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.3: Page 69"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.3\n",
+ "# Page: 69\n",
+ "\n",
+ "print'Illustration 3.3 - Page: 69\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# Heat transfer analog to Eqn. 3.12\n",
+ "# The Eqn. remains the same with the dimensionless conc. ratio replaced by ((tl-to)/(ti-to))\n",
+ "\n",
+ "# The dimensionless group:\n",
+ "# eta = 2*Dab*L/(3*del**2*velocity);\n",
+ "# eta = (2/3)*(Dab/(del*velocity))*(L/del);\n",
+ "# Ped = Peclet no. for mass transfer\n",
+ "# eta = (2/3)*(1/Ped)*(L/del);\n",
+ "\n",
+ "# For heat transfer is replaced by\n",
+ "# Peh = Peclet no. for heat transfer\n",
+ "# eta = (2/3)*(1/Peh)*(L/del);\n",
+ "# eta = (2/3)*(alpha/(del*velocity))*(L/del);\n",
+ "# eta = (2*alpha*L)/(3*del**2*velocity);\n",
+ "print'Heat transfer analog to Eqn. 3.21 is eta = (2*alpha*L)/(3*del**2*velocity)'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.3 - Page: 69\n",
+ "\n",
+ "\n",
+ "Heat transfer analog to Eqn. 3.21 is eta = (2*alpha*L)/(3*del**2*velocity)\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.4: Page-69"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.4\n",
+ "# Page: 69\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 3.4 - Page: 69\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = UF6 b = air\n",
+ "# The average heat transfer coefficient: Nu_avg = 0.43+0.532(Re^0.5)(Pr^0.31)\n",
+ "# The analogus expression for mass transfer coefficient: Sh_avg = 0.43+0.532(Re^0.5)(Sc^0.31)\n",
+ "d = 0.006;# [m]\n",
+ "velocity = 3.0;# [m/s]\n",
+ "surf_temp = 43.0;# [C]\n",
+ "bulk_temp = 60.0;# [C]\n",
+ "avg_temp = (surf_temp+bulk_temp)/2; #[C]\n",
+ "density = 4.10;# [kg/cubic m]\n",
+ "viscosity = 2.7*10**(-5);# [kg/m.s]\n",
+ "Dab = 9.04*10**(-6);# [square m/s]\n",
+ "press = 53.32;# [kN/square m]\n",
+ "tot_press = 101.33;# [kN/square m]\n",
+ "#******#\n",
+ "\n",
+ "avg_press = press/2.0; # [kN/square m]\n",
+ "Xa = avg_press/tot_press;\n",
+ "Xb = 1-Xa;\n",
+ "Re = d*velocity*density/viscosity;\n",
+ "Sc = viscosity/(density*Dab);\n",
+ "Sh_avg = 0.43+(0.532*(2733**0.5)*(0.728**0.5));\n",
+ "c = 273.2/(22.41*(273.2+avg_temp));# [kmol/cubic m]\n",
+ "F_avg = Sh_avg*c*Dab/d;#[kmol/cubic m]\n",
+ "Nb = 0.0;\n",
+ "Ca1_by_C = press/tot_press;\n",
+ "Ca2_by_C = 0.0;\n",
+ "Flux_a = 1.0;\n",
+ "# Using Eqn. 3.1\n",
+ "Na = Flux_a*F_avg*math.log((Flux_a-Ca2_by_C)/(Flux_a-Ca1_by_C));#[kmol UF6/square m.s]\n",
+ "print'Rate of sublimation is',round(Na,8),' kmol UF6/square m.s'\n",
+ "# the answer is slightly different in textbook due to approximation"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.4 - Page: 69\n",
+ "\n",
+ "\n",
+ "Rate of sublimation is 0.00102088 kmol UF6/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.5: Page 73"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.5\n",
+ "# Page: 73\n",
+ "\n",
+ "print'Illustration 3.5 - Page: 73\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "velocity = 15.0;# [m/s]\n",
+ "G = 21.3;# [kg/square m.s]\n",
+ "#******#\n",
+ "\n",
+ "# Since the experimental data do not include the effects of changing Prandtl number.\n",
+ "\n",
+ "# Jh = (h/(Cp*density*viscosity)) = (h/Cp*G)*(Pr^(2/3)) = Shi(Re);\n",
+ "\n",
+ "# Shi(Re) must be compatible with 21.3*(G**0.6);\n",
+ "# Let Shi(Re) = b*(Re**n);\n",
+ "# Re = (l*G)/viscosity;\n",
+ "\n",
+ "# h = (Cp*G/(Pr**(2/3)))*b*(Re**n);\n",
+ "# h = (Cp*G/(Pr**(2/3)))*b*((l*b/viscosity)**n) = 21.3*(G**0.6);\n",
+ "\n",
+ "n = 0.6-1;\n",
+ "# b = 21.3*((Pr**(2/3))/Cp)*((l/viscosity)**(-n));\n",
+ "\n",
+ "# Using data for air at 38 C & 1 std atm.\n",
+ "Cp1 = 1002;# [kJ/kg.K]\n",
+ "viscosity1 = 1.85*10**(-5);#[kg/m.s]\n",
+ "k1 = 0.0273;#[W/m.K]\n",
+ "Pr1 = (Cp1*viscosity1)/k1;\n",
+ "b_prime = 21.3*(Pr1**(2.0/3)/Cp1)*((1/viscosity1)**0.4);\n",
+ "# b = b_prime*l**(0.4);\n",
+ "# Jh = (h/(Cp*G))*Pr**(2/3) = b_prime*((l/Re)**(0.4)) = Shi(Re);\n",
+ "\n",
+ "# The heat mass transfer analogy will be used to estimate the mass transfer coefficient. (Jd = Jh)\n",
+ "\n",
+ "# Jd = (KG*Pbm*Mav*Sc**(2/3))/(density*viscosity) = Shi(Re) = b_prime*((l/Re)**0.4);\n",
+ "\n",
+ "# KG*Pbm = F = (b_prime*density*viscosity)/(Re^0.4*Mav*Sc**(2/3)) = (b_prime*(density*velocity)**0.6*(viscosity^0.4))/(Mav*Sc**(2/3));\n",
+ "\n",
+ "# For H2-H20, 38 C, 1std atm\n",
+ "viscosity2 = 9*10**(-6);# [kg/m.s]\n",
+ "density2 = 0.0794;# [kg/cubic m]\n",
+ "Dab = 7.75*10**(-5);# [square m/s]\n",
+ "Sc = viscosity2/(density2*Dab);\n",
+ "\n",
+ "# Assuming desity, Molecular weight and viscosity of the gas are essentially those of H2\n",
+ "\n",
+ "Mav = 2.02;# [kg/kmol]\n",
+ "F = (b_prime*(density2*velocity)**0.6*(viscosity2**0.4))/(Mav*Sc**(2.0/3));# [kmol/square m.s]\n",
+ "print'The required mass transfer: ',round(F,5),' kmol/square m.s'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.5 - Page: 73\n",
+ "\n",
+ "\n",
+ "The required mass transfer: 0.00525 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.6:Page 77"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.6\n",
+ "# Page: 77\n",
+ "\n",
+ "print'Illustration 3.6 - Page: 77\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy import integrate\n",
+ "import math \n",
+ "#***Data***#\n",
+ "Dp = 0.0125;# [m]\n",
+ "viscosity = 2.4*10**(-5);# [kg/m.s]\n",
+ "Sc = 2.0;\n",
+ "E = 0.3;\n",
+ "Go = (2*10**(-3))/0.1;# molar superficial mass velocity [kmol/square m.s]\n",
+ "#********#\n",
+ "\n",
+ "# a = CO b = Ni(CO)4\n",
+ "# Nb = -(Na/4);\n",
+ "Flux_a = 4.0/3;\n",
+ "Ca2_by_C = 0;# At the metal interface\n",
+ "# Ca1_by_C = Ya #mole fraction of CO in the bulk\n",
+ "\n",
+ "# Eqn. 3.1 becomes: Na = (4/3)*F*log((4/3)/((4/3)-Ya));\n",
+ "\n",
+ "# Let G = kmol gas/(square m bed cross section).s\n",
+ "# a = specific metal surface\n",
+ "# z = depth \n",
+ "# Therefore, Na = -(diff(Ya*G))/(a*diff(z));# [kmol/((square m metal surface).s)];\n",
+ "# For each kmol of CO consumed, (1/4)kmol Ni(CO)4 forms, representing a loss of (3/4) kmol per kmol of CO consumed.\n",
+ "# The CO consumed through bed depth dz is therefore (Go-G)(4/3) kmol;\n",
+ "# Ya = (Go-(Go-G)*(4/3))/G;\n",
+ "# G = Go/(4-(3*Ya));\n",
+ "# diff(YaG) = ((4*Go)/(4-3*Ya)**2)*diff(Ya);\n",
+ "\n",
+ "# Substituting in Eqn. 3.64\n",
+ "# -(4*Go/((4-3*Ya)**2*a))*(diff(Ya)/diff(z)) = (4/3)*F*log(4/(4-3*Ya));\n",
+ "\n",
+ "# At depth z:\n",
+ "# Mass velocity of CO = (Go-(Go-G)/(4/3))*28;\n",
+ "# Mass velocity of Ni(CO)4 = ((Go-G)*(1/3))*170.7;\n",
+ "# G_prime = 47.6*Go-19.6G; # total mass velocity [kg/square m.s]\n",
+ "# Substituting G leads to:\n",
+ "# G_prime = Go*(47.6-19.6*(4-3*Ya));# [kg/m.s]\n",
+ "# Re = (Dp*G')/viscosity\n",
+ "\n",
+ "# With Go = 0.002 kmol/square m.s & Ya in the range 1-0.005, the range of Re is 292-444;\n",
+ "# From table 3.3:\n",
+ "# Jd = (F/G)*(Sc**(2/3)) = (2.06/E)*Re**(-0.575);\n",
+ "# F = (2.06/E*(Sc)**(2/3))*(Go/(4-3*Ya))*Re**(-0.575);\n",
+ "\n",
+ "a = 6*(1-E)/Dp;\n",
+ "\n",
+ "# Result after arrangement:\n",
+ "\n",
+ "X2=lambda Ya:-((4*Go)/((4-(3*Ya))**2.0*a))*(3.0/4)*(E*(Sc**(2.0/3))*(4-(3*Ya))/(2.06*Go)*(1/math.log(4.0/(4-(3*Ya)))))*(((Dp/viscosity)*(Go*(47.6-(19.6/(4.0-(3*Ya))))))**0.575);# [m]\n",
+ "Z = integrate.quad(X2,1,0.005);\n",
+ "print'The bed depth required to reduce the CO content to 0.005 is',round(Z[0],3),'m'\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.6 - Page: 77\n",
+ "\n",
+ "\n",
+ "The bed depth required to reduce the CO content to 0.005 is 0.132 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.7: Page 80"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.7\n",
+ "# Page: 80\n",
+ "\n",
+ "print'Illustration 3.7 - Page: 80\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = water b = air\n",
+ "out_dia = 0.0254;# [m]\n",
+ "wall_thick = 0.00165;# [m]\n",
+ "avg_velocity = 4.6;# [m/s]\n",
+ "T1 = 66.0;# [C]\n",
+ "P = 1.0;# [atm]\n",
+ "Pa1 = 0.24;# [atm]\n",
+ "k1 = 11400.0;# [W/(square m.K)]\n",
+ "T2 = 24.0;# [C]\n",
+ "k2 = 570.0;# [W/square m.K]\n",
+ "k_Cu = 381.0;# [w/square m.K]\n",
+ "#******#\n",
+ "\n",
+ "# For the metal tube\n",
+ "int_dia = out_dia-(2*wall_thick);# [m]\n",
+ "avg_dia = (out_dia+int_dia)/2;# [mm]\n",
+ "Nb = 0;\n",
+ "Flux_a = 1;\n",
+ "Ya1 = 0.24;\n",
+ "Yb1 = 1-Ya1;\n",
+ "Mav = (Ya1*18.02)+(Yb1*29);# [kg/kmol]\n",
+ "density = (Mav/22.41)*(273/(273+T1));# [kg/cubic m]\n",
+ "viscosity = 1.75*10**(-5);# [kg/m.s]\n",
+ "Cpa = 1880.0;# [J/kg.K]\n",
+ "Cpmix = 1145.0;# [J/kg.K]\n",
+ "Sc = 0.6;\n",
+ "Pr = 0.75;\n",
+ "G_prime = avg_velocity*density;# [kg/square m.s]\n",
+ "G = G_prime/Mav;# [kmol/square m.s]\n",
+ "Re = avg_dia*G_prime/viscosity;\n",
+ "# From Table 3.3:\n",
+ "# Jd = Std*Sc**(2/3) = (F/G)*Sc**(2/3) = 0.023*Re**(-0.17);\n",
+ "Jd = 0.023*Re**(-0.17);\n",
+ "F = (0.023*G)*(Re**(-0.17)/Sc**(2.0/3));\n",
+ "\n",
+ "# The heat transfer coeffecient in the absence of mass transfer will be estimated through Jd = Jh\n",
+ "# Jh = Sth*Pr^(2/3) = (h/Cp*G_prime)*(Pr^(2/3)) = Jd\n",
+ "h = Jd*Cpmix*G_prime/(Pr**(2.0/3));\n",
+ "\n",
+ "U = 1/((1/k1)+((wall_thick/k_Cu)*(int_dia/avg_dia))+((1/k2)*(int_dia/out_dia)));# W/square m.K\n",
+ "\n",
+ "# Using Eqn. 3.70 & 3.71 with Nb = 0\n",
+ "# Qt = (Na*18.02*Cpa/1-exp(-(Na*18.02*Cpa/h)))*(T1-Ti)+(Lambda_a*Na);\n",
+ "# Qt = 618*(Ti-T2);\n",
+ "# Using Eqn. 3.67, with Nb = 0, Cai/C = pai, Ca1/C = Ya1 = 0.24;\n",
+ "# Na = F*log(((Flux_a)-(pai))/((Flux_a)-(Ya1));\n",
+ "\n",
+ "# Solving above three Eqn. simultaneously:\n",
+ "Ti = 42.2;# [C]\n",
+ "pai = 0.0806;# [atm]\n",
+ "Lambda_a = 43.4*10**6;# [J/kmol]\n",
+ "Na = F*log(((Flux_a)-(pai))/((Flux_a)-(Ya1)));# [kmol/square m.s]\n",
+ "Qt1 = 618*(Ti-T2);# [W/square m]\n",
+ "Qt2 = ((Na*18.02*Cpa/(1-exp(-(Na*18.02*Cpa/h))))*(T1-Ti))+(Lambda_a*Na);# [W/square m]\n",
+ "\n",
+ "# since the value of Qt1 & Qt2 are relatively close\n",
+ "print'The local rate of condensation of water is ',round(Na,6),' kmol/square m.s'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.7 - Page: 80\n",
+ "\n",
+ "\n",
+ "The local rate of condensation of water is 0.000232 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.8: Page 81"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.8\n",
+ "# Page: 81\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 3.8 - Page: 81\\n\\n'\n",
+ "print'Illustration 3.8 (a)\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = water b = air\n",
+ "Nb = 0;\n",
+ "h = 1100.0;# [W/square m]\n",
+ "#*****#\n",
+ "\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Cpa = 2090;# [J/kg.K]\n",
+ "T1 = 600.0;# [C]\n",
+ "Ti = 260;# [C]\n",
+ "# The positive dirn. is taken to be from the bulk gas to the surface.\n",
+ "Has = 2.684*(10**6);# enthapy of saturated steam at 1.2 std atm, rel. to the liquid at 0 C in [J/kg]\n",
+ "Hai = 2.994*(10**6);# enthalpy of steam at 1 std atm, 260 C in [J/kg]\n",
+ "\n",
+ "# Radiation contributions to the heat transfer from the gas to the surface are negligible. Eqn. 3.70 reduces to\n",
+ "Na = -((h/(Ma*Cpa))*log(1-((Cpa*(T1-Ti))/(Has-Hai))));# [kmol/square m.s]\n",
+ "print'The rate of steam flow reqd. is',round(Na,4),' kmol/square m.s\\n\\n'\n",
+ "# negative sign indicates that the mass flux is into the gas\n",
+ "\n",
+ "print'Illustration 3.8 (b)\\n\\n'\n",
+ " \n",
+ "# Solution (b)\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = water b = air\n",
+ "h = 572.0;# [W/square m]\n",
+ "T1 = 25.0;# [C]\n",
+ "#******#\n",
+ "\n",
+ "Ti = 260.0;# [C]\n",
+ "# The positive dirn. is taken to be from the bulk gas to the surface.\n",
+ "Has = 1.047*10**(5);# enthapy of saturated steam at 1.2 std atm, rel. to the liquid at 0 C in [J/kg]\n",
+ "Hai = 2.994*(10**6);# enthalpy of steam at 1 std atm, 260 C in [J/kg]\n",
+ "\n",
+ "# Radiation contributions to the heat transfer from the gas to the surface are negligible. Eqn. 3.70 reduces to\n",
+ "Na = -((h/(Ma*Cpa))*math.log(1-((Cpa*(T1-Ti))/(Has-Hai))));# [kmol/square m.s]\n",
+ "print'The rate of steam flow reqd. is',round(Na,4),' kmol/square m.s'\n",
+ "# negative sign indicates that the mass flux is into \n",
+ "# the answer of part B in textbook is incorrect"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.8 - Page: 81\n",
+ "\n",
+ "\n",
+ "Illustration 3.8 (a)\n",
+ "\n",
+ "\n",
+ "The rate of steam flow reqd. is -0.0348 kmol/square m.s\n",
+ "\n",
+ "\n",
+ "Illustration 3.8 (b)\n",
+ "\n",
+ "\n",
+ "The rate of steam flow reqd. is 0.0028 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 42
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter3_2.ipynb b/Mass_-_Transfer_Operations/Chapter3_2.ipynb
new file mode 100755
index 00000000..36f53f4d
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter3_2.ipynb
@@ -0,0 +1,628 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:64d9a62e17838716bd10cd86f93be8c39dc69462337a3d3adc3d6ea158cbc575"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 3: Mass-Transfer Coefficients"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.1:Page 53"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.1\n",
+ "# Page: 53\n",
+ "\n",
+ "print'Illustration 3.1 - Page: 53\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = CO2 b = H2O\n",
+ "Ca0 = 0;#[kmol/cubic m]\n",
+ "Cai = 0.0336;#[kmol/cubic m]\n",
+ "Dab = 1.96*10**(-9);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "density = 998.0;# [kg/cubic m]\n",
+ "viscosity = 8.94*10**(-4);#[kg/m.s]\n",
+ "rate = 0.05;#[kg/m.s] mass flow rate of liquid\n",
+ "L = 1;#[m]\n",
+ "g = 9.81;#[m/square s]\n",
+ "# From Eqn. 3.10\n",
+ "Del = ((3*viscosity*rate)/((density**2)*g))**(1.0/3);# [m]\n",
+ "Re = 4*rate/viscosity;\n",
+ "# Flow comes out to be laminar\n",
+ "# From Eqn. 3.19\n",
+ "Kl_avg = ((6*Dab*rate)/(3.141*density*Del*L))**(1.0/2);#[kmol/square m.s.(kmol/cubic m)]\n",
+ "bulk_avg_velocity = rate/(density*Del);#[m/s]\n",
+ "# At the top: Cai-Ca = Cai_Ca0 = Cai\n",
+ "#At the bottom: Cai-Cal\n",
+ "# From Eqn. 3.21 & 3.22\n",
+ "Cal = Cai*(1-(1.0/(exp(Kl_avg/(bulk_avg_velocity*Del)))));# [kmol/cubic m]\n",
+ "rate_absorption = bulk_avg_velocity*Del*(Cal-Ca0);# [kmol/s].(m of width)\n",
+ "print'The rate of absorption is ',round(rate_absorption,8),' kmol/sec.(m of width)'\n",
+ "# The actual value may be substantially larger."
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.1 - Page: 53\n",
+ "\n",
+ "\n",
+ "The rate of absorption is 7.2e-07 kmol/sec.(m of width)\n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.2: Page 56"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.2\n",
+ "# Page: 56\n",
+ "\n",
+ "print'Illustration 3.2 - Page: 56\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "d = 0.025;# [m]\n",
+ "avg_velocity = 3;# [m/s]\n",
+ "viscosity = 8.937*10**(-4);# [kg/m.s]\n",
+ "density = 997;# [kg/m**3]\n",
+ "#*********#\n",
+ "\n",
+ "kinematic_viscosity = viscosity/density;# [square m/s]\n",
+ "Re = d*avg_velocity*density/viscosity;\n",
+ "# Reynold's number comes out to be 83670\n",
+ "# At this Reynold's number fanning factor = 0.0047\n",
+ "f = 0.0047;\n",
+ "L = 1;# [m]\n",
+ "press_drop = 2*density*f*L*(avg_velocity**2)/(d);# [N/square m]\n",
+ "P = 3.141*(d**2)*avg_velocity*press_drop/4;# [N.m/s] for 1m pipe\n",
+ "m = 3.141*(d**2)*L*density/4;\n",
+ "# From Eqn. 3.24\n",
+ "Ld = ((kinematic_viscosity**3)*m/P)**(1.0/4);# [m]\n",
+ "# From Eqn. 3.25\n",
+ "Ud = (kinematic_viscosity*P/m)**(1.0/4);# [m/s]\n",
+ "print'Velocity of small eddies is',round(Ud,4),'m/s'\n",
+ "print'Length scale of small eddies is',round(Ld,7),'m'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.2 - Page: 56\n",
+ "\n",
+ "\n",
+ "Velocity of small eddies is 0.0549 m/s\n",
+ "Length scale of small eddies is 1.63e-05 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.3: Page 69"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.3\n",
+ "# Page: 69\n",
+ "\n",
+ "print'Illustration 3.3 - Page: 69\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# Heat transfer analog to Eqn. 3.12\n",
+ "# The Eqn. remains the same with the dimensionless conc. ratio replaced by ((tl-to)/(ti-to))\n",
+ "\n",
+ "# The dimensionless group:\n",
+ "# eta = 2*Dab*L/(3*del**2*velocity);\n",
+ "# eta = (2/3)*(Dab/(del*velocity))*(L/del);\n",
+ "# Ped = Peclet no. for mass transfer\n",
+ "# eta = (2/3)*(1/Ped)*(L/del);\n",
+ "\n",
+ "# For heat transfer is replaced by\n",
+ "# Peh = Peclet no. for heat transfer\n",
+ "# eta = (2/3)*(1/Peh)*(L/del);\n",
+ "# eta = (2/3)*(alpha/(del*velocity))*(L/del);\n",
+ "# eta = (2*alpha*L)/(3*del**2*velocity);\n",
+ "print'Heat transfer analog to Eqn. 3.21 is eta = (2*alpha*L)/(3*del**2*velocity)'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.3 - Page: 69\n",
+ "\n",
+ "\n",
+ "Heat transfer analog to Eqn. 3.21 is eta = (2*alpha*L)/(3*del**2*velocity)\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.4: Page-69"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.4\n",
+ "# Page: 69\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 3.4 - Page: 69\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = UF6 b = air\n",
+ "# The average heat transfer coefficient: Nu_avg = 0.43+0.532(Re^0.5)(Pr^0.31)\n",
+ "# The analogus expression for mass transfer coefficient: Sh_avg = 0.43+0.532(Re^0.5)(Sc^0.31)\n",
+ "d = 0.006;# [m]\n",
+ "velocity = 3.0;# [m/s]\n",
+ "surf_temp = 43.0;# [C]\n",
+ "bulk_temp = 60.0;# [C]\n",
+ "avg_temp = (surf_temp+bulk_temp)/2; #[C]\n",
+ "density = 4.10;# [kg/cubic m]\n",
+ "viscosity = 2.7*10**(-5);# [kg/m.s]\n",
+ "Dab = 9.04*10**(-6);# [square m/s]\n",
+ "press = 53.32;# [kN/square m]\n",
+ "tot_press = 101.33;# [kN/square m]\n",
+ "#******#\n",
+ "\n",
+ "avg_press = press/2.0; # [kN/square m]\n",
+ "Xa = avg_press/tot_press;\n",
+ "Xb = 1-Xa;\n",
+ "Re = d*velocity*density/viscosity;\n",
+ "Sc = viscosity/(density*Dab);\n",
+ "Sh_avg = 0.43+(0.532*(2733**0.5)*(0.728**0.5));\n",
+ "c = 273.2/(22.41*(273.2+avg_temp));# [kmol/cubic m]\n",
+ "F_avg = Sh_avg*c*Dab/d;#[kmol/cubic m]\n",
+ "Nb = 0.0;\n",
+ "Ca1_by_C = press/tot_press;\n",
+ "Ca2_by_C = 0.0;\n",
+ "Flux_a = 1.0;\n",
+ "# Using Eqn. 3.1\n",
+ "Na = Flux_a*F_avg*math.log((Flux_a-Ca2_by_C)/(Flux_a-Ca1_by_C));#[kmol UF6/square m.s]\n",
+ "print'Rate of sublimation is',round(Na,8),' kmol UF6/square m.s'\n",
+ "# the answer is slightly different in textbook due to approximation"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.4 - Page: 69\n",
+ "\n",
+ "\n",
+ "Rate of sublimation is 0.00102088 kmol UF6/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.5: Page 73"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.5\n",
+ "# Page: 73\n",
+ "\n",
+ "print'Illustration 3.5 - Page: 73\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "velocity = 15.0;# [m/s]\n",
+ "G = 21.3;# [kg/square m.s]\n",
+ "#******#\n",
+ "\n",
+ "# Since the experimental data do not include the effects of changing Prandtl number.\n",
+ "\n",
+ "# Jh = (h/(Cp*density*viscosity)) = (h/Cp*G)*(Pr^(2/3)) = Shi(Re);\n",
+ "\n",
+ "# Shi(Re) must be compatible with 21.3*(G**0.6);\n",
+ "# Let Shi(Re) = b*(Re**n);\n",
+ "# Re = (l*G)/viscosity;\n",
+ "\n",
+ "# h = (Cp*G/(Pr**(2/3)))*b*(Re**n);\n",
+ "# h = (Cp*G/(Pr**(2/3)))*b*((l*b/viscosity)**n) = 21.3*(G**0.6);\n",
+ "\n",
+ "n = 0.6-1;\n",
+ "# b = 21.3*((Pr**(2/3))/Cp)*((l/viscosity)**(-n));\n",
+ "\n",
+ "# Using data for air at 38 C & 1 std atm.\n",
+ "Cp1 = 1002;# [kJ/kg.K]\n",
+ "viscosity1 = 1.85*10**(-5);#[kg/m.s]\n",
+ "k1 = 0.0273;#[W/m.K]\n",
+ "Pr1 = (Cp1*viscosity1)/k1;\n",
+ "b_prime = 21.3*(Pr1**(2.0/3)/Cp1)*((1/viscosity1)**0.4);\n",
+ "# b = b_prime*l**(0.4);\n",
+ "# Jh = (h/(Cp*G))*Pr**(2/3) = b_prime*((l/Re)**(0.4)) = Shi(Re);\n",
+ "\n",
+ "# The heat mass transfer analogy will be used to estimate the mass transfer coefficient. (Jd = Jh)\n",
+ "\n",
+ "# Jd = (KG*Pbm*Mav*Sc**(2/3))/(density*viscosity) = Shi(Re) = b_prime*((l/Re)**0.4);\n",
+ "\n",
+ "# KG*Pbm = F = (b_prime*density*viscosity)/(Re^0.4*Mav*Sc**(2/3)) = (b_prime*(density*velocity)**0.6*(viscosity^0.4))/(Mav*Sc**(2/3));\n",
+ "\n",
+ "# For H2-H20, 38 C, 1std atm\n",
+ "viscosity2 = 9*10**(-6);# [kg/m.s]\n",
+ "density2 = 0.0794;# [kg/cubic m]\n",
+ "Dab = 7.75*10**(-5);# [square m/s]\n",
+ "Sc = viscosity2/(density2*Dab);\n",
+ "\n",
+ "# Assuming desity, Molecular weight and viscosity of the gas are essentially those of H2\n",
+ "\n",
+ "Mav = 2.02;# [kg/kmol]\n",
+ "F = (b_prime*(density2*velocity)**0.6*(viscosity2**0.4))/(Mav*Sc**(2.0/3));# [kmol/square m.s]\n",
+ "print'The required mass transfer: ',round(F,5),' kmol/square m.s'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.5 - Page: 73\n",
+ "\n",
+ "\n",
+ "The required mass transfer: 0.00525 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.6:Page 77"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.6\n",
+ "# Page: 77\n",
+ "\n",
+ "print'Illustration 3.6 - Page: 77\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy import integrate\n",
+ "import math \n",
+ "#***Data***#\n",
+ "Dp = 0.0125;# [m]\n",
+ "viscosity = 2.4*10**(-5);# [kg/m.s]\n",
+ "Sc = 2.0;\n",
+ "E = 0.3;\n",
+ "Go = (2*10**(-3))/0.1;# molar superficial mass velocity [kmol/square m.s]\n",
+ "#********#\n",
+ "\n",
+ "# a = CO b = Ni(CO)4\n",
+ "# Nb = -(Na/4);\n",
+ "Flux_a = 4.0/3;\n",
+ "Ca2_by_C = 0;# At the metal interface\n",
+ "# Ca1_by_C = Ya #mole fraction of CO in the bulk\n",
+ "\n",
+ "# Eqn. 3.1 becomes: Na = (4/3)*F*log((4/3)/((4/3)-Ya));\n",
+ "\n",
+ "# Let G = kmol gas/(square m bed cross section).s\n",
+ "# a = specific metal surface\n",
+ "# z = depth \n",
+ "# Therefore, Na = -(diff(Ya*G))/(a*diff(z));# [kmol/((square m metal surface).s)];\n",
+ "# For each kmol of CO consumed, (1/4)kmol Ni(CO)4 forms, representing a loss of (3/4) kmol per kmol of CO consumed.\n",
+ "# The CO consumed through bed depth dz is therefore (Go-G)(4/3) kmol;\n",
+ "# Ya = (Go-(Go-G)*(4/3))/G;\n",
+ "# G = Go/(4-(3*Ya));\n",
+ "# diff(YaG) = ((4*Go)/(4-3*Ya)**2)*diff(Ya);\n",
+ "\n",
+ "# Substituting in Eqn. 3.64\n",
+ "# -(4*Go/((4-3*Ya)**2*a))*(diff(Ya)/diff(z)) = (4/3)*F*log(4/(4-3*Ya));\n",
+ "\n",
+ "# At depth z:\n",
+ "# Mass velocity of CO = (Go-(Go-G)/(4/3))*28;\n",
+ "# Mass velocity of Ni(CO)4 = ((Go-G)*(1/3))*170.7;\n",
+ "# G_prime = 47.6*Go-19.6G; # total mass velocity [kg/square m.s]\n",
+ "# Substituting G leads to:\n",
+ "# G_prime = Go*(47.6-19.6*(4-3*Ya));# [kg/m.s]\n",
+ "# Re = (Dp*G')/viscosity\n",
+ "\n",
+ "# With Go = 0.002 kmol/square m.s & Ya in the range 1-0.005, the range of Re is 292-444;\n",
+ "# From table 3.3:\n",
+ "# Jd = (F/G)*(Sc**(2/3)) = (2.06/E)*Re**(-0.575);\n",
+ "# F = (2.06/E*(Sc)**(2/3))*(Go/(4-3*Ya))*Re**(-0.575);\n",
+ "\n",
+ "a = 6*(1-E)/Dp;\n",
+ "\n",
+ "# Result after arrangement:\n",
+ "\n",
+ "X2=lambda Ya:-((4*Go)/((4-(3*Ya))**2.0*a))*(3.0/4)*(E*(Sc**(2.0/3))*(4-(3*Ya))/(2.06*Go)*(1/math.log(4.0/(4-(3*Ya)))))*(((Dp/viscosity)*(Go*(47.6-(19.6/(4.0-(3*Ya))))))**0.575);# [m]\n",
+ "Z = integrate.quad(X2,1,0.005);\n",
+ "print'The bed depth required to reduce the CO content to 0.005 is',round(Z[0],3),'m'\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.6 - Page: 77\n",
+ "\n",
+ "\n",
+ "The bed depth required to reduce the CO content to 0.005 is 0.132 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.7: Page 80"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.7\n",
+ "# Page: 80\n",
+ "\n",
+ "print'Illustration 3.7 - Page: 80\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = water b = air\n",
+ "out_dia = 0.0254;# [m]\n",
+ "wall_thick = 0.00165;# [m]\n",
+ "avg_velocity = 4.6;# [m/s]\n",
+ "T1 = 66.0;# [C]\n",
+ "P = 1.0;# [atm]\n",
+ "Pa1 = 0.24;# [atm]\n",
+ "k1 = 11400.0;# [W/(square m.K)]\n",
+ "T2 = 24.0;# [C]\n",
+ "k2 = 570.0;# [W/square m.K]\n",
+ "k_Cu = 381.0;# [w/square m.K]\n",
+ "#******#\n",
+ "\n",
+ "# For the metal tube\n",
+ "int_dia = out_dia-(2*wall_thick);# [m]\n",
+ "avg_dia = (out_dia+int_dia)/2;# [mm]\n",
+ "Nb = 0;\n",
+ "Flux_a = 1;\n",
+ "Ya1 = 0.24;\n",
+ "Yb1 = 1-Ya1;\n",
+ "Mav = (Ya1*18.02)+(Yb1*29);# [kg/kmol]\n",
+ "density = (Mav/22.41)*(273/(273+T1));# [kg/cubic m]\n",
+ "viscosity = 1.75*10**(-5);# [kg/m.s]\n",
+ "Cpa = 1880.0;# [J/kg.K]\n",
+ "Cpmix = 1145.0;# [J/kg.K]\n",
+ "Sc = 0.6;\n",
+ "Pr = 0.75;\n",
+ "G_prime = avg_velocity*density;# [kg/square m.s]\n",
+ "G = G_prime/Mav;# [kmol/square m.s]\n",
+ "Re = avg_dia*G_prime/viscosity;\n",
+ "# From Table 3.3:\n",
+ "# Jd = Std*Sc**(2/3) = (F/G)*Sc**(2/3) = 0.023*Re**(-0.17);\n",
+ "Jd = 0.023*Re**(-0.17);\n",
+ "F = (0.023*G)*(Re**(-0.17)/Sc**(2.0/3));\n",
+ "\n",
+ "# The heat transfer coeffecient in the absence of mass transfer will be estimated through Jd = Jh\n",
+ "# Jh = Sth*Pr^(2/3) = (h/Cp*G_prime)*(Pr^(2/3)) = Jd\n",
+ "h = Jd*Cpmix*G_prime/(Pr**(2.0/3));\n",
+ "\n",
+ "U = 1/((1/k1)+((wall_thick/k_Cu)*(int_dia/avg_dia))+((1/k2)*(int_dia/out_dia)));# W/square m.K\n",
+ "\n",
+ "# Using Eqn. 3.70 & 3.71 with Nb = 0\n",
+ "# Qt = (Na*18.02*Cpa/1-exp(-(Na*18.02*Cpa/h)))*(T1-Ti)+(Lambda_a*Na);\n",
+ "# Qt = 618*(Ti-T2);\n",
+ "# Using Eqn. 3.67, with Nb = 0, Cai/C = pai, Ca1/C = Ya1 = 0.24;\n",
+ "# Na = F*log(((Flux_a)-(pai))/((Flux_a)-(Ya1));\n",
+ "\n",
+ "# Solving above three Eqn. simultaneously:\n",
+ "Ti = 42.2;# [C]\n",
+ "pai = 0.0806;# [atm]\n",
+ "Lambda_a = 43.4*10**6;# [J/kmol]\n",
+ "Na = F*log(((Flux_a)-(pai))/((Flux_a)-(Ya1)));# [kmol/square m.s]\n",
+ "Qt1 = 618*(Ti-T2);# [W/square m]\n",
+ "Qt2 = ((Na*18.02*Cpa/(1-exp(-(Na*18.02*Cpa/h))))*(T1-Ti))+(Lambda_a*Na);# [W/square m]\n",
+ "\n",
+ "# since the value of Qt1 & Qt2 are relatively close\n",
+ "print'The local rate of condensation of water is ',round(Na,6),' kmol/square m.s'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.7 - Page: 80\n",
+ "\n",
+ "\n",
+ "The local rate of condensation of water is 0.000232 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex3.8: Page 81"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 3.8\n",
+ "# Page: 81\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 3.8 - Page: 81\\n\\n'\n",
+ "print'Illustration 3.8 (a)\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = water b = air\n",
+ "Nb = 0;\n",
+ "h = 1100.0;# [W/square m]\n",
+ "#*****#\n",
+ "\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Cpa = 2090;# [J/kg.K]\n",
+ "T1 = 600.0;# [C]\n",
+ "Ti = 260;# [C]\n",
+ "# The positive dirn. is taken to be from the bulk gas to the surface.\n",
+ "Has = 2.684*(10**6);# enthapy of saturated steam at 1.2 std atm, rel. to the liquid at 0 C in [J/kg]\n",
+ "Hai = 2.994*(10**6);# enthalpy of steam at 1 std atm, 260 C in [J/kg]\n",
+ "\n",
+ "# Radiation contributions to the heat transfer from the gas to the surface are negligible. Eqn. 3.70 reduces to\n",
+ "Na = -((h/(Ma*Cpa))*log(1-((Cpa*(T1-Ti))/(Has-Hai))));# [kmol/square m.s]\n",
+ "print'The rate of steam flow reqd. is',round(Na,4),' kmol/square m.s\\n\\n'\n",
+ "# negative sign indicates that the mass flux is into the gas\n",
+ "\n",
+ "print'Illustration 3.8 (b)\\n\\n'\n",
+ " \n",
+ "# Solution (b)\n",
+ "\n",
+ "#***Data****#\n",
+ "# a = water b = air\n",
+ "h = 572.0;# [W/square m]\n",
+ "T1 = 25.0;# [C]\n",
+ "#******#\n",
+ "\n",
+ "Ti = 260.0;# [C]\n",
+ "# The positive dirn. is taken to be from the bulk gas to the surface.\n",
+ "Has = 1.047*10**(5);# enthapy of saturated steam at 1.2 std atm, rel. to the liquid at 0 C in [J/kg]\n",
+ "Hai = 2.994*(10**6);# enthalpy of steam at 1 std atm, 260 C in [J/kg]\n",
+ "\n",
+ "# Radiation contributions to the heat transfer from the gas to the surface are negligible. Eqn. 3.70 reduces to\n",
+ "Na = -((h/(Ma*Cpa))*math.log(1-((Cpa*(T1-Ti))/(Has-Hai))));# [kmol/square m.s]\n",
+ "print'The rate of steam flow reqd. is',round(Na,4),' kmol/square m.s'\n",
+ "# negative sign indicates that the mass flux is into \n",
+ "# the answer of part B in textbook is incorrect"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 3.8 - Page: 81\n",
+ "\n",
+ "\n",
+ "Illustration 3.8 (a)\n",
+ "\n",
+ "\n",
+ "The rate of steam flow reqd. is -0.0348 kmol/square m.s\n",
+ "\n",
+ "\n",
+ "Illustration 3.8 (b)\n",
+ "\n",
+ "\n",
+ "The rate of steam flow reqd. is 0.0028 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 42
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter4.ipynb b/Mass_-_Transfer_Operations/Chapter4.ipynb
new file mode 100755
index 00000000..73fd7beb
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter4.ipynb
@@ -0,0 +1,471 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:c24c5f13f06ad2e35494c2ba2e22ba1351ce2de7d258eb50313205ed297dec4a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 4: Diffusion In Solids"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.1: Page 89"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.1\n",
+ "# Page: 89\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 4.1 - Page: 89\\n\\n'\n",
+ " \n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "P = 2;# [atm]\n",
+ "a1 = 0.025;# [m]\n",
+ "a2 = 0.050;# [m]\n",
+ "solub = 0.053*P;# [cubic m H2 (STP)/(cubic m rubber)]\n",
+ "Ca1 = solub/22.41;# inner surface of the pipe\n",
+ "Ca2 = 0;# resistance to difusion of H2 away from the surface is negligible.\n",
+ "Da = 1.8*10**(-10);# [square m/s]\n",
+ "l = 1;# [m]\n",
+ "#********#\n",
+ "\n",
+ "z = (a2-a1)/2;# [m]\n",
+ "# Using Eqn. 4.4\n",
+ "Sav = (2*(math.pi)*l*(a2-a1))/(2*math.log(a2/a1));# [square m]\n",
+ "# Using Eqn. 4.3\n",
+ "w = (Da*Sav*(Ca1-Ca2))/z;# [kmol H2/s for 1m length]\n",
+ "w = w*2.02*10**3*3600;# [g H2/m.h]\n",
+ "print'The rate of loss of H2 by diffusion per m of pipe length:',round(w,6),' g H2/m.h'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.1 - Page: 89\n",
+ "\n",
+ "\n",
+ "The rate of loss of H2 by diffusion per m of pipe length: 5.6e-05 g H2/m.h\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.2: Page 92"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.2\n",
+ "# Page: 92\n",
+ "\n",
+ "print'Illustration 4.2 - Page: 92\\n\\n'\n",
+ "print'Illustration 4.2 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "# Given\n",
+ "a = 3.0/2;# [cm]\n",
+ "thetha = 68*3600;# [s]\n",
+ "# Ca can e calculated in terms of g/100 cubic cm\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 3.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#**********#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.6;\n",
+ "# From Fig. 4.2 (Pg 91): For diffusion from only one exposed surface D*thetha/(4*a^2) = 0.128\n",
+ "D = 0.128*4*(a**2)/thetha;# [square cm/s]\n",
+ "D = D*10**(-4);# [square m/s]\n",
+ "print'Diffusivity of urea in gel from only one exposed durface:',round(D,12),'square m/s\\n\\n'\n",
+ "\n",
+ "print'Illustration 4.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "#****Data****#\n",
+ "# Ca can e calculated in terms of g/100 cubic cm\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 1.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#*********#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.2;\n",
+ "# From Fig. 4.2 (Pg 91): For diffusion from only one exposed surface D*thetha/(4*a**2) = 0.568\n",
+ "D = 4.70*10**(-6);# From Illusration 4.2(a) [square cm/s]\n",
+ "thetha = 0.568*4*a**2/D;# [s]\n",
+ "thetha = thetha/3600.0;# [h]\n",
+ "print'The time taken for the avg. conc. to fall to 1g/100 cubic cm is:',round(thetha),' hours'\n",
+ "\n",
+ "print'Illustration 4.2 (c)\\n\\n'\n",
+ "\n",
+ "# solution (c)\n",
+ "\n",
+ "#****Data*****#\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 1.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#*******#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.2;\n",
+ "# From Fig. 4.2: For diffusion from two opposite exposed surface D*thetha/(a**2) = 0.568\n",
+ "D = 4.70*10**(-6);# From Illusration 4.2(a) [square cm/s]\n",
+ "thetha = 0.568*(a**2)/D;# [s]\n",
+ "thetha = thetha/3600.0;# [h]\n",
+ "print'The time taken for the avg. conc. to fall to 1g/100 cubic cm when two faces opposed is:',int(thetha),' hours'\n",
+ "# the solution in the textbook is wrong due to approximation\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.2 - Page: 92\n",
+ "\n",
+ "\n",
+ "Illustration 4.2 (a)\n",
+ "\n",
+ "\n",
+ "Diffusivity of urea in gel from only one exposed durface: 4.71e-10 square m/s\n",
+ "\n",
+ "\n",
+ "Illustration 4.2 (b)\n",
+ "\n",
+ "\n",
+ "The time taken for the avg. conc. to fall to 1g/100 cubic cm is: 302.0 hours\n",
+ "Illustration 4.2 (c)\n",
+ "\n",
+ "\n",
+ "The time taken for the avg. conc. to fall to 1g/100 cubic cm when two faces opposed is: 75 hours\n"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.3: Page 94"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.3\n",
+ "# Page: 94\n",
+ "\n",
+ "print'Illustration 4.3 - Page: 94\\n\\n'\n",
+ "\n",
+ "# solution \n",
+ "\n",
+ "#****Data****#\n",
+ "z = 0.1;# [cm]\n",
+ "pa1 = 1;# [cmHg]\n",
+ "pa2 = 0;# [cmHg]\n",
+ "Da = 1.1*10**(-10)*10**4;# [square cm/s]\n",
+ "#***********#\n",
+ "\n",
+ "# Solubility coeffecient in terms of Hg\n",
+ "Sa = 0.90/76;# [cubic cm gas (STP)/cubic cm.cmHg]\n",
+ "# Using Eqn. 4.15\n",
+ "Va = (Da*Sa*(pa1-pa2))/z;# [cubic cm(STP)/square cm.s]\n",
+ "# Using Eqn. 4.16\n",
+ "P = Da*Sa;# [cubic cm gas (STP)/square cm.s.(cmHg/cm)]\n",
+ "print'The rate of diffusion of CO is:',round(Va,8),'cubic cm(STP)/square cm.s'\n",
+ "print'The permeability of the membrane is',round(P,9),'cubic cm gas (STP)/square cm.s.(cmHg/cm)'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.3 - Page: 94\n",
+ "\n",
+ "\n",
+ "The rate of diffusion of CO is: 1.3e-07 cubic cm(STP)/square cm.s\n",
+ "The permeability of the membrane is 1.3e-08 cubic cm gas (STP)/square cm.s.(cmHg/cm)\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.4: Page 96"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ " \n",
+ "\n",
+ "# Illustration 4.4\n",
+ "# Page: 96\n",
+ "\n",
+ "print'Illustration 4.4 - Page: 96\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "a = 0.005;# [m]\n",
+ "# For the KCl diffusion\n",
+ "Dab1 = 1.84*10**(-9);# [square m/s]\n",
+ "thetha = 4.75*3600;# [s]\n",
+ "Ca_Inf = 0;\n",
+ "# For K2CrO4 diffusion\n",
+ "Cao = 0.28;# [g/cubic cm]\n",
+ "Ca_Inf = 0.002;# [g/cubic cm]\n",
+ "Dab2 = 1.14*10**(-9);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "E = 0.1;# For 90% removal of KCl\n",
+ "# From Fig. 4.2 (Pg 91): Deff*thetha/a^2 = 0.18\n",
+ "Deff = 0.18*a**2/thetha;# [square m/s]\n",
+ "Dab_by_Deff = Dab1/Deff;\n",
+ "Ca_thetha = 0.1*0.28;# [g/cubic cm]\n",
+ "Es = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# From Fig. 4.2 (Pg 91): Deff*thetha/a^2 = 0.30\n",
+ "Deff = Dab2/Dab_by_Deff;# [square m/s]\n",
+ "thetha = 0.3*a**2/Deff;# [s]\n",
+ "thetha = thetha/3600;# [h]\n",
+ "print'The time reqd. is:',round(thetha,3),'hours'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.4 - Page: 96\n",
+ "\n",
+ "\n",
+ "The time reqd. is: 12.778 hours\n"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.5: Page 98"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.5\n",
+ "# Page: 98\n",
+ "import math \n",
+ "\n",
+ "print'Illustration 4.5 - Page: 98\\n\\n'\n",
+ "print'Illustration 4.5 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = H2 b = N2\n",
+ "Dab_eff = 5.3*10**(-6);# [square m/s]\n",
+ "Dkb_eff = 1.17*10**(-5);# [square m/s]\n",
+ "Dab = 7.63*10**(-5);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "R = 8314;#[Nm/kmol]\n",
+ "Mb = 2.02;# [kg/kmol]\n",
+ "T = 293;# [K]\n",
+ "Dtrue_by_Deff = Dab/Dab_eff;\n",
+ "# Since the ratio is strictly a matter of the geometry of the solid.\n",
+ "Dkb = Dkb_eff*Dtrue_by_Deff;# [square m/s]\n",
+ "# From Eqn. 4.20\n",
+ "d = 3*Dkb*((math.pi*Mb)/(8*R*T))**0.5;# [m]\n",
+ "print'The equivalent pore diameter is: ',round(d,9),' m\\n\\n'\n",
+ "\n",
+ "print'Illustration 4.5 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = O2 b = N2 c = H2\n",
+ "Ya1 = 0.8;\n",
+ "Ya2 = 0.2;\n",
+ "Pt = 10133;# [N/square m]\n",
+ "z = 0.002;# [m]\n",
+ "T = 293;# [K]\n",
+ "#*******#\n",
+ "\n",
+ "# From Table 2.1 (Pg 31):\n",
+ "Dab = 1.81*10**(-5);# [square m/s] at STP\n",
+ "Dkc = 1.684*10**(-4);# [square m/s] From Illustration 4.5(a)\n",
+ "Mc = 2.02;# [kg/kmol]\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 28.02;# [kg/kmol]\n",
+ "Dab = Dab*(1/0.1)*((293/273)**1.5);# [square m/s] at 0.1 atm & 20 C\n",
+ "DabEff = Dab/14.4;# [square m/s] From Illustration 4.5(a)\n",
+ "Dka = Dkc*((Mc/Ma)**0.5);# [square m/s]\n",
+ "DkaEff = Dka/14.4;# [square m/s]\n",
+ "Nb_by_Na = -(Ma/Mb)**0.5;\n",
+ "# Na/(Na+Nb) = 1.0/(1+(Nb/Na))\n",
+ "Na_by_NaSumNb = 1.0/(1+(Nb_by_Na));\n",
+ "DabEff_by_DkaEff = DabEff/DkaEff;\n",
+ "# By Eqn. 4.23\n",
+ "Na = (Na_by_NaSumNb)*(DabEff*Pt/(R*T*z))*log((((Na_by_NaSumNb)*(1+DabEff_by_DkaEff))-Ya2)/(((Na_by_NaSumNb)*(1+DabEff_by_DkaEff))-Ya1));# [kmol/square m.s]\n",
+ "Nb = Na*(Nb_by_Na);# [kmol/square m.s]\n",
+ "print\"Diffusion flux of O2 is \",round(Na,8),\" kmol/square m.s\\n\"\n",
+ "print\"Diffusion flux of N2 is \",round(Nb,8),\" kmol/square m.s\\n\"\n",
+ "#the answer in textbook is slightly different due to approximation while here calculation is precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.5 - Page: 98\n",
+ "\n",
+ "\n",
+ "Illustration 4.5 (a)\n",
+ "\n",
+ "\n",
+ "The equivalent pore diameter is: 2.88e-07 m\n",
+ "\n",
+ "\n",
+ "Illustration 4.5 (b)\n",
+ "\n",
+ "\n",
+ "Diffusion flux of O2 is 2.95e-06 kmol/square m.s\n",
+ "\n",
+ "Diffusion flux of N2 is -3.16e-06 kmol/square m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.6: Page 100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.6\n",
+ "# Page: 100\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 4.6 - Page: 100\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = N2\n",
+ "# For N2 at 300K\n",
+ "viscosity1 = 1.8*10**(-5);# [kg/m.s]\n",
+ "Pt1 = 10133.0;# [N/square m.sec]\n",
+ "T = 300;# [K]\n",
+ "z = 0.0254;# [m]\n",
+ "T2 = 393.0;# [K]\n",
+ "#***********#\n",
+ "\n",
+ "Ma = 28.02;# [kg/kmol]\n",
+ "R = 8314.0;# [J/K.kgmol]\n",
+ "#From Eqn 4.22\n",
+ "Lambda = (3.2*viscosity1/Pt1)*(R*T/(2*(math.pi)*Ma))**0.5;\n",
+ "d = 10**(-4);# [m]\n",
+ "d_by_lambda = d/Lambda;\n",
+ "# Kundsen flow will not occur\n",
+ "# N2 flow corresponding to 9 cubic ft/square ft.min at 300K & 1 std atm = 0.0457 cubic m/square m.min\n",
+ "Na1 = 0.0457*(273.0/T)*(1/22.41);# [kmol/square m.s]\n",
+ "Pt1_diff_Pt2 = 2*3386/13.6;# [N/square m]\n",
+ "Ptav = Pt1+(Pt1_diff_Pt2/2.0);# [N/square m]\n",
+ "# From Eqn. 4.26\n",
+ "k1 = Na1*R*T*z/(Ptav*(Pt1_diff_Pt2));# [m**4/N.s]\n",
+ "\n",
+ "#For N2 at 393K\n",
+ "viscosity2 = 2.2*10**(-5);# [kg/m.s]\n",
+ "k2 = (k1*viscosity1)/(viscosity2);# [m^4/N.s]\n",
+ "# From Eqn 4.26\n",
+ "Na = (k2*Ptav*Pt1_diff_Pt2)/(R*T2*z);# [kmol/square m.s]\n",
+ "print\"Flow rate to be expected is\",round(Na,6),\" kmol/square m.s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.6 - Page: 100\n",
+ "\n",
+ "\n",
+ "Flow rate to be expected is 0.001159 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter4_1.ipynb b/Mass_-_Transfer_Operations/Chapter4_1.ipynb
new file mode 100755
index 00000000..73fd7beb
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter4_1.ipynb
@@ -0,0 +1,471 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:c24c5f13f06ad2e35494c2ba2e22ba1351ce2de7d258eb50313205ed297dec4a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 4: Diffusion In Solids"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.1: Page 89"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.1\n",
+ "# Page: 89\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 4.1 - Page: 89\\n\\n'\n",
+ " \n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "P = 2;# [atm]\n",
+ "a1 = 0.025;# [m]\n",
+ "a2 = 0.050;# [m]\n",
+ "solub = 0.053*P;# [cubic m H2 (STP)/(cubic m rubber)]\n",
+ "Ca1 = solub/22.41;# inner surface of the pipe\n",
+ "Ca2 = 0;# resistance to difusion of H2 away from the surface is negligible.\n",
+ "Da = 1.8*10**(-10);# [square m/s]\n",
+ "l = 1;# [m]\n",
+ "#********#\n",
+ "\n",
+ "z = (a2-a1)/2;# [m]\n",
+ "# Using Eqn. 4.4\n",
+ "Sav = (2*(math.pi)*l*(a2-a1))/(2*math.log(a2/a1));# [square m]\n",
+ "# Using Eqn. 4.3\n",
+ "w = (Da*Sav*(Ca1-Ca2))/z;# [kmol H2/s for 1m length]\n",
+ "w = w*2.02*10**3*3600;# [g H2/m.h]\n",
+ "print'The rate of loss of H2 by diffusion per m of pipe length:',round(w,6),' g H2/m.h'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.1 - Page: 89\n",
+ "\n",
+ "\n",
+ "The rate of loss of H2 by diffusion per m of pipe length: 5.6e-05 g H2/m.h\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.2: Page 92"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.2\n",
+ "# Page: 92\n",
+ "\n",
+ "print'Illustration 4.2 - Page: 92\\n\\n'\n",
+ "print'Illustration 4.2 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "# Given\n",
+ "a = 3.0/2;# [cm]\n",
+ "thetha = 68*3600;# [s]\n",
+ "# Ca can e calculated in terms of g/100 cubic cm\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 3.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#**********#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.6;\n",
+ "# From Fig. 4.2 (Pg 91): For diffusion from only one exposed surface D*thetha/(4*a^2) = 0.128\n",
+ "D = 0.128*4*(a**2)/thetha;# [square cm/s]\n",
+ "D = D*10**(-4);# [square m/s]\n",
+ "print'Diffusivity of urea in gel from only one exposed durface:',round(D,12),'square m/s\\n\\n'\n",
+ "\n",
+ "print'Illustration 4.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "#****Data****#\n",
+ "# Ca can e calculated in terms of g/100 cubic cm\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 1.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#*********#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.2;\n",
+ "# From Fig. 4.2 (Pg 91): For diffusion from only one exposed surface D*thetha/(4*a**2) = 0.568\n",
+ "D = 4.70*10**(-6);# From Illusration 4.2(a) [square cm/s]\n",
+ "thetha = 0.568*4*a**2/D;# [s]\n",
+ "thetha = thetha/3600.0;# [h]\n",
+ "print'The time taken for the avg. conc. to fall to 1g/100 cubic cm is:',round(thetha),' hours'\n",
+ "\n",
+ "print'Illustration 4.2 (c)\\n\\n'\n",
+ "\n",
+ "# solution (c)\n",
+ "\n",
+ "#****Data*****#\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 1.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#*******#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.2;\n",
+ "# From Fig. 4.2: For diffusion from two opposite exposed surface D*thetha/(a**2) = 0.568\n",
+ "D = 4.70*10**(-6);# From Illusration 4.2(a) [square cm/s]\n",
+ "thetha = 0.568*(a**2)/D;# [s]\n",
+ "thetha = thetha/3600.0;# [h]\n",
+ "print'The time taken for the avg. conc. to fall to 1g/100 cubic cm when two faces opposed is:',int(thetha),' hours'\n",
+ "# the solution in the textbook is wrong due to approximation\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.2 - Page: 92\n",
+ "\n",
+ "\n",
+ "Illustration 4.2 (a)\n",
+ "\n",
+ "\n",
+ "Diffusivity of urea in gel from only one exposed durface: 4.71e-10 square m/s\n",
+ "\n",
+ "\n",
+ "Illustration 4.2 (b)\n",
+ "\n",
+ "\n",
+ "The time taken for the avg. conc. to fall to 1g/100 cubic cm is: 302.0 hours\n",
+ "Illustration 4.2 (c)\n",
+ "\n",
+ "\n",
+ "The time taken for the avg. conc. to fall to 1g/100 cubic cm when two faces opposed is: 75 hours\n"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.3: Page 94"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.3\n",
+ "# Page: 94\n",
+ "\n",
+ "print'Illustration 4.3 - Page: 94\\n\\n'\n",
+ "\n",
+ "# solution \n",
+ "\n",
+ "#****Data****#\n",
+ "z = 0.1;# [cm]\n",
+ "pa1 = 1;# [cmHg]\n",
+ "pa2 = 0;# [cmHg]\n",
+ "Da = 1.1*10**(-10)*10**4;# [square cm/s]\n",
+ "#***********#\n",
+ "\n",
+ "# Solubility coeffecient in terms of Hg\n",
+ "Sa = 0.90/76;# [cubic cm gas (STP)/cubic cm.cmHg]\n",
+ "# Using Eqn. 4.15\n",
+ "Va = (Da*Sa*(pa1-pa2))/z;# [cubic cm(STP)/square cm.s]\n",
+ "# Using Eqn. 4.16\n",
+ "P = Da*Sa;# [cubic cm gas (STP)/square cm.s.(cmHg/cm)]\n",
+ "print'The rate of diffusion of CO is:',round(Va,8),'cubic cm(STP)/square cm.s'\n",
+ "print'The permeability of the membrane is',round(P,9),'cubic cm gas (STP)/square cm.s.(cmHg/cm)'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.3 - Page: 94\n",
+ "\n",
+ "\n",
+ "The rate of diffusion of CO is: 1.3e-07 cubic cm(STP)/square cm.s\n",
+ "The permeability of the membrane is 1.3e-08 cubic cm gas (STP)/square cm.s.(cmHg/cm)\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.4: Page 96"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ " \n",
+ "\n",
+ "# Illustration 4.4\n",
+ "# Page: 96\n",
+ "\n",
+ "print'Illustration 4.4 - Page: 96\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "a = 0.005;# [m]\n",
+ "# For the KCl diffusion\n",
+ "Dab1 = 1.84*10**(-9);# [square m/s]\n",
+ "thetha = 4.75*3600;# [s]\n",
+ "Ca_Inf = 0;\n",
+ "# For K2CrO4 diffusion\n",
+ "Cao = 0.28;# [g/cubic cm]\n",
+ "Ca_Inf = 0.002;# [g/cubic cm]\n",
+ "Dab2 = 1.14*10**(-9);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "E = 0.1;# For 90% removal of KCl\n",
+ "# From Fig. 4.2 (Pg 91): Deff*thetha/a^2 = 0.18\n",
+ "Deff = 0.18*a**2/thetha;# [square m/s]\n",
+ "Dab_by_Deff = Dab1/Deff;\n",
+ "Ca_thetha = 0.1*0.28;# [g/cubic cm]\n",
+ "Es = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# From Fig. 4.2 (Pg 91): Deff*thetha/a^2 = 0.30\n",
+ "Deff = Dab2/Dab_by_Deff;# [square m/s]\n",
+ "thetha = 0.3*a**2/Deff;# [s]\n",
+ "thetha = thetha/3600;# [h]\n",
+ "print'The time reqd. is:',round(thetha,3),'hours'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.4 - Page: 96\n",
+ "\n",
+ "\n",
+ "The time reqd. is: 12.778 hours\n"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.5: Page 98"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.5\n",
+ "# Page: 98\n",
+ "import math \n",
+ "\n",
+ "print'Illustration 4.5 - Page: 98\\n\\n'\n",
+ "print'Illustration 4.5 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = H2 b = N2\n",
+ "Dab_eff = 5.3*10**(-6);# [square m/s]\n",
+ "Dkb_eff = 1.17*10**(-5);# [square m/s]\n",
+ "Dab = 7.63*10**(-5);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "R = 8314;#[Nm/kmol]\n",
+ "Mb = 2.02;# [kg/kmol]\n",
+ "T = 293;# [K]\n",
+ "Dtrue_by_Deff = Dab/Dab_eff;\n",
+ "# Since the ratio is strictly a matter of the geometry of the solid.\n",
+ "Dkb = Dkb_eff*Dtrue_by_Deff;# [square m/s]\n",
+ "# From Eqn. 4.20\n",
+ "d = 3*Dkb*((math.pi*Mb)/(8*R*T))**0.5;# [m]\n",
+ "print'The equivalent pore diameter is: ',round(d,9),' m\\n\\n'\n",
+ "\n",
+ "print'Illustration 4.5 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = O2 b = N2 c = H2\n",
+ "Ya1 = 0.8;\n",
+ "Ya2 = 0.2;\n",
+ "Pt = 10133;# [N/square m]\n",
+ "z = 0.002;# [m]\n",
+ "T = 293;# [K]\n",
+ "#*******#\n",
+ "\n",
+ "# From Table 2.1 (Pg 31):\n",
+ "Dab = 1.81*10**(-5);# [square m/s] at STP\n",
+ "Dkc = 1.684*10**(-4);# [square m/s] From Illustration 4.5(a)\n",
+ "Mc = 2.02;# [kg/kmol]\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 28.02;# [kg/kmol]\n",
+ "Dab = Dab*(1/0.1)*((293/273)**1.5);# [square m/s] at 0.1 atm & 20 C\n",
+ "DabEff = Dab/14.4;# [square m/s] From Illustration 4.5(a)\n",
+ "Dka = Dkc*((Mc/Ma)**0.5);# [square m/s]\n",
+ "DkaEff = Dka/14.4;# [square m/s]\n",
+ "Nb_by_Na = -(Ma/Mb)**0.5;\n",
+ "# Na/(Na+Nb) = 1.0/(1+(Nb/Na))\n",
+ "Na_by_NaSumNb = 1.0/(1+(Nb_by_Na));\n",
+ "DabEff_by_DkaEff = DabEff/DkaEff;\n",
+ "# By Eqn. 4.23\n",
+ "Na = (Na_by_NaSumNb)*(DabEff*Pt/(R*T*z))*log((((Na_by_NaSumNb)*(1+DabEff_by_DkaEff))-Ya2)/(((Na_by_NaSumNb)*(1+DabEff_by_DkaEff))-Ya1));# [kmol/square m.s]\n",
+ "Nb = Na*(Nb_by_Na);# [kmol/square m.s]\n",
+ "print\"Diffusion flux of O2 is \",round(Na,8),\" kmol/square m.s\\n\"\n",
+ "print\"Diffusion flux of N2 is \",round(Nb,8),\" kmol/square m.s\\n\"\n",
+ "#the answer in textbook is slightly different due to approximation while here calculation is precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.5 - Page: 98\n",
+ "\n",
+ "\n",
+ "Illustration 4.5 (a)\n",
+ "\n",
+ "\n",
+ "The equivalent pore diameter is: 2.88e-07 m\n",
+ "\n",
+ "\n",
+ "Illustration 4.5 (b)\n",
+ "\n",
+ "\n",
+ "Diffusion flux of O2 is 2.95e-06 kmol/square m.s\n",
+ "\n",
+ "Diffusion flux of N2 is -3.16e-06 kmol/square m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.6: Page 100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.6\n",
+ "# Page: 100\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 4.6 - Page: 100\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = N2\n",
+ "# For N2 at 300K\n",
+ "viscosity1 = 1.8*10**(-5);# [kg/m.s]\n",
+ "Pt1 = 10133.0;# [N/square m.sec]\n",
+ "T = 300;# [K]\n",
+ "z = 0.0254;# [m]\n",
+ "T2 = 393.0;# [K]\n",
+ "#***********#\n",
+ "\n",
+ "Ma = 28.02;# [kg/kmol]\n",
+ "R = 8314.0;# [J/K.kgmol]\n",
+ "#From Eqn 4.22\n",
+ "Lambda = (3.2*viscosity1/Pt1)*(R*T/(2*(math.pi)*Ma))**0.5;\n",
+ "d = 10**(-4);# [m]\n",
+ "d_by_lambda = d/Lambda;\n",
+ "# Kundsen flow will not occur\n",
+ "# N2 flow corresponding to 9 cubic ft/square ft.min at 300K & 1 std atm = 0.0457 cubic m/square m.min\n",
+ "Na1 = 0.0457*(273.0/T)*(1/22.41);# [kmol/square m.s]\n",
+ "Pt1_diff_Pt2 = 2*3386/13.6;# [N/square m]\n",
+ "Ptav = Pt1+(Pt1_diff_Pt2/2.0);# [N/square m]\n",
+ "# From Eqn. 4.26\n",
+ "k1 = Na1*R*T*z/(Ptav*(Pt1_diff_Pt2));# [m**4/N.s]\n",
+ "\n",
+ "#For N2 at 393K\n",
+ "viscosity2 = 2.2*10**(-5);# [kg/m.s]\n",
+ "k2 = (k1*viscosity1)/(viscosity2);# [m^4/N.s]\n",
+ "# From Eqn 4.26\n",
+ "Na = (k2*Ptav*Pt1_diff_Pt2)/(R*T2*z);# [kmol/square m.s]\n",
+ "print\"Flow rate to be expected is\",round(Na,6),\" kmol/square m.s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.6 - Page: 100\n",
+ "\n",
+ "\n",
+ "Flow rate to be expected is 0.001159 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter4_2.ipynb b/Mass_-_Transfer_Operations/Chapter4_2.ipynb
new file mode 100755
index 00000000..73fd7beb
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter4_2.ipynb
@@ -0,0 +1,471 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:c24c5f13f06ad2e35494c2ba2e22ba1351ce2de7d258eb50313205ed297dec4a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 4: Diffusion In Solids"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.1: Page 89"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.1\n",
+ "# Page: 89\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 4.1 - Page: 89\\n\\n'\n",
+ " \n",
+ "# solution\n",
+ "\n",
+ "#***Data****#\n",
+ "P = 2;# [atm]\n",
+ "a1 = 0.025;# [m]\n",
+ "a2 = 0.050;# [m]\n",
+ "solub = 0.053*P;# [cubic m H2 (STP)/(cubic m rubber)]\n",
+ "Ca1 = solub/22.41;# inner surface of the pipe\n",
+ "Ca2 = 0;# resistance to difusion of H2 away from the surface is negligible.\n",
+ "Da = 1.8*10**(-10);# [square m/s]\n",
+ "l = 1;# [m]\n",
+ "#********#\n",
+ "\n",
+ "z = (a2-a1)/2;# [m]\n",
+ "# Using Eqn. 4.4\n",
+ "Sav = (2*(math.pi)*l*(a2-a1))/(2*math.log(a2/a1));# [square m]\n",
+ "# Using Eqn. 4.3\n",
+ "w = (Da*Sav*(Ca1-Ca2))/z;# [kmol H2/s for 1m length]\n",
+ "w = w*2.02*10**3*3600;# [g H2/m.h]\n",
+ "print'The rate of loss of H2 by diffusion per m of pipe length:',round(w,6),' g H2/m.h'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.1 - Page: 89\n",
+ "\n",
+ "\n",
+ "The rate of loss of H2 by diffusion per m of pipe length: 5.6e-05 g H2/m.h\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.2: Page 92"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.2\n",
+ "# Page: 92\n",
+ "\n",
+ "print'Illustration 4.2 - Page: 92\\n\\n'\n",
+ "print'Illustration 4.2 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "# Given\n",
+ "a = 3.0/2;# [cm]\n",
+ "thetha = 68*3600;# [s]\n",
+ "# Ca can e calculated in terms of g/100 cubic cm\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 3.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#**********#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.6;\n",
+ "# From Fig. 4.2 (Pg 91): For diffusion from only one exposed surface D*thetha/(4*a^2) = 0.128\n",
+ "D = 0.128*4*(a**2)/thetha;# [square cm/s]\n",
+ "D = D*10**(-4);# [square m/s]\n",
+ "print'Diffusivity of urea in gel from only one exposed durface:',round(D,12),'square m/s\\n\\n'\n",
+ "\n",
+ "print'Illustration 4.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "#****Data****#\n",
+ "# Ca can e calculated in terms of g/100 cubic cm\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 1.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#*********#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.2;\n",
+ "# From Fig. 4.2 (Pg 91): For diffusion from only one exposed surface D*thetha/(4*a**2) = 0.568\n",
+ "D = 4.70*10**(-6);# From Illusration 4.2(a) [square cm/s]\n",
+ "thetha = 0.568*4*a**2/D;# [s]\n",
+ "thetha = thetha/3600.0;# [h]\n",
+ "print'The time taken for the avg. conc. to fall to 1g/100 cubic cm is:',round(thetha),' hours'\n",
+ "\n",
+ "print'Illustration 4.2 (c)\\n\\n'\n",
+ "\n",
+ "# solution (c)\n",
+ "\n",
+ "#****Data*****#\n",
+ "Cao = 5.0;# [g/100 cubic cm]\n",
+ "Ca_thetha = 1.0;# [g/100 cubic cm]\n",
+ "Ca_Inf = 0.0;# [g/100 cubic cm]\n",
+ "#*******#\n",
+ "\n",
+ "E = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# E = 0.2;\n",
+ "# From Fig. 4.2: For diffusion from two opposite exposed surface D*thetha/(a**2) = 0.568\n",
+ "D = 4.70*10**(-6);# From Illusration 4.2(a) [square cm/s]\n",
+ "thetha = 0.568*(a**2)/D;# [s]\n",
+ "thetha = thetha/3600.0;# [h]\n",
+ "print'The time taken for the avg. conc. to fall to 1g/100 cubic cm when two faces opposed is:',int(thetha),' hours'\n",
+ "# the solution in the textbook is wrong due to approximation\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.2 - Page: 92\n",
+ "\n",
+ "\n",
+ "Illustration 4.2 (a)\n",
+ "\n",
+ "\n",
+ "Diffusivity of urea in gel from only one exposed durface: 4.71e-10 square m/s\n",
+ "\n",
+ "\n",
+ "Illustration 4.2 (b)\n",
+ "\n",
+ "\n",
+ "The time taken for the avg. conc. to fall to 1g/100 cubic cm is: 302.0 hours\n",
+ "Illustration 4.2 (c)\n",
+ "\n",
+ "\n",
+ "The time taken for the avg. conc. to fall to 1g/100 cubic cm when two faces opposed is: 75 hours\n"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.3: Page 94"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.3\n",
+ "# Page: 94\n",
+ "\n",
+ "print'Illustration 4.3 - Page: 94\\n\\n'\n",
+ "\n",
+ "# solution \n",
+ "\n",
+ "#****Data****#\n",
+ "z = 0.1;# [cm]\n",
+ "pa1 = 1;# [cmHg]\n",
+ "pa2 = 0;# [cmHg]\n",
+ "Da = 1.1*10**(-10)*10**4;# [square cm/s]\n",
+ "#***********#\n",
+ "\n",
+ "# Solubility coeffecient in terms of Hg\n",
+ "Sa = 0.90/76;# [cubic cm gas (STP)/cubic cm.cmHg]\n",
+ "# Using Eqn. 4.15\n",
+ "Va = (Da*Sa*(pa1-pa2))/z;# [cubic cm(STP)/square cm.s]\n",
+ "# Using Eqn. 4.16\n",
+ "P = Da*Sa;# [cubic cm gas (STP)/square cm.s.(cmHg/cm)]\n",
+ "print'The rate of diffusion of CO is:',round(Va,8),'cubic cm(STP)/square cm.s'\n",
+ "print'The permeability of the membrane is',round(P,9),'cubic cm gas (STP)/square cm.s.(cmHg/cm)'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.3 - Page: 94\n",
+ "\n",
+ "\n",
+ "The rate of diffusion of CO is: 1.3e-07 cubic cm(STP)/square cm.s\n",
+ "The permeability of the membrane is 1.3e-08 cubic cm gas (STP)/square cm.s.(cmHg/cm)\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.4: Page 96"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ " \n",
+ "\n",
+ "# Illustration 4.4\n",
+ "# Page: 96\n",
+ "\n",
+ "print'Illustration 4.4 - Page: 96\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "a = 0.005;# [m]\n",
+ "# For the KCl diffusion\n",
+ "Dab1 = 1.84*10**(-9);# [square m/s]\n",
+ "thetha = 4.75*3600;# [s]\n",
+ "Ca_Inf = 0;\n",
+ "# For K2CrO4 diffusion\n",
+ "Cao = 0.28;# [g/cubic cm]\n",
+ "Ca_Inf = 0.002;# [g/cubic cm]\n",
+ "Dab2 = 1.14*10**(-9);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "E = 0.1;# For 90% removal of KCl\n",
+ "# From Fig. 4.2 (Pg 91): Deff*thetha/a^2 = 0.18\n",
+ "Deff = 0.18*a**2/thetha;# [square m/s]\n",
+ "Dab_by_Deff = Dab1/Deff;\n",
+ "Ca_thetha = 0.1*0.28;# [g/cubic cm]\n",
+ "Es = (Ca_thetha-Ca_Inf)/(Cao-Ca_Inf);\n",
+ "# From Fig. 4.2 (Pg 91): Deff*thetha/a^2 = 0.30\n",
+ "Deff = Dab2/Dab_by_Deff;# [square m/s]\n",
+ "thetha = 0.3*a**2/Deff;# [s]\n",
+ "thetha = thetha/3600;# [h]\n",
+ "print'The time reqd. is:',round(thetha,3),'hours'"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.4 - Page: 96\n",
+ "\n",
+ "\n",
+ "The time reqd. is: 12.778 hours\n"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.5: Page 98"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.5\n",
+ "# Page: 98\n",
+ "import math \n",
+ "\n",
+ "print'Illustration 4.5 - Page: 98\\n\\n'\n",
+ "print'Illustration 4.5 (a)\\n\\n'\n",
+ "\n",
+ "# solution (a)\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = H2 b = N2\n",
+ "Dab_eff = 5.3*10**(-6);# [square m/s]\n",
+ "Dkb_eff = 1.17*10**(-5);# [square m/s]\n",
+ "Dab = 7.63*10**(-5);# [square m/s]\n",
+ "#*******#\n",
+ "\n",
+ "R = 8314;#[Nm/kmol]\n",
+ "Mb = 2.02;# [kg/kmol]\n",
+ "T = 293;# [K]\n",
+ "Dtrue_by_Deff = Dab/Dab_eff;\n",
+ "# Since the ratio is strictly a matter of the geometry of the solid.\n",
+ "Dkb = Dkb_eff*Dtrue_by_Deff;# [square m/s]\n",
+ "# From Eqn. 4.20\n",
+ "d = 3*Dkb*((math.pi*Mb)/(8*R*T))**0.5;# [m]\n",
+ "print'The equivalent pore diameter is: ',round(d,9),' m\\n\\n'\n",
+ "\n",
+ "print'Illustration 4.5 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a = O2 b = N2 c = H2\n",
+ "Ya1 = 0.8;\n",
+ "Ya2 = 0.2;\n",
+ "Pt = 10133;# [N/square m]\n",
+ "z = 0.002;# [m]\n",
+ "T = 293;# [K]\n",
+ "#*******#\n",
+ "\n",
+ "# From Table 2.1 (Pg 31):\n",
+ "Dab = 1.81*10**(-5);# [square m/s] at STP\n",
+ "Dkc = 1.684*10**(-4);# [square m/s] From Illustration 4.5(a)\n",
+ "Mc = 2.02;# [kg/kmol]\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 28.02;# [kg/kmol]\n",
+ "Dab = Dab*(1/0.1)*((293/273)**1.5);# [square m/s] at 0.1 atm & 20 C\n",
+ "DabEff = Dab/14.4;# [square m/s] From Illustration 4.5(a)\n",
+ "Dka = Dkc*((Mc/Ma)**0.5);# [square m/s]\n",
+ "DkaEff = Dka/14.4;# [square m/s]\n",
+ "Nb_by_Na = -(Ma/Mb)**0.5;\n",
+ "# Na/(Na+Nb) = 1.0/(1+(Nb/Na))\n",
+ "Na_by_NaSumNb = 1.0/(1+(Nb_by_Na));\n",
+ "DabEff_by_DkaEff = DabEff/DkaEff;\n",
+ "# By Eqn. 4.23\n",
+ "Na = (Na_by_NaSumNb)*(DabEff*Pt/(R*T*z))*log((((Na_by_NaSumNb)*(1+DabEff_by_DkaEff))-Ya2)/(((Na_by_NaSumNb)*(1+DabEff_by_DkaEff))-Ya1));# [kmol/square m.s]\n",
+ "Nb = Na*(Nb_by_Na);# [kmol/square m.s]\n",
+ "print\"Diffusion flux of O2 is \",round(Na,8),\" kmol/square m.s\\n\"\n",
+ "print\"Diffusion flux of N2 is \",round(Nb,8),\" kmol/square m.s\\n\"\n",
+ "#the answer in textbook is slightly different due to approximation while here calculation is precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.5 - Page: 98\n",
+ "\n",
+ "\n",
+ "Illustration 4.5 (a)\n",
+ "\n",
+ "\n",
+ "The equivalent pore diameter is: 2.88e-07 m\n",
+ "\n",
+ "\n",
+ "Illustration 4.5 (b)\n",
+ "\n",
+ "\n",
+ "Diffusion flux of O2 is 2.95e-06 kmol/square m.s\n",
+ "\n",
+ "Diffusion flux of N2 is -3.16e-06 kmol/square m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex4.6: Page 100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 4.6\n",
+ "# Page: 100\n",
+ "\n",
+ "import math\n",
+ "print'Illustration 4.6 - Page: 100\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = N2\n",
+ "# For N2 at 300K\n",
+ "viscosity1 = 1.8*10**(-5);# [kg/m.s]\n",
+ "Pt1 = 10133.0;# [N/square m.sec]\n",
+ "T = 300;# [K]\n",
+ "z = 0.0254;# [m]\n",
+ "T2 = 393.0;# [K]\n",
+ "#***********#\n",
+ "\n",
+ "Ma = 28.02;# [kg/kmol]\n",
+ "R = 8314.0;# [J/K.kgmol]\n",
+ "#From Eqn 4.22\n",
+ "Lambda = (3.2*viscosity1/Pt1)*(R*T/(2*(math.pi)*Ma))**0.5;\n",
+ "d = 10**(-4);# [m]\n",
+ "d_by_lambda = d/Lambda;\n",
+ "# Kundsen flow will not occur\n",
+ "# N2 flow corresponding to 9 cubic ft/square ft.min at 300K & 1 std atm = 0.0457 cubic m/square m.min\n",
+ "Na1 = 0.0457*(273.0/T)*(1/22.41);# [kmol/square m.s]\n",
+ "Pt1_diff_Pt2 = 2*3386/13.6;# [N/square m]\n",
+ "Ptav = Pt1+(Pt1_diff_Pt2/2.0);# [N/square m]\n",
+ "# From Eqn. 4.26\n",
+ "k1 = Na1*R*T*z/(Ptav*(Pt1_diff_Pt2));# [m**4/N.s]\n",
+ "\n",
+ "#For N2 at 393K\n",
+ "viscosity2 = 2.2*10**(-5);# [kg/m.s]\n",
+ "k2 = (k1*viscosity1)/(viscosity2);# [m^4/N.s]\n",
+ "# From Eqn 4.26\n",
+ "Na = (k2*Ptav*Pt1_diff_Pt2)/(R*T2*z);# [kmol/square m.s]\n",
+ "print\"Flow rate to be expected is\",round(Na,6),\" kmol/square m.s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 4.6 - Page: 100\n",
+ "\n",
+ "\n",
+ "Flow rate to be expected is 0.001159 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter5.ipynb b/Mass_-_Transfer_Operations/Chapter5.ipynb
new file mode 100755
index 00000000..b860f069
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter5.ipynb
@@ -0,0 +1,384 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:7993b2e1b1cd4e665f8a316544ebc9b7c9af58ff099db1ddb24eed947b6e95bc"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 5: Interphase Mass Transfer"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex5.1: Page 114"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 5.1\n",
+ "# Page: 114\n",
+ "\n",
+ "print'Illustration 5.1 - Page: 114\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = NH3, b = H2O\n",
+ "d = 2.54*10**(-2);# [m]\n",
+ "Yag = 0.80;\n",
+ "Xal = 0.05;\n",
+ "T = 273+26.7;# [K]\n",
+ "Kl = 2.87*10**(-5);# [kmol/square m.s.(kmol/cubic m)]\n",
+ "Sh = 40;\n",
+ "Da = 2.297*10**(-5);# [square m.s]\n",
+ "P = 1.0133*10**(5);# [N/square m]\n",
+ "Xbm = 1.0;\n",
+ "#*********#\n",
+ "\n",
+ "Ma = 18.0;# [kg/kmol]\n",
+ "# Liquid:\n",
+ "# Because of large conc. of ammonia in gas F's rather than k's are used.\n",
+ "# Molecular weight of water and ammonia are nearly same.\n",
+ "# The density of the solution is practically that of water.\n",
+ "MolarDensity1 = 1000/Ma;# [kmol/cubic m]\n",
+ "# Kl is determined for dilute soln. where Xbm is practically 1.0\n",
+ "Fl = Kl*Xbm*MolarDensity1;# [kmol/square m.s]\n",
+ "Ma = 18;# [kg-/kmol]\n",
+ "# Gas:\n",
+ "MolarDensity2 = (1/22.41)*(273/(273+26.7));# [kmol/cubic m]\n",
+ "Fg = Sh*MolarDensity2*Da/d;# [kmol/square m.s]\n",
+ "\n",
+ "# Mass Transfer Flux\n",
+ "# Th eqb. distribuion data for NH3 from \"The Chemical Engineers Handbook\" 5th Edt. p3-68:\n",
+ "# Data = [Xa,pa]\n",
+ "# Xa = NH3 mole fraction in gas phas\n",
+ "# pa = NH3 partial pressure in N/square m\n",
+ "Data = [(0 ,0),(0.05 ,7171),(0.10, 13652),(0.25 ,59917),(0.30 ,93220)];\n",
+ "\n",
+ "X = numpy.zeros(5);\n",
+ "for i in range(1,5) :\n",
+ " X[i]=Data[i][0]\n",
+ " \n",
+ "\n",
+ "# Ya_star = mole fraction of NH3 in gas phase at eqb.\n",
+ "Ya_star = numpy.zeros(5);\n",
+ "for i in range(0,5) :\n",
+ " Ya_star[i] = (Data[i][1])/P\n",
+ "\n",
+ "# For transfer of only one component\n",
+ "Na_by_SummationN = 1.0;\n",
+ "Ya = numpy.zeros(5);\n",
+ "for i in range(0,5):\n",
+ " Ya[i] = 1-((1-Yag)*(1.0-Xal)/(1-Data[i][0]));\n",
+ "\n",
+ "plt.plot(X,Ya_star,'g',label='Equilibrium Line')\n",
+ "plt.plot(X,Ya,'r',label='Operating Line')\n",
+ "ax = pylab.gca()\n",
+ "ax.grid('on')\n",
+ "ax.set_xlabel('Xa = mole fraction of NH3 in liquid phase');\n",
+ "ax.set_ylabel('Ya = mole fraction of NH3 in gas phase');\n",
+ "pylab.legend(loc='lower right')\n",
+ "plt.title('Ya Vs Xa');\n",
+ "plt.show()\n",
+ "\n",
+ "# From intersection of operating line & Eqb. line\n",
+ "Xai = 0.274;\n",
+ "Yai = 0.732;\n",
+ "\n",
+ "# From Eqn.5.20\n",
+ "Na = Na_by_SummationN*Fg*log((Na_by_SummationN-Yai)/(Na_by_SummationN-Yag));# [kmol NH3 absorbed/square m.s]\n",
+ "print\"Local mass transfer flux for ammonia is \",round(Na,6),\" kmol/square m.s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 5.1 - Page: 114\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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tRCQOeBFIBk4DrhaRpn7WewaYCRTLa+u83i7q5fiKWmyqykdrP+K0l09j2/5t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\nrPVZ5zLgLQBV/UZEEkSkpqr+nteODx4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+ "text": [
+ "<matplotlib.figure.Figure at 0x7a704e0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Local mass transfer flux for ammonia is 0.00043 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex5.2: Page 130"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Illustration 5.2\n",
+ "# Page: 130\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "\n",
+ "print'Illustration 5.2 - Page: 130\\n\\n'\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data***#\n",
+ "# Eqb. data\n",
+ "# Data = [Wt% of moisture in the soap,Partial pressure of water in air(mm Hg)]\n",
+ "Data = [(0,0),( 2.40, 9.66),(3.76 ,19.20),(4.76 ,28.4),(6.10, 37.2),(7.83, 46.4),(9.90, 55.0),(12.63, 63.2),(15.40, 71.9),(19.02 ,79.5)];\n",
+ "P = 760.0;# [mm Hg]\n",
+ "# Initial air\n",
+ "p1 = 12;# [mm Hg]\n",
+ "T = 273+75.0;# [K]\n",
+ "#******#\n",
+ "\n",
+ "# Y = kg water/kg dry air\n",
+ "# X = kg water/kg dry soap\n",
+ "# E = Air water phase\n",
+ "# R = Soap water phase\n",
+ "Y = numpy.zeros(10);\n",
+ "X = numpy.zeros(10);\n",
+ "for i in range(1,10):\n",
+ " Y[i] = Data[i][1]/(P-Data[i][1])*(18.02/29);\n",
+ " X[i] = Data[i][0]/(100.0-Data[i][0]);\n",
+ "\n",
+ "\n",
+ "print'Illustration 5.2 (a)\\n\\n'\n",
+ "\n",
+ "import pylab\n",
+ "# Soln. (a)\n",
+ "# First operation\n",
+ "Y1 = p1/(P-p1);# [kg water/kg dry soap]\n",
+ "# Initial Soap\n",
+ "S1 = 16.7/(100-16.7);# [kg water/kg dry soap]\n",
+ "# Final soap\n",
+ "S2 = 13.0/(100-13);# [kg water/kg dry soap]\n",
+ "Rs = 10.0*(1-0.167);# [kg dry soap]\n",
+ "# Using ideal gas law\n",
+ "Es = 10.0*((760-p1)/760.0)*(273.0/T)*(29.0/22.41);# [kg dry air]\n",
+ "slopeOperat = -Rs/Es;\n",
+ "\n",
+ "def f2(x):\n",
+ " return slopeOperat*(x-S1)+Y1\n",
+ "x = numpy.arange(S1,S2,-0.01);\n",
+ "X1=S2;\n",
+ "def f3(S):\n",
+ " return slopeOperat*(S-X1)+Y1\n",
+ "S=numpy.arange(0,S1,0.01);\n",
+ "\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f2(x),'g',label='First Process')\n",
+ "plt.plot(S,f3(S),'r',label='Second Process')\n",
+ "ax = pylab.gca()\n",
+ "plt.title(\"Illustration 5.2(a)\")\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "plt.show()\n",
+ "\n",
+ "# Results for First Process\n",
+ "# The condition at abcissa S2 correspond to the end of first operation\n",
+ "print \"Conditions corresponding to First Operation \\n\"\n",
+ "print \"X = kg water/kg dry soap\\n\",S2\n",
+ "print \"Y = kg water/kg dry air\\n\",f2(S2)\n",
+ "\n",
+ "# Results for Second Process\n",
+ "# The point at which the line meets the equilibrium line corresponds to the final value\n",
+ "X2 = 0.103;\n",
+ "Y2 = (X2/(1+X2));\n",
+ "print\"Final moisture content of soap is \",round(Y2*100,3),'%'\n",
+ "\n",
+ "\n",
+ "print'\\n\\n Illustration 5.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "Rs = 1*(1-0.167);# [kg dry soap/h]\n",
+ "# Entering soap\n",
+ "X1 = 0.20;# [kg water/kg dry soap]\n",
+ "# Leaving soap\n",
+ "x = 0.04;\n",
+ "X2 = x/(1-x);# [kg water/kg dry soap]\n",
+ "# Entering air\n",
+ "Y2 = 0.00996;# [from Illustration 5.2(a), kg water/kg dry air]\n",
+ "# The operating line of least slope giving rise to eqb. condition will indicate least amount of air usable.\n",
+ "# At X1 = 0.20; the eqb. condition:\n",
+ "Y1 = 0.0675;# [kg water/kg dry air]\n",
+ "\n",
+ "def f4(x):\n",
+ " return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
+ "x = numpy.arange(X2,0.24,0.01);\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f4(x),'g',label='Operating line')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.title(\"Illustration 5.2(b)\")\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "plt.show()\n",
+ "# By Eqn. 5.35\n",
+ "\n",
+ "Es = Rs*(X1-X2)/(Y1-Y2);# [kg dry air/h]\n",
+ "Esv = (Es/29)*22.41*(P/(P-p1))*(T/273.0); #[cubic m/kg dry soap]\n",
+ "print\"Minimum amount of air required is\",round(Esv,4),\" cubic m/kg dry soap\\n\\n\"\n",
+ "\n",
+ "print'Illustration 5.2 (c)\\n\\n'\n",
+ "\n",
+ "# solution (c)\n",
+ "\n",
+ "Esnew = 1.30*Es;# [kg dry air/h]\n",
+ "Y1 = Rs*((X1-X2)/Esnew)+Y2;\n",
+ "\n",
+ "def f5(x):\n",
+ " return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
+ "x = numpy.arange(X2,0.24,0.01);\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f5(x),'g',label='Operating line')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.title(\"Illustration 5.2(c)\")\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "plt.show()\n",
+ "# with final coordinates X = X1 & y = Y1\n",
+ "# From figure, Total number of eqb . stages = 3\n",
+ "N = 3;\n",
+ "print\"Moisture content of air leaving the drier is \",round(Y1,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Total number of eqb. stages = \",N\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 5.2 - Page: 130\n",
+ "\n",
+ "\n",
+ "Illustration 5.2 (a)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Qd7lcOn/+/KyRwvr167POBTJS8PYm7q0db3rVrFlTVVX79OmjAwYMOOs+u3bt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\nAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7a26828>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Conditions corresponding to First Operation \n",
+ "\n",
+ "X = kg water/kg dry soap\n",
+ "0.149425287356\n",
+ "Y = kg water/kg dry air\n",
+ "0.0586080045715\n",
+ "Final moisture content of soap is 9.338 %\n",
+ "\n",
+ "\n",
+ " Illustration 5.2 (b)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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kzes0Hj/yCOTJ4/v4E9c9+Gj1RwxrNoyih4ry3tvv+ez4aZWPiYlJmjo72P5/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\nWfgMr976Kk/VeSrD01wbYwIjrSuF+7M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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c30128>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Minimum amount of air required is 2.2941 cubic m/kg dry soap\n",
+ "\n",
+ "\n",
+ "Illustration 5.2 (c)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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GpENkJEyYAB9/DDfe6Iw8fvJJCMo6Tdp+7+CZg/Re1Ju5u+fSL6QfL9V4KcOW\nwvQHyf0m3wKPAetIfB6jMl6JyKRbYvOimOxdL8ePO2sYjBgBdevCuHFQr55no46zc72kVWJ1cu7y\nOQYuH8jw34fToWYHdr22i/zX5s/84LzMBq8Zk4Xt2uVcFXz3HTRv7jQTVazo66iyl5jYGMZtGEef\nxX14sMyDDHhoAKULZO3h3ekevCYihYDyQO6411T114wJzxiTWsuXO/0Fy5c7o4537oSiRX0dVfYz\nb888us7rSsHcBZnx7AzuKnGXr0PyuhSTgoi8DLwOlALW49yNtAJ40LuhGWPcuXceHzvmXBVMmgR5\ns95dj35v6/GtdJ3fld0ndzOwwUCerPRktuhE9kSKzUcisgW4C2cq6+oiUgl4X1WfyowAk2PNRyYQ\nWOdx5jl27hh9w/syfft0et/Xm053dSJXUC5fh5Xh0tt8dFFVL4gIIpJbVXeIiLVaGuNl6ek8Nqlz\nIeoCn6z8hI9XfMzz1Z5nR+gObrjuBl+H5ROeDGo/4OpT+BGYLyIzgQivRmXSxaZzSFxWqZddu5x+\ngooVnSkpli6FH3+Ee+/1TkLIKvXiDXHLYFYaXol1R9ax6qVVfNzwYzat2uTr0HwmxaSgqk+p6ilV\n7Qe8BXwJPOntwEzaBfIfeXL8vV6WL4ennnI+/IsWdTqPR43y/t1E/l4v3rJ031Jqf1mbz1Z/xtdP\nf83U5lO59YZbgcCtE0jl1NmqGu6lOIwJSNZ5nPn++PsPeizowboj63j/ofdpUaUFOcRmAoyTfYbh\nGZOFWOdx5jt54STvLHmHSZsm0bVuV75++muuy3mdr8PyO5YUjMlEf/3ldB5//jncc491HmeGyzGX\nGb56OO8ve59mtzVj26vbKJrXBnUkxZNxCq8DE1X1VCbEY0y2lHDk8dKlNvLY21SV6dun02NBDyoW\nrkh4m3BuK3Kbr8Pye55cKRQDfheRdcBYYK6ngwNEpBEwBAgCvlTVDxMpMxR4FIgE2qjqetfrETiT\n8cUAUapqazp4yOaySZwv6iVu5PGyZdCpk7OmQbFimR5GsrLj+2X1odWEzQvjzKUzjHx8JA3KNkjV\n/tmxTjzl0dxHIpIDeARoA9QCvgfGqOqeZPYJAnYCDYBDwO9AS1Xd7lamMRCqqo1FpDbwqarWcW37\nE6ipqieTOYcNXjN+J7HO4zZtrPM4M+w7vY9eC3uxZN8S+tfvzwvVXshSq55lluQGr3nU5a6qscBR\n4BjON/d5mAY4AAAgAElEQVRCwFQR+SiZ3e4GdqtqhKpGAZOBJxKUaQpMcJ1jFVBQRNy/R1lLq8ky\nIiOdgWaVKsGHHzrJYNcuePVVSwje9s/Ff+i5oCc1Rtegwo0V2Bm6k3Z3trOEkAae9Cl0Bp4H/sYZ\no9BVVaNcVw9/AN2S2LUEcMDt+UGgtgdlSuAkHwUWiEgMMEpVv0j51zEm8yXsPB471nsDzcyVomOj\n+WLtF7y95G0al2/Mpo6bKJHf1hdND0/6FG4AnlbVfe4vqmqsiDRJZj9P23WS+tO5V1UPi0gRnJHU\nO1R1acJC7m1/wcHBBAcHExISkmibYHh4eKKDUqy8lU9L+b//dpa43LEjhFatQvj1V+cqIavEn5XL\nqyp/nPyD+XvmU7paaX7p/AvVi1fPMvFndvm47Z5Itk9BRK4Btqpqqu+TEJE6QD9VbeR63guIde9s\nFpGRQLiqTnY93wE8oKrHEhyrL3BOVQcneN36FEym++03Z83juM7jV1/1v87j7Gzj0Y2EzQvj0NlD\nDHp4EI3LNw6YGUwzSpr7FFQ1GtghIrek4bxrgPIiEiwiuYAWwMwEZWbiNE3FJZHTqnpMRPKISD7X\n63lxOrk3pyGGgBTIQ/STk556iYmB6dOdielat4YGDSAiAt55J+snhKzyfjl89jDtZrSj4aSGPF35\naTZ13MRjFR7zSkLIKnXiDZ50NN8AbBWRRSLyk+uR8MP9Kq6EEgrMBbYB36nqdhHpICIdXGVmA3tF\nZDcwCnjFtXtxYKmIbABWAbNUdV6qf7sAFchv6OSkpV4CofPY398v5y+fp194P6qOqErRvEXZGbqT\nV+56hZxBOb12Tn+vE2/ypE/hrbQeXFXnAHMSvDYqwfPQRPbbC1RP+LoxmUUVpk6Fzp3hrrus89gX\nYmJjmLBxAm8tfov7b7mfte3XElww2NdhZXspJgWbBM8EmsOHnSuBnTth2jTnjiKTuRbsXUDXeV3J\nmysv05tPp3bJhDcuGm9JMimIyDmSvoNIVTW/d0IyxjdUYcwYeOMNZz2DyZPh2mt9HVVg2f7XdrrN\n78b2E9v5sMGHPFP5GetEzmRJJgVVvR5ARN4FDgOTXJtaATd7PzRjMs+ePdC+PZw5AwsWwB13+Dqi\nwHL8/HH6hfdjyrYp9Lq3F9OaT+Paaywj+4InHc1NVfVzVT3jeozg6pHJxo8E8rwtyUmsXmJinInq\nateGxo2dcQeBlhB8+X65GH2RD5Z9wG3DbyNXUC52vLqDLvd08XlCCOS/oRTnPhKRFcBw4FvXS88C\nr6pqXS/HliIbp2DSY8sWePFFyJMHvvgCypXzdUSBQ1WZvGUyvRb2osZNNfiwwYeUv7G8r8MKGMmN\nU/AkKZQBPgXiksByoLOqRmRkkGlhScGkxaVL8P77ztQUAwbASy/ZXUWZafn+5XSZ14WY2Bg+bvgx\n999yv69DCjjJJQVP7j76E2fiOmOyvJUrnauDcuVgwwYoYdPkZJo9J/fQY0EPVh9azYCHBvDfqv+1\nZTD9kP2PmIBw/jz83//BU09B377w44+WEDLLqQun6DK3C7W/rE2Nm2qwM3Qnz93xnCUEP2X/Kybb\nW7AAqlZ1JrDbssVZ+cyai7zvcsxlhqwcQsVhFYmMimTrK1t54743bF1kP2dJIRsK5CH67k6dcpqK\nXnzRmda6XbtwbrzR11H5n4x+v6gqP2z/gds/v515e+ax+IXFjHx8JMWuzzqTRAXy31CKSUFEwkSk\ni+vfuJ9fFBGbhsJPBfIbOs706VClinNn0ZYt0KiR1UtSMrJe1hxeQ8iEEPqG92V44+HMbjWb24ve\nnmHHzyyB/F7xZO6jmjhLcP6Es/bBYzgzlnYUkamJrbtsjK8cPQqhoU4i+O47Z74i4337/9nPGwvf\nYNGfi3in/ju0rd7WVj3LojxpPioF1FDVMFXtgpMkigIP4KzZbIzPqcL48VCtmjOj6YYNlhAyw9lL\nZ+m9sDd3jrqTsoXKsuu1XbxU4yVLCFmYJ1cKRYDLbs+jgGKqGikiF70TljGe+/NP6NDB6UieOxeq\nW8Om10XHRjNm3Rj6LenHI7c+wsaOGymZv6SvwzIZwJOk8DWwSkR+xGk+agJ841r8Zps3gzMmOTEx\nMGwY9O8P3bs7ax1c48k72qSZqvLL7l/oOr8rRfMW5ef//kyNm2r4OiyTgTwZvNZfRH4B6uHMmtpB\nVde4NrfyZnAmbQJh3pZt25y7inLlcpbHrFAh5X0CoV7SwtN62XRsE13ndWXfP/v46OGPaFKhSbad\nwTSQ3yueTHPxoqqOSfDaB6ra06uRecCmuQg8ly87K6ANHQrvvgsvvww57MZqrzpy9ghvLX6Ln3b9\nxFv3v0WHmh28uuqZ8b50TXMBNBORS6o6yXWw4YCNPjGZ7vffnauD0qVh/XooaU3YXnX+8nkGrxjM\np6s+pV31duwM3UnB3AV9HZbxMk+SwtPATBGJAR4FTqlqO++GZcy/IiOhTx+YNAk++QSefdZGJHtT\nrMYyceNEei/qTb3S9Vjz8hrKFCrj67BMJklu5bUb3J6+BMwAlgFvi8gNqnoypYOLSCNgCBAEfJnY\nmAYRGYqTbCKBNqq63m1bELAGOKiqTTz7lUx2snix00RUuzZs3gxFivg6ouxt8Z+LCZsXxrXXXMuU\n/0zhnlK2FmmgSe5KYR1XLscZN3DtMdfrZZM7sOsDfRjQADgE/C4iM1V1u1uZxkA5VS0vIrWBEUAd\nt8N0xrnDKZ/Hv5HJFk6fdu4o+uUXGDECHnvM1xFlbztP7KTb/G5sPr6ZDx76gOa3N8+2ncgmeUl2\n0alqsKqWcXu4P082IbjcDexW1QhVjQImc/WKbU2BCa7zrQIKikgxABEpCTQGvsRJSMZDWX2I/uzZ\nzhQV11zjjEzOqISQ1evFG05EnuCpD56i3th63Ff6Pra/up0WVVoEfEII5PeKN+/bKAEccHt+0PWa\np2U+AboBsd4KMLvKqm9oVRg40Fkr+euvnUns8ufPuONn1XrxhovRF/lo+UdUHl6ZQ5sPsSN0B93q\ndSP3Nbl9HZpfCOT3ijeH+nh6r2jCryQiIo8Dx1V1vYiEJLez+/3EwcHBBAcHExISkuh9xuHh4Yn+\nZ1t535evWzeEjh2du4pWrnTuLMpK8WeV8osXL2brX1tZsHcBxa4vRouyLTiX8xyF8xTOEvFnVvnw\n8HD69evnN/Gkt3zcdo+oaqIPIGdS2zx54PQN/OL2vBfQI0GZkcCzbs93AMWBAThXEH8CR4DzwFeJ\nnEPN1fr27evrEFLlr79U779f9cknVc+e9d55slq9ZLTf9v+mdb6so3eOvFMX7V0U/3qg10tisnud\nuD47E/3sTq75aIWIzBCRjiIS7FmKucIaoLyIBItILqAFMDNBmZnA8wAiUgc4rapHVfUNVS2lqmWA\nZ4FFqvp8GmIwfm7HDqhTB+65B6ZNg+uv93VE2c/eU3tpPqU5zac2p1OtTqxpv4b6Zer7Oizjp5Js\nPlLVWiJSBmgEDHF1/C4F5gBLVPVScgdW1WgRCQXm4tySOkZVt4tIB9f2Uao6W0Qai8hunKuBtkkd\nLtW/mfF7CxZAq1bwwQfQNqn/eZNmpy+e5r1f32PshrH8r/b/GP/kePLkzOPrsIyfS7ZPQVX/xLlN\ndITr2/59OEniXRH5S1WTvS9EVefgJBH310YleB6awjGWAEuSK2OulBXmbRk5Evr1g++/hwceyJxz\nZoV6yQhRMVGMXDOSd5e+S9MKTdnSaQs35bspyfKBUi+pEch1kuLcR0nuKFJSVQ9mcDypjUHTGr/x\njZgYCAtzxh/MmgXlyvk6ouxDVflp1090m9+N4ILBDHp4EFWLVfV1WMYPpXfuo0T5OiGYrOfMGWjZ\nEi5dghUroFAhX0eUfaw7so6weWH8df4vPm30KY3KNfJ1SCaLsvklTaaIiIB69aBUKZgzxxJCRjl4\n5iAv/PgCj33zGC2rtGRDxw2WEEy6JJsURCRIRAZlVjAme1qxAurWhZdecqasyGmzLqfbucvneGvR\nW1QbWY1S+UuxK3QX7Wu255octsqQSZ+UOppjRORescZ7k0bffAOdOzvrJ9v8RekXExvDuA3j6LO4\nDw+VfYgNHTZQqkApX4dlshFPmo82ADNEpLWIPON6PO3twEza+cMQ/dhYZ7rrN96ARYv8IyH4Q72k\nx7w987hz1J1M3DSRmS1nMvGpiRmSELJ6vXhDINeJJ0khN3ASeBB43PWwaaz9mK/f0BcuOB3K8+fD\nqlVQ1U9ugPF1vaTV1uNbefTrRwmdHco79d8h/IVwat1cK8OOn1XrxZsCuU48WaO5TSbEYbKJo0fh\niSfg1ludtRBy2/xqaXbs3DH6hvdl+vbp9L6vN52e7USuoFy+DstkcyleKYhIRRFZKCJbXc/vEJE3\nvR+ayWo2bnQWw3nsMWeWU0sIaXMh6gIDlg7g9s9vJ2/OvOwM3UnnOp0tIZhM4Unz0RfAG8Bl1/PN\nQEuvRWSypJ9+ggYNnKmv+/Sx5TLTIlZjmbRpEhWHVWT90fWsemkVgxsOptB1dv+uyTye3L+WR1VX\nxS26oaoqIlHeDctkFarw8ccweLAzQrl2bV9HlDX9uu9XwuaFESRBfPvMt9QrXc/XIZkA5UlS+EtE\n4icjEJFmONNZGz+VWfO2XL4Mr74Kq1c7ayCULp0pp00zf5zPZtffu+ixoAfrj6zngwYf0OL2zF/1\nzB/rxdcCuU5SnPtIRG4FRgP3AKdx1jhopaoRXo8uBTZ8wndOnoRmzSBvXmcsQj5bRTtV/o78m3eW\nvMPXm7+mW91udK7T2VY9M5kmubmPPOlTiFXVh4CiQCVVrYetmRzQdu1y1kCoUQN+/NESQmpcir7E\n4N8GU2l4JaJjo9n+6nZ63NvDEoLxG540H00H7lTVc26vTQVqeick488WLXLGILz7Lrz8sq+jyTpU\nlanbptJzYU9uK3Ibv7b5lcpFKvs6LGOukmRSEJHKwG1AAdcIZsFZ7CY/zoA2E2C++ALefBO+/RYe\nfNDX0WQdKw+uJGxeGOcvn2f046N5qOxDvg7JmCQld6VQAWfkcgGuHMF8FrDviAEkJga6d3duO126\nFCpU8HVEWUPE6Qh6LezFr/t+5d367/J8tecJyhHk67CMSVaSfQqqOsM1mrmJqrZ1e7yuqr9lXogm\ntTJyiH5MjLNk5rp1zh1GWTkhZNbUBf9c/Ice83tQc3RNKt1YiV2hu2h7Z1u/TQiBPKVDUgK5Tjzp\naF4vIqEi8rmIjBORsSIy1uuRmTTLqDd0bKwz3fXffztrINxwQ4Yc1me8/YceFRPF8NXDqTCsAn9F\n/sXmTpvpG9KXvLnyevW86RXIH4BJCeQ68SQpTASK4azNHA6UAs4lt0McEWkkIjtE5A8R6ZFEmaGu\n7RtF5E7Xa7lFZJWIbBCRbSLyvke/jckwqvD66/DHH84dRjZlRdJUlVm7ZlF1RFV+2PEDc5+by9gn\nxnJzvpt9HZoxqebJ3UflVLWZiDyhqhNE5BtgWUo7iUgQMAxoABwCfheRmaq63a1MY9fxy4tIbWAE\nUEdVL4pIfVWNFJFrgGUicq+qpnhek36q0LOn01y0cKEzFsEkbsPRDYTNC+PI2SMMfmQwjcs3zvTB\nZ8ZkJE+uFOLmPPpHRKoCBYEiHux3N7BbVSNUNQqYDDyRoExTYAKAqq4CCopIMdfzSFeZXEAQzvTd\nJhO8+y7Mng1z50KBAr6Oxj8dOnOItjPa0mhSI5pVbsamTpt4rMJjlhBMlufRhHgicgPwJjAT2AYM\n9GC/EsABt+cHXa+lVKYkxC8FugE4BixW1W0enNOk08cfw8SJzloIN97o62j8z7nL5+i7uC93jLyD\n4nmLszN0J53u6mTLYJpsw5P1FL5w/bgEKJOKY3s6/0TCr1bqOm8MUF1ECgBzRSREVcMT7uw+R0lw\ncDDBwcGEhIQkOndJeHh4oh1I2a183M+pPX5YWDhjxoTTti2MHOm7+L1VPuE+qTl+TGwMvcb0YsTU\nEQQXDKZ1mdZcu+xaPln2id/+vp6WL1iw4FWv+TIefygfERFBv379/Cae9JaP2+4JT+Y+2gOsBJYC\nS1V1q0cHFqkD9FPVRq7nvXCmzPjQrcxIIFxVJ7ue7wAeUNVjCY71FnBBVQcleN3mPsogkyY5/Qjh\n4VCuXIrFA8qCvQsImxdGvlz5+Ljhx9xd4m5fh2RMuiQ395En17y3A7WBe4FBIlIR2KSqT6aw3xqg\nvIgEA4eBFly9DsNMIBSY7Eoip1X1mIgUBqJV9bSIXAc8DLztQawmDaZPh65dnU5lSwj/2vbXNrrN\n78bOEzv5sMGHPF35aeszMNmeJ0khGogCYoBY4DhOO3+yVDVaREKBuTgdxWNUdbuIdHBtH6Wqs0Wk\nsYjsBs4DbV273wRMEJEcOP0eE1V1YSp/N+OBX36Bjh2df2+/3dfR+Ifj54/Td3Ffpm2fRq97e/FD\nix9s1TMTMDxpPorEWW3tY2Chqp7IjMA8Yc1H6bNkiTP99YwZULeur6PxvQtRFxiycgiDVwym9R2t\neeuBt7jhuiw+Ys+YRCTXfORJUngCuA+4C+eK4TfgV1VdkNGBppYlhbRbtQoefxwmT4aHAnx+tliN\nZfKWybyx8A1q3lyTDxt8SLkbrB3NZF/pWk/BNQdSV6ADMBtoA8zK0AhNhkrpLoONG6FpUxg/PrAS\nQmL1smz/Mup8WYdPVn7CxKcmMq35tIBLCIE8pUNSArlOUkwKIjLNdQfSUCAP0BqwlcT9WHJv6B07\n4NFHYdgweOyxzIvJH7jXy+6Tu3nm+2doNb0VnWt3ZtVLq7jvlvt8F5wPBfIHYFICuU486Wj+AFiv\nqtHeDsZ419698PDD8P778J//+Doa3zh54ST9l/Rn4qaJhN0TxqSnJnFdzut8HZYxfsOT5qPfLSFk\nfQcPQoMG0KsXvPCCr6PJfJdjLrPiwAoqDavEhegLbH1lK73u62UJwZgEbGx+ADh+3EkInTrBK6/4\nOprMpapM3z6dHgt6IKeExS8s5vaidu+tMUmxpJDNnTzpNBm1aAHduvk6msz1+6Hf6TKvC/9c/IcR\nj41g+cnllhCMSUGKSUFEanL1PEb/APusWck/xc11cvas06ncoAG4pnEJCPtO7+ONRW+w+M/F9K/f\nnzbV2xCUI4icITl9HZpfSmwenUAXyHXiyTiFlUBNYJPrparAVpy1mzup6lyvRph8bDZOIQmRkU5C\nqFwZRoyAQJid4cylM7y/9H1GrxvNq3e9Svd63bk+1/W+DssYv5OucQo48xZVV9WaqloTqA7sxZmP\nyJMptE0mu3QJnnkGSpeGzz/P/gkhOjaakWtGUnFYRY6cO8LGjht5p/47lhCMSQNP+hQqus+Mqqrb\nRKSSqu4REfua7meio6FlS7juOhg3DnJ4kvazKFVlzu45dJvfjWJ5izH7v7O586Y7fR2WMVmaJ0lh\nq4iMwFk5TYDmwDYRuRZn2gvjR7p1c5qOZsyAa7LxbQSbjm0ibF4Y+//Zz0cPf0STCk1sBlNjMoAn\nfQp5gFeAeq6XlgOfAxeBvKp61qsRJh+b9Sm4WbwYnnsONm3KvqumHTl7hDcXvcmsP2bx1v1v0aFm\nB3IGWQeyMamR3j6Fyqo6SFWfcj0GAQ+qaqwvE4K50tmz0K4djB4NmzeH+zqcDHf+8nneWfIOVUZU\n4cY8N7IzdCehd4emKiEE8tQFybF6uVog14mnazRXjXsiIi2BPt4LyaRFWJgzud1jj2WvN3SsxjJ+\nw3gqDqvItr+2seblNQx8eCAFcye+hGRyslO9ZCSrl6sFcp140urcDJgqIv/FmUL7eZw7j4yfmDMH\n5s1zmo2yk0V/LiJsXhjXXXMdU5tPpU7JOr4OyZhsL8WkoKp7XVcHPwL7gIaqGun1yIxHTp6El1+G\niRMhf35fR5MxdpzYQff53dlyfAsfNviQZrc1s05kYzJJkklBRDYneOkGnOamVa4O3ju8GpnxyGuv\nOWMS6tf3dSTp99f5v3h7ydt8t/U7etbryZT/TOHaa671dVjGBJTkrhSaZFoUJk2mToXff4cNG3wd\nSfpcjL7I0FVD+ei3j/hvlf+y49Ud3Jgnm94+ZYyfSzIpqGpERpxARBoBQ4Ag4EtV/TCRMkOBR4FI\noI2qrheRUsBXQFGcuZdGq+rQjIgpOzh2DEJD4YcfIE+eK7dllXlbVJXvt35Pz4U9qV68OsvbLafC\njRW8dr6sUi+ZzerlaoFcJymOU0jXwUWCgJ1AA+AQ8DvQUlW3u5VpDISqamMRqQ18qqp1RKQ4UFxV\nN4jI9cBa4MkE+wbkOAVVeOopZ16j99/3dTRp89uB3wibF8blmMt8/MjHPBD8gK9DMiZgJDdOwdtj\nXu8GdsdddYjIZOAJYLtbmabABABVXSUiBUWkmKoeBY66Xj8nItuBmxPsG5AmTnRWUfvuO19Hknp7\nT+2l54KerDy4kvcefI9Wd7Qih2TjuTiMyWK8/ddYAjjg9vyg67WUypR0LyAiwcCdwKoMjzCLOXAA\nunaFr76Ca7NQH+ypC6foOq8rd39xN9WKVWNH6A5aV2ttCcEYP+PtKwVP23YSXsbE7+dqOpoKdFbV\ncwl3dG/7Cw4OJjg4mJCQkETbBMPDwxMdlJJVyi9eHM5LL4VTpQr8+KPz8Pf4L8dcZuSakby39D3u\nunwXbc61IWpRFAMXDUy0vL/Fb+WtfHYoH7fdE97uU6gD9FPVRq7nvYBY985mERkJhKvqZNfzHcAD\nqnpMRHICs4A5qjokkeMHVJ/CyJEwdiz89pv/T3anqszYOYPu87tTtlBZBj0yiCpFq/g6LGMM6Z/7\nKD3WAOVFJFhEcgEtgJkJyszEGSUdl0ROuxKCAGOAbYklhECzZw+89ZbTbJRSQvD1EP01h9cQMiGE\ntxa/xWePfsYvz/3iFwnB1/Xir6xerhbIdeLVpOBarjMUmAtsA75T1e0i0kFEOrjKzAb2ishuYBTO\njKzgzMr6HFBfRNa7Ho28Ga+/iomBNm3gjTegUqWUy/vqDX3gnwO0/qE1Tb5twnNVn2N9h/U0LNfQ\nJ7EkJpD/0JNj9XK1QK4TrzdCqOocYE6C10YleB6ayH7L8P6VTJYwZIizWE7nzr6OJHFnL53lg2Uf\nMHLtSDrV6sSu0F3kuzafr8MyxqSBn7dMm23bnLEIq1f73ypq0bHRjFk3hn5L+vFw2YfZ0GEDpQqU\n8nVYxph0sKTgx6Ki4Pnn4b33oGxZX0dzpV92/0LXeV0pnKcws1rOoubNNX0dkjEmA1hS8GPvvw+F\nC0P79r6O5F+bj22m6/yu/HnqTz56+COaVmxqM5gak41YUvBT69bBsGGwfj2k9jPXG/O2HD13lLcW\nvcWMnTN48/436VirI7mCcmX4ebwpkOezSY7Vy9UCuU68Ok7B27LrOIVLl6BmTejZ01lz2ZcioyL5\neMXHfLLyE9pWb0vv+3pT6LpCvg3KGJMuvpz7yKRBr15QoQK0auW7GGI1lkmbJtF7UW/uKXkPv7/8\nO2UL+VnHhjEmw1lS8DOffw6zZjmjln3VVB8eEU7YvDByBeXiu2bfUbdUXd8EYozJdJYU/Mj06c6d\nRkuXOh3MmW3X37voPr87G49t5IOHPqD57c2tE9mYAONnd74HrmXLoGNH+OmnzL/99ETkCV6f8zr1\nxtajXql6bH91Oy2qtLCEYEwAsqTgB7Ztc9ZZ/vprqFEj/cfzdIj+pehLDPptEJWHVyZWY9n2yja6\n1etG7mtypz8IPxTIUxckx+rlaoFcJ5YUfOzgQXj0URg0CB5+OGOOmdIbWlWZsnUKlYdX5td9v7K0\n7VKGNR5GkbxFMiYAPxXIf+jJsXq5WiDXifUp+NDp005CeOUVaN06c8658uBKwuaFERkVyZimY6hf\npn7mnNgYkyVYUvCRS5ecdZbr14fu3b1/vj9P/Umvhb1Ytn8Z7z74Lq3vaE1QjiDvn9gYk6VY85EP\nxMY6cxoVLgyffOLdW09PXzxN9/ndqfVFLW4rchs7Q3fSpnobSwjGmETZlUImU4WwMDh6FObOhSAv\nfTZHxUQxeu1o3vn1HZpUaMKWTlu4Kd9N3jmZMSbbsKSQyQYPhvnznbEIub1wk4+qkqtsLqqOqEqp\nAqWY33o+dxS7I+NPlAUF8nw2ybF6uVog14nNfZSJvvnGmc/ot9+gZMmMP/76I+sJmxfG0XNHGfTI\nIB4t96iNNTDGXMXmPvIDCxfC//2f829GJ4RDZw7Re1Fv5u6ZS98H+vJSjZe4Jof91xpjUs/rHc0i\n0khEdojIHyLSI4kyQ13bN4rInW6vjxWRYyKy2dtxetOGDdCyJUyZAlUycP36c5fP0WdxH+4YeQc3\n57uZnaE76ViroyUEY0yaeTUpiEgQMAxoBNwGtBSRygnKNAbKqWp5oD0wwm3zONe+WVZEBDz2mDPR\n3f33Z8wxY2JjGLNuDBWHVWTvqb2s77CeAQ8NIP+1+TPmBMaYgOXtr5R3A7tVNQJARCYDTwDb3co0\nBSYAqOoqESkoIsVV9aiqLhWRYC/H6DV//w2NGjn9CM2aZcwx5++ZT9f5XSlwbQF+bPEjd5W4K2MO\nbIwxeL/5qARwwO35QddrqS2T5URGQpMm8OST8Npr6T/e1uNbafx1Y16Z/Qp9H+jLkjZLkkwIgTxE\nPzlWL4mzerlaINeJt5OCp7cGJewFzzq3FCUiOtrpQyhXzllnOT3OXjpLx1kdqT+hPo/c+ghbX9nK\n05WfTvauokB+QyfH6iVxVi9XC+Q68Xbz0SGglNvzUjhXAsmVKel6zSPu9xMHBwcTHBxMSEhIovcZ\nh4eHJ/qfnZHlFy8OZ9YsZ16j//4X3n47fce/Lud1lMpfih2hO9i0ahMD+g/wavxWPvDKR0REXPWa\nL+Pxh/Lh4eH069fPb+JJb/m47R5RVa89cJLOHiAYyAVsAConKNMYmO36uQ6wMsH2YGBzEsdXf/PO\nO1JQfJ8AAAuKSURBVKo1aqieOeO7GPr27eu7k/sxq5fEWb1cLbvXieuzM9HPba9eKahqtIiEAnOB\nIGCMqm4XkQ6u7aNUdbaINBaR3cB5oG3c/iLyLfAAcKOIHAD6qOo4b8acHmPGwPjxsHw55Mvn62iM\nMSb1vH5Du6rOAeYkeG1UguehSezb0ouhZaiff4Y334QlS6B4cV9HY4wxaWOjnDLAqlXQtq2zlGaF\nCr6OJrDnbUmO1UvirF6uFsh1YnMfpYMqjB3rjEMYNw4ef9xnoRhjjMds7iMv+PNPePll5y6jBQug\nWjVfR2SMMelni+ykUkwMfPop3HUXPPIIrFxpCcEYk33YlUIqbN8OL74I11zjTH/tD/0HxhiTkexK\nwQNRUfDee86Edq1bQ3i4JQRjTPZkSSEF69Y5TUXLlsHatdCpE+Tw81oL5CH6ybF6SZzVy9UCuU78\n/OPNdy5ccO4qevRRZ03l2bOhdGlfR+WZQH5DJ8fqJXFWL1cL5DqxPoVELFvm9B1UqwabNkGxYr6O\nyBhjMoclBTdnz0KvXvDDDzBsGDz1lK8jMsaYzGXNRy5z50LVqs46CFu2WEIwxgSmgL9SOHkSunRx\n5iz64gt4+GFfR2SMMb4T0FcK06ZBlSqQPz9s3px9EkIgz9uSHKuXxFm9XC2Q6yQg5z46ehRefRW2\nbnWmu65XzwvBGWOMn0pu7qOAulJQddY7uOMOqFQJNmywhGCMMe4Cpk9h3z7o0AGOHXM6le+809cR\nGWOM/8n2Vwqxsc7tpTVrwgMPwOrVlhCMMSYp2fpKYedOZxCaqjMgrVIlX0dkjDH+LVteKURFwQcf\nOP0FLVrA0qWBlRACeYh+cqxeEmf1crVArhOvJgURaSQiO0TkDxHpkUSZoa7tG0XkztTsm5gNG6B2\nbVi0CNasgdde8/8J7DJaIL+hk2P1kjirl6sFcp147eNSRIKAYUAj4DagpYhUTlCmMVBOVcsD7YER\nnu6b0MWL0Lu3s/DN6687ncnBwRn9W2UNERERvg7BL1m9JM7q5WqBXCfe/A59N7BbVSNUNQqYDDyR\noExTYAKAqq4CCopIcQ/3jffbb07n8fbtsHEjtGkDkugduIEhkN/QybF6SZzVy9UCuU682dFcAjjg\n9vwgUNuDMiWAmz3YF4DOnWHKFBg6FJo1S3fMxhgT0LyZFDwdapyu7/SnTztTVNx4Y3qOYowxBryb\nFA4Bpdyel8L5xp9cmZKuMjk92BeAr74Svvoq3bFmOxLI7WfJsHpJnNXL1QK1TryZFNYA5UUkGDgM\ntABaJigzEwgFJotIHf6/vfOPkauq4vjnWwoUWmuoQTQhtqXGQk2N/GiIpQhiNEhBIlajFiGFNFUC\n1FgSNQGNsSYYjPEPlZZCbQ1QA1igxhKCBWytlE3Zll03orRpSyg/UpuC/ZFKicc/7pnZ18fM7szO\nzs7M2/NJbua++965c+/Zu+/cH3PPhbfM7E1J+2uQreq7IwiCIBgaTTMKZvaupJuBJ4ETgPvM7B+S\nFvn95Wa2XtIVknYAh4EFA8k2q6xBEARBoqO9pAZBEATDS9tu62rFxrdOoEG97JbUI2mbpK6RK3Vz\nGUwnks6W9Jyko5KW1CPbyTSol0K2FahJL/P9f6dH0mZJn6hVthCYWdsF0pTRDmAKadF5O3BO7pkr\ngPUevxDYUqtsp4ZG9OLXu4BJra5HC3RyOnABsBRYUo9sp4ZG9FLUtlKHXj4FvN/jl4+Gd0s2tOtI\nYcQ2vnUYQ9XLGZn7RVucH1QnZrbPzLYCx+qV7WAa0UuJorUVqE0vz5nZ2375POlXkTXJFoF2NQrV\nNrXV8kyljW952U6lEb1A2jvyZ0lbJS1sWilHllp00gzZdqfRuhWxrUD9erkRWD9E2Y6kXV1nj8jG\ntw6kUb3MMbPXJJ0OPCXpJTPbNExlaxWN/FKiyL+yaLRuF5nZ6wVrK1CHXiR9BrgBKJ3PWOT2UqZd\nRwqNbHyrRbZTGape9gKY2Wv+uQ94lDQc7nQa+XuP9rZSFTN73T+L1FagRr344vIK4ItmdqAe2U6n\nXY1CeeObpJNIm9fW5Z5ZB1wHkN34VqNspzJkvUg6VdL7PH088Hmgd+SK3jTq+XvnR1Cjva2UOE4v\nBW4rUINeJH0EWAtca2Y76pEtBK1e6a4WgC8A/ySt9v/A0xYBizLP/MrvvwicN5BsUcJQ9QKcRfq1\nxHbg70XSy2A6AT5Emgt+GzgAvAJMGO1tpZpeitxWatTLvcB+YJuHroFkixZi81oQBEFQpl2nj4Ig\nCIIWEEYhCIIgKBNGIQiCICgTRiEIgiAoE0YhCIIgKBNGIQiCICgTRiFoGb4JaMQ3RUm6WtI5w5TX\nVkkn5tJ2S5o0TPkfGo58gqBWwigEo5EvATPqEZB0QoW0qcBeSx4zswzn5p/35CWpXX2WBQUgjELQ\nFkg6S1K3pPPdzcJDkvokrZW0RdL5uednSfqDx6+WdETSWEnjJO309IWSuiRtl/SIpFMkzQauAu7y\nA2SmSpom6Qnv9W+UNN3lV0laJmkL8LMKxb4ceGKAOp3i+d7o13f4AS2bJD2YP9jGn5nqB9/0SFqa\nSb/U5R4H+iT9WNLizP2fSro1l9d4SX/y+vdK+qqnf9Z13SPpPnfZUCpflz+7PJPPs5J+6frqlTSr\nWp2DAtDqLdURRm8gHVbSC0wHuoGZnn4bcLfHP07y939eTnYssNPjPyf5vZ8NXAI84OmTMs//BLjZ\n478Frsnc2wB81OMXAhs8vork20ZVyv8YMKVC+i5gMvAUyX8OwCySy4STSK4k/gV8t4LsuozMTcBB\nj18KHAIm+/Vk4AWPjyG5XTgtl9eXgXsy1xOBcSR3FqX6rgYWe/y0zLO/A670+DPAco9fDPS2uu1E\naF6IkULQaj5Ierl+w8xK6wsXkQ4wwcz6gJ68kJm9C+yUdDbphfsL4NPAHKDk4nmm9657gPkcP2Uk\nAEkTSCdtPSxpG7CM5BMI0tTNw+Zvwyzeuz7TzHZXqJOAx4GVZnZ/pk6Pmdk7ZnYI+GOpDDlmA2s8\nfn/uXpeZ7fH67wH2S/okyWFdt/V78yzRA3xO0p2S5pjZf0gGeJf1O3pbTdIbwGU+KusBLuN4fa3x\n790ETJQ0sULZgwIQc5NBq3kL2EPqgb6USa/lrIyNpONHj5F6+6tJvebb/P4qkuvjXknXk3rbJUov\n+jEkT7LnUpkjVdIvpt/45DHgryTnaWsyadk6DeUskMO563uBBcAZwMr3FMLsZaUzuucCSyVtIBmr\nLCXjeDLwG9KIbK+kH5FGFdUIp2kFJUYKQat5B7gGuE7S1z1tM1Ca/54BzKwiuwn4DvA3M/s38AFg\nuo8uIE3TvOG/DrqW/hfZQdJUCt573iVpnn+flDmofQAGXE8AfggckPTrTJ2uknSyj07mUvnFuhn4\nmsfnD1KGR70cFwBP5m9K+jBw1MweIE2xnUvy8DlF0jR/7JvAsyQDYKTRxwTgK9msSG6ikTSHZEQP\nDlK2oEOJkULQaszMjki6knTC10FSj3W1pD7S6KGP5N45Txdp+mmjX79I6jWXuIO01rDPPyd4+u+B\nFZJuAeaRXr53S7qddCD7GvqnrKr1iC8Bbq9WJ6/YYkkrJd1pZt+XtM7zfZO0llKpTouBByV9j9Sr\nz37/cWUxs2OSngYOVJriIhnTuyT9jzSa+paZ/VfSAtJ02ViSDpd5XitIrrLfIOkr+71HJXWT3hk3\nVKl3UADCdXbQdkgaA5zoL7BppAXbj/k6QsuRdCZp4XVunXLjzeywpFOBvwALzWx7A+UYA7wAzDOz\nnUPNp4bveQZYYmbdzfqOoH2IkULQjowHnvZpHwHfbheDAGBmr5Kmf+rlHp8OGwesatAgzCAtVq9t\npkEIRh8xUgiCIAjKxEJzEARBUCaMQhAEQVAmjEIQBEFQJoxCEARBUCaMQhAEQVAmjEIQBEFQ5v+r\nNVWVme/qqwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7e5c940>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Moisture content of air leaving the drier is 0.0542 kg water/kg dry air\n",
+ "\n",
+ "Total number of eqb. stages = 3\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter5_1.ipynb b/Mass_-_Transfer_Operations/Chapter5_1.ipynb
new file mode 100755
index 00000000..756e424b
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter5_1.ipynb
@@ -0,0 +1,385 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:5b0eee15396b4ea69bab7ccfb1908ab0b8c6f2630bddedaada99660ed07a1ef9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 5: Interphase Mass Transfer"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex5.1: Page 114"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 5.1\n",
+ "# Page: 114\n",
+ "\n",
+ "print'Illustration 5.1 - Page: 114\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = NH3, b = H2O\n",
+ "d = 2.54*10**(-2);# [m]\n",
+ "Yag = 0.80;\n",
+ "Xal = 0.05;\n",
+ "T = 273+26.7;# [K]\n",
+ "Kl = 2.87*10**(-5);# [kmol/square m.s.(kmol/cubic m)]\n",
+ "Sh = 40;\n",
+ "Da = 2.297*10**(-5);# [square m.s]\n",
+ "P = 1.0133*10**(5);# [N/square m]\n",
+ "Xbm = 1.0;\n",
+ "#*********#\n",
+ "\n",
+ "Ma = 18.0;# [kg/kmol]\n",
+ "# Liquid:\n",
+ "# Because of large conc. of ammonia in gas F's rather than k's are used.\n",
+ "# Molecular weight of water and ammonia are nearly same.\n",
+ "# The density of the solution is practically that of water.\n",
+ "MolarDensity1 = 1000/Ma;# [kmol/cubic m]\n",
+ "# Kl is determined for dilute soln. where Xbm is practically 1.0\n",
+ "Fl = Kl*Xbm*MolarDensity1;# [kmol/square m.s]\n",
+ "Ma = 18;# [kg-/kmol]\n",
+ "# Gas:\n",
+ "MolarDensity2 = (1/22.41)*(273/(273+26.7));# [kmol/cubic m]\n",
+ "Fg = Sh*MolarDensity2*Da/d;# [kmol/square m.s]\n",
+ "\n",
+ "# Mass Transfer Flux\n",
+ "# Th eqb. distribuion data for NH3 from \"The Chemical Engineers Handbook\" 5th Edt. p3-68:\n",
+ "# Data = [Xa,pa]\n",
+ "# Xa = NH3 mole fraction in gas phas\n",
+ "# pa = NH3 partial pressure in N/square m\n",
+ "Data = [(0 ,0),(0.05 ,7171),(0.10, 13652),(0.25 ,59917),(0.30 ,93220)];\n",
+ "\n",
+ "X = numpy.zeros(5);\n",
+ "for i in range(1,5) :\n",
+ " X[i]=Data[i][0]\n",
+ " \n",
+ "\n",
+ "# Ya_star = mole fraction of NH3 in gas phase at eqb.\n",
+ "Ya_star = numpy.zeros(5);\n",
+ "for i in range(0,5) :\n",
+ " Ya_star[i] = (Data[i][1])/P\n",
+ "\n",
+ "# For transfer of only one component\n",
+ "Na_by_SummationN = 1.0;\n",
+ "Ya = numpy.zeros(5);\n",
+ "for i in range(0,5):\n",
+ " Ya[i] = 1-((1-Yag)*(1.0-Xal)/(1-Data[i][0]));\n",
+ "\n",
+ "plt.plot(X,Ya_star,'g',label='Equilibrium Line')\n",
+ "plt.plot(X,Ya,'r',label='Operating Line')\n",
+ "ax = pylab.gca()\n",
+ "ax.grid('on')\n",
+ "ax.set_xlabel('Xa = mole fraction of NH3 in liquid phase');\n",
+ "ax.set_ylabel('Ya = mole fraction of NH3 in gas phase');\n",
+ "pylab.legend(loc='lower right')\n",
+ "plt.title('Ya Vs Xa');\n",
+ "plt.show()\n",
+ "\n",
+ "# From intersection of operating line & Eqb. line\n",
+ "Xai = 0.274;\n",
+ "Yai = 0.732;\n",
+ "\n",
+ "# From Eqn.5.20\n",
+ "Na = Na_by_SummationN*Fg*log((Na_by_SummationN-Yai)/(Na_by_SummationN-Yag));# [kmol NH3 absorbed/square m.s]\n",
+ "print\"Local mass transfer flux for ammonia is \",round(Na,6),\" kmol/square m.s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 5.1 - Page: 114\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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tRCQOeBFIBk4DrhaRpn7WewaYCRTLa+u83i7q5fiKWmyqykdrP+K0l09j2/5t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\nrPVZ5zLgLQBV/UZEEkSkpqr+nteODx4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+ "text": [
+ "<matplotlib.figure.Figure at 0x7765208>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Local mass transfer flux for ammonia is 0.00043 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex5.2: Page 130"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Illustration 5.2\n",
+ "# Page: 130\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "\n",
+ "print'Illustration 5.2 - Page: 130\\n\\n'\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data***#\n",
+ "# Eqb. data\n",
+ "# Data = [Wt% of moisture in the soap,Partial pressure of water in air(mm Hg)]\n",
+ "Data = [(0,0),( 2.40, 9.66),(3.76 ,19.20),(4.76 ,28.4),(6.10, 37.2),(7.83, 46.4),(9.90, 55.0),(12.63, 63.2),(15.40, 71.9),(19.02 ,79.5)];\n",
+ "P = 760.0;# [mm Hg]\n",
+ "# Initial air\n",
+ "p1 = 12;# [mm Hg]\n",
+ "T = 273+75.0;# [K]\n",
+ "#******#\n",
+ "\n",
+ "# Y = kg water/kg dry air\n",
+ "# X = kg water/kg dry soap\n",
+ "# E = Air water phase\n",
+ "# R = Soap water phase\n",
+ "Y = numpy.zeros(10);\n",
+ "X = numpy.zeros(10);\n",
+ "for i in range(1,10):\n",
+ " Y[i] = Data[i][1]/(P-Data[i][1])*(18.02/29);\n",
+ " X[i] = Data[i][0]/(100.0-Data[i][0]);\n",
+ "\n",
+ "\n",
+ "print'Illustration 5.2 (a)\\n\\n'\n",
+ "\n",
+ "import pylab\n",
+ "# Soln. (a)\n",
+ "# First operation\n",
+ "Y1 = p1/(P-p1);# [kg water/kg dry soap]\n",
+ "# Initial Soap\n",
+ "S1 = 16.7/(100-16.7);# [kg water/kg dry soap]\n",
+ "# Final soap\n",
+ "S2 = 13.0/(100-13);# [kg water/kg dry soap]\n",
+ "Rs = 10.0*(1-0.167);# [kg dry soap]\n",
+ "# Using ideal gas law\n",
+ "Es = 10.0*((760-p1)/760.0)*(273.0/T)*(29.0/22.41);# [kg dry air]\n",
+ "slopeOperat = -Rs/Es;\n",
+ "\n",
+ "def f2(x):\n",
+ " return slopeOperat*(x-S1)+Y1\n",
+ "x = numpy.arange(S1,S2,-0.01);\n",
+ "X1=S2;\n",
+ "def f3(S):\n",
+ " return slopeOperat*(S-X1)+Y1\n",
+ "S=numpy.arange(0,S1,0.01);\n",
+ "\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f2(x),'g',label='First Process')\n",
+ "plt.plot(S,f3(S),'r',label='Second Process')\n",
+ "ax = pylab.gca()\n",
+ "plt.title(\"Illustration 5.2(a)\")\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "plt.show()\n",
+ "\n",
+ "# Results for First Process\n",
+ "# The condition at abcissa S2 correspond to the end of first operation\n",
+ "print \"Conditions corresponding to First Operation \\n\"\n",
+ "print \"X = kg water/kg dry soap\\n\",S2\n",
+ "print \"Y = kg water/kg dry air\\n\",f2(S2)\n",
+ "\n",
+ "# Results for Second Process\n",
+ "# The point at which the line meets the equilibrium line corresponds to the final value\n",
+ "X2 = 0.103;\n",
+ "Y2 = (X2/(1+X2));\n",
+ "print\"Final moisture content of soap is \",round(Y2*100,3),'%'\n",
+ "\n",
+ "\n",
+ "print'\\n\\n Illustration 5.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "Rs = 1*(1-0.167);# [kg dry soap/h]\n",
+ "# Entering soap\n",
+ "X1 = 0.20;# [kg water/kg dry soap]\n",
+ "# Leaving soap\n",
+ "x = 0.04;\n",
+ "X2 = x/(1-x);# [kg water/kg dry soap]\n",
+ "# Entering air\n",
+ "Y2 = 0.00996;# [from Illustration 5.2(a), kg water/kg dry air]\n",
+ "# The operating line of least slope giving rise to eqb. condition will indicate least amount of air usable.\n",
+ "# At X1 = 0.20; the eqb. condition:\n",
+ "Y1 = 0.0675;# [kg water/kg dry air]\n",
+ "\n",
+ "def f4(x):\n",
+ " return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
+ "x = numpy.arange(X2,0.24,0.01);\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f4(x),'g',label='Operating line')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.title(\"Illustration 5.2(b)\")\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "plt.show()\n",
+ "# By Eqn. 5.35\n",
+ "\n",
+ "Es = Rs*(X1-X2)/(Y1-Y2);# [kg dry air/h]\n",
+ "Esv = (Es/29)*22.41*(P/(P-p1))*(T/273.0); #[cubic m/kg dry soap]\n",
+ "print\"Minimum amount of air required is\",round(Esv,4),\" cubic m/kg dry soap\\n\\n\"\n",
+ "\n",
+ "print'Illustration 5.2 (c)\\n\\n'\n",
+ "\n",
+ "# solution (c)\n",
+ "\n",
+ "Esnew = 1.30*Es;# [kg dry air/h]\n",
+ "Y1 = Rs*((X1-X2)/Esnew)+Y2;\n",
+ "\n",
+ "def f5(x):\n",
+ " return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
+ "x = numpy.arange(X2,0.24,0.01);\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f5(x),'g',label='Operating line')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.title(\"Illustration 5.2(c)\")\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "plt.show()\n",
+ "# with final coordinates X = X1 & y = Y1\n",
+ "# From figure, Total number of eqb . stages = 3\n",
+ "N = 3;\n",
+ "print\"Moisture content of air leaving the drier is \",round(Y1,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Total number of eqb. stages = \",N\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 5.2 - Page: 130\n",
+ "\n",
+ "\n",
+ "Illustration 5.2 (a)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Qd7lcOn/+/KyRwvr167POBTJS8PYm7q0db3rVrFlTVVX79OmjAwYMOOs+u3bt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\nAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7a26828>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Conditions corresponding to First Operation \n",
+ "\n",
+ "X = kg water/kg dry soap\n",
+ "0.149425287356\n",
+ "Y = kg water/kg dry air\n",
+ "0.0586080045715\n",
+ "Final moisture content of soap is 9.338 %\n",
+ "\n",
+ "\n",
+ " Illustration 5.2 (b)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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kzes0Hj/yCOTJ4/v4E9c9+Gj1RwxrNoyih4ry3tvv+ez4aZWPiYlJmjo72P5/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\nWfgMr976Kk/VeSrD01wbYwIjrSuF+7M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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c30128>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Minimum amount of air required is 2.2941 cubic m/kg dry soap\n",
+ "\n",
+ "\n",
+ "Illustration 5.2 (c)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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GpENkJEyYAB9/DDfe6Iw8fvJJCMo6Tdp+7+CZg/Re1Ju5u+fSL6QfL9V4KcOW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\nNVWVme/qqwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7e5c940>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Moisture content of air leaving the drier is 0.0542 kg water/kg dry air\n",
+ "\n",
+ "Total number of eqb. stages = 3\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter5_2.ipynb b/Mass_-_Transfer_Operations/Chapter5_2.ipynb
new file mode 100755
index 00000000..756e424b
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter5_2.ipynb
@@ -0,0 +1,385 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:5b0eee15396b4ea69bab7ccfb1908ab0b8c6f2630bddedaada99660ed07a1ef9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 5: Interphase Mass Transfer"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex5.1: Page 114"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 5.1\n",
+ "# Page: 114\n",
+ "\n",
+ "print'Illustration 5.1 - Page: 114\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "%matplotlib inline\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "# a = NH3, b = H2O\n",
+ "d = 2.54*10**(-2);# [m]\n",
+ "Yag = 0.80;\n",
+ "Xal = 0.05;\n",
+ "T = 273+26.7;# [K]\n",
+ "Kl = 2.87*10**(-5);# [kmol/square m.s.(kmol/cubic m)]\n",
+ "Sh = 40;\n",
+ "Da = 2.297*10**(-5);# [square m.s]\n",
+ "P = 1.0133*10**(5);# [N/square m]\n",
+ "Xbm = 1.0;\n",
+ "#*********#\n",
+ "\n",
+ "Ma = 18.0;# [kg/kmol]\n",
+ "# Liquid:\n",
+ "# Because of large conc. of ammonia in gas F's rather than k's are used.\n",
+ "# Molecular weight of water and ammonia are nearly same.\n",
+ "# The density of the solution is practically that of water.\n",
+ "MolarDensity1 = 1000/Ma;# [kmol/cubic m]\n",
+ "# Kl is determined for dilute soln. where Xbm is practically 1.0\n",
+ "Fl = Kl*Xbm*MolarDensity1;# [kmol/square m.s]\n",
+ "Ma = 18;# [kg-/kmol]\n",
+ "# Gas:\n",
+ "MolarDensity2 = (1/22.41)*(273/(273+26.7));# [kmol/cubic m]\n",
+ "Fg = Sh*MolarDensity2*Da/d;# [kmol/square m.s]\n",
+ "\n",
+ "# Mass Transfer Flux\n",
+ "# Th eqb. distribuion data for NH3 from \"The Chemical Engineers Handbook\" 5th Edt. p3-68:\n",
+ "# Data = [Xa,pa]\n",
+ "# Xa = NH3 mole fraction in gas phas\n",
+ "# pa = NH3 partial pressure in N/square m\n",
+ "Data = [(0 ,0),(0.05 ,7171),(0.10, 13652),(0.25 ,59917),(0.30 ,93220)];\n",
+ "\n",
+ "X = numpy.zeros(5);\n",
+ "for i in range(1,5) :\n",
+ " X[i]=Data[i][0]\n",
+ " \n",
+ "\n",
+ "# Ya_star = mole fraction of NH3 in gas phase at eqb.\n",
+ "Ya_star = numpy.zeros(5);\n",
+ "for i in range(0,5) :\n",
+ " Ya_star[i] = (Data[i][1])/P\n",
+ "\n",
+ "# For transfer of only one component\n",
+ "Na_by_SummationN = 1.0;\n",
+ "Ya = numpy.zeros(5);\n",
+ "for i in range(0,5):\n",
+ " Ya[i] = 1-((1-Yag)*(1.0-Xal)/(1-Data[i][0]));\n",
+ "\n",
+ "plt.plot(X,Ya_star,'g',label='Equilibrium Line')\n",
+ "plt.plot(X,Ya,'r',label='Operating Line')\n",
+ "ax = pylab.gca()\n",
+ "ax.grid('on')\n",
+ "ax.set_xlabel('Xa = mole fraction of NH3 in liquid phase');\n",
+ "ax.set_ylabel('Ya = mole fraction of NH3 in gas phase');\n",
+ "pylab.legend(loc='lower right')\n",
+ "plt.title('Ya Vs Xa');\n",
+ "plt.show()\n",
+ "\n",
+ "# From intersection of operating line & Eqb. line\n",
+ "Xai = 0.274;\n",
+ "Yai = 0.732;\n",
+ "\n",
+ "# From Eqn.5.20\n",
+ "Na = Na_by_SummationN*Fg*log((Na_by_SummationN-Yai)/(Na_by_SummationN-Yag));# [kmol NH3 absorbed/square m.s]\n",
+ "print\"Local mass transfer flux for ammonia is \",round(Na,6),\" kmol/square m.s\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 5.1 - Page: 114\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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tRCQOeBFIBk4DrhaRpn7WewaYCRTLa+u83i7q5fiKWmyqykdrP+K0l09j2/5t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\nrPVZ5zLgLQBV/UZEEkSkpqr+nteODx4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+ "text": [
+ "<matplotlib.figure.Figure at 0x7765208>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Local mass transfer flux for ammonia is 0.00043 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex5.2: Page 130"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Illustration 5.2\n",
+ "# Page: 130\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "\n",
+ "print'Illustration 5.2 - Page: 130\\n\\n'\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data***#\n",
+ "# Eqb. data\n",
+ "# Data = [Wt% of moisture in the soap,Partial pressure of water in air(mm Hg)]\n",
+ "Data = [(0,0),( 2.40, 9.66),(3.76 ,19.20),(4.76 ,28.4),(6.10, 37.2),(7.83, 46.4),(9.90, 55.0),(12.63, 63.2),(15.40, 71.9),(19.02 ,79.5)];\n",
+ "P = 760.0;# [mm Hg]\n",
+ "# Initial air\n",
+ "p1 = 12;# [mm Hg]\n",
+ "T = 273+75.0;# [K]\n",
+ "#******#\n",
+ "\n",
+ "# Y = kg water/kg dry air\n",
+ "# X = kg water/kg dry soap\n",
+ "# E = Air water phase\n",
+ "# R = Soap water phase\n",
+ "Y = numpy.zeros(10);\n",
+ "X = numpy.zeros(10);\n",
+ "for i in range(1,10):\n",
+ " Y[i] = Data[i][1]/(P-Data[i][1])*(18.02/29);\n",
+ " X[i] = Data[i][0]/(100.0-Data[i][0]);\n",
+ "\n",
+ "\n",
+ "print'Illustration 5.2 (a)\\n\\n'\n",
+ "\n",
+ "import pylab\n",
+ "# Soln. (a)\n",
+ "# First operation\n",
+ "Y1 = p1/(P-p1);# [kg water/kg dry soap]\n",
+ "# Initial Soap\n",
+ "S1 = 16.7/(100-16.7);# [kg water/kg dry soap]\n",
+ "# Final soap\n",
+ "S2 = 13.0/(100-13);# [kg water/kg dry soap]\n",
+ "Rs = 10.0*(1-0.167);# [kg dry soap]\n",
+ "# Using ideal gas law\n",
+ "Es = 10.0*((760-p1)/760.0)*(273.0/T)*(29.0/22.41);# [kg dry air]\n",
+ "slopeOperat = -Rs/Es;\n",
+ "\n",
+ "def f2(x):\n",
+ " return slopeOperat*(x-S1)+Y1\n",
+ "x = numpy.arange(S1,S2,-0.01);\n",
+ "X1=S2;\n",
+ "def f3(S):\n",
+ " return slopeOperat*(S-X1)+Y1\n",
+ "S=numpy.arange(0,S1,0.01);\n",
+ "\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f2(x),'g',label='First Process')\n",
+ "plt.plot(S,f3(S),'r',label='Second Process')\n",
+ "ax = pylab.gca()\n",
+ "plt.title(\"Illustration 5.2(a)\")\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "plt.show()\n",
+ "\n",
+ "# Results for First Process\n",
+ "# The condition at abcissa S2 correspond to the end of first operation\n",
+ "print \"Conditions corresponding to First Operation \\n\"\n",
+ "print \"X = kg water/kg dry soap\\n\",S2\n",
+ "print \"Y = kg water/kg dry air\\n\",f2(S2)\n",
+ "\n",
+ "# Results for Second Process\n",
+ "# The point at which the line meets the equilibrium line corresponds to the final value\n",
+ "X2 = 0.103;\n",
+ "Y2 = (X2/(1+X2));\n",
+ "print\"Final moisture content of soap is \",round(Y2*100,3),'%'\n",
+ "\n",
+ "\n",
+ "print'\\n\\n Illustration 5.2 (b)\\n\\n'\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "Rs = 1*(1-0.167);# [kg dry soap/h]\n",
+ "# Entering soap\n",
+ "X1 = 0.20;# [kg water/kg dry soap]\n",
+ "# Leaving soap\n",
+ "x = 0.04;\n",
+ "X2 = x/(1-x);# [kg water/kg dry soap]\n",
+ "# Entering air\n",
+ "Y2 = 0.00996;# [from Illustration 5.2(a), kg water/kg dry air]\n",
+ "# The operating line of least slope giving rise to eqb. condition will indicate least amount of air usable.\n",
+ "# At X1 = 0.20; the eqb. condition:\n",
+ "Y1 = 0.0675;# [kg water/kg dry air]\n",
+ "\n",
+ "def f4(x):\n",
+ " return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
+ "x = numpy.arange(X2,0.24,0.01);\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f4(x),'g',label='Operating line')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.title(\"Illustration 5.2(b)\")\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "plt.show()\n",
+ "# By Eqn. 5.35\n",
+ "\n",
+ "Es = Rs*(X1-X2)/(Y1-Y2);# [kg dry air/h]\n",
+ "Esv = (Es/29)*22.41*(P/(P-p1))*(T/273.0); #[cubic m/kg dry soap]\n",
+ "print\"Minimum amount of air required is\",round(Esv,4),\" cubic m/kg dry soap\\n\\n\"\n",
+ "\n",
+ "print'Illustration 5.2 (c)\\n\\n'\n",
+ "\n",
+ "# solution (c)\n",
+ "\n",
+ "Esnew = 1.30*Es;# [kg dry air/h]\n",
+ "Y1 = Rs*((X1-X2)/Esnew)+Y2;\n",
+ "\n",
+ "def f5(x):\n",
+ " return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
+ "x = numpy.arange(X2,0.24,0.01);\n",
+ "plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
+ "plt.plot(x,f5(x),'g',label='Operating line')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel('kg water / kg dry soap')\n",
+ "ax.set_ylabel('kg water / kg dry air')\n",
+ "ax.set_autoscale_on('False')\n",
+ "pylab.axis([0.0,0.24, 0,0.08])\n",
+ "plt.title(\"Illustration 5.2(c)\")\n",
+ "plt.grid(b=None, which='major', axis='both')\n",
+ "ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
+ "pylab.legend(loc='upper left')\n",
+ "plt.show()\n",
+ "# with final coordinates X = X1 & y = Y1\n",
+ "# From figure, Total number of eqb . stages = 3\n",
+ "N = 3;\n",
+ "print\"Moisture content of air leaving the drier is \",round(Y1,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Total number of eqb. stages = \",N\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 5.2 - Page: 130\n",
+ "\n",
+ "\n",
+ "Illustration 5.2 (a)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Qd7lcOn/+/KyRwvr167POBTJS8PYm7q0db3rVrFlTVVX79OmjAwYMOOs+u3bt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\nAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7a26828>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Conditions corresponding to First Operation \n",
+ "\n",
+ "X = kg water/kg dry soap\n",
+ "0.149425287356\n",
+ "Y = kg water/kg dry air\n",
+ "0.0586080045715\n",
+ "Final moisture content of soap is 9.338 %\n",
+ "\n",
+ "\n",
+ " Illustration 5.2 (b)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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kzes0Hj/yCOTJ4/v4E9c9+Gj1RwxrNoyih4ry3tvv+ez4aZWPiYlJmjo72P5/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\nWfgMr976Kk/VeSrD01wbYwIjrSuF+7M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+ "text": [
+ "<matplotlib.figure.Figure at 0x7c30128>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Minimum amount of air required is 2.2941 cubic m/kg dry soap\n",
+ "\n",
+ "\n",
+ "Illustration 5.2 (c)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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GpENkJEyYAB9/DDfe6Iw8fvJJCMo6Tdp+7+CZg/Re1Ju5u+fSL6QfL9V4KcOW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\nNVWVme/qqwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7e5c940>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Moisture content of air leaving the drier is 0.0542 kg water/kg dry air\n",
+ "\n",
+ "Total number of eqb. stages = 3\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter6.ipynb b/Mass_-_Transfer_Operations/Chapter6.ipynb
new file mode 100755
index 00000000..330c2a7b
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter6.ipynb
@@ -0,0 +1,1050 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:154e89b2f76588eed1121bf1db9bf4372804b279864337e5f5dc9c9be7d3f365"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 6: Equipment For Gas-Liquid Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.1: Page 145"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.1\n",
+ "# Page: 145\n",
+ "\n",
+ "print'Illustration 6.1 - Page: 145\\n\\n'\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# w = Gas flow rate per orifice\n",
+ "w = 0.055/50;# [kg/s]\n",
+ "L = 8*10**(-4);# [liquid flow rate, cubic m/s]\n",
+ "d = 0.003;# [diameter of the orifice,m]\n",
+ "viscocity_gas = 1.8*10**(-5);# [kg/m.s]\n",
+ "#******#\n",
+ "\n",
+ "Re = 4*w/(math.pi*d*viscocity_gas);\n",
+ "Dp = 0.0071*Re**(-0.05);# [m]\n",
+ "h = 3.0;# [height of vessel,m]\n",
+ "P_atm = 101.3;# [kN/square m]\n",
+ "Density_water = 1000.0;# [kg/cubic m]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "Temp = 273+25;# [K]\n",
+ "P_orifice = P_atm+(h*Density_water*g/1000);# [kN/square m]\n",
+ "P_avg = P_atm+((h/2.0)*Density_water*g/1000);# [kN/square m]\n",
+ "Density_gas = (29/22.41)*(273.0/Temp)*(P_avg/P_atm);# [kg/cubic m]\n",
+ "D = 1.0;# [dia of vessel,m]\n",
+ "Area = (math.pi*D**2)/4;# [square m]\n",
+ "Vg = 0.055/(Area*Density_gas);# [m/s]\n",
+ "Vl = L/Area;# [m/s]\n",
+ "sigma = 0.072;# [N/m]\n",
+ "# From fig. 6.2 (Pg 143)\n",
+ "abscissa = 0.0516;# [m/s]\n",
+ "Vg_by_Vs = 0.11;\n",
+ "Vs = Vg/Vg_by_Vs;# [m/s]\n",
+ "def f6(shi_g):\n",
+ " return Vs-(Vg/shi_g)+(Vl/(1-shi_g)) \n",
+ "shi_g = fsolve(f6,0.5);\n",
+ "dp = ((Dp**3)*(P_orifice/P_avg))**(1.0/3);# [bubble diameter,m]\n",
+ "# From eqn. 6.9\n",
+ "a = 6.0*shi_g/dp;# [specific interfacial area,square m]\n",
+ "print\"The Specific Interfacial Area is \",round(a,2),\" square m/cubic m\\n\"\n",
+ "\n",
+ "# For diffsion of Cl2 in H20\n",
+ "Dl = 1.44*10**(-9);# [square m/s]\n",
+ "viscocity_water = 8.937*10**(-4);# [kg/m.s]\n",
+ "Reg = dp*Vs*Density_water/viscocity_water;\n",
+ "Scl = viscocity_water/(Density_water*Dl);\n",
+ "# From Eqn.6.11\n",
+ "Shl = 2+(0.0187*(Reg**0.779)*(Scl**0.546)*(dp*(g**(1.0/3))/(Dl**(2.0/3)))**0.116);\n",
+ "# For dilute soln. of Cl2 in H20\n",
+ "c = 1000/18.02;# [kmol/cubic m]\n",
+ "Fl = (c*Dl*Shl)/dp;# [kmol/square m.s]\n",
+ "print\"Mass Transfer coeffecient is \",round(Fl,5),\" kmol/square m.s\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.1 - Page: 145\n",
+ "\n",
+ "\n",
+ "The Specific Interfacial Area is 148.13 square m/cubic m\n",
+ "\n",
+ "Mass Transfer coeffecient is 0.01335 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.2: Page 157"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.2\n",
+ "# Page: 157\n",
+ "\n",
+ "print'Illustration 6.2 - Page: 157\\n\\n'\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = N2 b = H2O\n",
+ "L = 9.5*10**(-4);# [cubic m/s]\n",
+ "G = 0.061;# [kg/s]\n",
+ "Temp = 273.0+25;# [K]\n",
+ "#*****#\n",
+ "\n",
+ "print\"Construction Arrangement\\n\"\n",
+ "print\"Use 4 vertical wall baffles, 100 mm wide at 90 degree intervals.\\n\"\n",
+ "print\"Use a 305 mm dameter, a six bladed disk flat blade turbine impeller, arranged axially, 300 mm from the bottom of vessel\\n\"\n",
+ "print\"The sparger underneath the impeller will be in the form of a 240 mm dameter ring made of 12.7 mm tubing drilled in the top with 3.18 mm dia holes\\n\"\n",
+ "Di = 0.305;# [m]\n",
+ "Do = 0.00316;# [m]\n",
+ "viscocity_a = 1.8*10**(-5);# [kg/m.s]\n",
+ "Re_g = 35000;\n",
+ "Ma = 28.02;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# w = Gas flow rate per orifice\n",
+ "w = Re_g*math.pi*Do*viscocity_a/4.0;# [kg/s]\n",
+ "N_holes = G/w;\n",
+ "Interval = math.pi*240/round(N_holes);\n",
+ "print\"The number of holes is \",round(N_holes),\" at approx \",round(Interval),\" mm interval around the sparger ring\\n\"\n",
+ "\n",
+ "viscocity_b = 8.9*10**(-4);# [kg/m.s]\n",
+ "Sigma = 0.072;# [N/m]\n",
+ "Density_b = 1000.0;# [kg/cubic m]\n",
+ "D = 1.0;# [dia of vessel,m]\n",
+ "g = 9.81;# [m/s**2]\n",
+ "# From Eqn. 6.18\n",
+ "def f7(N):\n",
+ " return (N*Di/(Sigma*g/Density_b)**0.25)-1.22-(1.25*D/Di)\n",
+ "N_min = fsolve(f7,2);# [r/s]\n",
+ "N = 5.0;# [r/s]\n",
+ "Re_l = ((Di**2)*N*Density_b/viscocity_b);\n",
+ "# From fig 6.5 (Pg 152)\n",
+ "Po = 5.0;\n",
+ "P = Po*Density_b*(N**3)*(Di**5);\n",
+ "h = 0.7;# [m]\n",
+ "P_atm = 101.33;# [kN/square m]\n",
+ "P_gas = P_atm+(h*Density_b*g/1000.0);# [kN/square m]\n",
+ "Qg = (G/Ma)*22.41*(Temp/273.0)*(P_atm/P_gas);# [cubic m/s]\n",
+ "# From Fig.6.7 (Pg 155)\n",
+ "abcissa = Qg/(N*(Di**3));\n",
+ "# abcissa is off scale\n",
+ "Pg_by_P = 0.43;\n",
+ "Pg = 0.43*P;# [W]\n",
+ "Vg = Qg/(math.pi*(D**2)/4);# [superficial gas velocity,m/s]\n",
+ "check_value = (Re_l**0.7)*((N*Di/Vg)**0.3);\n",
+ "vl = math.pi*(D**2)/4;# [cubic m]\n",
+ "# Since value<30000\n",
+ "# From Eqn. 6.21, Eqn.6.23 & Eqn. 6.24\n",
+ "K = 2.25;\n",
+ "m = 0.4;\n",
+ "Vt = 0.250;# [m/s]\n",
+ "shi = 1.0;\n",
+ "err = 1.0;\n",
+ "while (err>10**(-3)):\n",
+ " a = 1.44*((Pg/vl)**0.4)*((Density_b/(Sigma**3))**0.2)*((Vg/Vt)**0.5);# [square m/cubic m]\n",
+ " shin = (0.24*K*((viscocity_a/viscocity_b)**0.25)*((Vg/Vt)**0.5))**(1.0/(1-m));\n",
+ " Dp = K*((vl/Pg)**0.4)*((Sigma**3/Density_b)**0.2)*(shin**m)*((viscocity_a/viscocity_b)**0.25);# [m]\n",
+ " err = abs(shi-shin);\n",
+ " Vt = Vt-0.002;# [m/s]\n",
+ " shi = shin;\n",
+ "\n",
+ "\n",
+ "# For N2 in H2\n",
+ "Dl = 1.9*10**(-9);# [square m/s]\n",
+ "Ra = 1.514*10**(6);\n",
+ "# By Eqn. 6.25\n",
+ "Shl = 2.0+(0.31*(Ra**(1.0/3)));\n",
+ "# For dilute soln.\n",
+ "c = 1000.0/Mb;# [kmol/cubic m]\n",
+ "Fl = Shl*c*Dl*1.0/Dp;# [kmol/square m.s]\n",
+ "print\"The average gas-bubble diameter is \",(\"{:.2e}\".format(Dp)),\" m\\n\",\n",
+ "print\"Gas Holdup:\\n\",round(shi,5)\n",
+ "print\"Interfacial area:\",round(a,4),\" square m/cubic m \\n\"\n",
+ "print\"Mass transfer coffecient:\",(\"{:.2e}\".format(Fl)),\"kmol/square m.s\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.2 - Page: 157\n",
+ "\n",
+ "\n",
+ "Construction Arrangement\n",
+ "\n",
+ "Use 4 vertical wall baffles, 100 mm wide at 90 degree intervals.\n",
+ "\n",
+ "Use a 305 mm dameter, a six bladed disk flat blade turbine impeller, arranged axially, 300 mm from the bottom of vessel\n",
+ "\n",
+ "The sparger underneath the impeller will be in the form of a 240 mm dameter ring made of 12.7 mm tubing drilled in the top with 3.18 mm dia holes\n",
+ "\n",
+ "The number of holes is 39.0 at approx 19.0 mm interval around the sparger ring\n",
+ "\n",
+ "The average gas-bubble diameter is 6.35e-04 m\n",
+ "Gas Holdup:\n",
+ "0.02265\n",
+ "Interfacial area: 214.0106 square m/cubic m \n",
+ "\n",
+ "Mass transfer coffecient: 6.24e-03 kmol/square m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.3: Page 174"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.3\n",
+ "# Page: 174\n",
+ "\n",
+ "print'Illustration 6.3 - Page: 174\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = methanol b = water\n",
+ "G = 0.100;# [kmol/s]\n",
+ "L = 0.25;# [kmol/s]\n",
+ "Temp = 273+95;# [K]\n",
+ "XaG = 0.18;# [mol % in gas phase]\n",
+ "MaL = 0.15;# [mass % in liquid phase]\n",
+ "#*****#\n",
+ "\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "Mavg_G = XaG*Ma+((1-XaG)*Mb);# [kg/kmol]\n",
+ "Density_G = (Mavg_G/22.41)*(273.0/Temp);# [kg/cubic cm]\n",
+ "Q = G*22.41*(Temp/273.0);# [cubic cm/s]\n",
+ "Density_L = 961.0;# [kg/cubic cm]\n",
+ "Mavg_L = 1.0/((MaL/Ma)+(1-MaL)/Mb);# [kg/kmol]\n",
+ "q = L*Mavg_L/Density_L;\n",
+ "\n",
+ "# Perforations\n",
+ "print\"Perforations\\n\"\n",
+ "print\"Do = 4.5mm on an equilateral triangle pitch 12 mm between the hole centres, punched in sheet metal 2 mm thick\\n\"\n",
+ "Do = 0.0045;# [m]\n",
+ "pitch = 0.012;# [m]\n",
+ "# By Eqn.6.31\n",
+ "Ao_by_Aa = 0.907*(Do/pitch)**2;\n",
+ "print\"The ratio of Hole Area By Active Area is:\",round(Ao_by_Aa,4),\"\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Tower Diameter\n",
+ "print\"Tower Diameter\\n\"\n",
+ "t = 0.50;# [tray spacing,m]\n",
+ "print\"Tower Spacing:\",t,\" m\\n\"\n",
+ "# abcissa = (L/G)*(Density_G/Density_L)^0.5 = (q/Q)*(Density_L/Density_G)**0.5\n",
+ "abcissa = (q/Q)*(Density_L/Density_G)**0.5;\n",
+ "# From Table 6.2 (Pg 169)\n",
+ "alpha = (0.0744*t)+0.01173;\n",
+ "beeta = (0.0304*t)+0.015;\n",
+ "if (abcissa<0.1):\n",
+ " abcissa = 0.1;\n",
+ "\n",
+ "sigma = 0.040;# [N/m]\n",
+ "# From Eqn.6.30\n",
+ "Cf = ((alpha*math.log10(1.0/abcissa))+beeta)*(sigma/0.02)**0.2;\n",
+ "# From Eqn. 6.29\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1/2);# [m/s]\n",
+ "# Using 80% of flooding velocity\n",
+ "V = 0.8*Vf;# [m/s]\n",
+ "An = Q/V;# [square m]\n",
+ "# The tray area used by one downspout = 8.8%\n",
+ "At = An/(1-0.088);# [square m]\n",
+ "D = (4*At/math.pi)**(1.0/2);# [m]\n",
+ "# Take D = 1.25 m\n",
+ "D = 1.25; #[m]\n",
+ "At = math.pi*(D**2)/4;# [corrected At, square m]\n",
+ "W = 0.7*D;# [weir length,m]\n",
+ "Ad = 0.088*At;# [square m]\n",
+ "# For a design similar to Fig 6.14 (Pg 168)\n",
+ "# A 40 mm wide supporting ring, beams between downspouts and a 50 mm wide disengaging & distributing zones these areas total 0.222 square m\n",
+ "Aa = At-(2.0*Ad)-0.222;\n",
+ "print\"Weir Length:\",round(W,4),\"\\n\"\n",
+ "print\"Area for perforated sheet: \",round(Aa,4),\" square m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Weir crest h1 & Weir height hw\n",
+ "print\"Weir crest h1 & Weir height hw\\n\"\n",
+ "h1 = 0.025;# [m]\n",
+ "h1_by_D = h1/D;\n",
+ "D_by_W = D/W;\n",
+ "# From Eqn. 6.34\n",
+ "Weff_by_W = math.sqrt(((D_by_W)**2)-((((D_by_W)**2-1)**0.5)+(2*h1_by_D*D_by_W))**2);\n",
+ "# Set hw to 50 mm\n",
+ "hw = 0.05;# [m]\n",
+ "print\"Weir crest: \",h1,\" m\\n\"\n",
+ "print\"Weir height: \",hw,\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Dry Pressure Drop\n",
+ "print\"Dry Pressure Drop\\n\"\n",
+ "l = 0.002;# [m]\n",
+ "# From Eqn. 6.37\n",
+ "Co = 1.09*(Do/l)**0.25;\n",
+ "Ao = 0.1275*Aa;# [square m]\n",
+ "Vo = Q/Ao;# [m/sec]\n",
+ "viscocity_G = 1.25*10**(-5);# [kg/m.s]\n",
+ "Re = Do*Vo*Density_G/viscocity_G;\n",
+ "# From \"The Chemical Engineers Handbook,\" 5th Edition fig 5.26\n",
+ "fr = 0.008;\n",
+ "g = 9.81;# [m/s**2]\n",
+ "# From Eqn. 6.36\n",
+ "def f(hd):\n",
+ " return (2*hd*g*Density_L/(Vo**2*Density_G))-(Co*(0.40*(1.25-(Ao/An))+(4*l*fr/Do)+(1-(Ao/An))**2))\n",
+ "hd = fsolve(f,1);\n",
+ "print\"Dry Pressure Drop:\",round(hd,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Hydraulic head hl\n",
+ "print\"Hydraulic head hl\"\n",
+ "Va = Q/Aa;# [m/s]\n",
+ "z = (D+W)/2.0;# [m]\n",
+ "# From Eqn. 6.38\n",
+ "hl = 6.10*10**(-3)+(0.725*hw)-(0.238*hw*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "print\"Hydraulic head: \",round(hl,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#Residual Pressure drop hr\n",
+ "print\"Residual Pressure drop hr\\n\"\n",
+ "# From Eqn. 6.42\n",
+ "hr = 6*sigma/(Density_L*Do*g);# m\n",
+ "print\"Residual Pressure Drop:\",round(hr,4),\"m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Total Gas pressure Drop hg\n",
+ "print\"Total Gas pressure Drop hg\\n\"\n",
+ "# From Eqn. 6.35\n",
+ "hg = hd+hl+hr;# [m]\n",
+ "print\"Total gas pressure Drop: \",round(hg,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Pressure loss at liquid entrance h2\n",
+ "print\"Pressure loss at liquid entrance h2\\n\"\n",
+ "# Al: Area for the liquid flow under the apron\n",
+ "Al = 0.025*W;# [square m]\n",
+ "Ada = min(Al,Ad);\n",
+ "# From Eqn. 6.43\n",
+ "h2 = (3.0/(2*g))*(q/Ada)**2;\n",
+ "print\"Pressure loss at liquid entrance:\",round(h2,4),\"m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Backup in Downspout h3\n",
+ "print\"Backup in Downspout h3\\n\"\n",
+ "# From Eqn.6.44\n",
+ "h3 = hg+h2;\n",
+ "print\"Backup in Downspout:\",round(h3,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Check on Flooding\n",
+ "print\"Check on Flooding\\n\"\n",
+ "if((hw+h1+h3)<(t/2.0)):\n",
+ " print\"Choosen Tower spacing is satisfactory\\n\"\n",
+ "else:\n",
+ " print\"Choosen Tower spacing is not satisfactory\\n\"\n",
+ "\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Weeping Velocity\n",
+ "print\"Weeping Velocity\\n\"\n",
+ "print\"For W/D ratio \",W/D,\" weir is set at \",0.3296*D,\" m from the center from the tower\\n\",\n",
+ "Z = 2*(0.3296*D);# [m]\n",
+ "# From Eqn.6.46\n",
+ "def f8(Vow):\n",
+ " return (Vow*viscocity_G/(sigma))-(0.0229*((viscocity_G**2/(sigma*Density_G*Do))*(Density_L/Density_G))**0.379)*((l/Do)**0.293)*(2*Aa*Do/(math.sqrt(3.0)*(pitch**3)))**(2.8/((Z/Do)**0.724))\n",
+ "Vow = fsolve(f8,0.1);# [m/s]\n",
+ "print\"The minimum gas velocity through the holes below which excessive weeping is likely:\",round(Vow,3),\" m/s\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Entrainment\n",
+ "print\"Entrainment\\n\"\n",
+ "V_by_Vf = V/Vf;\n",
+ "# From Fig.6.17 (Pg 173), V/Vf = 0.8 & abcissa = 0.0622\n",
+ "E = 0.05;\n",
+ "print\"Entrainment:\\n\",E\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.3 - Page: 174\n",
+ "\n",
+ "\n",
+ "Perforations\n",
+ "\n",
+ "Do = 4.5mm on an equilateral triangle pitch 12 mm between the hole centres, punched in sheet metal 2 mm thick\n",
+ "\n",
+ "The ratio of Hole Area By Active Area is: 0.1275 \n",
+ "\n",
+ "\n",
+ "\n",
+ "Tower Diameter\n",
+ "\n",
+ "Tower Spacing: 0.5 m\n",
+ "\n",
+ "Weir Length: 0.875 \n",
+ "\n",
+ "Area for perforated sheet: 0.7892 square m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Weir crest h1 & Weir height hw\n",
+ "\n",
+ "Weir crest: 0.025 m\n",
+ "\n",
+ "Weir height: 0.05 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Dry Pressure Drop\n",
+ "\n",
+ "Dry Pressure Drop: 0.0654 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Hydraulic head hl\n",
+ "Hydraulic head: 0.0106 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Residual Pressure drop hr\n",
+ "\n",
+ "Residual Pressure Drop: 0.0057 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Total Gas pressure Drop hg\n",
+ "\n",
+ "Total gas pressure Drop: 0.0816 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Pressure loss at liquid entrance h2\n",
+ "\n",
+ "Pressure loss at liquid entrance: 0.008 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Backup in Downspout h3\n",
+ "\n",
+ "Backup in Downspout: 0.0897 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Check on Flooding\n",
+ "\n",
+ "Choosen Tower spacing is satisfactory\n",
+ "\n",
+ "\n",
+ "\n",
+ "Weeping Velocity\n",
+ "\n",
+ "For W/D ratio 0.7 weir is set at 0.412 m from the center from the tower\n",
+ "The minimum gas velocity through the holes below which excessive weeping is likely: 8.703 m/s\n",
+ "\n",
+ "\n",
+ "\n",
+ "Entrainment\n",
+ "\n",
+ "Entrainment:\n",
+ "0.05\n"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.4: Page 183"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.4\n",
+ "# Page: 183\n",
+ "\n",
+ "print'Illustration 6.4 - Page: 183\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "#****Data****#\n",
+ "#From Illustrtion 6.3:\n",
+ "G = 0.100;# [kmol/s]\n",
+ "Density_G = 0.679;# [kg/cubic m]\n",
+ "q = 5*10**(-3);# [cubic m/s]\n",
+ "Va = 3.827;# [m/s]\n",
+ "z = 1.063;# [m]\n",
+ "L = 0.25;# [kmol/s]\n",
+ "hL = 0.0106;# [m]\n",
+ "hW = 0.05;# [m]\n",
+ "Z = 0.824;# [m]\n",
+ "E = 0.05;\n",
+ "ya = 0.18;# [mole fraction methanol]\n",
+ "\n",
+ "# a:CH3OH b:H2O\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "# From Chapter 2:\n",
+ "ScG = 0.865;\n",
+ "Dl = 5.94*10**(-9);# [square m/s]\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/ScG**0.5;\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;# [square m/s]\n",
+ "thethaL = hL*z*Z/q;# [s]\n",
+ "NtL = 40000*Dl**0.5*((0.213*Va*Density_G**0.5)+0.15)*thethaL;\n",
+ "# For 15 mass% methanol:\n",
+ "xa = (15.0/Ma)/((15.0/Ma)+(85.0/Mb));\n",
+ "# From Fig 6.23 (Pg 184)\n",
+ "mAC = -(NtL*L)/(NtG*G);# [Slope of AC line]\n",
+ "meqb = 2.50;# [slope of equilibrium line]\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1.0/((1/NtG)+(meqb*G/L)*(1.0/NtL));\n",
+ "# From Eqn. 6.51:\n",
+ "EOG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thethaL);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2.0)*((1+(4*meqb*G*EOG/(L*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EOG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+(math.exp(eta)-1)/(eta*(1+eta/(eta+Pe))));\n",
+ "# From Eqn. 6.60:\n",
+ "EMGE = EMG/(1+(EMG*E/(1-E)));\n",
+ "print\"Efficiency of Sieve trays: \",round(EMGE,1)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.4 - Page: 183\n",
+ "\n",
+ "\n",
+ "Effeciency of Sieve trays: 0.7\n"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.5: Page 200"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.5\n",
+ "# Page: 200\n",
+ "\n",
+ "print'Illustration 6.5 - Page: 200\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "# ****Data****#\n",
+ "G = 0.80;# [cubic m/s]\n",
+ "P = 10**2;# [kN/square m]\n",
+ "XaG = 0.07;\n",
+ "Temp = 273+30.0;# [K]\n",
+ "L = 3.8;# [kg/s]\n",
+ "Density_L = 1235.0;# [kg/cubic m]\n",
+ "viscocity_L = 2.5*10**(-3);# [kg/m.s]\n",
+ "#******#\n",
+ "\n",
+ "# a = SO2 b = air\n",
+ "\n",
+ "# Solution (a) \n",
+ "\n",
+ "# Since the larger flow quantities are at the bottom for an absorber, the diameter will be choosen to accomodate the bottom condition\n",
+ "Mavg_G = XaG*64+((1-XaG)*29);# [kg/kmol]\n",
+ "G1 = G*(273/Temp)*(P/101.33)*(1/22.41);# [kmol/s]\n",
+ "G2 = G1*Mavg_G;# [kg/s]\n",
+ "Density_G = G2/G;# [kg/cubic m]\n",
+ "# Assuming Complete absorption of SO2\n",
+ "sulphur_removed = G1*XaG*64;# [kg/s]\n",
+ "abcissa = (L/G)*((Density_G/Density_L)**0.5);\n",
+ "#From Fig. 6.24, using gas pressure drop of 400 (N/square m)/m\n",
+ "ordinate = 0.061;\n",
+ "# For 25 mm ceramic Intalox Saddle:\n",
+ "Cf = 98.0;# [Table 6.3 Pg 196]\n",
+ "J = 1;\n",
+ "G_prime = (ordinate*Density_G*(Density_L-Density_G)/(Cf*viscocity_L**0.1*J))**0.5;# [kg/square m.s]\n",
+ "A = G2/G_prime;# [square m]\n",
+ "D = (4*A/math.pi)**0.5;# [m]\n",
+ "print\"The Tower Diameter is \",round(D,4),\" m\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "# Let\n",
+ "D = 1.0;# [m]\n",
+ "A = math.pi*D**2.0/4;# [square m]\n",
+ "# The pressure drop for 8 m of irrigated packing\n",
+ "delta_p = 400*8.0;# [N/square m]\n",
+ "# For dry packing\n",
+ "G_prime = (G2-sulphur_removed)/A;# [kg/square m.s]\n",
+ "P = P-(delta_p/1000.0);# [kN/square m]\n",
+ "Density_G = (29/22.41)*(273.0/Temp)*(P/101.33);# [kg/cubic m]\n",
+ "# From Table 6.3 (Pg 196)\n",
+ "Cd = 241.5;\n",
+ "# From Eqn. 6.68\n",
+ "delta_p_by_z = Cd*G_prime**2/Density_G;# [N/square m for 1m of packing]\n",
+ "pressure_drop = delta_p+delta_p_by_z;# [N/square m]\n",
+ "V = 7.5;# [m/s]\n",
+ "head_loss = 1.5*V**2.0/2;# [N.m/kg]\n",
+ "head_loss = head_loss*Density_G;# [N/square m]\n",
+ "Power = (pressure_drop+head_loss)*(G2-sulphur_removed)/(Density_G*1000.0);# [kW]\n",
+ "eta = 0.6;\n",
+ "Power = Power/eta;# [kW]\n",
+ "print\"The Power for the fan motor is \",round(Power,2),\" kW\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.5 - Page: 200\n",
+ "\n",
+ "\n",
+ "The Tower Diameter is 0.981 m\n",
+ "\n",
+ "The Power for the fan motor is 4.49 kW\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.6: Page 204"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.6\n",
+ "# Page: 204\n",
+ "\n",
+ "print'Illustration 6.6 - Page: 204\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# Gas\n",
+ "Mavg_G = 11.0;# [kg/kmol]\n",
+ "viscocity_G = 10**(-5);# [kg/m.s]\n",
+ "Pt = 107.0;# [kN/square m]\n",
+ "Dg = 1.30*10**(-5);# [square m/s]\n",
+ "Temp = 273.0+27;# [K]\n",
+ "G_prime = 0.716;# [kg/square m.s]\n",
+ "\n",
+ "# Liquid:\n",
+ "Mavg_L = 260.0;\n",
+ "viscocity_L = 2*10**(-3);# [kg/m.s]\n",
+ "Density_L = 840.0;# [kg/cubic m]\n",
+ "sigma = 3*10.0**(-2);# [N/m]\n",
+ "Dl = 4.71*10**(-10);# [square m/s]\n",
+ "#******#\n",
+ "\n",
+ "#Gas:\n",
+ "Density_G = (Mavg_G/22.41)*(Pt/101.33)*(273/Temp);# [kg/cubic m]\n",
+ "ScG = viscocity_G/(Density_G*Dg);\n",
+ "G = G_prime/Mavg_G;# [kmol/square m.s]\n",
+ "\n",
+ "# Liquid:\n",
+ "L_prime = 2.71;# [kg/square m.s]\n",
+ "ScL = viscocity_L/(Density_L*Dl);\n",
+ "\n",
+ "# Holdup:\n",
+ "# From Table 6.5 (Pg 206), L_prime = 2.71 kg/square m.s\n",
+ "Ds = 0.0472;# [m]\n",
+ "beeta = 1.508*Ds**0.376;\n",
+ "shiLsW = 5.014*10**(-5)/Ds**1.56;# [square m/cubic m]\n",
+ "shiLtW = (2.32*10**(-6))*(737.5*L_prime)**beeta/(Ds**2);# [square m/cubic m]\n",
+ "shiLoW = shiLtW-shiLsW;# [square m/cubic m]\n",
+ "H = (1404*(L_prime**0.57)*(viscocity_L**0.13)/((Density_L**0.84)*((3.24*L_prime**0.413)-1)))*(sigma/0.073)**(0.2817-0.262*math.log10(L_prime));\n",
+ "shiLo = shiLoW*H;# [square m/cubic m]\n",
+ "shiLs = 4.23*10**(-3)*(viscocity_L**0.04)*(sigma**0.55)/((Ds**1.56)*(Density_L**0.37));# [square m/cubic m]\n",
+ "shiLt = shiLo+shiLs;# [square m/cubic m]\n",
+ "\n",
+ "# Interfacial Area:\n",
+ "# From Table 6.4 (Pg 205)\n",
+ "m = 62.4;\n",
+ "n = (0.0240*L_prime)-0.0996;\n",
+ "p = -0.1355;\n",
+ "aAW = m*((808*G_prime/(Density_G**0.5))**n)*(L_prime**p);# [square m/cubic m]\n",
+ "# From Eqn. 6.73\n",
+ "aA = aAW*shiLo/shiLoW;# [square m/cubic m]\n",
+ "# From Table 6.3 (Pg 196)\n",
+ "e = 0.75;\n",
+ "# From Eqn. 6.71\n",
+ "eLo = e-shiLt;\n",
+ "# From Eqn. 6.70\n",
+ "def f9(Fg):\n",
+ " return ((Fg*ScG**(2.0/3))/G)-1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36) \n",
+ "Fg = fsolve(f9,1);# [kmol/square m.s]\n",
+ "# From Eqn. 6.72:\n",
+ "def f10(Kl):\n",
+ " return (Kl*Ds/Dl)-(25.1*(Ds*L_prime/viscocity_L)**0.45)*ScL**0.5\n",
+ "Kl = fsolve(f10,1);# [(kmol/square m.s).(kmol/cubic m)]\n",
+ "# Since the value of Kl is taken at low conc., it can be converted into Fl\n",
+ "c = (Density_L/Mavg_L);# [kmol/cubic m]\n",
+ "Fl = Kl*c;# [kmol/cubic m]\n",
+ "print\"The volumetric coeffecients are\\n\"\n",
+ "print\"Based on Gas Phase \",round(Fg*aA,3),\" kmol/cubic m.s\\n\"\n",
+ "print\"based on Liquid Phase\",round(Fl*aA,3),\" kmol/cubic m.s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.6 - Page: 204\n",
+ "\n",
+ "\n",
+ "The volumetric coeffecients are\n",
+ "\n",
+ "Based on Gas Phase 0.071 kmol/cubic m.s\n",
+ "\n",
+ "based on Liquid Phase 0.014 kmol/cubic m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.7: Page 207"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.7\n",
+ "# Page: 207\n",
+ "\n",
+ "print'Illustration 6.7 - Page: 207\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#****Data****#\n",
+ "# Air\n",
+ "G_prime = 1.10;# [kg/square m.s]\n",
+ "viscocity_G = 1.8*10**(-5);# [kg/m.s]\n",
+ "ScG = 0.6;# [for air water mixture]\n",
+ "Temp1 = 273+20.0;# [K]\n",
+ "\n",
+ "# Water\n",
+ "L_prime = 5.5;# [kg/square m.s]\n",
+ "#*****#\n",
+ "\n",
+ "# Air:\n",
+ "Ma = 29.0;# [kg/kmol]\n",
+ "G = G_prime/Ma;# [kmol/square m.s]\n",
+ "Density_G = (Ma/22.41)*(273.0/Temp1);\n",
+ "Cpa = 1005.0;# [N.m/kg.K]\n",
+ "PrG = 0.74;\n",
+ "\n",
+ "# Liquid:\n",
+ "kth = 0.587;# [W/m.K]\n",
+ "Cpb = 4187.0;# [N.m/kg.K]\n",
+ "viscocity_L = 1.14*10**(-3);# [kg/m.s]\n",
+ "\n",
+ "# From Table 6.5 (Pg 206)\n",
+ "Ds = 0.0725;# [m]\n",
+ "beeta = 1.508*(Ds**0.376);\n",
+ "shiLtW = (2.09*10**(-6))*(737.5*L_prime)**beeta/(Ds**2);# [square m/cubic m]\n",
+ "shiLsW = 2.47*10**(-4)/(Ds**1.21);# [square m/cubic m]\n",
+ "shiLoW = shiLtW-shiLsW;# [square m/cubic m]\n",
+ "# From Table 6.4 (Pg 205)\n",
+ "m = 34.03;\n",
+ "n = 0.0;\n",
+ "p = 0.362;\n",
+ "aAW = m*(808.0*G_prime/Density_G**0.5)**(n)*L_prime**p;# [square m/cubic m]\n",
+ "# From Eqn. 6.75\n",
+ "aVW = 0.85*aAW*shiLtW/shiLoW;# [square m/cubic m]\n",
+ "# From Table 6.3\n",
+ "e = 0.74;\n",
+ "eLo = e-shiLtW;\n",
+ "# From Eqn. 6.70\n",
+ "def f11(Fg):\n",
+ " return ((Fg*ScG**(2.0/3))/G)-1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36)\n",
+ "Fg = fsolve(f11,1);# [kmol/square m.s]\n",
+ "# Since the liquid is pure water. It has no mass trnsfer coeffecient.\n",
+ "# For such process we need convective heat transfer coeffecient for both liquid & gas.\n",
+ "# Asuming Jd = Jh\n",
+ "# From Eqn. 6.70\n",
+ "Jh = 1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36);\n",
+ "Hg = Jh*Cpa*G_prime/(PrG**(2.0/3));# [W/square m.K]\n",
+ "PrL = Cpb*viscocity_L/kth;\n",
+ "# Heat transfer analog of Eqn. 6.72\n",
+ "Hl = 25.1*(kth/Ds)*(Ds*L_prime/viscocity_L)**0.45*PrL**0.5;# [W/square m.K]\n",
+ "print\"The volumetric coeffecients are\\n\"\n",
+ "print\"Based on Gas Phase \",round(Hg*aVW), \"W/cubic m.K\\n\"\n",
+ "print\"based on Liquid Phase\",round(Hl*aVW,2),\" W/cubic m.K\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.7 - Page: 207\n",
+ "\n",
+ "\n",
+ "The volumetric coeffecients are\n",
+ "\n",
+ "Based on Gas Phase 3183.0 W/cubic m.K\n",
+ "\n",
+ "based on Liquid Phase 503701.46 W/cubic m.K\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.1\n",
+ "# Page: 494\n",
+ "\n",
+ "print'Illustration 10.1 - Page: 494\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "xF = 0.30;# [mol fraction]\n",
+ "yS = 0;# [mol fraction]\n",
+ "S1 = 40.0;# [kg]\n",
+ "B1 = 40.0;# [kg]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data at 20 OC:\n",
+ "# Wa: Wt. percent of a\n",
+ "# Wb: Wt. percent of b\n",
+ "# Wc: Wt. percent of c\n",
+ "# Data1 = [Wc Wa Wb]\n",
+ "# Data1: water layer\n",
+ "Data1 = numpy.array([(0.69 ,98.1, 1.2),(1.41, 97.1 ,1.5),(2.89 ,95.5 ,1.6),(6.42 ,91.7 ,1.9),(13.30, 84.4, 2.3),(25.50 ,71.1 ,3.4),(36.70 ,58.9 ,4.4),(44.30 ,45.1 ,10.6),(46.40 ,37.1 ,16.5)])\n",
+ "# Data2: isopropyl ether layer\n",
+ "Data2 = numpy.array([(0.18 ,0.5 ,99.3),(0.37, 0.7 ,98.9),(0.79, 0.8, 98.4),(1.93 ,1, 97.1),(4.82, 1.9, 93.3),(11.40, 3.9, 84.7),(21.60, 6.9, 71.5),(31.10, 10.8, 58.1),(36.20 ,15.1 ,48.7)])\n",
+ "\n",
+ "plt.plot((Data1[:,2])/100,(Data1[:,0])/100,label=\"x Vs fraction ether\")\n",
+ "plt.plot((Data2[:,2])/100,(Data2[:,0])/100,label=\"y Vs fraction ether\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='lower center');\n",
+ "ax=pylab.gca()\n",
+ "ax.set_xlabel(\"Wt fraction of isopropyl ether\");\n",
+ "ax.set_ylabel(\"Wt fraction of acetic acid\");\n",
+ "plt.ylim((0,0.3))\n",
+ "plt.xlim((0,1))\n",
+ "plt.show();\n",
+ "# x: Wt fraction of acetic acid in water layer.\n",
+ "# y: Wt fraction of acetic acid in isopropyl layer.\n",
+ "\n",
+ "# The rectangular coordinates of Fig 10.9(a) will be used but only upto x = 0.30\n",
+ "\n",
+ "# Stage 1:\n",
+ "F = 100;# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "M1 = F+S1;# [kg]\n",
+ "# From Eqn. 10.5:\n",
+ "xM1 = ((F*xF)+(S1*yS))/M1;\n",
+ "# From Fig. 10.15 (Pg 495):\n",
+ "# Point M1 is located on the line FB and with the help of tie line passing through M1:\n",
+ "x1 = 0.258;# [mol fraction]\n",
+ "y1 = 0.117;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E1 = (M1*(xM1-x1)/(y1-x1));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R1 = M1-E1;# [kg]\n",
+ "\n",
+ "# Stage 2:\n",
+ "S2 = 40;# [kg]\n",
+ "B2 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M2 = R1+B2;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM2 = ((R1*x1)+(S2*yS))/M2;\n",
+ "# Point M2 is located on the line R1B and the tie line passing through R2E2 through M2:\n",
+ "x2 = 0.227;\n",
+ "y2 = 0.095;\n",
+ "# From Eqn. 10.8:\n",
+ "E2 = (M2*(xM2-x2)/(y2-x2));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R2 = M2-E2;# [kg]\n",
+ "\n",
+ "# Stage 3:\n",
+ "S3 = 40;# [kg]\n",
+ "B3 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M3 = R2+B3;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM3 = ((R2*x2)+(S3*yS))/M3;\n",
+ "# Point M3 is located on the line R2B and the tie line passing through R3E3 through M3:\n",
+ "x3 = 0.20;# [mol fraction]\n",
+ "y3 = 0.078;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E3 = (M3*(xM3-x3)/(y3-x3));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R3 = M3-E3;# [kg]\n",
+ "Ac = x3*R3;\n",
+ "print\"The composited extract is\",round((E1+E2+E3),2),\" kg\\n\"\n",
+ "print\"The acid content is \",round(((E1*y1)+(E2*y2)+(E3*y3)),2),\" kg\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# If an extraction to give the same final raffinate concentration were to be done in single stage, the point M would be at the intersection of tie line R3E3 and the line BF.\n",
+ "x = 0.20;# [mol fraction]\n",
+ "xM = 0.12;# [mol fraction]\n",
+ "# From Eqn. 10.6:\n",
+ "S = F*(xF-xM)/(xM-yS);# [kg]\n",
+ "print round(S,2),\"kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.1 - Page: 494\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F6AM8U4J9C5SIVVLGmMhIrpzM4+c+Tp+P+3BAY2J4oDLjdTyM0cB+4BxgHDDB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\niRUYxhhjPPl/ptoLDFmMWJUAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x917b748>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The composited extract is 135.05 kg\n",
+ "\n",
+ "The acid content is 13.01 kg\n",
+ "\n",
+ "\n",
+ "\n",
+ "150.0 kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 82
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter6_1.ipynb b/Mass_-_Transfer_Operations/Chapter6_1.ipynb
new file mode 100755
index 00000000..d9b08193
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter6_1.ipynb
@@ -0,0 +1,1059 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:ce06499e0802b1a354db3b54c156495e1f9e00501c129efee55c325fbd5e394e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 6: Equipment For Gas-Liquid Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.1: Page 145"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.1\n",
+ "# Page: 145\n",
+ "\n",
+ "print'Illustration 6.1 - Page: 145\\n\\n'\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# w = Gas flow rate per orifice\n",
+ "w = 0.055/50;# [kg/s]\n",
+ "L = 8*10**(-4);# [liquid flow rate, cubic m/s]\n",
+ "d = 0.003;# [diameter of the orifice,m]\n",
+ "viscocity_gas = 1.8*10**(-5);# [kg/m.s]\n",
+ "#******#\n",
+ "\n",
+ "Re = 4*w/(math.pi*d*viscocity_gas);\n",
+ "Dp = 0.0071*Re**(-0.05);# [m]\n",
+ "h = 3.0;# [height of vessel,m]\n",
+ "P_atm = 101.3;# [kN/square m]\n",
+ "Density_water = 1000.0;# [kg/cubic m]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "Temp = 273+25;# [K]\n",
+ "P_orifice = P_atm+(h*Density_water*g/1000);# [kN/square m]\n",
+ "P_avg = P_atm+((h/2.0)*Density_water*g/1000);# [kN/square m]\n",
+ "Density_gas = (29/22.41)*(273.0/Temp)*(P_avg/P_atm);# [kg/cubic m]\n",
+ "D = 1.0;# [dia of vessel,m]\n",
+ "Area = (math.pi*D**2)/4;# [square m]\n",
+ "Vg = 0.055/(Area*Density_gas);# [m/s]\n",
+ "Vl = L/Area;# [m/s]\n",
+ "sigma = 0.072;# [N/m]\n",
+ "# From fig. 6.2 (Pg 143)\n",
+ "abscissa = 0.0516;# [m/s]\n",
+ "Vg_by_Vs = 0.11;\n",
+ "Vs = Vg/Vg_by_Vs;# [m/s]\n",
+ "def f6(shi_g):\n",
+ " return Vs-(Vg/shi_g)+(Vl/(1-shi_g)) \n",
+ "shi_g = fsolve(f6,0.5);\n",
+ "dp = ((Dp**3)*(P_orifice/P_avg))**(1.0/3);# [bubble diameter,m]\n",
+ "# From eqn. 6.9\n",
+ "a = 6.0*shi_g/dp;# [specific interfacial area,square m]\n",
+ "print\"The Specific Interfacial Area is \",round(a,2),\" square m/cubic m\\n\"\n",
+ "\n",
+ "# For diffsion of Cl2 in H20\n",
+ "Dl = 1.44*10**(-9);# [square m/s]\n",
+ "viscocity_water = 8.937*10**(-4);# [kg/m.s]\n",
+ "Reg = dp*Vs*Density_water/viscocity_water;\n",
+ "Scl = viscocity_water/(Density_water*Dl);\n",
+ "# From Eqn.6.11\n",
+ "Shl = 2+(0.0187*(Reg**0.779)*(Scl**0.546)*(dp*(g**(1.0/3))/(Dl**(2.0/3)))**0.116);\n",
+ "# For dilute soln. of Cl2 in H20\n",
+ "c = 1000/18.02;# [kmol/cubic m]\n",
+ "Fl = (c*Dl*Shl)/dp;# [kmol/square m.s]\n",
+ "print\"Mass Transfer coeffecient is \",round(Fl,5),\" kmol/square m.s\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.1 - Page: 145\n",
+ "\n",
+ "\n",
+ "The Specific Interfacial Area is 148.13 square m/cubic m\n",
+ "\n",
+ "Mass Transfer coeffecient is 0.01335 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.2: Page 157"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.2\n",
+ "# Page: 157\n",
+ "\n",
+ "print'Illustration 6.2 - Page: 157\\n\\n'\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = N2 b = H2O\n",
+ "L = 9.5*10**(-4);# [cubic m/s]\n",
+ "G = 0.061;# [kg/s]\n",
+ "Temp = 273.0+25;# [K]\n",
+ "#*****#\n",
+ "\n",
+ "print\"Construction Arrangement\\n\"\n",
+ "print\"Use 4 vertical wall baffles, 100 mm wide at 90 degree intervals.\\n\"\n",
+ "print\"Use a 305 mm dameter, a six bladed disk flat blade turbine impeller, arranged axially, 300 mm from the bottom of vessel\\n\"\n",
+ "print\"The sparger underneath the impeller will be in the form of a 240 mm dameter ring made of 12.7 mm tubing drilled in the top with 3.18 mm dia holes\\n\"\n",
+ "Di = 0.305;# [m]\n",
+ "Do = 0.00316;# [m]\n",
+ "viscocity_a = 1.8*10**(-5);# [kg/m.s]\n",
+ "Re_g = 35000;\n",
+ "Ma = 28.02;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# w = Gas flow rate per orifice\n",
+ "w = Re_g*math.pi*Do*viscocity_a/4.0;# [kg/s]\n",
+ "N_holes = G/w;\n",
+ "Interval = math.pi*240/round(N_holes);\n",
+ "print\"The number of holes is \",round(N_holes),\" at approx \",round(Interval),\" mm interval around the sparger ring\\n\"\n",
+ "\n",
+ "viscocity_b = 8.9*10**(-4);# [kg/m.s]\n",
+ "Sigma = 0.072;# [N/m]\n",
+ "Density_b = 1000.0;# [kg/cubic m]\n",
+ "D = 1.0;# [dia of vessel,m]\n",
+ "g = 9.81;# [m/s**2]\n",
+ "# From Eqn. 6.18\n",
+ "def f7(N):\n",
+ " return (N*Di/(Sigma*g/Density_b)**0.25)-1.22-(1.25*D/Di)\n",
+ "N_min = fsolve(f7,2);# [r/s]\n",
+ "N = 5.0;# [r/s]\n",
+ "Re_l = ((Di**2)*N*Density_b/viscocity_b);\n",
+ "# From fig 6.5 (Pg 152)\n",
+ "Po = 5.0;\n",
+ "P = Po*Density_b*(N**3)*(Di**5);\n",
+ "h = 0.7;# [m]\n",
+ "P_atm = 101.33;# [kN/square m]\n",
+ "P_gas = P_atm+(h*Density_b*g/1000.0);# [kN/square m]\n",
+ "Qg = (G/Ma)*22.41*(Temp/273.0)*(P_atm/P_gas);# [cubic m/s]\n",
+ "# From Fig.6.7 (Pg 155)\n",
+ "abcissa = Qg/(N*(Di**3));\n",
+ "# abcissa is off scale\n",
+ "Pg_by_P = 0.43;\n",
+ "Pg = 0.43*P;# [W]\n",
+ "Vg = Qg/(math.pi*(D**2)/4);# [superficial gas velocity,m/s]\n",
+ "check_value = (Re_l**0.7)*((N*Di/Vg)**0.3);\n",
+ "vl = math.pi*(D**2)/4;# [cubic m]\n",
+ "# Since value<30000\n",
+ "# From Eqn. 6.21, Eqn.6.23 & Eqn. 6.24\n",
+ "K = 2.25;\n",
+ "m = 0.4;\n",
+ "Vt = 0.250;# [m/s]\n",
+ "shi = 1.0;\n",
+ "err = 1.0;\n",
+ "while (err>10**(-3)):\n",
+ " a = 1.44*((Pg/vl)**0.4)*((Density_b/(Sigma**3))**0.2)*((Vg/Vt)**0.5);# [square m/cubic m]\n",
+ " shin = (0.24*K*((viscocity_a/viscocity_b)**0.25)*((Vg/Vt)**0.5))**(1.0/(1-m));\n",
+ " Dp = K*((vl/Pg)**0.4)*((Sigma**3/Density_b)**0.2)*(shin**m)*((viscocity_a/viscocity_b)**0.25);# [m]\n",
+ " err = abs(shi-shin);\n",
+ " Vt = Vt-0.002;# [m/s]\n",
+ " shi = shin;\n",
+ "\n",
+ "\n",
+ "# For N2 in H2\n",
+ "Dl = 1.9*10**(-9);# [square m/s]\n",
+ "Ra = 1.514*10**(6);\n",
+ "# By Eqn. 6.25\n",
+ "Shl = 2.0+(0.31*(Ra**(1.0/3)));\n",
+ "# For dilute soln.\n",
+ "c = 1000.0/Mb;# [kmol/cubic m]\n",
+ "Fl = Shl*c*Dl*1.0/Dp;# [kmol/square m.s]\n",
+ "print\"The average gas-bubble diameter is \",(\"{:.2e}\".format(Dp)),\" m\\n\",\n",
+ "print\"Gas Holdup:\\n\",round(shi,5)\n",
+ "print\"Interfacial area:\",round(a,4),\" square m/cubic m \\n\"\n",
+ "print\"Mass transfer coffecient:\",(\"{:.2e}\".format(Fl)),\"kmol/square m.s\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.2 - Page: 157\n",
+ "\n",
+ "\n",
+ "Construction Arrangement\n",
+ "\n",
+ "Use 4 vertical wall baffles, 100 mm wide at 90 degree intervals.\n",
+ "\n",
+ "Use a 305 mm dameter, a six bladed disk flat blade turbine impeller, arranged axially, 300 mm from the bottom of vessel\n",
+ "\n",
+ "The sparger underneath the impeller will be in the form of a 240 mm dameter ring made of 12.7 mm tubing drilled in the top with 3.18 mm dia holes\n",
+ "\n",
+ "The number of holes is 39.0 at approx 19.0 mm interval around the sparger ring\n",
+ "\n",
+ "The average gas-bubble diameter is 6.35e-04 m\n",
+ "Gas Holdup:\n",
+ "0.02265\n",
+ "Interfacial area: 214.0106 square m/cubic m \n",
+ "\n",
+ "Mass transfer coffecient: 6.24e-03 kmol/square m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.3: Page 174"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.3\n",
+ "# Page: 174\n",
+ "\n",
+ "print'Illustration 6.3 - Page: 174\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = methanol b = water\n",
+ "G = 0.100;# [kmol/s]\n",
+ "L = 0.25;# [kmol/s]\n",
+ "Temp = 273+95;# [K]\n",
+ "XaG = 0.18;# [mol % in gas phase]\n",
+ "MaL = 0.15;# [mass % in liquid phase]\n",
+ "#*****#\n",
+ "\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "Mavg_G = XaG*Ma+((1-XaG)*Mb);# [kg/kmol]\n",
+ "Density_G = (Mavg_G/22.41)*(273.0/Temp);# [kg/cubic cm]\n",
+ "Q = G*22.41*(Temp/273.0);# [cubic cm/s]\n",
+ "Density_L = 961.0;# [kg/cubic cm]\n",
+ "Mavg_L = 1.0/((MaL/Ma)+(1-MaL)/Mb);# [kg/kmol]\n",
+ "q = L*Mavg_L/Density_L;\n",
+ "\n",
+ "# Perforations\n",
+ "print\"Perforations\\n\"\n",
+ "print\"Do = 4.5mm on an equilateral triangle pitch 12 mm between the hole centres, punched in sheet metal 2 mm thick\\n\"\n",
+ "Do = 0.0045;# [m]\n",
+ "pitch = 0.012;# [m]\n",
+ "# By Eqn.6.31\n",
+ "Ao_by_Aa = 0.907*(Do/pitch)**2;\n",
+ "print\"The ratio of Hole Area By Active Area is:\",round(Ao_by_Aa,4),\"\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Tower Diameter\n",
+ "print\"Tower Diameter\\n\"\n",
+ "t = 0.50;# [tray spacing,m]\n",
+ "print\"Tower Spacing:\",t,\" m\\n\"\n",
+ "# abcissa = (L/G)*(Density_G/Density_L)^0.5 = (q/Q)*(Density_L/Density_G)**0.5\n",
+ "abcissa = (q/Q)*(Density_L/Density_G)**0.5;\n",
+ "# From Table 6.2 (Pg 169)\n",
+ "alpha = (0.0744*t)+0.01173;\n",
+ "beeta = (0.0304*t)+0.015;\n",
+ "if (abcissa<0.1):\n",
+ " abcissa = 0.1;\n",
+ "\n",
+ "sigma = 0.040;# [N/m]\n",
+ "# From Eqn.6.30\n",
+ "Cf = ((alpha*math.log10(1.0/abcissa))+beeta)*(sigma/0.02)**0.2;\n",
+ "# From Eqn. 6.29\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1/2);# [m/s]\n",
+ "# Using 80% of flooding velocity\n",
+ "V = 0.8*Vf;# [m/s]\n",
+ "An = Q/V;# [square m]\n",
+ "# The tray area used by one downspout = 8.8%\n",
+ "At = An/(1-0.088);# [square m]\n",
+ "D = (4*At/math.pi)**(1.0/2);# [m]\n",
+ "# Take D = 1.25 m\n",
+ "D = 1.25; #[m]\n",
+ "At = math.pi*(D**2)/4;# [corrected At, square m]\n",
+ "W = 0.7*D;# [weir length,m]\n",
+ "Ad = 0.088*At;# [square m]\n",
+ "# For a design similar to Fig 6.14 (Pg 168)\n",
+ "# A 40 mm wide supporting ring, beams between downspouts and a 50 mm wide disengaging & distributing zones these areas total 0.222 square m\n",
+ "Aa = At-(2.0*Ad)-0.222;\n",
+ "print\"Weir Length:\",round(W,4),\"\\n\"\n",
+ "print\"Area for perforated sheet: \",round(Aa,4),\" square m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Weir crest h1 & Weir height hw\n",
+ "print\"Weir crest h1 & Weir height hw\\n\"\n",
+ "h1 = 0.025;# [m]\n",
+ "h1_by_D = h1/D;\n",
+ "D_by_W = D/W;\n",
+ "# From Eqn. 6.34\n",
+ "Weff_by_W = math.sqrt(((D_by_W)**2)-((((D_by_W)**2-1)**0.5)+(2*h1_by_D*D_by_W))**2);\n",
+ "# Set hw to 50 mm\n",
+ "hw = 0.05;# [m]\n",
+ "print\"Weir crest: \",h1,\" m\\n\"\n",
+ "print\"Weir height: \",hw,\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Dry Pressure Drop\n",
+ "print\"Dry Pressure Drop\\n\"\n",
+ "l = 0.002;# [m]\n",
+ "# From Eqn. 6.37\n",
+ "Co = 1.09*(Do/l)**0.25;\n",
+ "Ao = 0.1275*Aa;# [square m]\n",
+ "Vo = Q/Ao;# [m/sec]\n",
+ "viscocity_G = 1.25*10**(-5);# [kg/m.s]\n",
+ "Re = Do*Vo*Density_G/viscocity_G;\n",
+ "# From \"The Chemical Engineers Handbook,\" 5th Edition fig 5.26\n",
+ "fr = 0.008;\n",
+ "g = 9.81;# [m/s**2]\n",
+ "# From Eqn. 6.36\n",
+ "def f(hd):\n",
+ " return (2*hd*g*Density_L/(Vo**2*Density_G))-(Co*(0.40*(1.25-(Ao/An))+(4*l*fr/Do)+(1-(Ao/An))**2))\n",
+ "hd = fsolve(f,1);\n",
+ "print\"Dry Pressure Drop:\",round(hd,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Hydraulic head hl\n",
+ "print\"Hydraulic head hl\"\n",
+ "Va = Q/Aa;# [m/s]\n",
+ "z = (D+W)/2.0;# [m]\n",
+ "# From Eqn. 6.38\n",
+ "hl = 6.10*10**(-3)+(0.725*hw)-(0.238*hw*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "print\"Hydraulic head: \",round(hl,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#Residual Pressure drop hr\n",
+ "print\"Residual Pressure drop hr\\n\"\n",
+ "# From Eqn. 6.42\n",
+ "hr = 6*sigma/(Density_L*Do*g);# m\n",
+ "print\"Residual Pressure Drop:\",round(hr,4),\"m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Total Gas pressure Drop hg\n",
+ "print\"Total Gas pressure Drop hg\\n\"\n",
+ "# From Eqn. 6.35\n",
+ "hg = hd+hl+hr;# [m]\n",
+ "print\"Total gas pressure Drop: \",round(hg,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Pressure loss at liquid entrance h2\n",
+ "print\"Pressure loss at liquid entrance h2\\n\"\n",
+ "# Al: Area for the liquid flow under the apron\n",
+ "Al = 0.025*W;# [square m]\n",
+ "Ada = min(Al,Ad);\n",
+ "# From Eqn. 6.43\n",
+ "h2 = (3.0/(2*g))*(q/Ada)**2;\n",
+ "print\"Pressure loss at liquid entrance:\",round(h2,4),\"m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Backup in Downspout h3\n",
+ "print\"Backup in Downspout h3\\n\"\n",
+ "# From Eqn.6.44\n",
+ "h3 = hg+h2;\n",
+ "print\"Backup in Downspout:\",round(h3,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Check on Flooding\n",
+ "print\"Check on Flooding\\n\"\n",
+ "if((hw+h1+h3)<(t/2.0)):\n",
+ " print\"Choosen Tower spacing is satisfactory\\n\"\n",
+ "else:\n",
+ " print\"Choosen Tower spacing is not satisfactory\\n\"\n",
+ "\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Weeping Velocity\n",
+ "print\"Weeping Velocity\\n\"\n",
+ "print\"For W/D ratio \",W/D,\" weir is set at \",0.3296*D,\" m from the center from the tower\\n\",\n",
+ "Z = 2*(0.3296*D);# [m]\n",
+ "# From Eqn.6.46\n",
+ "def f8(Vow):\n",
+ " return (Vow*viscocity_G/(sigma))-(0.0229*((viscocity_G**2/(sigma*Density_G*Do))*(Density_L/Density_G))**0.379)*((l/Do)**0.293)*(2*Aa*Do/(math.sqrt(3.0)*(pitch**3)))**(2.8/((Z/Do)**0.724))\n",
+ "Vow = fsolve(f8,0.1);# [m/s]\n",
+ "print\"The minimum gas velocity through the holes below which excessive weeping is likely:\",round(Vow,3),\" m/s\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Entrainment\n",
+ "print\"Entrainment\\n\"\n",
+ "V_by_Vf = V/Vf;\n",
+ "# From Fig.6.17 (Pg 173), V/Vf = 0.8 & abcissa = 0.0622\n",
+ "E = 0.05;\n",
+ "print\"Entrainment:\\n\",E\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.3 - Page: 174\n",
+ "\n",
+ "\n",
+ "Perforations\n",
+ "\n",
+ "Do = 4.5mm on an equilateral triangle pitch 12 mm between the hole centres, punched in sheet metal 2 mm thick\n",
+ "\n",
+ "The ratio of Hole Area By Active Area is: 0.1275 \n",
+ "\n",
+ "\n",
+ "\n",
+ "Tower Diameter\n",
+ "\n",
+ "Tower Spacing: 0.5 m\n",
+ "\n",
+ "Weir Length: 0.875 \n",
+ "\n",
+ "Area for perforated sheet: 0.7892 square m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Weir crest h1 & Weir height hw\n",
+ "\n",
+ "Weir crest: 0.025 m\n",
+ "\n",
+ "Weir height: 0.05 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Dry Pressure Drop\n",
+ "\n",
+ "Dry Pressure Drop: 0.0654 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Hydraulic head hl\n",
+ "Hydraulic head: 0.0106 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Residual Pressure drop hr\n",
+ "\n",
+ "Residual Pressure Drop: 0.0057 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Total Gas pressure Drop hg\n",
+ "\n",
+ "Total gas pressure Drop: 0.0816 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Pressure loss at liquid entrance h2\n",
+ "\n",
+ "Pressure loss at liquid entrance: 0.008 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Backup in Downspout h3\n",
+ "\n",
+ "Backup in Downspout: 0.0897 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Check on Flooding\n",
+ "\n",
+ "Choosen Tower spacing is satisfactory\n",
+ "\n",
+ "\n",
+ "\n",
+ "Weeping Velocity\n",
+ "\n",
+ "For W/D ratio 0.7 weir is set at 0.412 m from the center from the tower\n",
+ "The minimum gas velocity through the holes below which excessive weeping is likely: 8.703 m/s\n",
+ "\n",
+ "\n",
+ "\n",
+ "Entrainment\n",
+ "\n",
+ "Entrainment:\n",
+ "0.05\n"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.4: Page 183"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.4\n",
+ "# Page: 183\n",
+ "\n",
+ "print'Illustration 6.4 - Page: 183\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "#****Data****#\n",
+ "#From Illustrtion 6.3:\n",
+ "G = 0.100;# [kmol/s]\n",
+ "Density_G = 0.679;# [kg/cubic m]\n",
+ "q = 5*10**(-3);# [cubic m/s]\n",
+ "Va = 3.827;# [m/s]\n",
+ "z = 1.063;# [m]\n",
+ "L = 0.25;# [kmol/s]\n",
+ "hL = 0.0106;# [m]\n",
+ "hW = 0.05;# [m]\n",
+ "Z = 0.824;# [m]\n",
+ "E = 0.05;\n",
+ "ya = 0.18;# [mole fraction methanol]\n",
+ "\n",
+ "# a:CH3OH b:H2O\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "# From Chapter 2:\n",
+ "ScG = 0.865;\n",
+ "Dl = 5.94*10**(-9);# [square m/s]\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/ScG**0.5;\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;# [square m/s]\n",
+ "thethaL = hL*z*Z/q;# [s]\n",
+ "NtL = 40000*Dl**0.5*((0.213*Va*Density_G**0.5)+0.15)*thethaL;\n",
+ "# For 15 mass% methanol:\n",
+ "xa = (15.0/Ma)/((15.0/Ma)+(85.0/Mb));\n",
+ "# From Fig 6.23 (Pg 184)\n",
+ "mAC = -(NtL*L)/(NtG*G);# [Slope of AC line]\n",
+ "meqb = 2.50;# [slope of equilibrium line]\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1.0/((1/NtG)+(meqb*G/L)*(1.0/NtL));\n",
+ "# From Eqn. 6.51:\n",
+ "EOG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thethaL);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2.0)*((1+(4*meqb*G*EOG/(L*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EOG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+(math.exp(eta)-1)/(eta*(1+eta/(eta+Pe))));\n",
+ "# From Eqn. 6.60:\n",
+ "EMGE = EMG/(1+(EMG*E/(1-E)));\n",
+ "print\"Efficiency of Sieve trays: \",round(EMGE,1)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.4 - Page: 183\n",
+ "\n",
+ "\n",
+ "Effeciency of Sieve trays: 0.7\n"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.5: Page 200"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.5\n",
+ "# Page: 200\n",
+ "\n",
+ "print'Illustration 6.5 - Page: 200\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "# ****Data****#\n",
+ "G = 0.80;# [cubic m/s]\n",
+ "P = 10**2;# [kN/square m]\n",
+ "XaG = 0.07;\n",
+ "Temp = 273+30.0;# [K]\n",
+ "L = 3.8;# [kg/s]\n",
+ "Density_L = 1235.0;# [kg/cubic m]\n",
+ "viscocity_L = 2.5*10**(-3);# [kg/m.s]\n",
+ "#******#\n",
+ "\n",
+ "# a = SO2 b = air\n",
+ "\n",
+ "# Solution (a) \n",
+ "\n",
+ "# Since the larger flow quantities are at the bottom for an absorber, the diameter will be choosen to accomodate the bottom condition\n",
+ "Mavg_G = XaG*64+((1-XaG)*29);# [kg/kmol]\n",
+ "G1 = G*(273/Temp)*(P/101.33)*(1/22.41);# [kmol/s]\n",
+ "G2 = G1*Mavg_G;# [kg/s]\n",
+ "Density_G = G2/G;# [kg/cubic m]\n",
+ "# Assuming Complete absorption of SO2\n",
+ "sulphur_removed = G1*XaG*64;# [kg/s]\n",
+ "abcissa = (L/G)*((Density_G/Density_L)**0.5);\n",
+ "#From Fig. 6.24, using gas pressure drop of 400 (N/square m)/m\n",
+ "ordinate = 0.061;\n",
+ "# For 25 mm ceramic Intalox Saddle:\n",
+ "Cf = 98.0;# [Table 6.3 Pg 196]\n",
+ "J = 1;\n",
+ "G_prime = (ordinate*Density_G*(Density_L-Density_G)/(Cf*viscocity_L**0.1*J))**0.5;# [kg/square m.s]\n",
+ "A = G2/G_prime;# [square m]\n",
+ "D = (4*A/math.pi)**0.5;# [m]\n",
+ "print\"The Tower Diameter is \",round(D,4),\" m\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "# Let\n",
+ "D = 1.0;# [m]\n",
+ "A = math.pi*D**2.0/4;# [square m]\n",
+ "# The pressure drop for 8 m of irrigated packing\n",
+ "delta_p = 400*8.0;# [N/square m]\n",
+ "# For dry packing\n",
+ "G_prime = (G2-sulphur_removed)/A;# [kg/square m.s]\n",
+ "P = P-(delta_p/1000.0);# [kN/square m]\n",
+ "Density_G = (29/22.41)*(273.0/Temp)*(P/101.33);# [kg/cubic m]\n",
+ "# From Table 6.3 (Pg 196)\n",
+ "Cd = 241.5;\n",
+ "# From Eqn. 6.68\n",
+ "delta_p_by_z = Cd*G_prime**2/Density_G;# [N/square m for 1m of packing]\n",
+ "pressure_drop = delta_p+delta_p_by_z;# [N/square m]\n",
+ "V = 7.5;# [m/s]\n",
+ "head_loss = 1.5*V**2.0/2;# [N.m/kg]\n",
+ "head_loss = head_loss*Density_G;# [N/square m]\n",
+ "Power = (pressure_drop+head_loss)*(G2-sulphur_removed)/(Density_G*1000.0);# [kW]\n",
+ "eta = 0.6;\n",
+ "Power = Power/eta;# [kW]\n",
+ "print\"The Power for the fan motor is \",round(Power,2),\" kW\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.5 - Page: 200\n",
+ "\n",
+ "\n",
+ "The Tower Diameter is 0.981 m\n",
+ "\n",
+ "The Power for the fan motor is 4.49 kW\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.6: Page 204"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.6\n",
+ "# Page: 204\n",
+ "\n",
+ "print'Illustration 6.6 - Page: 204\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# Gas\n",
+ "Mavg_G = 11.0;# [kg/kmol]\n",
+ "viscocity_G = 10**(-5);# [kg/m.s]\n",
+ "Pt = 107.0;# [kN/square m]\n",
+ "Dg = 1.30*10**(-5);# [square m/s]\n",
+ "Temp = 273.0+27;# [K]\n",
+ "G_prime = 0.716;# [kg/square m.s]\n",
+ "\n",
+ "# Liquid:\n",
+ "Mavg_L = 260.0;\n",
+ "viscocity_L = 2*10**(-3);# [kg/m.s]\n",
+ "Density_L = 840.0;# [kg/cubic m]\n",
+ "sigma = 3*10.0**(-2);# [N/m]\n",
+ "Dl = 4.71*10**(-10);# [square m/s]\n",
+ "#******#\n",
+ "\n",
+ "#Gas:\n",
+ "Density_G = (Mavg_G/22.41)*(Pt/101.33)*(273/Temp);# [kg/cubic m]\n",
+ "ScG = viscocity_G/(Density_G*Dg);\n",
+ "G = G_prime/Mavg_G;# [kmol/square m.s]\n",
+ "\n",
+ "# Liquid:\n",
+ "L_prime = 2.71;# [kg/square m.s]\n",
+ "ScL = viscocity_L/(Density_L*Dl);\n",
+ "\n",
+ "# Holdup:\n",
+ "# From Table 6.5 (Pg 206), L_prime = 2.71 kg/square m.s\n",
+ "Ds = 0.0472;# [m]\n",
+ "beeta = 1.508*Ds**0.376;\n",
+ "shiLsW = 5.014*10**(-5)/Ds**1.56;# [square m/cubic m]\n",
+ "shiLtW = (2.32*10**(-6))*(737.5*L_prime)**beeta/(Ds**2);# [square m/cubic m]\n",
+ "shiLoW = shiLtW-shiLsW;# [square m/cubic m]\n",
+ "H = (1404*(L_prime**0.57)*(viscocity_L**0.13)/((Density_L**0.84)*((3.24*L_prime**0.413)-1)))*(sigma/0.073)**(0.2817-0.262*math.log10(L_prime));\n",
+ "shiLo = shiLoW*H;# [square m/cubic m]\n",
+ "shiLs = 4.23*10**(-3)*(viscocity_L**0.04)*(sigma**0.55)/((Ds**1.56)*(Density_L**0.37));# [square m/cubic m]\n",
+ "shiLt = shiLo+shiLs;# [square m/cubic m]\n",
+ "\n",
+ "# Interfacial Area:\n",
+ "# From Table 6.4 (Pg 205)\n",
+ "m = 62.4;\n",
+ "n = (0.0240*L_prime)-0.0996;\n",
+ "p = -0.1355;\n",
+ "aAW = m*((808*G_prime/(Density_G**0.5))**n)*(L_prime**p);# [square m/cubic m]\n",
+ "# From Eqn. 6.73\n",
+ "aA = aAW*shiLo/shiLoW;# [square m/cubic m]\n",
+ "# From Table 6.3 (Pg 196)\n",
+ "e = 0.75;\n",
+ "# From Eqn. 6.71\n",
+ "eLo = e-shiLt;\n",
+ "# From Eqn. 6.70\n",
+ "def f9(Fg):\n",
+ " return ((Fg*ScG**(2.0/3))/G)-1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36) \n",
+ "Fg = fsolve(f9,1);# [kmol/square m.s]\n",
+ "# From Eqn. 6.72:\n",
+ "def f10(Kl):\n",
+ " return (Kl*Ds/Dl)-(25.1*(Ds*L_prime/viscocity_L)**0.45)*ScL**0.5\n",
+ "Kl = fsolve(f10,1);# [(kmol/square m.s).(kmol/cubic m)]\n",
+ "# Since the value of Kl is taken at low conc., it can be converted into Fl\n",
+ "c = (Density_L/Mavg_L);# [kmol/cubic m]\n",
+ "Fl = Kl*c;# [kmol/cubic m]\n",
+ "print\"The volumetric coeffecients are\\n\"\n",
+ "print\"Based on Gas Phase \",round(Fg*aA,3),\" kmol/cubic m.s\\n\"\n",
+ "print\"based on Liquid Phase\",round(Fl*aA,3),\" kmol/cubic m.s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.6 - Page: 204\n",
+ "\n",
+ "\n",
+ "The volumetric coeffecients are\n",
+ "\n",
+ "Based on Gas Phase 0.071 kmol/cubic m.s\n",
+ "\n",
+ "based on Liquid Phase 0.014 kmol/cubic m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.7: Page 207"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.7\n",
+ "# Page: 207\n",
+ "\n",
+ "print'Illustration 6.7 - Page: 207\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#****Data****#\n",
+ "# Air\n",
+ "G_prime = 1.10;# [kg/square m.s]\n",
+ "viscocity_G = 1.8*10**(-5);# [kg/m.s]\n",
+ "ScG = 0.6;# [for air water mixture]\n",
+ "Temp1 = 273+20.0;# [K]\n",
+ "\n",
+ "# Water\n",
+ "L_prime = 5.5;# [kg/square m.s]\n",
+ "#*****#\n",
+ "\n",
+ "# Air:\n",
+ "Ma = 29.0;# [kg/kmol]\n",
+ "G = G_prime/Ma;# [kmol/square m.s]\n",
+ "Density_G = (Ma/22.41)*(273.0/Temp1);\n",
+ "Cpa = 1005.0;# [N.m/kg.K]\n",
+ "PrG = 0.74;\n",
+ "\n",
+ "# Liquid:\n",
+ "kth = 0.587;# [W/m.K]\n",
+ "Cpb = 4187.0;# [N.m/kg.K]\n",
+ "viscocity_L = 1.14*10**(-3);# [kg/m.s]\n",
+ "\n",
+ "# From Table 6.5 (Pg 206)\n",
+ "Ds = 0.0725;# [m]\n",
+ "beeta = 1.508*(Ds**0.376);\n",
+ "shiLtW = (2.09*10**(-6))*(737.5*L_prime)**beeta/(Ds**2);# [square m/cubic m]\n",
+ "shiLsW = 2.47*10**(-4)/(Ds**1.21);# [square m/cubic m]\n",
+ "shiLoW = shiLtW-shiLsW;# [square m/cubic m]\n",
+ "# From Table 6.4 (Pg 205)\n",
+ "m = 34.03;\n",
+ "n = 0.0;\n",
+ "p = 0.362;\n",
+ "aAW = m*(808.0*G_prime/Density_G**0.5)**(n)*L_prime**p;# [square m/cubic m]\n",
+ "# From Eqn. 6.75\n",
+ "aVW = 0.85*aAW*shiLtW/shiLoW;# [square m/cubic m]\n",
+ "# From Table 6.3\n",
+ "e = 0.74;\n",
+ "eLo = e-shiLtW;\n",
+ "# From Eqn. 6.70\n",
+ "def f11(Fg):\n",
+ " return ((Fg*ScG**(2.0/3))/G)-1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36)\n",
+ "Fg = fsolve(f11,1);# [kmol/square m.s]\n",
+ "# Since the liquid is pure water. It has no mass trnsfer coeffecient.\n",
+ "# For such process we need convective heat transfer coeffecient for both liquid & gas.\n",
+ "# Asuming Jd = Jh\n",
+ "# From Eqn. 6.70\n",
+ "Jh = 1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36);\n",
+ "Hg = Jh*Cpa*G_prime/(PrG**(2.0/3));# [W/square m.K]\n",
+ "PrL = Cpb*viscocity_L/kth;\n",
+ "# Heat transfer analog of Eqn. 6.72\n",
+ "Hl = 25.1*(kth/Ds)*(Ds*L_prime/viscocity_L)**0.45*PrL**0.5;# [W/square m.K]\n",
+ "print\"The volumetric coeffecients are\\n\"\n",
+ "print\"Based on Gas Phase \",round(Hg*aVW), \"W/cubic m.K\\n\"\n",
+ "print\"based on Liquid Phase\",round(Hl*aVW,2),\" W/cubic m.K\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.7 - Page: 207\n",
+ "\n",
+ "\n",
+ "The volumetric coeffecients are\n",
+ "\n",
+ "Based on Gas Phase 3183.0 W/cubic m.K\n",
+ "\n",
+ "based on Liquid Phase 503701.46 W/cubic m.K\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.1\n",
+ "# Page: 494\n",
+ "\n",
+ "print'Illustration 10.1 - Page: 494\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "%matplotlib inline\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "xF = 0.30;# [mol fraction]\n",
+ "yS = 0;# [mol fraction]\n",
+ "S1 = 40.0;# [kg]\n",
+ "B1 = 40.0;# [kg]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data at 20 OC:\n",
+ "# Wa: Wt. percent of a\n",
+ "# Wb: Wt. percent of b\n",
+ "# Wc: Wt. percent of c\n",
+ "# Data1 = [Wc Wa Wb]\n",
+ "# Data1: water layer\n",
+ "Data1 = numpy.array([(0.69 ,98.1, 1.2),(1.41, 97.1 ,1.5),(2.89 ,95.5 ,1.6),(6.42 ,91.7 ,1.9),(13.30, 84.4, 2.3),(25.50 ,71.1 ,3.4),(36.70 ,58.9 ,4.4),(44.30 ,45.1 ,10.6),(46.40 ,37.1 ,16.5)])\n",
+ "# Data2: isopropyl ether layer\n",
+ "Data2 = numpy.array([(0.18 ,0.5 ,99.3),(0.37, 0.7 ,98.9),(0.79, 0.8, 98.4),(1.93 ,1, 97.1),(4.82, 1.9, 93.3),(11.40, 3.9, 84.7),(21.60, 6.9, 71.5),(31.10, 10.8, 58.1),(36.20 ,15.1 ,48.7)])\n",
+ "\n",
+ "plt.plot((Data1[:,2])/100,(Data1[:,0])/100,label=\"x Vs fraction ether\")\n",
+ "plt.plot((Data2[:,2])/100,(Data2[:,0])/100,label=\"y Vs fraction ether\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='lower center');\n",
+ "ax=pylab.gca()\n",
+ "ax.set_xlabel(\"Wt fraction of isopropyl ether\");\n",
+ "ax.set_ylabel(\"Wt fraction of acetic acid\");\n",
+ "plt.ylim((0,0.3))\n",
+ "plt.xlim((0,1))\n",
+ "plt.show();\n",
+ "# x: Wt fraction of acetic acid in water layer.\n",
+ "# y: Wt fraction of acetic acid in isopropyl layer.\n",
+ "\n",
+ "# The rectangular coordinates of Fig 10.9(a) will be used but only upto x = 0.30\n",
+ "\n",
+ "# Stage 1:\n",
+ "F = 100;# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "M1 = F+S1;# [kg]\n",
+ "# From Eqn. 10.5:\n",
+ "xM1 = ((F*xF)+(S1*yS))/M1;\n",
+ "# From Fig. 10.15 (Pg 495):\n",
+ "# Point M1 is located on the line FB and with the help of tie line passing through M1:\n",
+ "x1 = 0.258;# [mol fraction]\n",
+ "y1 = 0.117;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E1 = (M1*(xM1-x1)/(y1-x1));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R1 = M1-E1;# [kg]\n",
+ "\n",
+ "# Stage 2:\n",
+ "S2 = 40;# [kg]\n",
+ "B2 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M2 = R1+B2;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM2 = ((R1*x1)+(S2*yS))/M2;\n",
+ "# Point M2 is located on the line R1B and the tie line passing through R2E2 through M2:\n",
+ "x2 = 0.227;\n",
+ "y2 = 0.095;\n",
+ "# From Eqn. 10.8:\n",
+ "E2 = (M2*(xM2-x2)/(y2-x2));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R2 = M2-E2;# [kg]\n",
+ "\n",
+ "# Stage 3:\n",
+ "S3 = 40;# [kg]\n",
+ "B3 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M3 = R2+B3;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM3 = ((R2*x2)+(S3*yS))/M3;\n",
+ "# Point M3 is located on the line R2B and the tie line passing through R3E3 through M3:\n",
+ "x3 = 0.20;# [mol fraction]\n",
+ "y3 = 0.078;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E3 = (M3*(xM3-x3)/(y3-x3));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R3 = M3-E3;# [kg]\n",
+ "Ac = x3*R3;\n",
+ "print\"The composited extract is\",round((E1+E2+E3),2),\" kg\\n\"\n",
+ "print\"The acid content is \",round(((E1*y1)+(E2*y2)+(E3*y3)),2),\" kg\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# If an extraction to give the same final raffinate concentration were to be done in single stage, the point M would be at the intersection of tie line R3E3 and the line BF.\n",
+ "x = 0.20;# [mol fraction]\n",
+ "xM = 0.12;# [mol fraction]\n",
+ "# From Eqn. 10.6:\n",
+ "S = F*(xF-xM)/(xM-yS);# [kg]\n",
+ "print round(S,2),\"kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.1 - Page: 494\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F6AM8U4J9C5SIVVLGmMhIrpzM4+c+Tp+P+3BAY2J4oDLjdTyM0cB+4BxgHDDB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\niRUYxhhjPPl/ptoLDFmMWJUAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7890240>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The composited extract is 135.05 kg\n",
+ "\n",
+ "The acid content is 13.01 kg\n",
+ "\n",
+ "\n",
+ "\n",
+ "150.0 kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter6_2.ipynb b/Mass_-_Transfer_Operations/Chapter6_2.ipynb
new file mode 100755
index 00000000..d9b08193
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter6_2.ipynb
@@ -0,0 +1,1059 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:ce06499e0802b1a354db3b54c156495e1f9e00501c129efee55c325fbd5e394e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 6: Equipment For Gas-Liquid Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.1: Page 145"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.1\n",
+ "# Page: 145\n",
+ "\n",
+ "print'Illustration 6.1 - Page: 145\\n\\n'\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# w = Gas flow rate per orifice\n",
+ "w = 0.055/50;# [kg/s]\n",
+ "L = 8*10**(-4);# [liquid flow rate, cubic m/s]\n",
+ "d = 0.003;# [diameter of the orifice,m]\n",
+ "viscocity_gas = 1.8*10**(-5);# [kg/m.s]\n",
+ "#******#\n",
+ "\n",
+ "Re = 4*w/(math.pi*d*viscocity_gas);\n",
+ "Dp = 0.0071*Re**(-0.05);# [m]\n",
+ "h = 3.0;# [height of vessel,m]\n",
+ "P_atm = 101.3;# [kN/square m]\n",
+ "Density_water = 1000.0;# [kg/cubic m]\n",
+ "g = 9.81;# [m/s^2]\n",
+ "Temp = 273+25;# [K]\n",
+ "P_orifice = P_atm+(h*Density_water*g/1000);# [kN/square m]\n",
+ "P_avg = P_atm+((h/2.0)*Density_water*g/1000);# [kN/square m]\n",
+ "Density_gas = (29/22.41)*(273.0/Temp)*(P_avg/P_atm);# [kg/cubic m]\n",
+ "D = 1.0;# [dia of vessel,m]\n",
+ "Area = (math.pi*D**2)/4;# [square m]\n",
+ "Vg = 0.055/(Area*Density_gas);# [m/s]\n",
+ "Vl = L/Area;# [m/s]\n",
+ "sigma = 0.072;# [N/m]\n",
+ "# From fig. 6.2 (Pg 143)\n",
+ "abscissa = 0.0516;# [m/s]\n",
+ "Vg_by_Vs = 0.11;\n",
+ "Vs = Vg/Vg_by_Vs;# [m/s]\n",
+ "def f6(shi_g):\n",
+ " return Vs-(Vg/shi_g)+(Vl/(1-shi_g)) \n",
+ "shi_g = fsolve(f6,0.5);\n",
+ "dp = ((Dp**3)*(P_orifice/P_avg))**(1.0/3);# [bubble diameter,m]\n",
+ "# From eqn. 6.9\n",
+ "a = 6.0*shi_g/dp;# [specific interfacial area,square m]\n",
+ "print\"The Specific Interfacial Area is \",round(a,2),\" square m/cubic m\\n\"\n",
+ "\n",
+ "# For diffsion of Cl2 in H20\n",
+ "Dl = 1.44*10**(-9);# [square m/s]\n",
+ "viscocity_water = 8.937*10**(-4);# [kg/m.s]\n",
+ "Reg = dp*Vs*Density_water/viscocity_water;\n",
+ "Scl = viscocity_water/(Density_water*Dl);\n",
+ "# From Eqn.6.11\n",
+ "Shl = 2+(0.0187*(Reg**0.779)*(Scl**0.546)*(dp*(g**(1.0/3))/(Dl**(2.0/3)))**0.116);\n",
+ "# For dilute soln. of Cl2 in H20\n",
+ "c = 1000/18.02;# [kmol/cubic m]\n",
+ "Fl = (c*Dl*Shl)/dp;# [kmol/square m.s]\n",
+ "print\"Mass Transfer coeffecient is \",round(Fl,5),\" kmol/square m.s\\n\",\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.1 - Page: 145\n",
+ "\n",
+ "\n",
+ "The Specific Interfacial Area is 148.13 square m/cubic m\n",
+ "\n",
+ "Mass Transfer coeffecient is 0.01335 kmol/square m.s\n"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.2: Page 157"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.2\n",
+ "# Page: 157\n",
+ "\n",
+ "print'Illustration 6.2 - Page: 157\\n\\n'\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = N2 b = H2O\n",
+ "L = 9.5*10**(-4);# [cubic m/s]\n",
+ "G = 0.061;# [kg/s]\n",
+ "Temp = 273.0+25;# [K]\n",
+ "#*****#\n",
+ "\n",
+ "print\"Construction Arrangement\\n\"\n",
+ "print\"Use 4 vertical wall baffles, 100 mm wide at 90 degree intervals.\\n\"\n",
+ "print\"Use a 305 mm dameter, a six bladed disk flat blade turbine impeller, arranged axially, 300 mm from the bottom of vessel\\n\"\n",
+ "print\"The sparger underneath the impeller will be in the form of a 240 mm dameter ring made of 12.7 mm tubing drilled in the top with 3.18 mm dia holes\\n\"\n",
+ "Di = 0.305;# [m]\n",
+ "Do = 0.00316;# [m]\n",
+ "viscocity_a = 1.8*10**(-5);# [kg/m.s]\n",
+ "Re_g = 35000;\n",
+ "Ma = 28.02;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# w = Gas flow rate per orifice\n",
+ "w = Re_g*math.pi*Do*viscocity_a/4.0;# [kg/s]\n",
+ "N_holes = G/w;\n",
+ "Interval = math.pi*240/round(N_holes);\n",
+ "print\"The number of holes is \",round(N_holes),\" at approx \",round(Interval),\" mm interval around the sparger ring\\n\"\n",
+ "\n",
+ "viscocity_b = 8.9*10**(-4);# [kg/m.s]\n",
+ "Sigma = 0.072;# [N/m]\n",
+ "Density_b = 1000.0;# [kg/cubic m]\n",
+ "D = 1.0;# [dia of vessel,m]\n",
+ "g = 9.81;# [m/s**2]\n",
+ "# From Eqn. 6.18\n",
+ "def f7(N):\n",
+ " return (N*Di/(Sigma*g/Density_b)**0.25)-1.22-(1.25*D/Di)\n",
+ "N_min = fsolve(f7,2);# [r/s]\n",
+ "N = 5.0;# [r/s]\n",
+ "Re_l = ((Di**2)*N*Density_b/viscocity_b);\n",
+ "# From fig 6.5 (Pg 152)\n",
+ "Po = 5.0;\n",
+ "P = Po*Density_b*(N**3)*(Di**5);\n",
+ "h = 0.7;# [m]\n",
+ "P_atm = 101.33;# [kN/square m]\n",
+ "P_gas = P_atm+(h*Density_b*g/1000.0);# [kN/square m]\n",
+ "Qg = (G/Ma)*22.41*(Temp/273.0)*(P_atm/P_gas);# [cubic m/s]\n",
+ "# From Fig.6.7 (Pg 155)\n",
+ "abcissa = Qg/(N*(Di**3));\n",
+ "# abcissa is off scale\n",
+ "Pg_by_P = 0.43;\n",
+ "Pg = 0.43*P;# [W]\n",
+ "Vg = Qg/(math.pi*(D**2)/4);# [superficial gas velocity,m/s]\n",
+ "check_value = (Re_l**0.7)*((N*Di/Vg)**0.3);\n",
+ "vl = math.pi*(D**2)/4;# [cubic m]\n",
+ "# Since value<30000\n",
+ "# From Eqn. 6.21, Eqn.6.23 & Eqn. 6.24\n",
+ "K = 2.25;\n",
+ "m = 0.4;\n",
+ "Vt = 0.250;# [m/s]\n",
+ "shi = 1.0;\n",
+ "err = 1.0;\n",
+ "while (err>10**(-3)):\n",
+ " a = 1.44*((Pg/vl)**0.4)*((Density_b/(Sigma**3))**0.2)*((Vg/Vt)**0.5);# [square m/cubic m]\n",
+ " shin = (0.24*K*((viscocity_a/viscocity_b)**0.25)*((Vg/Vt)**0.5))**(1.0/(1-m));\n",
+ " Dp = K*((vl/Pg)**0.4)*((Sigma**3/Density_b)**0.2)*(shin**m)*((viscocity_a/viscocity_b)**0.25);# [m]\n",
+ " err = abs(shi-shin);\n",
+ " Vt = Vt-0.002;# [m/s]\n",
+ " shi = shin;\n",
+ "\n",
+ "\n",
+ "# For N2 in H2\n",
+ "Dl = 1.9*10**(-9);# [square m/s]\n",
+ "Ra = 1.514*10**(6);\n",
+ "# By Eqn. 6.25\n",
+ "Shl = 2.0+(0.31*(Ra**(1.0/3)));\n",
+ "# For dilute soln.\n",
+ "c = 1000.0/Mb;# [kmol/cubic m]\n",
+ "Fl = Shl*c*Dl*1.0/Dp;# [kmol/square m.s]\n",
+ "print\"The average gas-bubble diameter is \",(\"{:.2e}\".format(Dp)),\" m\\n\",\n",
+ "print\"Gas Holdup:\\n\",round(shi,5)\n",
+ "print\"Interfacial area:\",round(a,4),\" square m/cubic m \\n\"\n",
+ "print\"Mass transfer coffecient:\",(\"{:.2e}\".format(Fl)),\"kmol/square m.s\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.2 - Page: 157\n",
+ "\n",
+ "\n",
+ "Construction Arrangement\n",
+ "\n",
+ "Use 4 vertical wall baffles, 100 mm wide at 90 degree intervals.\n",
+ "\n",
+ "Use a 305 mm dameter, a six bladed disk flat blade turbine impeller, arranged axially, 300 mm from the bottom of vessel\n",
+ "\n",
+ "The sparger underneath the impeller will be in the form of a 240 mm dameter ring made of 12.7 mm tubing drilled in the top with 3.18 mm dia holes\n",
+ "\n",
+ "The number of holes is 39.0 at approx 19.0 mm interval around the sparger ring\n",
+ "\n",
+ "The average gas-bubble diameter is 6.35e-04 m\n",
+ "Gas Holdup:\n",
+ "0.02265\n",
+ "Interfacial area: 214.0106 square m/cubic m \n",
+ "\n",
+ "Mass transfer coffecient: 6.24e-03 kmol/square m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.3: Page 174"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.3\n",
+ "# Page: 174\n",
+ "\n",
+ "print'Illustration 6.3 - Page: 174\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a = methanol b = water\n",
+ "G = 0.100;# [kmol/s]\n",
+ "L = 0.25;# [kmol/s]\n",
+ "Temp = 273+95;# [K]\n",
+ "XaG = 0.18;# [mol % in gas phase]\n",
+ "MaL = 0.15;# [mass % in liquid phase]\n",
+ "#*****#\n",
+ "\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "Mavg_G = XaG*Ma+((1-XaG)*Mb);# [kg/kmol]\n",
+ "Density_G = (Mavg_G/22.41)*(273.0/Temp);# [kg/cubic cm]\n",
+ "Q = G*22.41*(Temp/273.0);# [cubic cm/s]\n",
+ "Density_L = 961.0;# [kg/cubic cm]\n",
+ "Mavg_L = 1.0/((MaL/Ma)+(1-MaL)/Mb);# [kg/kmol]\n",
+ "q = L*Mavg_L/Density_L;\n",
+ "\n",
+ "# Perforations\n",
+ "print\"Perforations\\n\"\n",
+ "print\"Do = 4.5mm on an equilateral triangle pitch 12 mm between the hole centres, punched in sheet metal 2 mm thick\\n\"\n",
+ "Do = 0.0045;# [m]\n",
+ "pitch = 0.012;# [m]\n",
+ "# By Eqn.6.31\n",
+ "Ao_by_Aa = 0.907*(Do/pitch)**2;\n",
+ "print\"The ratio of Hole Area By Active Area is:\",round(Ao_by_Aa,4),\"\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Tower Diameter\n",
+ "print\"Tower Diameter\\n\"\n",
+ "t = 0.50;# [tray spacing,m]\n",
+ "print\"Tower Spacing:\",t,\" m\\n\"\n",
+ "# abcissa = (L/G)*(Density_G/Density_L)^0.5 = (q/Q)*(Density_L/Density_G)**0.5\n",
+ "abcissa = (q/Q)*(Density_L/Density_G)**0.5;\n",
+ "# From Table 6.2 (Pg 169)\n",
+ "alpha = (0.0744*t)+0.01173;\n",
+ "beeta = (0.0304*t)+0.015;\n",
+ "if (abcissa<0.1):\n",
+ " abcissa = 0.1;\n",
+ "\n",
+ "sigma = 0.040;# [N/m]\n",
+ "# From Eqn.6.30\n",
+ "Cf = ((alpha*math.log10(1.0/abcissa))+beeta)*(sigma/0.02)**0.2;\n",
+ "# From Eqn. 6.29\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1/2);# [m/s]\n",
+ "# Using 80% of flooding velocity\n",
+ "V = 0.8*Vf;# [m/s]\n",
+ "An = Q/V;# [square m]\n",
+ "# The tray area used by one downspout = 8.8%\n",
+ "At = An/(1-0.088);# [square m]\n",
+ "D = (4*At/math.pi)**(1.0/2);# [m]\n",
+ "# Take D = 1.25 m\n",
+ "D = 1.25; #[m]\n",
+ "At = math.pi*(D**2)/4;# [corrected At, square m]\n",
+ "W = 0.7*D;# [weir length,m]\n",
+ "Ad = 0.088*At;# [square m]\n",
+ "# For a design similar to Fig 6.14 (Pg 168)\n",
+ "# A 40 mm wide supporting ring, beams between downspouts and a 50 mm wide disengaging & distributing zones these areas total 0.222 square m\n",
+ "Aa = At-(2.0*Ad)-0.222;\n",
+ "print\"Weir Length:\",round(W,4),\"\\n\"\n",
+ "print\"Area for perforated sheet: \",round(Aa,4),\" square m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Weir crest h1 & Weir height hw\n",
+ "print\"Weir crest h1 & Weir height hw\\n\"\n",
+ "h1 = 0.025;# [m]\n",
+ "h1_by_D = h1/D;\n",
+ "D_by_W = D/W;\n",
+ "# From Eqn. 6.34\n",
+ "Weff_by_W = math.sqrt(((D_by_W)**2)-((((D_by_W)**2-1)**0.5)+(2*h1_by_D*D_by_W))**2);\n",
+ "# Set hw to 50 mm\n",
+ "hw = 0.05;# [m]\n",
+ "print\"Weir crest: \",h1,\" m\\n\"\n",
+ "print\"Weir height: \",hw,\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Dry Pressure Drop\n",
+ "print\"Dry Pressure Drop\\n\"\n",
+ "l = 0.002;# [m]\n",
+ "# From Eqn. 6.37\n",
+ "Co = 1.09*(Do/l)**0.25;\n",
+ "Ao = 0.1275*Aa;# [square m]\n",
+ "Vo = Q/Ao;# [m/sec]\n",
+ "viscocity_G = 1.25*10**(-5);# [kg/m.s]\n",
+ "Re = Do*Vo*Density_G/viscocity_G;\n",
+ "# From \"The Chemical Engineers Handbook,\" 5th Edition fig 5.26\n",
+ "fr = 0.008;\n",
+ "g = 9.81;# [m/s**2]\n",
+ "# From Eqn. 6.36\n",
+ "def f(hd):\n",
+ " return (2*hd*g*Density_L/(Vo**2*Density_G))-(Co*(0.40*(1.25-(Ao/An))+(4*l*fr/Do)+(1-(Ao/An))**2))\n",
+ "hd = fsolve(f,1);\n",
+ "print\"Dry Pressure Drop:\",round(hd,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Hydraulic head hl\n",
+ "print\"Hydraulic head hl\"\n",
+ "Va = Q/Aa;# [m/s]\n",
+ "z = (D+W)/2.0;# [m]\n",
+ "# From Eqn. 6.38\n",
+ "hl = 6.10*10**(-3)+(0.725*hw)-(0.238*hw*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "print\"Hydraulic head: \",round(hl,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#Residual Pressure drop hr\n",
+ "print\"Residual Pressure drop hr\\n\"\n",
+ "# From Eqn. 6.42\n",
+ "hr = 6*sigma/(Density_L*Do*g);# m\n",
+ "print\"Residual Pressure Drop:\",round(hr,4),\"m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Total Gas pressure Drop hg\n",
+ "print\"Total Gas pressure Drop hg\\n\"\n",
+ "# From Eqn. 6.35\n",
+ "hg = hd+hl+hr;# [m]\n",
+ "print\"Total gas pressure Drop: \",round(hg,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Pressure loss at liquid entrance h2\n",
+ "print\"Pressure loss at liquid entrance h2\\n\"\n",
+ "# Al: Area for the liquid flow under the apron\n",
+ "Al = 0.025*W;# [square m]\n",
+ "Ada = min(Al,Ad);\n",
+ "# From Eqn. 6.43\n",
+ "h2 = (3.0/(2*g))*(q/Ada)**2;\n",
+ "print\"Pressure loss at liquid entrance:\",round(h2,4),\"m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Backup in Downspout h3\n",
+ "print\"Backup in Downspout h3\\n\"\n",
+ "# From Eqn.6.44\n",
+ "h3 = hg+h2;\n",
+ "print\"Backup in Downspout:\",round(h3,4),\" m\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Check on Flooding\n",
+ "print\"Check on Flooding\\n\"\n",
+ "if((hw+h1+h3)<(t/2.0)):\n",
+ " print\"Choosen Tower spacing is satisfactory\\n\"\n",
+ "else:\n",
+ " print\"Choosen Tower spacing is not satisfactory\\n\"\n",
+ "\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Weeping Velocity\n",
+ "print\"Weeping Velocity\\n\"\n",
+ "print\"For W/D ratio \",W/D,\" weir is set at \",0.3296*D,\" m from the center from the tower\\n\",\n",
+ "Z = 2*(0.3296*D);# [m]\n",
+ "# From Eqn.6.46\n",
+ "def f8(Vow):\n",
+ " return (Vow*viscocity_G/(sigma))-(0.0229*((viscocity_G**2/(sigma*Density_G*Do))*(Density_L/Density_G))**0.379)*((l/Do)**0.293)*(2*Aa*Do/(math.sqrt(3.0)*(pitch**3)))**(2.8/((Z/Do)**0.724))\n",
+ "Vow = fsolve(f8,0.1);# [m/s]\n",
+ "print\"The minimum gas velocity through the holes below which excessive weeping is likely:\",round(Vow,3),\" m/s\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Entrainment\n",
+ "print\"Entrainment\\n\"\n",
+ "V_by_Vf = V/Vf;\n",
+ "# From Fig.6.17 (Pg 173), V/Vf = 0.8 & abcissa = 0.0622\n",
+ "E = 0.05;\n",
+ "print\"Entrainment:\\n\",E\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.3 - Page: 174\n",
+ "\n",
+ "\n",
+ "Perforations\n",
+ "\n",
+ "Do = 4.5mm on an equilateral triangle pitch 12 mm between the hole centres, punched in sheet metal 2 mm thick\n",
+ "\n",
+ "The ratio of Hole Area By Active Area is: 0.1275 \n",
+ "\n",
+ "\n",
+ "\n",
+ "Tower Diameter\n",
+ "\n",
+ "Tower Spacing: 0.5 m\n",
+ "\n",
+ "Weir Length: 0.875 \n",
+ "\n",
+ "Area for perforated sheet: 0.7892 square m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Weir crest h1 & Weir height hw\n",
+ "\n",
+ "Weir crest: 0.025 m\n",
+ "\n",
+ "Weir height: 0.05 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Dry Pressure Drop\n",
+ "\n",
+ "Dry Pressure Drop: 0.0654 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Hydraulic head hl\n",
+ "Hydraulic head: 0.0106 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Residual Pressure drop hr\n",
+ "\n",
+ "Residual Pressure Drop: 0.0057 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Total Gas pressure Drop hg\n",
+ "\n",
+ "Total gas pressure Drop: 0.0816 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Pressure loss at liquid entrance h2\n",
+ "\n",
+ "Pressure loss at liquid entrance: 0.008 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Backup in Downspout h3\n",
+ "\n",
+ "Backup in Downspout: 0.0897 m\n",
+ "\n",
+ "\n",
+ "\n",
+ "Check on Flooding\n",
+ "\n",
+ "Choosen Tower spacing is satisfactory\n",
+ "\n",
+ "\n",
+ "\n",
+ "Weeping Velocity\n",
+ "\n",
+ "For W/D ratio 0.7 weir is set at 0.412 m from the center from the tower\n",
+ "The minimum gas velocity through the holes below which excessive weeping is likely: 8.703 m/s\n",
+ "\n",
+ "\n",
+ "\n",
+ "Entrainment\n",
+ "\n",
+ "Entrainment:\n",
+ "0.05\n"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.4: Page 183"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.4\n",
+ "# Page: 183\n",
+ "\n",
+ "print'Illustration 6.4 - Page: 183\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "#****Data****#\n",
+ "#From Illustrtion 6.3:\n",
+ "G = 0.100;# [kmol/s]\n",
+ "Density_G = 0.679;# [kg/cubic m]\n",
+ "q = 5*10**(-3);# [cubic m/s]\n",
+ "Va = 3.827;# [m/s]\n",
+ "z = 1.063;# [m]\n",
+ "L = 0.25;# [kmol/s]\n",
+ "hL = 0.0106;# [m]\n",
+ "hW = 0.05;# [m]\n",
+ "Z = 0.824;# [m]\n",
+ "E = 0.05;\n",
+ "ya = 0.18;# [mole fraction methanol]\n",
+ "\n",
+ "# a:CH3OH b:H2O\n",
+ "Ma = 32;# [kg/kmol]\n",
+ "Mb = 18;# [kg/kmol]\n",
+ "# From Chapter 2:\n",
+ "ScG = 0.865;\n",
+ "Dl = 5.94*10**(-9);# [square m/s]\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/ScG**0.5;\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;# [square m/s]\n",
+ "thethaL = hL*z*Z/q;# [s]\n",
+ "NtL = 40000*Dl**0.5*((0.213*Va*Density_G**0.5)+0.15)*thethaL;\n",
+ "# For 15 mass% methanol:\n",
+ "xa = (15.0/Ma)/((15.0/Ma)+(85.0/Mb));\n",
+ "# From Fig 6.23 (Pg 184)\n",
+ "mAC = -(NtL*L)/(NtG*G);# [Slope of AC line]\n",
+ "meqb = 2.50;# [slope of equilibrium line]\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1.0/((1/NtG)+(meqb*G/L)*(1.0/NtL));\n",
+ "# From Eqn. 6.51:\n",
+ "EOG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thethaL);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2.0)*((1+(4*meqb*G*EOG/(L*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EOG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+(math.exp(eta)-1)/(eta*(1+eta/(eta+Pe))));\n",
+ "# From Eqn. 6.60:\n",
+ "EMGE = EMG/(1+(EMG*E/(1-E)));\n",
+ "print\"Efficiency of Sieve trays: \",round(EMGE,1)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.4 - Page: 183\n",
+ "\n",
+ "\n",
+ "Effeciency of Sieve trays: 0.7\n"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.5: Page 200"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.5\n",
+ "# Page: 200\n",
+ "\n",
+ "print'Illustration 6.5 - Page: 200\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "# ****Data****#\n",
+ "G = 0.80;# [cubic m/s]\n",
+ "P = 10**2;# [kN/square m]\n",
+ "XaG = 0.07;\n",
+ "Temp = 273+30.0;# [K]\n",
+ "L = 3.8;# [kg/s]\n",
+ "Density_L = 1235.0;# [kg/cubic m]\n",
+ "viscocity_L = 2.5*10**(-3);# [kg/m.s]\n",
+ "#******#\n",
+ "\n",
+ "# a = SO2 b = air\n",
+ "\n",
+ "# Solution (a) \n",
+ "\n",
+ "# Since the larger flow quantities are at the bottom for an absorber, the diameter will be choosen to accomodate the bottom condition\n",
+ "Mavg_G = XaG*64+((1-XaG)*29);# [kg/kmol]\n",
+ "G1 = G*(273/Temp)*(P/101.33)*(1/22.41);# [kmol/s]\n",
+ "G2 = G1*Mavg_G;# [kg/s]\n",
+ "Density_G = G2/G;# [kg/cubic m]\n",
+ "# Assuming Complete absorption of SO2\n",
+ "sulphur_removed = G1*XaG*64;# [kg/s]\n",
+ "abcissa = (L/G)*((Density_G/Density_L)**0.5);\n",
+ "#From Fig. 6.24, using gas pressure drop of 400 (N/square m)/m\n",
+ "ordinate = 0.061;\n",
+ "# For 25 mm ceramic Intalox Saddle:\n",
+ "Cf = 98.0;# [Table 6.3 Pg 196]\n",
+ "J = 1;\n",
+ "G_prime = (ordinate*Density_G*(Density_L-Density_G)/(Cf*viscocity_L**0.1*J))**0.5;# [kg/square m.s]\n",
+ "A = G2/G_prime;# [square m]\n",
+ "D = (4*A/math.pi)**0.5;# [m]\n",
+ "print\"The Tower Diameter is \",round(D,4),\" m\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "\n",
+ "# Let\n",
+ "D = 1.0;# [m]\n",
+ "A = math.pi*D**2.0/4;# [square m]\n",
+ "# The pressure drop for 8 m of irrigated packing\n",
+ "delta_p = 400*8.0;# [N/square m]\n",
+ "# For dry packing\n",
+ "G_prime = (G2-sulphur_removed)/A;# [kg/square m.s]\n",
+ "P = P-(delta_p/1000.0);# [kN/square m]\n",
+ "Density_G = (29/22.41)*(273.0/Temp)*(P/101.33);# [kg/cubic m]\n",
+ "# From Table 6.3 (Pg 196)\n",
+ "Cd = 241.5;\n",
+ "# From Eqn. 6.68\n",
+ "delta_p_by_z = Cd*G_prime**2/Density_G;# [N/square m for 1m of packing]\n",
+ "pressure_drop = delta_p+delta_p_by_z;# [N/square m]\n",
+ "V = 7.5;# [m/s]\n",
+ "head_loss = 1.5*V**2.0/2;# [N.m/kg]\n",
+ "head_loss = head_loss*Density_G;# [N/square m]\n",
+ "Power = (pressure_drop+head_loss)*(G2-sulphur_removed)/(Density_G*1000.0);# [kW]\n",
+ "eta = 0.6;\n",
+ "Power = Power/eta;# [kW]\n",
+ "print\"The Power for the fan motor is \",round(Power,2),\" kW\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.5 - Page: 200\n",
+ "\n",
+ "\n",
+ "The Tower Diameter is 0.981 m\n",
+ "\n",
+ "The Power for the fan motor is 4.49 kW\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.6: Page 204"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.6\n",
+ "# Page: 204\n",
+ "\n",
+ "print'Illustration 6.6 - Page: 204\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# Gas\n",
+ "Mavg_G = 11.0;# [kg/kmol]\n",
+ "viscocity_G = 10**(-5);# [kg/m.s]\n",
+ "Pt = 107.0;# [kN/square m]\n",
+ "Dg = 1.30*10**(-5);# [square m/s]\n",
+ "Temp = 273.0+27;# [K]\n",
+ "G_prime = 0.716;# [kg/square m.s]\n",
+ "\n",
+ "# Liquid:\n",
+ "Mavg_L = 260.0;\n",
+ "viscocity_L = 2*10**(-3);# [kg/m.s]\n",
+ "Density_L = 840.0;# [kg/cubic m]\n",
+ "sigma = 3*10.0**(-2);# [N/m]\n",
+ "Dl = 4.71*10**(-10);# [square m/s]\n",
+ "#******#\n",
+ "\n",
+ "#Gas:\n",
+ "Density_G = (Mavg_G/22.41)*(Pt/101.33)*(273/Temp);# [kg/cubic m]\n",
+ "ScG = viscocity_G/(Density_G*Dg);\n",
+ "G = G_prime/Mavg_G;# [kmol/square m.s]\n",
+ "\n",
+ "# Liquid:\n",
+ "L_prime = 2.71;# [kg/square m.s]\n",
+ "ScL = viscocity_L/(Density_L*Dl);\n",
+ "\n",
+ "# Holdup:\n",
+ "# From Table 6.5 (Pg 206), L_prime = 2.71 kg/square m.s\n",
+ "Ds = 0.0472;# [m]\n",
+ "beeta = 1.508*Ds**0.376;\n",
+ "shiLsW = 5.014*10**(-5)/Ds**1.56;# [square m/cubic m]\n",
+ "shiLtW = (2.32*10**(-6))*(737.5*L_prime)**beeta/(Ds**2);# [square m/cubic m]\n",
+ "shiLoW = shiLtW-shiLsW;# [square m/cubic m]\n",
+ "H = (1404*(L_prime**0.57)*(viscocity_L**0.13)/((Density_L**0.84)*((3.24*L_prime**0.413)-1)))*(sigma/0.073)**(0.2817-0.262*math.log10(L_prime));\n",
+ "shiLo = shiLoW*H;# [square m/cubic m]\n",
+ "shiLs = 4.23*10**(-3)*(viscocity_L**0.04)*(sigma**0.55)/((Ds**1.56)*(Density_L**0.37));# [square m/cubic m]\n",
+ "shiLt = shiLo+shiLs;# [square m/cubic m]\n",
+ "\n",
+ "# Interfacial Area:\n",
+ "# From Table 6.4 (Pg 205)\n",
+ "m = 62.4;\n",
+ "n = (0.0240*L_prime)-0.0996;\n",
+ "p = -0.1355;\n",
+ "aAW = m*((808*G_prime/(Density_G**0.5))**n)*(L_prime**p);# [square m/cubic m]\n",
+ "# From Eqn. 6.73\n",
+ "aA = aAW*shiLo/shiLoW;# [square m/cubic m]\n",
+ "# From Table 6.3 (Pg 196)\n",
+ "e = 0.75;\n",
+ "# From Eqn. 6.71\n",
+ "eLo = e-shiLt;\n",
+ "# From Eqn. 6.70\n",
+ "def f9(Fg):\n",
+ " return ((Fg*ScG**(2.0/3))/G)-1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36) \n",
+ "Fg = fsolve(f9,1);# [kmol/square m.s]\n",
+ "# From Eqn. 6.72:\n",
+ "def f10(Kl):\n",
+ " return (Kl*Ds/Dl)-(25.1*(Ds*L_prime/viscocity_L)**0.45)*ScL**0.5\n",
+ "Kl = fsolve(f10,1);# [(kmol/square m.s).(kmol/cubic m)]\n",
+ "# Since the value of Kl is taken at low conc., it can be converted into Fl\n",
+ "c = (Density_L/Mavg_L);# [kmol/cubic m]\n",
+ "Fl = Kl*c;# [kmol/cubic m]\n",
+ "print\"The volumetric coeffecients are\\n\"\n",
+ "print\"Based on Gas Phase \",round(Fg*aA,3),\" kmol/cubic m.s\\n\"\n",
+ "print\"based on Liquid Phase\",round(Fl*aA,3),\" kmol/cubic m.s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.6 - Page: 204\n",
+ "\n",
+ "\n",
+ "The volumetric coeffecients are\n",
+ "\n",
+ "Based on Gas Phase 0.071 kmol/cubic m.s\n",
+ "\n",
+ "based on Liquid Phase 0.014 kmol/cubic m.s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex6.7: Page 207"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 6.7\n",
+ "# Page: 207\n",
+ "\n",
+ "print'Illustration 6.7 - Page: 207\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#****Data****#\n",
+ "# Air\n",
+ "G_prime = 1.10;# [kg/square m.s]\n",
+ "viscocity_G = 1.8*10**(-5);# [kg/m.s]\n",
+ "ScG = 0.6;# [for air water mixture]\n",
+ "Temp1 = 273+20.0;# [K]\n",
+ "\n",
+ "# Water\n",
+ "L_prime = 5.5;# [kg/square m.s]\n",
+ "#*****#\n",
+ "\n",
+ "# Air:\n",
+ "Ma = 29.0;# [kg/kmol]\n",
+ "G = G_prime/Ma;# [kmol/square m.s]\n",
+ "Density_G = (Ma/22.41)*(273.0/Temp1);\n",
+ "Cpa = 1005.0;# [N.m/kg.K]\n",
+ "PrG = 0.74;\n",
+ "\n",
+ "# Liquid:\n",
+ "kth = 0.587;# [W/m.K]\n",
+ "Cpb = 4187.0;# [N.m/kg.K]\n",
+ "viscocity_L = 1.14*10**(-3);# [kg/m.s]\n",
+ "\n",
+ "# From Table 6.5 (Pg 206)\n",
+ "Ds = 0.0725;# [m]\n",
+ "beeta = 1.508*(Ds**0.376);\n",
+ "shiLtW = (2.09*10**(-6))*(737.5*L_prime)**beeta/(Ds**2);# [square m/cubic m]\n",
+ "shiLsW = 2.47*10**(-4)/(Ds**1.21);# [square m/cubic m]\n",
+ "shiLoW = shiLtW-shiLsW;# [square m/cubic m]\n",
+ "# From Table 6.4 (Pg 205)\n",
+ "m = 34.03;\n",
+ "n = 0.0;\n",
+ "p = 0.362;\n",
+ "aAW = m*(808.0*G_prime/Density_G**0.5)**(n)*L_prime**p;# [square m/cubic m]\n",
+ "# From Eqn. 6.75\n",
+ "aVW = 0.85*aAW*shiLtW/shiLoW;# [square m/cubic m]\n",
+ "# From Table 6.3\n",
+ "e = 0.74;\n",
+ "eLo = e-shiLtW;\n",
+ "# From Eqn. 6.70\n",
+ "def f11(Fg):\n",
+ " return ((Fg*ScG**(2.0/3))/G)-1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36)\n",
+ "Fg = fsolve(f11,1);# [kmol/square m.s]\n",
+ "# Since the liquid is pure water. It has no mass trnsfer coeffecient.\n",
+ "# For such process we need convective heat transfer coeffecient for both liquid & gas.\n",
+ "# Asuming Jd = Jh\n",
+ "# From Eqn. 6.70\n",
+ "Jh = 1.195*((Ds*G_prime)/(viscocity_G*(1-eLo)))**(-0.36);\n",
+ "Hg = Jh*Cpa*G_prime/(PrG**(2.0/3));# [W/square m.K]\n",
+ "PrL = Cpb*viscocity_L/kth;\n",
+ "# Heat transfer analog of Eqn. 6.72\n",
+ "Hl = 25.1*(kth/Ds)*(Ds*L_prime/viscocity_L)**0.45*PrL**0.5;# [W/square m.K]\n",
+ "print\"The volumetric coeffecients are\\n\"\n",
+ "print\"Based on Gas Phase \",round(Hg*aVW), \"W/cubic m.K\\n\"\n",
+ "print\"based on Liquid Phase\",round(Hl*aVW,2),\" W/cubic m.K\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 6.7 - Page: 207\n",
+ "\n",
+ "\n",
+ "The volumetric coeffecients are\n",
+ "\n",
+ "Based on Gas Phase 3183.0 W/cubic m.K\n",
+ "\n",
+ "based on Liquid Phase 503701.46 W/cubic m.K\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 10.1\n",
+ "# Page: 494\n",
+ "\n",
+ "print'Illustration 10.1 - Page: 494\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pylab\n",
+ "%matplotlib inline\n",
+ "#****Data****#\n",
+ "# a:water b:isopropyl ether c:acetic acid\n",
+ "xF = 0.30;# [mol fraction]\n",
+ "yS = 0;# [mol fraction]\n",
+ "S1 = 40.0;# [kg]\n",
+ "B1 = 40.0;# [kg]\n",
+ "#*******#\n",
+ "\n",
+ "# Equilibrium data at 20 OC:\n",
+ "# Wa: Wt. percent of a\n",
+ "# Wb: Wt. percent of b\n",
+ "# Wc: Wt. percent of c\n",
+ "# Data1 = [Wc Wa Wb]\n",
+ "# Data1: water layer\n",
+ "Data1 = numpy.array([(0.69 ,98.1, 1.2),(1.41, 97.1 ,1.5),(2.89 ,95.5 ,1.6),(6.42 ,91.7 ,1.9),(13.30, 84.4, 2.3),(25.50 ,71.1 ,3.4),(36.70 ,58.9 ,4.4),(44.30 ,45.1 ,10.6),(46.40 ,37.1 ,16.5)])\n",
+ "# Data2: isopropyl ether layer\n",
+ "Data2 = numpy.array([(0.18 ,0.5 ,99.3),(0.37, 0.7 ,98.9),(0.79, 0.8, 98.4),(1.93 ,1, 97.1),(4.82, 1.9, 93.3),(11.40, 3.9, 84.7),(21.60, 6.9, 71.5),(31.10, 10.8, 58.1),(36.20 ,15.1 ,48.7)])\n",
+ "\n",
+ "plt.plot((Data1[:,2])/100,(Data1[:,0])/100,label=\"x Vs fraction ether\")\n",
+ "plt.plot((Data2[:,2])/100,(Data2[:,0])/100,label=\"y Vs fraction ether\")\n",
+ "plt.grid('on');\n",
+ "plt.legend(loc='lower center');\n",
+ "ax=pylab.gca()\n",
+ "ax.set_xlabel(\"Wt fraction of isopropyl ether\");\n",
+ "ax.set_ylabel(\"Wt fraction of acetic acid\");\n",
+ "plt.ylim((0,0.3))\n",
+ "plt.xlim((0,1))\n",
+ "plt.show();\n",
+ "# x: Wt fraction of acetic acid in water layer.\n",
+ "# y: Wt fraction of acetic acid in isopropyl layer.\n",
+ "\n",
+ "# The rectangular coordinates of Fig 10.9(a) will be used but only upto x = 0.30\n",
+ "\n",
+ "# Stage 1:\n",
+ "F = 100;# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "M1 = F+S1;# [kg]\n",
+ "# From Eqn. 10.5:\n",
+ "xM1 = ((F*xF)+(S1*yS))/M1;\n",
+ "# From Fig. 10.15 (Pg 495):\n",
+ "# Point M1 is located on the line FB and with the help of tie line passing through M1:\n",
+ "x1 = 0.258;# [mol fraction]\n",
+ "y1 = 0.117;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E1 = (M1*(xM1-x1)/(y1-x1));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R1 = M1-E1;# [kg]\n",
+ "\n",
+ "# Stage 2:\n",
+ "S2 = 40;# [kg]\n",
+ "B2 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M2 = R1+B2;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM2 = ((R1*x1)+(S2*yS))/M2;\n",
+ "# Point M2 is located on the line R1B and the tie line passing through R2E2 through M2:\n",
+ "x2 = 0.227;\n",
+ "y2 = 0.095;\n",
+ "# From Eqn. 10.8:\n",
+ "E2 = (M2*(xM2-x2)/(y2-x2));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R2 = M2-E2;# [kg]\n",
+ "\n",
+ "# Stage 3:\n",
+ "S3 = 40;# [kg]\n",
+ "B3 = 40;# [kg]\n",
+ "# From Eqn. 10.15:\n",
+ "M3 = R2+B3;# [kg]\n",
+ "# From Eqn. 10.16:\n",
+ "xM3 = ((R2*x2)+(S3*yS))/M3;\n",
+ "# Point M3 is located on the line R2B and the tie line passing through R3E3 through M3:\n",
+ "x3 = 0.20;# [mol fraction]\n",
+ "y3 = 0.078;# [mol fraction]\n",
+ "# From Eqn. 10.8:\n",
+ "E3 = (M3*(xM3-x3)/(y3-x3));# [kg]\n",
+ "# From Eqn. 10.4:\n",
+ "R3 = M3-E3;# [kg]\n",
+ "Ac = x3*R3;\n",
+ "print\"The composited extract is\",round((E1+E2+E3),2),\" kg\\n\"\n",
+ "print\"The acid content is \",round(((E1*y1)+(E2*y2)+(E3*y3)),2),\" kg\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# If an extraction to give the same final raffinate concentration were to be done in single stage, the point M would be at the intersection of tie line R3E3 and the line BF.\n",
+ "x = 0.20;# [mol fraction]\n",
+ "xM = 0.12;# [mol fraction]\n",
+ "# From Eqn. 10.6:\n",
+ "S = F*(xF-xM)/(xM-yS);# [kg]\n",
+ "print round(S,2),\"kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 10.1 - Page: 494\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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F6AM8U4J9C5SIVVLGmMhIrpzM4+c+Tp+P+3BAY2J4oDLjdTyM0cB+4BxgHDDB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\niRUYxhhjPPl/ptoLDFmMWJUAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7890240>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The composited extract is 135.05 kg\n",
+ "\n",
+ "The acid content is 13.01 kg\n",
+ "\n",
+ "\n",
+ "\n",
+ "150.0 kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter7.ipynb b/Mass_-_Transfer_Operations/Chapter7.ipynb
new file mode 100755
index 00000000..14a0593d
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter7.ipynb
@@ -0,0 +1,1071 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:4bcd866f270e2f66ae7fbe911b2556c72aef74bc48c0c7488d977884f07ab7ad"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 7: Humidification Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.1: Page 222"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.1\n",
+ "# Page: 222\n",
+ "\n",
+ "print'Illustration 7.1 - Page: 222\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# ****Data****#\n",
+ "Temp1 = 273+26.1;# [K]\n",
+ "P1 = 100;# [mm Hg]\n",
+ "Temp2 = 273+60.6;# [K]\n",
+ "P2 = 400;# [mm Hg]\n",
+ "P = 200;# [mm Hg]\n",
+ "#*****#\n",
+ "\n",
+ "def f12(T):\n",
+ " return ((1/Temp1)-(1/T))/((1/Temp1)-(1/Temp2))-((math.log(P1)-math.log(P))/(math.log(P1)-math.log(P2)))\n",
+ "T = fsolve(f12,37);# [K]\n",
+ "print\"At\",round(T-273,1),\" degree C, the vapour pressure of benzene is 200 mm Hg\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.1 - Page: 222\n",
+ "\n",
+ "\n",
+ "At"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 42.4 degree C, the vapour pressure of benzene is 200 mm Hg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.2: Page 223"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.2:\n",
+ "# Page: 223\n",
+ "\n",
+ "print'Illustration 7.2 - Page: 223\\n\\n'\n",
+ "#part(a) and part(b) are table based and doesn't require an calculation\n",
+ "\n",
+ "print'Illustration 7.2 (c)\\n\\n'\n",
+ "\n",
+ "# Solution (c)\n",
+ "\n",
+ "# Reference: H20\n",
+ "# At 25 OC\n",
+ "m = 0.775;\n",
+ "Mr = 18.02;# [kg/kmol]\n",
+ "lambdar = 2443000;# [N/m.kg]\n",
+ "M = 78.05;# [kg/kmol]\n",
+ "# From Eqn. 7.6:\n",
+ "Lambda = m*lambdar*Mr/M;# [N/m.kg]\n",
+ "print\"Latent Heat of Vaporization at 25 degree C is\",round(Lambda/1000,2),\" kN/m.kg\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.2 - Page: 223\n",
+ "\n",
+ "\n",
+ "Illustration 7.2 (c)\n",
+ "\n",
+ "\n",
+ "Latent Heat of Vaporization at 25 degree C is 437.13 kN/m.kg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.3: Page 226"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.3\n",
+ "# Page: 226\n",
+ "\n",
+ "print'Illustration 7.3 - Page: 226\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# ****Data****#\n",
+ "m = 10;# [kg]\n",
+ "Cvap = 1.256;# [kJ/kg.K]\n",
+ "Cliq = 1.507;# [kJ/kg.K]\n",
+ "Temp1 = 100;# [OC]\n",
+ "Temp4 = 10;# [OC]\n",
+ "#******#\n",
+ "\n",
+ "# Using Fig 7.2 (Pg 224):\n",
+ "Temp2 = 25;# [OC]\n",
+ "# Using the notation of Fig. 7.3:\n",
+ "H1_diff_H2 = Cvap*(Temp1-Temp2);# [kJ/kg]\n",
+ "# From Illustration 7.2:\n",
+ "H2_diff_H3 = 434;# [Latent Heat of Vaporisation, kJ/kg]\n",
+ "H3_diff_H4 = Cliq*(Temp2-Temp4);# [kJ/kg]\n",
+ "H1_diff_H4 = H1_diff_H2+H2_diff_H3+H3_diff_H4;# [kJ/kg]\n",
+ "H = m*H1_diff_H4;# [kJ]\n",
+ "print\"Heat evolved for 10 kg Benzene is \",int(H),\" kJ\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.3 - Page: 226\n",
+ "\n",
+ "\n",
+ "Heat evolved for 10 kg Benzene is 5508 kJ\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.4: Page 227"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.4\n",
+ "# Page: 227\n",
+ "\n",
+ "print'Illustration 7.4 - Page: 227\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = benzene vapour; B = Nitrogen Gas\n",
+ "P = 800.0;# [mm Hg]\n",
+ "Temp = 273.0+60;# [K]\n",
+ "pA = 100.0;# [mm Hg]\n",
+ "#******#\n",
+ "\n",
+ "pB = P-pA;# [mm Hg]\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 28.08;# [kg/kmol]\n",
+ "\n",
+ "# Mole Fraction\n",
+ "print\"On the Basis of Mole Fraction\\n\"\n",
+ "yAm = pA/P;\n",
+ "yBm = pB/P;\n",
+ "print\"Mole Fraction of Benzene is \",yAm\n",
+ "print\"\\nMole Fraction of Nitrogen is \",yBm\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Volume Fraction\n",
+ "print\"On the Basis of Volume Fraction\\n\"\n",
+ "# Volume fraction is same as mole Fraction\n",
+ "yAv = yAm;\n",
+ "yBv = yBm;\n",
+ "print\"Volume Fraction of Benzene is \",yAv\n",
+ "print\"\\n Volume Fraction of Nitrogen is \",yBv\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Absolute Humidity\n",
+ "print\"On the basis of Absolute humidity\\n\"\n",
+ "Y = pA/pB;# [mol benzene/mol nitrogen]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg nitrogen]\n",
+ "print\"The concentration of benzene is \",round(Y_prime,3),\" kg benzene/kg nitrogen\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.4 - Page: 227\n",
+ "\n",
+ "\n",
+ "On the Basis of Mole Fraction\n",
+ "\n",
+ "Mole Fraction of Benzene is 0.125\n",
+ "\n",
+ "Mole Fraction of Nitrogen is 0.875\n",
+ "\n",
+ "\n",
+ "On the Basis of Volume Fraction\n",
+ "\n",
+ "Volume Fraction of Benzene is 0.125\n",
+ "\n",
+ " Volume Fraction of Nitrogen is 0.875\n",
+ "\n",
+ "\n",
+ "On the basis of Absolute humidity\n",
+ "\n",
+ "The concentration of benzene is 0.397 kg benzene/kg nitrogen\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.5: Page 228"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.5\n",
+ "# Page: 228\n",
+ "\n",
+ "print'Illustration 7.5 - Page: 228\\n\\n'\n",
+ "\n",
+ "print'Illustration 7.5 (a)\\n\\n'\n",
+ "# solution(a)\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = benzene vapour; B = Nitrogen Gas\n",
+ "P = 1.0;# [atm]\n",
+ "#*****#\n",
+ "\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 28.02;# [kg/kmol]\n",
+ "# Since gas is saturated, from Fig. 7.2 (Pg 224):\n",
+ "pA = 275.0/760;# [atm]\n",
+ "Y = pA/(P-pA);# [kmol benzene/kmol nitrogen]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg nitrogen]\n",
+ "print\"The concentration of benzene is \",round(Y_prime,3),\" kg benzene/kg nitrogen\\n\\n\"\n",
+ "\n",
+ "print'Illustration 7.5 (b)\\n\\n'\n",
+ "# solution(b)\n",
+ "\n",
+ "# A = benzene vapour; B = CO2\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 44.01;# [kg/kmol]\n",
+ "# Since gas is saturated, from Fig. 7.2:\n",
+ "pA = 275.0/760;# [atm]\n",
+ "Y = pA/(P-pA);# [kmol benzene/kmol CO2]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg CO2]\n",
+ "print\"The concentration of benzene is\",round(Y_prime,3),\" kg benzene/kg CO2\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.5 - Page: 228\n",
+ "\n",
+ "\n",
+ "Illustration 7.5 (a)\n",
+ "\n",
+ "\n",
+ "The concentration of benzene is 1.579 kg benzene/kg nitrogen\n",
+ "\n",
+ "\n",
+ "Illustration 7.5 (b)\n",
+ "\n",
+ "\n",
+ "The concentration of benzene is 1.006 kg benzene/kg CO2\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.6: Page 234"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.6\n",
+ "# Page: 234\n",
+ "\n",
+ "print'Illustration 7.6 - Page: 234\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = water vapour; B = air\n",
+ "TempG = 55;# [OC]\n",
+ "P = 1.0133*10**(5);# [N/square m]\n",
+ "Y_prime = 0.030;# [kg water/kg dry air]\n",
+ "#******#\n",
+ "\n",
+ "MA = 18.02;# [kg/kmol]\n",
+ "MB = 28.97;# [kg/kmol]\n",
+ "\n",
+ "# Percent Humidity\n",
+ "# From psychrometric chart, at 55 OC\n",
+ "Ys_prime = 0.115;# [kg water/kg dry air]\n",
+ "percent_Humidity = (Y_prime/Ys_prime)*100;\n",
+ "print\"The sample has percent Humidity =\",round(percent_Humidity,1),\"%\"\n",
+ "\n",
+ "# Molal Absolute Humidity\n",
+ "Y = Y_prime*(MB/MA);# [kmol water/kmol dry air]\n",
+ "print\"\\n Molal Absolute Humidity of the sample is\",round(Y,4),\" kmol water/kmol dry air\\n\"\n",
+ "\n",
+ "# Partial Pressure\n",
+ "pA = Y*P/(1+Y);# [N/square m]\n",
+ "print\"The Partial Pressure Of Water is\",int(pA),\" N/square m\\n\"\n",
+ "\n",
+ "# Relative Humidity\n",
+ "pa = 118*133.3;# [vapour pressure of water at 55 OC,N/square m]\n",
+ "relative_Humidity = (pA/pa)*100;\n",
+ "print\"The sample has relative Humidity = \",round(relative_Humidity,1),\" %\\n\"\n",
+ "\n",
+ "# Dew Point\n",
+ "# From psychrometric chart,\n",
+ "dew_point = 31.5;# [OC]\n",
+ "print\"Dew point Of the Sample is\",dew_point,\" degree C\\n\"\n",
+ "\n",
+ "# Humid Volume\n",
+ "# At 55 OC\n",
+ "vB = 0.93;# [specific volume of dry air,cubic m/kg]\n",
+ "vsB = 1.10;# [specific volume of saturated air,cubic m/kg]\n",
+ "vH = vB+((vsB-vB)*(percent_Humidity/100));# [cubic m/kg]\n",
+ "print\"The Humid Volume of the Sample is \",round(vH,3),\" cubic m/kg\\n\"\n",
+ "\n",
+ "# Humid Heat\n",
+ "CB = 1005;# [J/kg.K]\n",
+ "CA = 1884;# [J/kg.K]\n",
+ "Cs = CB+(Y_prime*CA);# [J/kg]\n",
+ "print\"The Humid Heat is \",round(Cs,1),\" J/kg dry air.K\\n\"\n",
+ "\n",
+ "# Enthalpy\n",
+ "HA = 56000;# [J/kg dry air]\n",
+ "HsA = 352000;# [J/kg dry air]\n",
+ "H_prime = HA+((HsA-HA)*(percent_Humidity/100));# [J/kg dry air]\n",
+ "print\"The Enthalphy of the sample is \",round(H_prime/1000,1),\"KJ/kg dry air\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.6 - Page: 234\n",
+ "\n",
+ "\n",
+ "The sample has percent Humidity = 26.1 %\n",
+ "\n",
+ " Molal Absolute Humidity of the sample is 0.0482 kmol water/kmol dry air\n",
+ "\n",
+ "The Partial Pressure Of Water is 4662 N/square m\n",
+ "\n",
+ "The sample has relative Humidity = 29.6 %\n",
+ "\n",
+ "Dew point Of the Sample is 31.5 degree C\n",
+ "\n",
+ "The Humid Volume of the Sample is 0.974 cubic m/kg\n",
+ "\n",
+ "The Humid Heat is 1061.5 J/kg dry air.K\n",
+ "\n",
+ "The Enthalphy of the sample is 133.2 KJ/kg dry air\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.7: Page 236"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.7\n",
+ "# Page: 236\n",
+ "\n",
+ "print'Illustration 7.7 - Page: 236\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = water vapour; B = air\n",
+ "V = 100;# [m**3]\n",
+ "Tempi = 55;# [OC]\n",
+ "Tempf = 110;# [OC]\n",
+ "#*****#\n",
+ "\n",
+ "# From Illustration 7.6\n",
+ "vH = 0.974;# [m**3/kg]\n",
+ "Cs = 1061.5;# [J/kg]\n",
+ "WB = V/vH;# [kg]\n",
+ "Q = WB*Cs*(Tempf-Tempi);# [J]\n",
+ "print\"Heat required is \",round(Q,3),\" J\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation in book"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.7 - Page: 236\n",
+ "\n",
+ "\n",
+ "Heat recquired is 5994096.509 J\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.9: Page 240"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.9\n",
+ "# Page:240\n",
+ "from scipy.optimize import fsolve \n",
+ "print'Illustration 7.9 - Page:240\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "Tempw = 35;# [OC]\n",
+ "Tempg = 65;# [OC]\n",
+ "#******#\n",
+ "\n",
+ "# From psychrometric chart\n",
+ "lambda_w = 2419300;# [J/kg]\n",
+ "Y_prime_w = 0.0365;# [kg H2O/kg dry air]\n",
+ "# From fig 7.5(a)\n",
+ "hG_by_kY = 950;# [J/kg]\n",
+ "# From Eqn. 7.26\n",
+ "def f13(Y_prime):\n",
+ " return (Tempg-Tempw)-((lambda_w*(Y_prime_w-Y_prime))/hG_by_kY)\n",
+ "Y_prime = fsolve(f13,2);# [kg H2O/kg dry air]\n",
+ "print\"Humidity of air is\",round(Y_prime[0],4),\"kg H2O/kg dry air\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.9 - Page:240\n",
+ "\n",
+ "\n",
+ "Humidity of air is 0.0247 kg H2O/kg dry air\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.10: Page 241"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.10\n",
+ "# Page:241\n",
+ "\n",
+ "print'Illustration 7.10 - Page:241\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "Tg = 60;# [OC]\n",
+ "Y_prime = 0.050;# [kg toulene/kg air]\n",
+ "#*****#\n",
+ "\n",
+ "# Wet Bulb temparature\n",
+ "Dab = 0.92*10**(-5);# [square m/s]\n",
+ "density_air = 1.060;# [kg/cubic cm];\n",
+ "viscocity_G = 1.95*10**(-5);# [kg/m.s]\n",
+ "Sc = viscocity_G/(density_air*Dab);\n",
+ "# From Eqn. 7.28\n",
+ "hG_by_kY = 1223*(Sc**0.567);# [J/kg.K]\n",
+ "# Soln. of Eqn. 7.26 by trial & error method:\n",
+ "# (Tg-Tw) = (Yas_prime-Y_prime)*(lambda_w/hG_by_kY)\n",
+ "Tw = 31.8;# [OC]\n",
+ "print\"Wet Bulb Temparature:\",Tw,\" degree C\\n\"\n",
+ "\n",
+ "# Adiabatic Saturation Temparature\n",
+ "C_air = 1005;# [J/kg.K]\n",
+ "C_toulene = 1256;# [J/kg.K]\n",
+ "Cs = C_air+(C_toulene*Y_prime);# [J/kg.K]\n",
+ "# Soln. of Eqn. 7.21 by trial & error method:\n",
+ "# (Tg-Tas) = (Yas_prime-Y_prime)*(lambda_as/Cs)\n",
+ "Tas = 25.7;# [OC]\n",
+ "print\"Adiabatic Saturation Temparature: \",round(Tas,1),\" degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.10 - Page:241\n",
+ "\n",
+ "\n",
+ "Wet Bulb Temparature: 31.8 degree C\n",
+ "\n",
+ "Adiabatic Saturation Temparature: 25.7 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.11: Page 249"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.11\n",
+ "# Page: 249\n",
+ "\n",
+ "print'Illustration 7.11 - Page: 249\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "\n",
+ "#****Data****#\n",
+ "L_min = 2.27;# [kg/square m.s]\n",
+ "G_min = 2;# [kg/square m.s]\n",
+ "L2_prime = 15;# [kg/s]\n",
+ "Q = 270.0;# [W]\n",
+ "Templ2 = 45.0;# [OC]\n",
+ "Tempg1 = 30.0;# [OC]\n",
+ "Tempw1 = 24.0;# [OC]\n",
+ "Kya = 0.90;# [kg/cubic m.s]\n",
+ "#*******#\n",
+ "\n",
+ "H1_prime = 72;# [kJ/kg dry air]\n",
+ "Y1_prime = 0.0160;# [kg water/kg dry air]\n",
+ "Templ1 = 29;# [OC]\n",
+ "Cal = 4.187;# [kJ/kg]\n",
+ "\n",
+ "# Tower cross section Area:\n",
+ "Al = L2_prime/L_min;# [square m]\n",
+ "Ag = Gs/G_min;# [square m]\n",
+ "A = min(Al,Ag);# [square m]\n",
+ "Area = 3.25;\n",
+ "# From Eqn. 7.54\n",
+ "def f16(Z):\n",
+ " return Area-(Kya*Z/G_min)\n",
+ "Z = fsolve(f16,2);\n",
+ "print\"The height of tower is\",round(Z,2),\" m\\n\"\n",
+ "NtoG = 3.25;\n",
+ "HtoG = G_min/Kya;# [m]\n",
+ "\n",
+ "# Make up water\n",
+ "# Assuming the outlet air is essentially saturated:\n",
+ "Y2_prime = 0.0475;# [kg water/kg dry air]\n",
+ "E = G_min*(A)*(Y2_prime-Y1_prime);# [kg/s]\n",
+ "# Windage loss estimated as 0.2 percent\n",
+ "W = 0.002*L2_prime;# [kg/s]\n",
+ "ppm_blowdown = 2000;# [ppm]\n",
+ "ppm_makeup = 500;# [ppm]\n",
+ "# Since the weight fraction are proportional to the corresponding ppm values:\n",
+ "B = (E*ppm_makeup/(ppm_blowdown-ppm_makeup))-W;# [kg/s]\n",
+ "M = B+E+W;# [kg/s]\n",
+ "print\"The makeup water requirement is estimated to be\",round(M,2),\" kg/s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.11 - Page: 249\n",
+ "\n",
+ "\n",
+ "The height of tower is 7.22 m\n",
+ "\n",
+ "The makeup water requirement is estimated to be 0.46 kg/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 81
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.13: Page 254"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.13\n",
+ "# Page: 254\n",
+ "\n",
+ "\n",
+ "print'Illustration 7.13\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve \n",
+ "# Given\n",
+ "Tempg1=65;# [OC]\n",
+ "Y1_prime=0.0170;# [kg water/kg dry air]\n",
+ "# Using adiabatic satursion line on Fig. 7.5 (Pg 232)\n",
+ "Tempas=32;# [OC]\n",
+ "Yas_prime=0.0309;# [kg water/kg dry air]\n",
+ "Tempg2=45;# [OC]\n",
+ "Z=2;# [m]\n",
+ "#*******#\n",
+ "\n",
+ "Y2_prime=0.0265;# [kg water/kg dry air]\n",
+ "def f19(Kya_by_Gs):\n",
+ " return math.log((Yas_prime-Y1_prime)/(Yas_prime-Y2_prime))-(Kya_by_Gs*Z)\n",
+ "Kya_by_Gs=fsolve(f19,1);# [1/m]\n",
+ "\n",
+ "# For the extended chamber:\n",
+ "Z=4;# [m]\n",
+ "def f20(Y2_prime):\n",
+ " return math.log((Yas_prime-Y1_prime)/(Yas_prime-Y2_prime))-(Kya_by_Gs*Z)\n",
+ "Y2_prime=fsolve(f20,0.029);#[kg water/kg dry air] \n",
+ "# With the same adiabatic curve:\n",
+ "Tempg2=34;# [OC] from the curve\n",
+ "print\"The Outlet Conditions are:\\n\"\n",
+ "print\"Absolute Humidity is\",round(Y2_prime,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Dry Bulb Temperature is\",round(Tempg2), \"degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.13\n",
+ "\n",
+ "\n",
+ "The Outlet Conditions are:\n",
+ "\n",
+ "Absolute Humidity is 0.0295 kg water/kg dry air\n",
+ "\n",
+ "Dry Bulb Temperature is 34.0 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 137
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.14: Page 256"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.14\n",
+ "# Page: 256\n",
+ "\n",
+ "print'Illustration 7.14 - Page: 256\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# a = N2 b = CO\n",
+ "# Entering gas\n",
+ "Y1_prime = 0.0;# [kg water/kg dry air]\n",
+ "Pt = 1.0;# [atm]\n",
+ "Tempg1 = 315.0;# [OC]\n",
+ "G_prime = 5.0;# [square m/s]\n",
+ "\n",
+ "# Temp of the tower:\n",
+ "Templ2 = 18.0;# [OC]\n",
+ "Density_L2 = 1000.0; #[kg/square m]\n",
+ "viscocity_L2 = 1.056*10**(-3);# [kg/m.s]\n",
+ "Tempg2 = 27.0;# [OC]\n",
+ "\n",
+ "Mb = 28.0;# [kg/kmol]\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Density_G1 = (Mb/22.41)*(273/(Tempg1+273));# [kg/square m]\n",
+ "G1 = G_prime*(Density_G1);# [kg/s]\n",
+ "\n",
+ "# Since the outlet gas is nearly saturated:\n",
+ "Y_prime = 0.024;# [kg water/kg dry air]\n",
+ "Y2_prime = 0.022;# [kg water/kg dry air, assumed]\n",
+ "G2 = G1*(1+Y2_prime);# [kg/s]\n",
+ "Mav = (1+Y2_prime)/((1/Mb)+(Y2_prime/Ma));# [kg/kmol]\n",
+ "Density_G2 = (Mav/22.4)*(273.0/(Templ2+273));# [kg/square m]\n",
+ "L2_by_G2 = 2.0;\n",
+ "abcissa = L2_by_G2*(Density_G2/(Density_L2-Density_G2))**(1/2);\n",
+ "# From Fig. 6.34:\n",
+ "# For a gas pressure drop of 400 N/square m/m\n",
+ "ordinate = 0.073;\n",
+ "# From Table 6.3:\n",
+ "Cf = 65.0;\n",
+ "J = 1.0;\n",
+ "def f21(G2_prime):\n",
+ " return ((G2_prime**2)*Cf*(viscocity_L2**0.1)*J/(Density_G2*(Density_L2-Density_G2)))-ordinate\n",
+ "# Tentative data:\n",
+ "G2_prime = fsolve(f21,2);# [kg/square m.s]\n",
+ "Area = G1/G2_prime;# [square m]\n",
+ "dia = math.sqrt(4*Area/math.pi);# [m]\n",
+ "\n",
+ "# Final data:\n",
+ "dia = 1.50;# [m]\n",
+ "Area = math.pi*dia**2.0/4;# [square m]\n",
+ "Gs_prime = G1/Area;# [kg/square m.s]\n",
+ "G2_prime = G2/Area;# [kg/square m.s]\n",
+ "L2_prime = L2_by_G2*G2_prime;# [kg/square m.s]\n",
+ "# From Eqn. 7.29:\n",
+ "def f22(L1_prime):\n",
+ " return (L2_prime-L1_prime)-(Gs_prime*(Y2_prime-Y1_prime))\n",
+ "L1_prime = fsolve(f22,2);\n",
+ "Cb = 1089;# [J/kg.K]\n",
+ "Ca = 1884;# [J/kg.K]\n",
+ "Cs1 = Cb+(Y1_prime*Ca);# [J/(kg dry air).K]\n",
+ "Cs2 = Cb+(Y2_prime*Ca);# [J/(kg dry air).K]\n",
+ "Tempo = Templ2;# [base temp.,K]\n",
+ "Lambda = 2.46*10**6;# [J/kg]\n",
+ "CaL = 4187;# [J/kg K]\n",
+ "# From Eqn. 7.31:\n",
+ "def f23(Templ1):\n",
+ " return ((L2_prime*CaL*(Templ2-Tempo))+(Gs_prime*Cs1*(Tempg1-Tempo)))-((L1_prime*CaL*(Templ1-Tempo))+(Gs_prime*(Cs2*(Tempg2-Tempo))+(Y2_prime*Lambda)))\n",
+ "Templ1 = fsolve(f23,2);\n",
+ "# At Templ1 = 49.2 OC\n",
+ "viscocity_L = 0.557*10**(-3);# [kg/m.s]\n",
+ "Density_L = 989.0;# [kg/square m]\n",
+ "K = 0.64;# [w/m.K]\n",
+ "Prl = CaL*viscocity_L/K;\n",
+ "\n",
+ "# For Entering Gas:\n",
+ "viscocity_G1 = 0.0288*10**(-3);# [kg*/m.s]\n",
+ "Dab = 0.8089*10**(-4);# [square m/s]\n",
+ "ScG = viscocity_G1/(Density_G1*Dab);\n",
+ "PrG = 0.74;\n",
+ "\n",
+ "# From Illustration 6.7:\n",
+ "a = 53.1;# [square m/square m]\n",
+ "Fga = 0.0736;# [kmol/square m]\n",
+ "Hga = 4440.0;# [W/square m.K]\n",
+ "Hla = 350500.0;# [W/square m.K]\n",
+ "# At the bottom, by several trial:\n",
+ "Tempi = 50.3;# [OC]\n",
+ "pai = 93.9/760;# [atm]\n",
+ "paG = 0;# [atm]\n",
+ "# By Eqn. 7.64:\n",
+ "dY_prime_by_dZ = -(Ma*Fga/Gs_prime)*math.log((1-(pai/Pt))/(1-(paG/Pt)));# [(kg H2O/kg dry gas)/m]\n",
+ "Hg_primea = -(Gs_prime*Ca*dY_prime_by_dZ)/(1-math.exp((Gs_prime*Ca*dY_prime_by_dZ)/(Hga)));# [W/square m.K]\n",
+ "dTempg_by_dZ = -(Hg_primea*(Tempg1-Tempi)/(Gs_prime*Cs1));# [OC/m]\n",
+ "Tempi = (Templ1)+((Gs_prime*(Cs1*dTempg_by_dZ)+((Ca*(Tempg1))-(CaL*(Templ1))+(((CaL-Ca)*(Tempo))+Lambda))*dY_prime_by_dZ)/((Gs_prime*CaL*dY_prime_by_dZ)-Hla));#[OC]\n",
+ "# Assume:\n",
+ "delta_Tempg = -30;# [OC]\n",
+ "delta_Z = delta_Tempg/(dTempg_by_dZ);# [m]\n",
+ "Tempg = Tempg1+delta_Tempg;# [OC]\n",
+ "Y_prime = Y1_prime+(dY_prime_by_dZ)*delta_Z;# [kg H2O/kg dry gas]\n",
+ "paG = Y_prime/(Y_prime+(Ma/Mb));# [atm]\n",
+ "Cs = Cb+Ca*(Y_prime);# [J/(kg dry air).K]\n",
+ "# Water balance, From Eqn. 7.29:\n",
+ "def f24(L_prime):\n",
+ " return (L2_prime-L_prime)-(Gs_prime*(Y_prime-Y1_prime))\n",
+ "L_prime = fsolve(f24,2);# [kg/square m.s]\n",
+ "\n",
+ "def f25(Templ):\n",
+ " return ((L_prime*CaL*(Templ-Tempo))+(Gs_prime*Cs1*(Tempg1-Tempo)))-((L1_prime*CaL*(Templ1-Tempo))+(Gs_prime*(Cs*(Tempg-Tempo))+(Y_prime*Lambda)))\n",
+ "Templ = fsolve(f25,2);\n",
+ "\n",
+ "# This process is repeated several times until gas temp falls to Tempg2\n",
+ "Z = 1.54;# [m] Z = sum of all delta_Z\n",
+ "# The value of Y2_prime was calculated to be 0.0222 which is sufficiently close to the assumed value.\n",
+ "print\"The diameter of tower is \",dia,\" m\\n\"\n",
+ "print\"The packed height is\",Z, \"m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.15: Page 267"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.15\n",
+ "# Page: 267\n",
+ "\n",
+ "print'Illustration 7.15 - Page: 267\\n\\n'\n",
+ "\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "import numpy\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "w = 0.75;# [m]\n",
+ "OD = 19.05/1000;# [m]\n",
+ "l = 3.75;# [m]\n",
+ "n = 20;\n",
+ "t = 1.65/1000;# [m]\n",
+ "Ws = 2.3;# [kg/s]\n",
+ "Wal = 10.0;# [kg/s]\n",
+ "Wt = 4.0;# [kg/s]\n",
+ "Density = 800;# [kg/cubic m]\n",
+ "viscocity = 0.005;# [kg/m.s]\n",
+ "K = 0.1436;# [W/m.K]\n",
+ "Ct = 2010.0;# [J/kg.K]\n",
+ "Cal = 4187.0;# [J/kg.K]\n",
+ "Y1_prime = 0.01;# [kg H2O/kg dry air]\n",
+ "Y2_prime = 0.06;# [kg H2O/kg dry air]\n",
+ "TempT = 95.0;# [OC]\n",
+ "#*****#\n",
+ "\n",
+ "Free_area = (w-(n*OD))*l;# [square m]\n",
+ "Gs_min = 2.3/Free_area;# [kg/square m.s]\n",
+ "M1 = 1.461;Yav_prime = (Y1_prime+Y2_prime)/2;# [kg H2O/kg dry air]\n",
+ "# From Eqn. 7.86:\n",
+ "ky = 0.0493*(Gs_min*(1+Yav_prime))**0.905;# [kg/square m.s.delta_Y_prime]\n",
+ "# From Fig. 7.5:\n",
+ "H1_prime = 56000.0;# [J/kg]\n",
+ "Ao = 400*math.pi*OD*l;# [square m]\n",
+ "# Cooling water is distributed over 40 tubes & since tubes are staggered\n",
+ "geta = Wal/(40.0*2*l);# [kg/m.s]\n",
+ "geta_by_OD = geta/OD;# [kg/square m.s]\n",
+ "# Assume:\n",
+ "TempL = 28.0;# [OC]\n",
+ "# From Eqn. 7.84:\n",
+ "hL_prime = (982+(15.58*TempL))*(geta_by_OD**(1/3));# [W/square m.K]\n",
+ "# From Eqn. 7.85:\n",
+ "hL_dprime = 11360;# [W/square m.K]\n",
+ "# From Fig. 7.5 (Pg 232)\n",
+ "m = 5000.0;# [J/kg.K]\n",
+ "Ky = 1.0/((1/ky)+(m/hL_dprime));\n",
+ "ID = (OD-(2.0*t));# [m]\n",
+ "Ai = math.pi*(ID**2)/4;# [square m]\n",
+ "Gt_prime = Wt/(n*Ai);# [kg/square m.s]\n",
+ "M2 = -0.7204;Re = ID*Gt_prime/viscocity;\n",
+ "Pr = Ct*viscocity/K;\n",
+ "# From a standard correlation:\n",
+ "hT = 364.0;# [W/square m.K]\n",
+ "Dav = (ID+OD)/2.0;# [m]\n",
+ "Zm = (OD-ID)/2;# [m]\n",
+ "Km = 112.5;# [W/m.K]\n",
+ "# From Eqn. 7.67:\n",
+ "Uo = 1/((OD/(ID*hT))+((OD/Dav)*(Zm/Km))+(1/hL_prime));# [W/square m.K]\n",
+ "# From Eqn. 7.75:\n",
+ "alpha1 = -(((Uo*Ao)/(Wt*Ct))+((Uo*Ao)/(Wal*Cal)));\n",
+ "alpha2 = m*Uo*Ao/(Wt*Ct);\n",
+ "# From Eqn. 7.76:\n",
+ "beeta1 = Ky*Ao/(Wal*Cal);\n",
+ "beeta2 = -((m*Ky*Ao/(Wal*Cal))-(Ky*Ao/Ws));\n",
+ "def f26(r):\n",
+ " return (r**2)+((alpha1+beeta2)*r)+((alpha1*beeta2)-(alpha2*beeta1))\n",
+ "r1 = fsolve(f26,10);\n",
+ "r2 = fsolve(f26,0);\n",
+ "beeta2 = 1.402;\n",
+ "# From Eqn. 7.83:\n",
+ "# N1-(M1*(r1+alpha1)/beeta1) = 0............................................(1)\n",
+ "# N2-(M2*(r2+alpha2)/beeta2) = 0............................................(2)\n",
+ "# From Eqn. 7.77:\n",
+ "# At the top:\n",
+ "x1 = 1.0;\n",
+ "# TempL2+(M1*exp(r1*x1))+(M2*exp(-(r2*x1))) = TempL.........................(3)\n",
+ "# From Eqn. 7.78:\n",
+ "# At the bottom:\n",
+ "x2 = 0.0;\n",
+ "# H1_star-N1-N2 = H1_prime..................................................(4)\n",
+ "# From Eqn. 7.80:\n",
+ "# ((M1/r1)*(exp(r1)-1))+((M2*r2)*(exp(r2)-1)) = (Tempt-TempL)...............(5)\n",
+ "# From Eqn. 7.81:\n",
+ "# ((N1/r1)*(exp(r1)-1))+((N2*r2)*(exp(r2)-1)) = (H1_star-H1_prime)..........(6)\n",
+ "# From Eqn. 7.91 & Eqn. 7.92:\n",
+ "# Uo*Ao*(TempT-TempL)=Ky*Ao*(H1_star-H1_prime)..............................(7)\n",
+ "\n",
+ "# Elimination of M's & N's by solving Eqn. (1) to (4) and (7) simultaneously:\n",
+ "# and from Fig. 7.5 (Pg 232):\n",
+ "TempL1=28.0;# [OC]\n",
+ "H1_star=(Uo*Ao*(TempT-TempL)/(Ky*Ao))+H1_prime;# [J/kmol]\n",
+ "\n",
+ "\n",
+ "N1 = 3594.0*M1\n",
+ "N2 =-43288.0*M2;\n",
+ "\n",
+ "# By Eqn. 5\n",
+ "delta_Temp = ((M1/r1)*(math.exp(r1)-1))+((M2*r2)*(math.exp(r2)-1));# [OC]\n",
+ "Q = Uo*delta_Temp*Ao;\n",
+ "TempT1 = TempT-(Q/(Wt*Ct));# [OC]\n",
+ "H2_prime = Q/(Ws)+H1_prime;# [J/kg]\n",
+ "print\"Temperature to which oil was cooled:\",int(TempT1),\" degree C\\n\"\n",
+ "# The solution in the textbook is wrong "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.15 - Page: 267\n",
+ "\n",
+ "\n",
+ "Temperature to which oil was cooled:"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 57 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter7_1.ipynb b/Mass_-_Transfer_Operations/Chapter7_1.ipynb
new file mode 100755
index 00000000..14a0593d
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter7_1.ipynb
@@ -0,0 +1,1071 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:4bcd866f270e2f66ae7fbe911b2556c72aef74bc48c0c7488d977884f07ab7ad"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 7: Humidification Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.1: Page 222"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.1\n",
+ "# Page: 222\n",
+ "\n",
+ "print'Illustration 7.1 - Page: 222\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# ****Data****#\n",
+ "Temp1 = 273+26.1;# [K]\n",
+ "P1 = 100;# [mm Hg]\n",
+ "Temp2 = 273+60.6;# [K]\n",
+ "P2 = 400;# [mm Hg]\n",
+ "P = 200;# [mm Hg]\n",
+ "#*****#\n",
+ "\n",
+ "def f12(T):\n",
+ " return ((1/Temp1)-(1/T))/((1/Temp1)-(1/Temp2))-((math.log(P1)-math.log(P))/(math.log(P1)-math.log(P2)))\n",
+ "T = fsolve(f12,37);# [K]\n",
+ "print\"At\",round(T-273,1),\" degree C, the vapour pressure of benzene is 200 mm Hg\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.1 - Page: 222\n",
+ "\n",
+ "\n",
+ "At"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 42.4 degree C, the vapour pressure of benzene is 200 mm Hg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.2: Page 223"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.2:\n",
+ "# Page: 223\n",
+ "\n",
+ "print'Illustration 7.2 - Page: 223\\n\\n'\n",
+ "#part(a) and part(b) are table based and doesn't require an calculation\n",
+ "\n",
+ "print'Illustration 7.2 (c)\\n\\n'\n",
+ "\n",
+ "# Solution (c)\n",
+ "\n",
+ "# Reference: H20\n",
+ "# At 25 OC\n",
+ "m = 0.775;\n",
+ "Mr = 18.02;# [kg/kmol]\n",
+ "lambdar = 2443000;# [N/m.kg]\n",
+ "M = 78.05;# [kg/kmol]\n",
+ "# From Eqn. 7.6:\n",
+ "Lambda = m*lambdar*Mr/M;# [N/m.kg]\n",
+ "print\"Latent Heat of Vaporization at 25 degree C is\",round(Lambda/1000,2),\" kN/m.kg\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.2 - Page: 223\n",
+ "\n",
+ "\n",
+ "Illustration 7.2 (c)\n",
+ "\n",
+ "\n",
+ "Latent Heat of Vaporization at 25 degree C is 437.13 kN/m.kg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.3: Page 226"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.3\n",
+ "# Page: 226\n",
+ "\n",
+ "print'Illustration 7.3 - Page: 226\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# ****Data****#\n",
+ "m = 10;# [kg]\n",
+ "Cvap = 1.256;# [kJ/kg.K]\n",
+ "Cliq = 1.507;# [kJ/kg.K]\n",
+ "Temp1 = 100;# [OC]\n",
+ "Temp4 = 10;# [OC]\n",
+ "#******#\n",
+ "\n",
+ "# Using Fig 7.2 (Pg 224):\n",
+ "Temp2 = 25;# [OC]\n",
+ "# Using the notation of Fig. 7.3:\n",
+ "H1_diff_H2 = Cvap*(Temp1-Temp2);# [kJ/kg]\n",
+ "# From Illustration 7.2:\n",
+ "H2_diff_H3 = 434;# [Latent Heat of Vaporisation, kJ/kg]\n",
+ "H3_diff_H4 = Cliq*(Temp2-Temp4);# [kJ/kg]\n",
+ "H1_diff_H4 = H1_diff_H2+H2_diff_H3+H3_diff_H4;# [kJ/kg]\n",
+ "H = m*H1_diff_H4;# [kJ]\n",
+ "print\"Heat evolved for 10 kg Benzene is \",int(H),\" kJ\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.3 - Page: 226\n",
+ "\n",
+ "\n",
+ "Heat evolved for 10 kg Benzene is 5508 kJ\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.4: Page 227"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.4\n",
+ "# Page: 227\n",
+ "\n",
+ "print'Illustration 7.4 - Page: 227\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = benzene vapour; B = Nitrogen Gas\n",
+ "P = 800.0;# [mm Hg]\n",
+ "Temp = 273.0+60;# [K]\n",
+ "pA = 100.0;# [mm Hg]\n",
+ "#******#\n",
+ "\n",
+ "pB = P-pA;# [mm Hg]\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 28.08;# [kg/kmol]\n",
+ "\n",
+ "# Mole Fraction\n",
+ "print\"On the Basis of Mole Fraction\\n\"\n",
+ "yAm = pA/P;\n",
+ "yBm = pB/P;\n",
+ "print\"Mole Fraction of Benzene is \",yAm\n",
+ "print\"\\nMole Fraction of Nitrogen is \",yBm\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Volume Fraction\n",
+ "print\"On the Basis of Volume Fraction\\n\"\n",
+ "# Volume fraction is same as mole Fraction\n",
+ "yAv = yAm;\n",
+ "yBv = yBm;\n",
+ "print\"Volume Fraction of Benzene is \",yAv\n",
+ "print\"\\n Volume Fraction of Nitrogen is \",yBv\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Absolute Humidity\n",
+ "print\"On the basis of Absolute humidity\\n\"\n",
+ "Y = pA/pB;# [mol benzene/mol nitrogen]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg nitrogen]\n",
+ "print\"The concentration of benzene is \",round(Y_prime,3),\" kg benzene/kg nitrogen\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.4 - Page: 227\n",
+ "\n",
+ "\n",
+ "On the Basis of Mole Fraction\n",
+ "\n",
+ "Mole Fraction of Benzene is 0.125\n",
+ "\n",
+ "Mole Fraction of Nitrogen is 0.875\n",
+ "\n",
+ "\n",
+ "On the Basis of Volume Fraction\n",
+ "\n",
+ "Volume Fraction of Benzene is 0.125\n",
+ "\n",
+ " Volume Fraction of Nitrogen is 0.875\n",
+ "\n",
+ "\n",
+ "On the basis of Absolute humidity\n",
+ "\n",
+ "The concentration of benzene is 0.397 kg benzene/kg nitrogen\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.5: Page 228"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.5\n",
+ "# Page: 228\n",
+ "\n",
+ "print'Illustration 7.5 - Page: 228\\n\\n'\n",
+ "\n",
+ "print'Illustration 7.5 (a)\\n\\n'\n",
+ "# solution(a)\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = benzene vapour; B = Nitrogen Gas\n",
+ "P = 1.0;# [atm]\n",
+ "#*****#\n",
+ "\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 28.02;# [kg/kmol]\n",
+ "# Since gas is saturated, from Fig. 7.2 (Pg 224):\n",
+ "pA = 275.0/760;# [atm]\n",
+ "Y = pA/(P-pA);# [kmol benzene/kmol nitrogen]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg nitrogen]\n",
+ "print\"The concentration of benzene is \",round(Y_prime,3),\" kg benzene/kg nitrogen\\n\\n\"\n",
+ "\n",
+ "print'Illustration 7.5 (b)\\n\\n'\n",
+ "# solution(b)\n",
+ "\n",
+ "# A = benzene vapour; B = CO2\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 44.01;# [kg/kmol]\n",
+ "# Since gas is saturated, from Fig. 7.2:\n",
+ "pA = 275.0/760;# [atm]\n",
+ "Y = pA/(P-pA);# [kmol benzene/kmol CO2]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg CO2]\n",
+ "print\"The concentration of benzene is\",round(Y_prime,3),\" kg benzene/kg CO2\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.5 - Page: 228\n",
+ "\n",
+ "\n",
+ "Illustration 7.5 (a)\n",
+ "\n",
+ "\n",
+ "The concentration of benzene is 1.579 kg benzene/kg nitrogen\n",
+ "\n",
+ "\n",
+ "Illustration 7.5 (b)\n",
+ "\n",
+ "\n",
+ "The concentration of benzene is 1.006 kg benzene/kg CO2\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.6: Page 234"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.6\n",
+ "# Page: 234\n",
+ "\n",
+ "print'Illustration 7.6 - Page: 234\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = water vapour; B = air\n",
+ "TempG = 55;# [OC]\n",
+ "P = 1.0133*10**(5);# [N/square m]\n",
+ "Y_prime = 0.030;# [kg water/kg dry air]\n",
+ "#******#\n",
+ "\n",
+ "MA = 18.02;# [kg/kmol]\n",
+ "MB = 28.97;# [kg/kmol]\n",
+ "\n",
+ "# Percent Humidity\n",
+ "# From psychrometric chart, at 55 OC\n",
+ "Ys_prime = 0.115;# [kg water/kg dry air]\n",
+ "percent_Humidity = (Y_prime/Ys_prime)*100;\n",
+ "print\"The sample has percent Humidity =\",round(percent_Humidity,1),\"%\"\n",
+ "\n",
+ "# Molal Absolute Humidity\n",
+ "Y = Y_prime*(MB/MA);# [kmol water/kmol dry air]\n",
+ "print\"\\n Molal Absolute Humidity of the sample is\",round(Y,4),\" kmol water/kmol dry air\\n\"\n",
+ "\n",
+ "# Partial Pressure\n",
+ "pA = Y*P/(1+Y);# [N/square m]\n",
+ "print\"The Partial Pressure Of Water is\",int(pA),\" N/square m\\n\"\n",
+ "\n",
+ "# Relative Humidity\n",
+ "pa = 118*133.3;# [vapour pressure of water at 55 OC,N/square m]\n",
+ "relative_Humidity = (pA/pa)*100;\n",
+ "print\"The sample has relative Humidity = \",round(relative_Humidity,1),\" %\\n\"\n",
+ "\n",
+ "# Dew Point\n",
+ "# From psychrometric chart,\n",
+ "dew_point = 31.5;# [OC]\n",
+ "print\"Dew point Of the Sample is\",dew_point,\" degree C\\n\"\n",
+ "\n",
+ "# Humid Volume\n",
+ "# At 55 OC\n",
+ "vB = 0.93;# [specific volume of dry air,cubic m/kg]\n",
+ "vsB = 1.10;# [specific volume of saturated air,cubic m/kg]\n",
+ "vH = vB+((vsB-vB)*(percent_Humidity/100));# [cubic m/kg]\n",
+ "print\"The Humid Volume of the Sample is \",round(vH,3),\" cubic m/kg\\n\"\n",
+ "\n",
+ "# Humid Heat\n",
+ "CB = 1005;# [J/kg.K]\n",
+ "CA = 1884;# [J/kg.K]\n",
+ "Cs = CB+(Y_prime*CA);# [J/kg]\n",
+ "print\"The Humid Heat is \",round(Cs,1),\" J/kg dry air.K\\n\"\n",
+ "\n",
+ "# Enthalpy\n",
+ "HA = 56000;# [J/kg dry air]\n",
+ "HsA = 352000;# [J/kg dry air]\n",
+ "H_prime = HA+((HsA-HA)*(percent_Humidity/100));# [J/kg dry air]\n",
+ "print\"The Enthalphy of the sample is \",round(H_prime/1000,1),\"KJ/kg dry air\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.6 - Page: 234\n",
+ "\n",
+ "\n",
+ "The sample has percent Humidity = 26.1 %\n",
+ "\n",
+ " Molal Absolute Humidity of the sample is 0.0482 kmol water/kmol dry air\n",
+ "\n",
+ "The Partial Pressure Of Water is 4662 N/square m\n",
+ "\n",
+ "The sample has relative Humidity = 29.6 %\n",
+ "\n",
+ "Dew point Of the Sample is 31.5 degree C\n",
+ "\n",
+ "The Humid Volume of the Sample is 0.974 cubic m/kg\n",
+ "\n",
+ "The Humid Heat is 1061.5 J/kg dry air.K\n",
+ "\n",
+ "The Enthalphy of the sample is 133.2 KJ/kg dry air\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.7: Page 236"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.7\n",
+ "# Page: 236\n",
+ "\n",
+ "print'Illustration 7.7 - Page: 236\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = water vapour; B = air\n",
+ "V = 100;# [m**3]\n",
+ "Tempi = 55;# [OC]\n",
+ "Tempf = 110;# [OC]\n",
+ "#*****#\n",
+ "\n",
+ "# From Illustration 7.6\n",
+ "vH = 0.974;# [m**3/kg]\n",
+ "Cs = 1061.5;# [J/kg]\n",
+ "WB = V/vH;# [kg]\n",
+ "Q = WB*Cs*(Tempf-Tempi);# [J]\n",
+ "print\"Heat required is \",round(Q,3),\" J\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation in book"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.7 - Page: 236\n",
+ "\n",
+ "\n",
+ "Heat recquired is 5994096.509 J\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.9: Page 240"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.9\n",
+ "# Page:240\n",
+ "from scipy.optimize import fsolve \n",
+ "print'Illustration 7.9 - Page:240\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "Tempw = 35;# [OC]\n",
+ "Tempg = 65;# [OC]\n",
+ "#******#\n",
+ "\n",
+ "# From psychrometric chart\n",
+ "lambda_w = 2419300;# [J/kg]\n",
+ "Y_prime_w = 0.0365;# [kg H2O/kg dry air]\n",
+ "# From fig 7.5(a)\n",
+ "hG_by_kY = 950;# [J/kg]\n",
+ "# From Eqn. 7.26\n",
+ "def f13(Y_prime):\n",
+ " return (Tempg-Tempw)-((lambda_w*(Y_prime_w-Y_prime))/hG_by_kY)\n",
+ "Y_prime = fsolve(f13,2);# [kg H2O/kg dry air]\n",
+ "print\"Humidity of air is\",round(Y_prime[0],4),\"kg H2O/kg dry air\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.9 - Page:240\n",
+ "\n",
+ "\n",
+ "Humidity of air is 0.0247 kg H2O/kg dry air\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.10: Page 241"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.10\n",
+ "# Page:241\n",
+ "\n",
+ "print'Illustration 7.10 - Page:241\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "Tg = 60;# [OC]\n",
+ "Y_prime = 0.050;# [kg toulene/kg air]\n",
+ "#*****#\n",
+ "\n",
+ "# Wet Bulb temparature\n",
+ "Dab = 0.92*10**(-5);# [square m/s]\n",
+ "density_air = 1.060;# [kg/cubic cm];\n",
+ "viscocity_G = 1.95*10**(-5);# [kg/m.s]\n",
+ "Sc = viscocity_G/(density_air*Dab);\n",
+ "# From Eqn. 7.28\n",
+ "hG_by_kY = 1223*(Sc**0.567);# [J/kg.K]\n",
+ "# Soln. of Eqn. 7.26 by trial & error method:\n",
+ "# (Tg-Tw) = (Yas_prime-Y_prime)*(lambda_w/hG_by_kY)\n",
+ "Tw = 31.8;# [OC]\n",
+ "print\"Wet Bulb Temparature:\",Tw,\" degree C\\n\"\n",
+ "\n",
+ "# Adiabatic Saturation Temparature\n",
+ "C_air = 1005;# [J/kg.K]\n",
+ "C_toulene = 1256;# [J/kg.K]\n",
+ "Cs = C_air+(C_toulene*Y_prime);# [J/kg.K]\n",
+ "# Soln. of Eqn. 7.21 by trial & error method:\n",
+ "# (Tg-Tas) = (Yas_prime-Y_prime)*(lambda_as/Cs)\n",
+ "Tas = 25.7;# [OC]\n",
+ "print\"Adiabatic Saturation Temparature: \",round(Tas,1),\" degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.10 - Page:241\n",
+ "\n",
+ "\n",
+ "Wet Bulb Temparature: 31.8 degree C\n",
+ "\n",
+ "Adiabatic Saturation Temparature: 25.7 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.11: Page 249"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.11\n",
+ "# Page: 249\n",
+ "\n",
+ "print'Illustration 7.11 - Page: 249\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "\n",
+ "#****Data****#\n",
+ "L_min = 2.27;# [kg/square m.s]\n",
+ "G_min = 2;# [kg/square m.s]\n",
+ "L2_prime = 15;# [kg/s]\n",
+ "Q = 270.0;# [W]\n",
+ "Templ2 = 45.0;# [OC]\n",
+ "Tempg1 = 30.0;# [OC]\n",
+ "Tempw1 = 24.0;# [OC]\n",
+ "Kya = 0.90;# [kg/cubic m.s]\n",
+ "#*******#\n",
+ "\n",
+ "H1_prime = 72;# [kJ/kg dry air]\n",
+ "Y1_prime = 0.0160;# [kg water/kg dry air]\n",
+ "Templ1 = 29;# [OC]\n",
+ "Cal = 4.187;# [kJ/kg]\n",
+ "\n",
+ "# Tower cross section Area:\n",
+ "Al = L2_prime/L_min;# [square m]\n",
+ "Ag = Gs/G_min;# [square m]\n",
+ "A = min(Al,Ag);# [square m]\n",
+ "Area = 3.25;\n",
+ "# From Eqn. 7.54\n",
+ "def f16(Z):\n",
+ " return Area-(Kya*Z/G_min)\n",
+ "Z = fsolve(f16,2);\n",
+ "print\"The height of tower is\",round(Z,2),\" m\\n\"\n",
+ "NtoG = 3.25;\n",
+ "HtoG = G_min/Kya;# [m]\n",
+ "\n",
+ "# Make up water\n",
+ "# Assuming the outlet air is essentially saturated:\n",
+ "Y2_prime = 0.0475;# [kg water/kg dry air]\n",
+ "E = G_min*(A)*(Y2_prime-Y1_prime);# [kg/s]\n",
+ "# Windage loss estimated as 0.2 percent\n",
+ "W = 0.002*L2_prime;# [kg/s]\n",
+ "ppm_blowdown = 2000;# [ppm]\n",
+ "ppm_makeup = 500;# [ppm]\n",
+ "# Since the weight fraction are proportional to the corresponding ppm values:\n",
+ "B = (E*ppm_makeup/(ppm_blowdown-ppm_makeup))-W;# [kg/s]\n",
+ "M = B+E+W;# [kg/s]\n",
+ "print\"The makeup water requirement is estimated to be\",round(M,2),\" kg/s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.11 - Page: 249\n",
+ "\n",
+ "\n",
+ "The height of tower is 7.22 m\n",
+ "\n",
+ "The makeup water requirement is estimated to be 0.46 kg/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 81
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.13: Page 254"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.13\n",
+ "# Page: 254\n",
+ "\n",
+ "\n",
+ "print'Illustration 7.13\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve \n",
+ "# Given\n",
+ "Tempg1=65;# [OC]\n",
+ "Y1_prime=0.0170;# [kg water/kg dry air]\n",
+ "# Using adiabatic satursion line on Fig. 7.5 (Pg 232)\n",
+ "Tempas=32;# [OC]\n",
+ "Yas_prime=0.0309;# [kg water/kg dry air]\n",
+ "Tempg2=45;# [OC]\n",
+ "Z=2;# [m]\n",
+ "#*******#\n",
+ "\n",
+ "Y2_prime=0.0265;# [kg water/kg dry air]\n",
+ "def f19(Kya_by_Gs):\n",
+ " return math.log((Yas_prime-Y1_prime)/(Yas_prime-Y2_prime))-(Kya_by_Gs*Z)\n",
+ "Kya_by_Gs=fsolve(f19,1);# [1/m]\n",
+ "\n",
+ "# For the extended chamber:\n",
+ "Z=4;# [m]\n",
+ "def f20(Y2_prime):\n",
+ " return math.log((Yas_prime-Y1_prime)/(Yas_prime-Y2_prime))-(Kya_by_Gs*Z)\n",
+ "Y2_prime=fsolve(f20,0.029);#[kg water/kg dry air] \n",
+ "# With the same adiabatic curve:\n",
+ "Tempg2=34;# [OC] from the curve\n",
+ "print\"The Outlet Conditions are:\\n\"\n",
+ "print\"Absolute Humidity is\",round(Y2_prime,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Dry Bulb Temperature is\",round(Tempg2), \"degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.13\n",
+ "\n",
+ "\n",
+ "The Outlet Conditions are:\n",
+ "\n",
+ "Absolute Humidity is 0.0295 kg water/kg dry air\n",
+ "\n",
+ "Dry Bulb Temperature is 34.0 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 137
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.14: Page 256"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.14\n",
+ "# Page: 256\n",
+ "\n",
+ "print'Illustration 7.14 - Page: 256\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# a = N2 b = CO\n",
+ "# Entering gas\n",
+ "Y1_prime = 0.0;# [kg water/kg dry air]\n",
+ "Pt = 1.0;# [atm]\n",
+ "Tempg1 = 315.0;# [OC]\n",
+ "G_prime = 5.0;# [square m/s]\n",
+ "\n",
+ "# Temp of the tower:\n",
+ "Templ2 = 18.0;# [OC]\n",
+ "Density_L2 = 1000.0; #[kg/square m]\n",
+ "viscocity_L2 = 1.056*10**(-3);# [kg/m.s]\n",
+ "Tempg2 = 27.0;# [OC]\n",
+ "\n",
+ "Mb = 28.0;# [kg/kmol]\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Density_G1 = (Mb/22.41)*(273/(Tempg1+273));# [kg/square m]\n",
+ "G1 = G_prime*(Density_G1);# [kg/s]\n",
+ "\n",
+ "# Since the outlet gas is nearly saturated:\n",
+ "Y_prime = 0.024;# [kg water/kg dry air]\n",
+ "Y2_prime = 0.022;# [kg water/kg dry air, assumed]\n",
+ "G2 = G1*(1+Y2_prime);# [kg/s]\n",
+ "Mav = (1+Y2_prime)/((1/Mb)+(Y2_prime/Ma));# [kg/kmol]\n",
+ "Density_G2 = (Mav/22.4)*(273.0/(Templ2+273));# [kg/square m]\n",
+ "L2_by_G2 = 2.0;\n",
+ "abcissa = L2_by_G2*(Density_G2/(Density_L2-Density_G2))**(1/2);\n",
+ "# From Fig. 6.34:\n",
+ "# For a gas pressure drop of 400 N/square m/m\n",
+ "ordinate = 0.073;\n",
+ "# From Table 6.3:\n",
+ "Cf = 65.0;\n",
+ "J = 1.0;\n",
+ "def f21(G2_prime):\n",
+ " return ((G2_prime**2)*Cf*(viscocity_L2**0.1)*J/(Density_G2*(Density_L2-Density_G2)))-ordinate\n",
+ "# Tentative data:\n",
+ "G2_prime = fsolve(f21,2);# [kg/square m.s]\n",
+ "Area = G1/G2_prime;# [square m]\n",
+ "dia = math.sqrt(4*Area/math.pi);# [m]\n",
+ "\n",
+ "# Final data:\n",
+ "dia = 1.50;# [m]\n",
+ "Area = math.pi*dia**2.0/4;# [square m]\n",
+ "Gs_prime = G1/Area;# [kg/square m.s]\n",
+ "G2_prime = G2/Area;# [kg/square m.s]\n",
+ "L2_prime = L2_by_G2*G2_prime;# [kg/square m.s]\n",
+ "# From Eqn. 7.29:\n",
+ "def f22(L1_prime):\n",
+ " return (L2_prime-L1_prime)-(Gs_prime*(Y2_prime-Y1_prime))\n",
+ "L1_prime = fsolve(f22,2);\n",
+ "Cb = 1089;# [J/kg.K]\n",
+ "Ca = 1884;# [J/kg.K]\n",
+ "Cs1 = Cb+(Y1_prime*Ca);# [J/(kg dry air).K]\n",
+ "Cs2 = Cb+(Y2_prime*Ca);# [J/(kg dry air).K]\n",
+ "Tempo = Templ2;# [base temp.,K]\n",
+ "Lambda = 2.46*10**6;# [J/kg]\n",
+ "CaL = 4187;# [J/kg K]\n",
+ "# From Eqn. 7.31:\n",
+ "def f23(Templ1):\n",
+ " return ((L2_prime*CaL*(Templ2-Tempo))+(Gs_prime*Cs1*(Tempg1-Tempo)))-((L1_prime*CaL*(Templ1-Tempo))+(Gs_prime*(Cs2*(Tempg2-Tempo))+(Y2_prime*Lambda)))\n",
+ "Templ1 = fsolve(f23,2);\n",
+ "# At Templ1 = 49.2 OC\n",
+ "viscocity_L = 0.557*10**(-3);# [kg/m.s]\n",
+ "Density_L = 989.0;# [kg/square m]\n",
+ "K = 0.64;# [w/m.K]\n",
+ "Prl = CaL*viscocity_L/K;\n",
+ "\n",
+ "# For Entering Gas:\n",
+ "viscocity_G1 = 0.0288*10**(-3);# [kg*/m.s]\n",
+ "Dab = 0.8089*10**(-4);# [square m/s]\n",
+ "ScG = viscocity_G1/(Density_G1*Dab);\n",
+ "PrG = 0.74;\n",
+ "\n",
+ "# From Illustration 6.7:\n",
+ "a = 53.1;# [square m/square m]\n",
+ "Fga = 0.0736;# [kmol/square m]\n",
+ "Hga = 4440.0;# [W/square m.K]\n",
+ "Hla = 350500.0;# [W/square m.K]\n",
+ "# At the bottom, by several trial:\n",
+ "Tempi = 50.3;# [OC]\n",
+ "pai = 93.9/760;# [atm]\n",
+ "paG = 0;# [atm]\n",
+ "# By Eqn. 7.64:\n",
+ "dY_prime_by_dZ = -(Ma*Fga/Gs_prime)*math.log((1-(pai/Pt))/(1-(paG/Pt)));# [(kg H2O/kg dry gas)/m]\n",
+ "Hg_primea = -(Gs_prime*Ca*dY_prime_by_dZ)/(1-math.exp((Gs_prime*Ca*dY_prime_by_dZ)/(Hga)));# [W/square m.K]\n",
+ "dTempg_by_dZ = -(Hg_primea*(Tempg1-Tempi)/(Gs_prime*Cs1));# [OC/m]\n",
+ "Tempi = (Templ1)+((Gs_prime*(Cs1*dTempg_by_dZ)+((Ca*(Tempg1))-(CaL*(Templ1))+(((CaL-Ca)*(Tempo))+Lambda))*dY_prime_by_dZ)/((Gs_prime*CaL*dY_prime_by_dZ)-Hla));#[OC]\n",
+ "# Assume:\n",
+ "delta_Tempg = -30;# [OC]\n",
+ "delta_Z = delta_Tempg/(dTempg_by_dZ);# [m]\n",
+ "Tempg = Tempg1+delta_Tempg;# [OC]\n",
+ "Y_prime = Y1_prime+(dY_prime_by_dZ)*delta_Z;# [kg H2O/kg dry gas]\n",
+ "paG = Y_prime/(Y_prime+(Ma/Mb));# [atm]\n",
+ "Cs = Cb+Ca*(Y_prime);# [J/(kg dry air).K]\n",
+ "# Water balance, From Eqn. 7.29:\n",
+ "def f24(L_prime):\n",
+ " return (L2_prime-L_prime)-(Gs_prime*(Y_prime-Y1_prime))\n",
+ "L_prime = fsolve(f24,2);# [kg/square m.s]\n",
+ "\n",
+ "def f25(Templ):\n",
+ " return ((L_prime*CaL*(Templ-Tempo))+(Gs_prime*Cs1*(Tempg1-Tempo)))-((L1_prime*CaL*(Templ1-Tempo))+(Gs_prime*(Cs*(Tempg-Tempo))+(Y_prime*Lambda)))\n",
+ "Templ = fsolve(f25,2);\n",
+ "\n",
+ "# This process is repeated several times until gas temp falls to Tempg2\n",
+ "Z = 1.54;# [m] Z = sum of all delta_Z\n",
+ "# The value of Y2_prime was calculated to be 0.0222 which is sufficiently close to the assumed value.\n",
+ "print\"The diameter of tower is \",dia,\" m\\n\"\n",
+ "print\"The packed height is\",Z, \"m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.15: Page 267"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.15\n",
+ "# Page: 267\n",
+ "\n",
+ "print'Illustration 7.15 - Page: 267\\n\\n'\n",
+ "\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "import numpy\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "w = 0.75;# [m]\n",
+ "OD = 19.05/1000;# [m]\n",
+ "l = 3.75;# [m]\n",
+ "n = 20;\n",
+ "t = 1.65/1000;# [m]\n",
+ "Ws = 2.3;# [kg/s]\n",
+ "Wal = 10.0;# [kg/s]\n",
+ "Wt = 4.0;# [kg/s]\n",
+ "Density = 800;# [kg/cubic m]\n",
+ "viscocity = 0.005;# [kg/m.s]\n",
+ "K = 0.1436;# [W/m.K]\n",
+ "Ct = 2010.0;# [J/kg.K]\n",
+ "Cal = 4187.0;# [J/kg.K]\n",
+ "Y1_prime = 0.01;# [kg H2O/kg dry air]\n",
+ "Y2_prime = 0.06;# [kg H2O/kg dry air]\n",
+ "TempT = 95.0;# [OC]\n",
+ "#*****#\n",
+ "\n",
+ "Free_area = (w-(n*OD))*l;# [square m]\n",
+ "Gs_min = 2.3/Free_area;# [kg/square m.s]\n",
+ "M1 = 1.461;Yav_prime = (Y1_prime+Y2_prime)/2;# [kg H2O/kg dry air]\n",
+ "# From Eqn. 7.86:\n",
+ "ky = 0.0493*(Gs_min*(1+Yav_prime))**0.905;# [kg/square m.s.delta_Y_prime]\n",
+ "# From Fig. 7.5:\n",
+ "H1_prime = 56000.0;# [J/kg]\n",
+ "Ao = 400*math.pi*OD*l;# [square m]\n",
+ "# Cooling water is distributed over 40 tubes & since tubes are staggered\n",
+ "geta = Wal/(40.0*2*l);# [kg/m.s]\n",
+ "geta_by_OD = geta/OD;# [kg/square m.s]\n",
+ "# Assume:\n",
+ "TempL = 28.0;# [OC]\n",
+ "# From Eqn. 7.84:\n",
+ "hL_prime = (982+(15.58*TempL))*(geta_by_OD**(1/3));# [W/square m.K]\n",
+ "# From Eqn. 7.85:\n",
+ "hL_dprime = 11360;# [W/square m.K]\n",
+ "# From Fig. 7.5 (Pg 232)\n",
+ "m = 5000.0;# [J/kg.K]\n",
+ "Ky = 1.0/((1/ky)+(m/hL_dprime));\n",
+ "ID = (OD-(2.0*t));# [m]\n",
+ "Ai = math.pi*(ID**2)/4;# [square m]\n",
+ "Gt_prime = Wt/(n*Ai);# [kg/square m.s]\n",
+ "M2 = -0.7204;Re = ID*Gt_prime/viscocity;\n",
+ "Pr = Ct*viscocity/K;\n",
+ "# From a standard correlation:\n",
+ "hT = 364.0;# [W/square m.K]\n",
+ "Dav = (ID+OD)/2.0;# [m]\n",
+ "Zm = (OD-ID)/2;# [m]\n",
+ "Km = 112.5;# [W/m.K]\n",
+ "# From Eqn. 7.67:\n",
+ "Uo = 1/((OD/(ID*hT))+((OD/Dav)*(Zm/Km))+(1/hL_prime));# [W/square m.K]\n",
+ "# From Eqn. 7.75:\n",
+ "alpha1 = -(((Uo*Ao)/(Wt*Ct))+((Uo*Ao)/(Wal*Cal)));\n",
+ "alpha2 = m*Uo*Ao/(Wt*Ct);\n",
+ "# From Eqn. 7.76:\n",
+ "beeta1 = Ky*Ao/(Wal*Cal);\n",
+ "beeta2 = -((m*Ky*Ao/(Wal*Cal))-(Ky*Ao/Ws));\n",
+ "def f26(r):\n",
+ " return (r**2)+((alpha1+beeta2)*r)+((alpha1*beeta2)-(alpha2*beeta1))\n",
+ "r1 = fsolve(f26,10);\n",
+ "r2 = fsolve(f26,0);\n",
+ "beeta2 = 1.402;\n",
+ "# From Eqn. 7.83:\n",
+ "# N1-(M1*(r1+alpha1)/beeta1) = 0............................................(1)\n",
+ "# N2-(M2*(r2+alpha2)/beeta2) = 0............................................(2)\n",
+ "# From Eqn. 7.77:\n",
+ "# At the top:\n",
+ "x1 = 1.0;\n",
+ "# TempL2+(M1*exp(r1*x1))+(M2*exp(-(r2*x1))) = TempL.........................(3)\n",
+ "# From Eqn. 7.78:\n",
+ "# At the bottom:\n",
+ "x2 = 0.0;\n",
+ "# H1_star-N1-N2 = H1_prime..................................................(4)\n",
+ "# From Eqn. 7.80:\n",
+ "# ((M1/r1)*(exp(r1)-1))+((M2*r2)*(exp(r2)-1)) = (Tempt-TempL)...............(5)\n",
+ "# From Eqn. 7.81:\n",
+ "# ((N1/r1)*(exp(r1)-1))+((N2*r2)*(exp(r2)-1)) = (H1_star-H1_prime)..........(6)\n",
+ "# From Eqn. 7.91 & Eqn. 7.92:\n",
+ "# Uo*Ao*(TempT-TempL)=Ky*Ao*(H1_star-H1_prime)..............................(7)\n",
+ "\n",
+ "# Elimination of M's & N's by solving Eqn. (1) to (4) and (7) simultaneously:\n",
+ "# and from Fig. 7.5 (Pg 232):\n",
+ "TempL1=28.0;# [OC]\n",
+ "H1_star=(Uo*Ao*(TempT-TempL)/(Ky*Ao))+H1_prime;# [J/kmol]\n",
+ "\n",
+ "\n",
+ "N1 = 3594.0*M1\n",
+ "N2 =-43288.0*M2;\n",
+ "\n",
+ "# By Eqn. 5\n",
+ "delta_Temp = ((M1/r1)*(math.exp(r1)-1))+((M2*r2)*(math.exp(r2)-1));# [OC]\n",
+ "Q = Uo*delta_Temp*Ao;\n",
+ "TempT1 = TempT-(Q/(Wt*Ct));# [OC]\n",
+ "H2_prime = Q/(Ws)+H1_prime;# [J/kg]\n",
+ "print\"Temperature to which oil was cooled:\",int(TempT1),\" degree C\\n\"\n",
+ "# The solution in the textbook is wrong "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.15 - Page: 267\n",
+ "\n",
+ "\n",
+ "Temperature to which oil was cooled:"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 57 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter7_2.ipynb b/Mass_-_Transfer_Operations/Chapter7_2.ipynb
new file mode 100755
index 00000000..83c83fea
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter7_2.ipynb
@@ -0,0 +1,1092 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:0627e1083498e0e37e89924d5c259017b46e7f004ea51c86bbdc5e0e6e777fe9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 7: Humidification Operations"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.1: Page 222"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.1\n",
+ "# Page: 222\n",
+ "\n",
+ "print'Illustration 7.1 - Page: 222\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "# ****Data****#\n",
+ "Temp1 = 273+26.1;# [K]\n",
+ "P1 = 100;# [mm Hg]\n",
+ "Temp2 = 273+60.6;# [K]\n",
+ "P2 = 400;# [mm Hg]\n",
+ "P = 200;# [mm Hg]\n",
+ "#*****#\n",
+ "\n",
+ "def f12(T):\n",
+ " return ((1/Temp1)-(1/T))/((1/Temp1)-(1/Temp2))-((math.log(P1)-math.log(P))/(math.log(P1)-math.log(P2)))\n",
+ "T = fsolve(f12,37);# [K]\n",
+ "print\"At\",round(T-273,1),\" degree C, the vapour pressure of benzene is 200 mm Hg\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.1 - Page: 222\n",
+ "\n",
+ "\n",
+ "At"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 42.4 degree C, the vapour pressure of benzene is 200 mm Hg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.2: Page 223"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.2:\n",
+ "# Page: 223\n",
+ "\n",
+ "print'Illustration 7.2 - Page: 223\\n\\n'\n",
+ "#part(a) and part(b) are table based and doesn't require an calculation\n",
+ "\n",
+ "print'Illustration 7.2 (c)\\n\\n'\n",
+ "\n",
+ "# Solution (c)\n",
+ "\n",
+ "# Reference: H20\n",
+ "# At 25 OC\n",
+ "m = 0.775;\n",
+ "Mr = 18.02;# [kg/kmol]\n",
+ "lambdar = 2443000;# [N/m.kg]\n",
+ "M = 78.05;# [kg/kmol]\n",
+ "# From Eqn. 7.6:\n",
+ "Lambda = m*lambdar*Mr/M;# [N/m.kg]\n",
+ "print\"Latent Heat of Vaporization at 25 degree C is\",round(Lambda/1000,2),\" kN/m.kg\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.2 - Page: 223\n",
+ "\n",
+ "\n",
+ "Illustration 7.2 (c)\n",
+ "\n",
+ "\n",
+ "Latent Heat of Vaporization at 25 degree C is 437.13 kN/m.kg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.3: Page 226"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.3\n",
+ "# Page: 226\n",
+ "\n",
+ "print'Illustration 7.3 - Page: 226\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# ****Data****#\n",
+ "m = 10;# [kg]\n",
+ "Cvap = 1.256;# [kJ/kg.K]\n",
+ "Cliq = 1.507;# [kJ/kg.K]\n",
+ "Temp1 = 100;# [OC]\n",
+ "Temp4 = 10;# [OC]\n",
+ "#******#\n",
+ "\n",
+ "# Using Fig 7.2 (Pg 224):\n",
+ "Temp2 = 25;# [OC]\n",
+ "# Using the notation of Fig. 7.3:\n",
+ "H1_diff_H2 = Cvap*(Temp1-Temp2);# [kJ/kg]\n",
+ "# From Illustration 7.2:\n",
+ "H2_diff_H3 = 434;# [Latent Heat of Vaporisation, kJ/kg]\n",
+ "H3_diff_H4 = Cliq*(Temp2-Temp4);# [kJ/kg]\n",
+ "H1_diff_H4 = H1_diff_H2+H2_diff_H3+H3_diff_H4;# [kJ/kg]\n",
+ "H = m*H1_diff_H4;# [kJ]\n",
+ "print\"Heat evolved for 10 kg Benzene is \",int(H),\" kJ\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.3 - Page: 226\n",
+ "\n",
+ "\n",
+ "Heat evolved for 10 kg Benzene is 5508 kJ\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.4: Page 227"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.4\n",
+ "# Page: 227\n",
+ "\n",
+ "print'Illustration 7.4 - Page: 227\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = benzene vapour; B = Nitrogen Gas\n",
+ "P = 800.0;# [mm Hg]\n",
+ "Temp = 273.0+60;# [K]\n",
+ "pA = 100.0;# [mm Hg]\n",
+ "#******#\n",
+ "\n",
+ "pB = P-pA;# [mm Hg]\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 28.08;# [kg/kmol]\n",
+ "\n",
+ "# Mole Fraction\n",
+ "print\"On the Basis of Mole Fraction\\n\"\n",
+ "yAm = pA/P;\n",
+ "yBm = pB/P;\n",
+ "print\"Mole Fraction of Benzene is \",yAm\n",
+ "print\"\\nMole Fraction of Nitrogen is \",yBm\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Volume Fraction\n",
+ "print\"On the Basis of Volume Fraction\\n\"\n",
+ "# Volume fraction is same as mole Fraction\n",
+ "yAv = yAm;\n",
+ "yBv = yBm;\n",
+ "print\"Volume Fraction of Benzene is \",yAv\n",
+ "print\"\\n Volume Fraction of Nitrogen is \",yBv\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Absolute Humidity\n",
+ "print\"On the basis of Absolute humidity\\n\"\n",
+ "Y = pA/pB;# [mol benzene/mol nitrogen]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg nitrogen]\n",
+ "print\"The concentration of benzene is \",round(Y_prime,3),\" kg benzene/kg nitrogen\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.4 - Page: 227\n",
+ "\n",
+ "\n",
+ "On the Basis of Mole Fraction\n",
+ "\n",
+ "Mole Fraction of Benzene is 0.125\n",
+ "\n",
+ "Mole Fraction of Nitrogen is 0.875\n",
+ "\n",
+ "\n",
+ "On the Basis of Volume Fraction\n",
+ "\n",
+ "Volume Fraction of Benzene is 0.125\n",
+ "\n",
+ " Volume Fraction of Nitrogen is 0.875\n",
+ "\n",
+ "\n",
+ "On the basis of Absolute humidity\n",
+ "\n",
+ "The concentration of benzene is 0.397 kg benzene/kg nitrogen\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.5: Page 228"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.5\n",
+ "# Page: 228\n",
+ "\n",
+ "print'Illustration 7.5 - Page: 228\\n\\n'\n",
+ "\n",
+ "print'Illustration 7.5 (a)\\n\\n'\n",
+ "# solution(a)\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = benzene vapour; B = Nitrogen Gas\n",
+ "P = 1.0;# [atm]\n",
+ "#*****#\n",
+ "\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 28.02;# [kg/kmol]\n",
+ "# Since gas is saturated, from Fig. 7.2 (Pg 224):\n",
+ "pA = 275.0/760;# [atm]\n",
+ "Y = pA/(P-pA);# [kmol benzene/kmol nitrogen]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg nitrogen]\n",
+ "print\"The concentration of benzene is \",round(Y_prime,3),\" kg benzene/kg nitrogen\\n\\n\"\n",
+ "\n",
+ "print'Illustration 7.5 (b)\\n\\n'\n",
+ "# solution(b)\n",
+ "\n",
+ "# A = benzene vapour; B = CO2\n",
+ "MA = 78.05;# [kg/kmol]\n",
+ "MB = 44.01;# [kg/kmol]\n",
+ "# Since gas is saturated, from Fig. 7.2:\n",
+ "pA = 275.0/760;# [atm]\n",
+ "Y = pA/(P-pA);# [kmol benzene/kmol CO2]\n",
+ "Y_prime = Y*(MA/MB);# [kg benzene/kg CO2]\n",
+ "print\"The concentration of benzene is\",round(Y_prime,3),\" kg benzene/kg CO2\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.5 - Page: 228\n",
+ "\n",
+ "\n",
+ "Illustration 7.5 (a)\n",
+ "\n",
+ "\n",
+ "The concentration of benzene is 1.579 kg benzene/kg nitrogen\n",
+ "\n",
+ "\n",
+ "Illustration 7.5 (b)\n",
+ "\n",
+ "\n",
+ "The concentration of benzene is 1.006 kg benzene/kg CO2\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.6: Page 234"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.6\n",
+ "# Page: 234\n",
+ "\n",
+ "print'Illustration 7.6 - Page: 234\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = water vapour; B = air\n",
+ "TempG = 55;# [OC]\n",
+ "P = 1.0133*10**(5);# [N/square m]\n",
+ "Y_prime = 0.030;# [kg water/kg dry air]\n",
+ "#******#\n",
+ "\n",
+ "MA = 18.02;# [kg/kmol]\n",
+ "MB = 28.97;# [kg/kmol]\n",
+ "\n",
+ "# Percent Humidity\n",
+ "# From psychrometric chart, at 55 OC\n",
+ "Ys_prime = 0.115;# [kg water/kg dry air]\n",
+ "percent_Humidity = (Y_prime/Ys_prime)*100;\n",
+ "print\"The sample has percent Humidity =\",round(percent_Humidity,1),\"%\"\n",
+ "\n",
+ "# Molal Absolute Humidity\n",
+ "Y = Y_prime*(MB/MA);# [kmol water/kmol dry air]\n",
+ "print\"\\n Molal Absolute Humidity of the sample is\",round(Y,4),\" kmol water/kmol dry air\\n\"\n",
+ "\n",
+ "# Partial Pressure\n",
+ "pA = Y*P/(1+Y);# [N/square m]\n",
+ "print\"The Partial Pressure Of Water is\",int(pA),\" N/square m\\n\"\n",
+ "\n",
+ "# Relative Humidity\n",
+ "pa = 118*133.3;# [vapour pressure of water at 55 OC,N/square m]\n",
+ "relative_Humidity = (pA/pa)*100;\n",
+ "print\"The sample has relative Humidity = \",round(relative_Humidity,1),\" %\\n\"\n",
+ "\n",
+ "# Dew Point\n",
+ "# From psychrometric chart,\n",
+ "dew_point = 31.5;# [OC]\n",
+ "print\"Dew point Of the Sample is\",dew_point,\" degree C\\n\"\n",
+ "\n",
+ "# Humid Volume\n",
+ "# At 55 OC\n",
+ "vB = 0.93;# [specific volume of dry air,cubic m/kg]\n",
+ "vsB = 1.10;# [specific volume of saturated air,cubic m/kg]\n",
+ "vH = vB+((vsB-vB)*(percent_Humidity/100));# [cubic m/kg]\n",
+ "print\"The Humid Volume of the Sample is \",round(vH,3),\" cubic m/kg\\n\"\n",
+ "\n",
+ "# Humid Heat\n",
+ "CB = 1005;# [J/kg.K]\n",
+ "CA = 1884;# [J/kg.K]\n",
+ "Cs = CB+(Y_prime*CA);# [J/kg]\n",
+ "print\"The Humid Heat is \",round(Cs,1),\" J/kg dry air.K\\n\"\n",
+ "\n",
+ "# Enthalpy\n",
+ "HA = 56000;# [J/kg dry air]\n",
+ "HsA = 352000;# [J/kg dry air]\n",
+ "H_prime = HA+((HsA-HA)*(percent_Humidity/100));# [J/kg dry air]\n",
+ "print\"The Enthalphy of the sample is \",round(H_prime/1000,1),\"KJ/kg dry air\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.6 - Page: 234\n",
+ "\n",
+ "\n",
+ "The sample has percent Humidity = 26.1 %\n",
+ "\n",
+ " Molal Absolute Humidity of the sample is 0.0482 kmol water/kmol dry air\n",
+ "\n",
+ "The Partial Pressure Of Water is 4662 N/square m\n",
+ "\n",
+ "The sample has relative Humidity = 29.6 %\n",
+ "\n",
+ "Dew point Of the Sample is 31.5 degree C\n",
+ "\n",
+ "The Humid Volume of the Sample is 0.974 cubic m/kg\n",
+ "\n",
+ "The Humid Heat is 1061.5 J/kg dry air.K\n",
+ "\n",
+ "The Enthalphy of the sample is 133.2 KJ/kg dry air\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.7: Page 236"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.7\n",
+ "# Page: 236\n",
+ "\n",
+ "print'Illustration 7.7 - Page: 236\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# A = water vapour; B = air\n",
+ "V = 100;# [m**3]\n",
+ "Tempi = 55;# [OC]\n",
+ "Tempf = 110;# [OC]\n",
+ "#*****#\n",
+ "\n",
+ "# From Illustration 7.6\n",
+ "vH = 0.974;# [m**3/kg]\n",
+ "Cs = 1061.5;# [J/kg]\n",
+ "WB = V/vH;# [kg]\n",
+ "Q = WB*Cs*(Tempf-Tempi);# [J]\n",
+ "print\"Heat required is \",round(Q,3),\" J\\n\"\n",
+ "# the answer is slightly different in textbook due to approximation in book"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.7 - Page: 236\n",
+ "\n",
+ "\n",
+ "Heat recquired is 5994096.509 J\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.9: Page 240"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.9\n",
+ "# Page:240\n",
+ "from scipy.optimize import fsolve \n",
+ "print'Illustration 7.9 - Page:240\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "Tempw = 35;# [OC]\n",
+ "Tempg = 65;# [OC]\n",
+ "#******#\n",
+ "\n",
+ "# From psychrometric chart\n",
+ "lambda_w = 2419300;# [J/kg]\n",
+ "Y_prime_w = 0.0365;# [kg H2O/kg dry air]\n",
+ "# From fig 7.5(a)\n",
+ "hG_by_kY = 950;# [J/kg]\n",
+ "# From Eqn. 7.26\n",
+ "def f13(Y_prime):\n",
+ " return (Tempg-Tempw)-((lambda_w*(Y_prime_w-Y_prime))/hG_by_kY)\n",
+ "Y_prime = fsolve(f13,2);# [kg H2O/kg dry air]\n",
+ "print\"Humidity of air is\",round(Y_prime[0],4),\"kg H2O/kg dry air\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.9 - Page:240\n",
+ "\n",
+ "\n",
+ "Humidity of air is 0.0247 kg H2O/kg dry air\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.10: Page 241"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.10\n",
+ "# Page:241\n",
+ "\n",
+ "print'Illustration 7.10 - Page:241\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "Tg = 60;# [OC]\n",
+ "Y_prime = 0.050;# [kg toulene/kg air]\n",
+ "#*****#\n",
+ "\n",
+ "# Wet Bulb temparature\n",
+ "Dab = 0.92*10**(-5);# [square m/s]\n",
+ "density_air = 1.060;# [kg/cubic cm];\n",
+ "viscocity_G = 1.95*10**(-5);# [kg/m.s]\n",
+ "Sc = viscocity_G/(density_air*Dab);\n",
+ "# From Eqn. 7.28\n",
+ "hG_by_kY = 1223*(Sc**0.567);# [J/kg.K]\n",
+ "# Soln. of Eqn. 7.26 by trial & error method:\n",
+ "# (Tg-Tw) = (Yas_prime-Y_prime)*(lambda_w/hG_by_kY)\n",
+ "Tw = 31.8;# [OC]\n",
+ "print\"Wet Bulb Temparature:\",Tw,\" degree C\\n\"\n",
+ "\n",
+ "# Adiabatic Saturation Temparature\n",
+ "C_air = 1005;# [J/kg.K]\n",
+ "C_toulene = 1256;# [J/kg.K]\n",
+ "Cs = C_air+(C_toulene*Y_prime);# [J/kg.K]\n",
+ "# Soln. of Eqn. 7.21 by trial & error method:\n",
+ "# (Tg-Tas) = (Yas_prime-Y_prime)*(lambda_as/Cs)\n",
+ "Tas = 25.7;# [OC]\n",
+ "print\"Adiabatic Saturation Temparature: \",round(Tas,1),\" degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.10 - Page:241\n",
+ "\n",
+ "\n",
+ "Wet Bulb Temparature: 31.8 degree C\n",
+ "\n",
+ "Adiabatic Saturation Temparature: 25.7 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.11: Page 249"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.11\n",
+ "# Page: 249\n",
+ "\n",
+ "print'Illustration 7.11 - Page: 249\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "\n",
+ "#****Data****#\n",
+ "L_min = 2.27;# [kg/square m.s]\n",
+ "G_min = 2;# [kg/square m.s]\n",
+ "L2_prime = 15;# [kg/s]\n",
+ "Q = 270.0;# [W]\n",
+ "Templ2 = 45.0;# [OC]\n",
+ "Tempg1 = 30.0;# [OC]\n",
+ "Tempw1 = 24.0;# [OC]\n",
+ "Kya = 0.90;# [kg/cubic m.s]\n",
+ "#*******#\n",
+ "\n",
+ "H1_prime = 72;# [kJ/kg dry air]\n",
+ "Y1_prime = 0.0160;# [kg water/kg dry air]\n",
+ "Templ1 = 29;# [OC]\n",
+ "Cal = 4.187;# [kJ/kg]\n",
+ "\n",
+ "# Tower cross section Area:\n",
+ "Al = L2_prime/L_min;# [square m]\n",
+ "Ag = Gs/G_min;# [square m]\n",
+ "A = min(Al,Ag);# [square m]\n",
+ "Area = 3.25;\n",
+ "# From Eqn. 7.54\n",
+ "def f16(Z):\n",
+ " return Area-(Kya*Z/G_min)\n",
+ "Z = fsolve(f16,2);\n",
+ "print\"The height of tower is\",round(Z,2),\" m\\n\"\n",
+ "NtoG = 3.25;\n",
+ "HtoG = G_min/Kya;# [m]\n",
+ "\n",
+ "# Make up water\n",
+ "# Assuming the outlet air is essentially saturated:\n",
+ "Y2_prime = 0.0475;# [kg water/kg dry air]\n",
+ "E = G_min*(A)*(Y2_prime-Y1_prime);# [kg/s]\n",
+ "# Windage loss estimated as 0.2 percent\n",
+ "W = 0.002*L2_prime;# [kg/s]\n",
+ "ppm_blowdown = 2000;# [ppm]\n",
+ "ppm_makeup = 500;# [ppm]\n",
+ "# Since the weight fraction are proportional to the corresponding ppm values:\n",
+ "B = (E*ppm_makeup/(ppm_blowdown-ppm_makeup))-W;# [kg/s]\n",
+ "M = B+E+W;# [kg/s]\n",
+ "print\"The makeup water requirement is estimated to be\",round(M,2),\" kg/s\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.11 - Page: 249\n",
+ "\n",
+ "\n",
+ "The height of tower is 7.22 m\n",
+ "\n",
+ "The makeup water requirement is estimated to be 0.46 kg/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 81
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.13: Page 254"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.13\n",
+ "# Page: 254\n",
+ "\n",
+ "\n",
+ "print'Illustration 7.13\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve \n",
+ "# Given\n",
+ "Tempg1=65;# [OC]\n",
+ "Y1_prime=0.0170;# [kg water/kg dry air]\n",
+ "# Using adiabatic satursion line on Fig. 7.5 (Pg 232)\n",
+ "Tempas=32;# [OC]\n",
+ "Yas_prime=0.0309;# [kg water/kg dry air]\n",
+ "Tempg2=45;# [OC]\n",
+ "Z=2;# [m]\n",
+ "#*******#\n",
+ "\n",
+ "Y2_prime=0.0265;# [kg water/kg dry air]\n",
+ "def f19(Kya_by_Gs):\n",
+ " return math.log((Yas_prime-Y1_prime)/(Yas_prime-Y2_prime))-(Kya_by_Gs*Z)\n",
+ "Kya_by_Gs=fsolve(f19,1);# [1/m]\n",
+ "\n",
+ "# For the extended chamber:\n",
+ "Z=4;# [m]\n",
+ "def f20(Y2_prime):\n",
+ " return math.log((Yas_prime-Y1_prime)/(Yas_prime-Y2_prime))-(Kya_by_Gs*Z)\n",
+ "Y2_prime=fsolve(f20,0.029);#[kg water/kg dry air] \n",
+ "# With the same adiabatic curve:\n",
+ "Tempg2=34;# [OC] from the curve\n",
+ "print\"The Outlet Conditions are:\\n\"\n",
+ "print\"Absolute Humidity is\",round(Y2_prime,4),\" kg water/kg dry air\\n\"\n",
+ "print\"Dry Bulb Temperature is\",round(Tempg2), \"degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.13\n",
+ "\n",
+ "\n",
+ "The Outlet Conditions are:\n",
+ "\n",
+ "Absolute Humidity is 0.0295 kg water/kg dry air\n",
+ "\n",
+ "Dry Bulb Temperature is 34.0 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 137
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.14: Page 256"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.14\n",
+ "# Page: 256\n",
+ "\n",
+ "print'Illustration 7.14 - Page: 256\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# a = N2 b = CO\n",
+ "# Entering gas\n",
+ "Y1_prime = 0.0;# [kg water/kg dry air]\n",
+ "Pt = 1.0;# [atm]\n",
+ "Tempg1 = 315.0;# [OC]\n",
+ "G_prime = 5.0;# [square m/s]\n",
+ "\n",
+ "# Temp of the tower:\n",
+ "Templ2 = 18.0;# [OC]\n",
+ "Density_L2 = 1000.0; #[kg/square m]\n",
+ "viscocity_L2 = 1.056*10**(-3);# [kg/m.s]\n",
+ "Tempg2 = 27.0;# [OC]\n",
+ "\n",
+ "Mb = 28.0;# [kg/kmol]\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Density_G1 = (Mb/22.41)*(273/(Tempg1+273));# [kg/square m]\n",
+ "G1 = G_prime*(Density_G1);# [kg/s]\n",
+ "\n",
+ "# Since the outlet gas is nearly saturated:\n",
+ "Y_prime = 0.024;# [kg water/kg dry air]\n",
+ "Y2_prime = 0.022;# [kg water/kg dry air, assumed]\n",
+ "G2 = G1*(1+Y2_prime);# [kg/s]\n",
+ "Mav = (1+Y2_prime)/((1/Mb)+(Y2_prime/Ma));# [kg/kmol]\n",
+ "Density_G2 = (Mav/22.4)*(273.0/(Templ2+273));# [kg/square m]\n",
+ "L2_by_G2 = 2.0;\n",
+ "abcissa = L2_by_G2*(Density_G2/(Density_L2-Density_G2))**(1/2);\n",
+ "# From Fig. 6.34:\n",
+ "# For a gas pressure drop of 400 N/square m/m\n",
+ "ordinate = 0.073;\n",
+ "# From Table 6.3:\n",
+ "Cf = 65.0;\n",
+ "J = 1.0;\n",
+ "def f21(G2_prime):\n",
+ " return ((G2_prime**2)*Cf*(viscocity_L2**0.1)*J/(Density_G2*(Density_L2-Density_G2)))-ordinate\n",
+ "# Tentative data:\n",
+ "G2_prime = fsolve(f21,2);# [kg/square m.s]\n",
+ "Area = G1/G2_prime;# [square m]\n",
+ "dia = math.sqrt(4*Area/math.pi);# [m]\n",
+ "\n",
+ "# Final data:\n",
+ "dia = 1.50;# [m]\n",
+ "Area = math.pi*dia**2.0/4;# [square m]\n",
+ "Gs_prime = G1/Area;# [kg/square m.s]\n",
+ "G2_prime = G2/Area;# [kg/square m.s]\n",
+ "L2_prime = L2_by_G2*G2_prime;# [kg/square m.s]\n",
+ "# From Eqn. 7.29:\n",
+ "def f22(L1_prime):\n",
+ " return (L2_prime-L1_prime)-(Gs_prime*(Y2_prime-Y1_prime))\n",
+ "L1_prime = fsolve(f22,2);\n",
+ "Cb = 1089;# [J/kg.K]\n",
+ "Ca = 1884;# [J/kg.K]\n",
+ "Cs1 = Cb+(Y1_prime*Ca);# [J/(kg dry air).K]\n",
+ "Cs2 = Cb+(Y2_prime*Ca);# [J/(kg dry air).K]\n",
+ "Tempo = Templ2;# [base temp.,K]\n",
+ "Lambda = 2.46*10**6;# [J/kg]\n",
+ "CaL = 4187;# [J/kg K]\n",
+ "# From Eqn. 7.31:\n",
+ "def f23(Templ1):\n",
+ " return ((L2_prime*CaL*(Templ2-Tempo))+(Gs_prime*Cs1*(Tempg1-Tempo)))-((L1_prime*CaL*(Templ1-Tempo))+(Gs_prime*(Cs2*(Tempg2-Tempo))+(Y2_prime*Lambda)))\n",
+ "Templ1 = fsolve(f23,2);\n",
+ "# At Templ1 = 49.2 OC\n",
+ "viscocity_L = 0.557*10**(-3);# [kg/m.s]\n",
+ "Density_L = 989.0;# [kg/square m]\n",
+ "K = 0.64;# [w/m.K]\n",
+ "Prl = CaL*viscocity_L/K;\n",
+ "\n",
+ "# For Entering Gas:\n",
+ "viscocity_G1 = 0.0288*10**(-3);# [kg*/m.s]\n",
+ "Dab = 0.8089*10**(-4);# [square m/s]\n",
+ "ScG = viscocity_G1/(Density_G1*Dab);\n",
+ "PrG = 0.74;\n",
+ "\n",
+ "# From Illustration 6.7:\n",
+ "a = 53.1;# [square m/square m]\n",
+ "Fga = 0.0736;# [kmol/square m]\n",
+ "Hga = 4440.0;# [W/square m.K]\n",
+ "Hla = 350500.0;# [W/square m.K]\n",
+ "# At the bottom, by several trial:\n",
+ "Tempi = 50.3;# [OC]\n",
+ "pai = 93.9/760;# [atm]\n",
+ "paG = 0;# [atm]\n",
+ "# By Eqn. 7.64:\n",
+ "dY_prime_by_dZ = -(Ma*Fga/Gs_prime)*math.log((1-(pai/Pt))/(1-(paG/Pt)));# [(kg H2O/kg dry gas)/m]\n",
+ "Hg_primea = -(Gs_prime*Ca*dY_prime_by_dZ)/(1-math.exp((Gs_prime*Ca*dY_prime_by_dZ)/(Hga)));# [W/square m.K]\n",
+ "dTempg_by_dZ = -(Hg_primea*(Tempg1-Tempi)/(Gs_prime*Cs1));# [OC/m]\n",
+ "Tempi = (Templ1)+((Gs_prime*(Cs1*dTempg_by_dZ)+((Ca*(Tempg1))-(CaL*(Templ1))+(((CaL-Ca)*(Tempo))+Lambda))*dY_prime_by_dZ)/((Gs_prime*CaL*dY_prime_by_dZ)-Hla));#[OC]\n",
+ "# Assume:\n",
+ "delta_Tempg = -30;# [OC]\n",
+ "delta_Z = delta_Tempg/(dTempg_by_dZ);# [m]\n",
+ "Tempg = Tempg1+delta_Tempg;# [OC]\n",
+ "Y_prime = Y1_prime+(dY_prime_by_dZ)*delta_Z;# [kg H2O/kg dry gas]\n",
+ "paG = Y_prime/(Y_prime+(Ma/Mb));# [atm]\n",
+ "Cs = Cb+Ca*(Y_prime);# [J/(kg dry air).K]\n",
+ "# Water balance, From Eqn. 7.29:\n",
+ "def f24(L_prime):\n",
+ " return (L2_prime-L_prime)-(Gs_prime*(Y_prime-Y1_prime))\n",
+ "L_prime = fsolve(f24,2);# [kg/square m.s]\n",
+ "\n",
+ "def f25(Templ):\n",
+ " return ((L_prime*CaL*(Templ-Tempo))+(Gs_prime*Cs1*(Tempg1-Tempo)))-((L1_prime*CaL*(Templ1-Tempo))+(Gs_prime*(Cs*(Tempg-Tempo))+(Y_prime*Lambda)))\n",
+ "Templ = fsolve(f25,2);\n",
+ "\n",
+ "# This process is repeated several times until gas temp falls to Tempg2\n",
+ "Z = 1.54;# [m] Z = sum of all delta_Z\n",
+ "# The value of Y2_prime was calculated to be 0.0222 which is sufficiently close to the assumed value.\n",
+ "print\"The diameter of tower is \",dia,\" m\\n\"\n",
+ "print\"The packed height is\",Z, \"m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.14 - Page: 256\n",
+ "\n",
+ "\n",
+ "The diameter of tower is "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 1.5 m\n",
+ "\n",
+ "The packed height is 1.54 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex7.15: Page 267"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 7.15\n",
+ "# Page: 267\n",
+ "\n",
+ "print'Illustration 7.15 - Page: 267\\n\\n'\n",
+ "\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "import numpy\n",
+ "# solution\n",
+ "\n",
+ "#***Data***#\n",
+ "w = 0.75;# [m]\n",
+ "OD = 19.05/1000;# [m]\n",
+ "l = 3.75;# [m]\n",
+ "n = 20;\n",
+ "t = 1.65/1000;# [m]\n",
+ "Ws = 2.3;# [kg/s]\n",
+ "Wal = 10.0;# [kg/s]\n",
+ "Wt = 4.0;# [kg/s]\n",
+ "Density = 800;# [kg/cubic m]\n",
+ "viscocity = 0.005;# [kg/m.s]\n",
+ "K = 0.1436;# [W/m.K]\n",
+ "Ct = 2010.0;# [J/kg.K]\n",
+ "Cal = 4187.0;# [J/kg.K]\n",
+ "Y1_prime = 0.01;# [kg H2O/kg dry air]\n",
+ "Y2_prime = 0.06;# [kg H2O/kg dry air]\n",
+ "TempT = 95.0;# [OC]\n",
+ "#*****#\n",
+ "\n",
+ "Free_area = (w-(n*OD))*l;# [square m]\n",
+ "Gs_min = 2.3/Free_area;# [kg/square m.s]\n",
+ "M1 = 1.461;Yav_prime = (Y1_prime+Y2_prime)/2;# [kg H2O/kg dry air]\n",
+ "# From Eqn. 7.86:\n",
+ "ky = 0.0493*(Gs_min*(1+Yav_prime))**0.905;# [kg/square m.s.delta_Y_prime]\n",
+ "# From Fig. 7.5:\n",
+ "H1_prime = 56000.0;# [J/kg]\n",
+ "Ao = 400*math.pi*OD*l;# [square m]\n",
+ "# Cooling water is distributed over 40 tubes & since tubes are staggered\n",
+ "geta = Wal/(40.0*2*l);# [kg/m.s]\n",
+ "geta_by_OD = geta/OD;# [kg/square m.s]\n",
+ "# Assume:\n",
+ "TempL = 28.0;# [OC]\n",
+ "# From Eqn. 7.84:\n",
+ "hL_prime = (982+(15.58*TempL))*(geta_by_OD**(1/3));# [W/square m.K]\n",
+ "# From Eqn. 7.85:\n",
+ "hL_dprime = 11360;# [W/square m.K]\n",
+ "# From Fig. 7.5 (Pg 232)\n",
+ "m = 5000.0;# [J/kg.K]\n",
+ "Ky = 1.0/((1/ky)+(m/hL_dprime));\n",
+ "ID = (OD-(2.0*t));# [m]\n",
+ "Ai = math.pi*(ID**2)/4;# [square m]\n",
+ "Gt_prime = Wt/(n*Ai);# [kg/square m.s]\n",
+ "M2 = -0.7204;Re = ID*Gt_prime/viscocity;\n",
+ "Pr = Ct*viscocity/K;\n",
+ "# From a standard correlation:\n",
+ "hT = 364.0;# [W/square m.K]\n",
+ "Dav = (ID+OD)/2.0;# [m]\n",
+ "Zm = (OD-ID)/2;# [m]\n",
+ "Km = 112.5;# [W/m.K]\n",
+ "# From Eqn. 7.67:\n",
+ "Uo = 1/((OD/(ID*hT))+((OD/Dav)*(Zm/Km))+(1/hL_prime));# [W/square m.K]\n",
+ "# From Eqn. 7.75:\n",
+ "alpha1 = -(((Uo*Ao)/(Wt*Ct))+((Uo*Ao)/(Wal*Cal)));\n",
+ "alpha2 = m*Uo*Ao/(Wt*Ct);\n",
+ "# From Eqn. 7.76:\n",
+ "beeta1 = Ky*Ao/(Wal*Cal);\n",
+ "beeta2 = -((m*Ky*Ao/(Wal*Cal))-(Ky*Ao/Ws));\n",
+ "def f26(r):\n",
+ " return (r**2)+((alpha1+beeta2)*r)+((alpha1*beeta2)-(alpha2*beeta1))\n",
+ "r1 = fsolve(f26,10);\n",
+ "r2 = fsolve(f26,0);\n",
+ "beeta2 = 1.402;\n",
+ "# From Eqn. 7.83:\n",
+ "# N1-(M1*(r1+alpha1)/beeta1) = 0............................................(1)\n",
+ "# N2-(M2*(r2+alpha2)/beeta2) = 0............................................(2)\n",
+ "# From Eqn. 7.77:\n",
+ "# At the top:\n",
+ "x1 = 1.0;\n",
+ "# TempL2+(M1*exp(r1*x1))+(M2*exp(-(r2*x1))) = TempL.........................(3)\n",
+ "# From Eqn. 7.78:\n",
+ "# At the bottom:\n",
+ "x2 = 0.0;\n",
+ "# H1_star-N1-N2 = H1_prime..................................................(4)\n",
+ "# From Eqn. 7.80:\n",
+ "# ((M1/r1)*(exp(r1)-1))+((M2*r2)*(exp(r2)-1)) = (Tempt-TempL)...............(5)\n",
+ "# From Eqn. 7.81:\n",
+ "# ((N1/r1)*(exp(r1)-1))+((N2*r2)*(exp(r2)-1)) = (H1_star-H1_prime)..........(6)\n",
+ "# From Eqn. 7.91 & Eqn. 7.92:\n",
+ "# Uo*Ao*(TempT-TempL)=Ky*Ao*(H1_star-H1_prime)..............................(7)\n",
+ "\n",
+ "# Elimination of M's & N's by solving Eqn. (1) to (4) and (7) simultaneously:\n",
+ "# and from Fig. 7.5 (Pg 232):\n",
+ "TempL1=28.0;# [OC]\n",
+ "H1_star=(Uo*Ao*(TempT-TempL)/(Ky*Ao))+H1_prime;# [J/kmol]\n",
+ "\n",
+ "\n",
+ "N1 = 3594.0*M1\n",
+ "N2 =-43288.0*M2;\n",
+ "\n",
+ "# By Eqn. 5\n",
+ "delta_Temp = ((M1/r1)*(math.exp(r1)-1))+((M2*r2)*(math.exp(r2)-1));# [OC]\n",
+ "Q = Uo*delta_Temp*Ao;\n",
+ "TempT1 = TempT-(Q/(Wt*Ct));# [OC]\n",
+ "H2_prime = Q/(Ws)+H1_prime;# [J/kg]\n",
+ "print\"Temperature to which oil was cooled:\",int(TempT1),\" degree C\\n\"\n",
+ "# The solution in the textbook is wrong "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 7.15 - Page: 267\n",
+ "\n",
+ "\n",
+ "Temperature to which oil was cooled:"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 57 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter8.ipynb b/Mass_-_Transfer_Operations/Chapter8.ipynb
new file mode 100755
index 00000000..3199859d
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter8.ipynb
@@ -0,0 +1,1462 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:6de1fbbc284930cae33ad8c4ecbf1043ba348d822deaa7750919e4f3aba2cece"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 8: Gas Absorption"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.1: Page 278"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.1\n",
+ "# Page: 278\n",
+ "\n",
+ "print'Illustration 8.1 - Page: 278\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "P_star = 2*10**(5);# [N/square m]\n",
+ "X_methane = 0.6;\n",
+ "X_ethane = 0.2;\n",
+ "X_propane = 0.08;\n",
+ "X_nbutane = 0.06;\n",
+ "X_npentane = 0.06;\n",
+ "#******#\n",
+ "\n",
+ "MoleFraction = [0.6, 0.2 ,0.08, 0.06 ,0.06]\n",
+ "Heading = [\"Component\", \"Equilibrium Partial Pressure\", \"Vapour Pressue \" ,\"Mole Fraction\"];\n",
+ "Component = [\"Methane\", \"Ethane \" ,\"Propane\" ,\"n-Butane\", \"n-Pentane\"];\n",
+ "VapPressure = [0 ,42.05, 8.96, 2.36 ,0.66];# [N/square m]\n",
+ "Sum = 0;\n",
+ "\n",
+ "print Heading[0],\"\\t \\t \\t \\t\",Heading[1],\"\\t \\t \\t \\t\",Heading[2],\"\\t \\t \\t \\t\",Heading[3],\"\\t \\n\"\n",
+ "\n",
+ "\n",
+ "for i in range(0,5):\n",
+ " print \"\\n \",Component[i],\" \\t \\t \\t \\t \\t\",(\"{:.2e}\".format(MoleFraction[i]*P_star)),\"\\t \\t \\t \\t \\t \\t \\t \\t \",(\"{:.2e}\".format(VapPressure[i]*10**(5))),\n",
+ " if VapPressure[i]==0:\n",
+ " Sum = Sum+0;\n",
+ " else:\n",
+ " \n",
+ " print \"\\t \\t \\t \\t \\t \\t \\t \\t \\t \\t\",(\"{:.2e}\".format((MoleFraction[i]*P_star)/(VapPressure[i]*10**(5)))),\"\\t\",\n",
+ " Sum = Sum+(MoleFraction[i]*P_star)/(VapPressure[i]*10**(5))\n",
+ "\n",
+ "\n",
+ "\n",
+ "print\"\\n Mole Fraction Of solvent Oil is \",round(1-Sum,3)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.1 - Page: 278\n",
+ "\n",
+ "\n",
+ "Component \t \t \t \tEquilibrium Partial Pressure \t \t \t \tVapour Pressue \t \t \t \tMole Fraction \t \n",
+ "\n",
+ "\n",
+ " Methane \t \t \t \t \t1.20e+05 \t \t \t \t \t \t \t \t 0.00e+00 \n",
+ " Ethane \t \t \t \t \t4.00e+04 \t \t \t \t \t \t \t \t 4.20e+06 \t \t \t \t \t \t \t \t \t \t9.51e-03 \t\n",
+ " Propane \t \t \t \t \t1.60e+04 \t \t \t \t \t \t \t \t 8.96e+05 \t \t \t \t \t \t \t \t \t \t1.79e-02 \t\n",
+ " n-Butane \t \t \t \t \t1.20e+04 \t \t \t \t \t \t \t \t 2.36e+05 \t \t \t \t \t \t \t \t \t \t5.08e-02 \t\n",
+ " n-Pentane \t \t \t \t \t1.20e+04 \t \t \t \t \t \t \t \t 6.60e+04 \t \t \t \t \t \t \t \t \t \t1.82e-01 \t\n",
+ " Mole Fraction Of solvent Oil is 0.74\n"
+ ]
+ }
+ ],
+ "prompt_number": 165
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.2: Page 286"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.2\n",
+ "# Page: 286\n",
+ "\n",
+ "print'Illustration 8.2 - Page: 286\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "#****Data****#\n",
+ "# Absorber:\n",
+ "G = 0.250;# [cubic m/s]\n",
+ "Temp1 = 273+26.0;# [K]\n",
+ "Pt = 1.07*10**(5);# [N/square m]\n",
+ "y1 = 0.02;\n",
+ "x2 = 0.005;\n",
+ "#******#\n",
+ "\n",
+ "G1 = G*(273.0/Temp1)*(Pt/(1.0133*10**(5)))*(1/22.41);# [kmol/s]\n",
+ "Y1 = y1/(1-y1);# [kmol benzene/kmol dry gas]\n",
+ "Gs = G1*(1.0-y1);# [kmol dry gas/s]\n",
+ "# For 95% removal of benzene:\n",
+ "Y2 = Y1*0.05;\n",
+ "X2 = x2/(1.0-x2);# [kmol benzene/kmol oil]\n",
+ "# Vapour pressure of benzene:\n",
+ "\n",
+ "P_star = 13330.0;# [N/square m]\n",
+ "X_star = numpy.zeros(20);\n",
+ "Y_star = numpy.zeros(20);\n",
+ "j = -1;\n",
+ "for i in range(1,21,1):\n",
+ " j = j+1;\n",
+ " x = i/100.0;\n",
+ " X_star[j] = i/100.0;\n",
+ " def f27(y):\n",
+ " return (y/(1+y))-(P_star/Pt)*(x/(1+x))\n",
+ " Y_star[j] = fsolve(f27,0.0);\n",
+ "\n",
+ "# For min flow rate:\n",
+ "X1 = 0.176;# [kmolbenzene/kmol oil]\n",
+ "DataMinFlow = numpy.array([[X2, Y2],[X1, Y1]]);\n",
+ "\n",
+ "plt.plot(X_star,Y_star,label=\"Equlibrium Line\")\n",
+ "plt.plot(DataMinFlow[:,0],DataMinFlow[:,1],label=\"Min Flow Rate Line\");\n",
+ "minLs = (Gs*(Y1-Y2)/(X1-X2));# [kmol/s]\n",
+ "# For 1.5 times the minimum:\n",
+ "Ls = 1.5*minLs;# [kmol/s]\n",
+ "X1_prime = (Gs*1.0*(Y1-Y2)/Ls)+X2;# [kmol benzene/kmol oil]\n",
+ "DataOperLine = numpy.array([[X2 ,Y2],[X1_prime ,Y1]]);\n",
+ "plt.plot(DataOperLine[:,0],DataOperLine[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "xlabel(\"moles of benzene / mole wash oil\");\n",
+ "ylabel(\"moles benzene / mole dry gas\");\n",
+ "legend(loc='lower right');\n",
+ "plt.title(\"Absorption\")\n",
+ "plt.show()\n",
+ "print\"The Oil circulation rate is \",(\"{:.2e}\".format(Ls)),\" kmol/s\\n\"\n",
+ "\n",
+ "# Stripping\n",
+ "Temp2 = 122+273;# [K]\n",
+ "# Vapour pressure at 122 OC\n",
+ "P_star = 319.9;# [kN/square m]\n",
+ "Pt = 101.33;# [kN/square m]\n",
+ "X_star = numpy.zeros(7);\n",
+ "Y_star = numpy.zeros(7);\n",
+ "j = -1;\n",
+ "for i in range(0,7,1):\n",
+ " j = j+1;\n",
+ " x = i/10.0;\n",
+ " X_star[j] = i/10.0;\n",
+ " def f28(y):\n",
+ " return (y/(1.0+y))-(P_star/Pt)*(x/(1.0+x))\n",
+ " Y_star[j] = fsolve(f28,0.0);\n",
+ "\n",
+ "X1 = X2;# [kmol benzene/kmol oil]\n",
+ "X2 = X1_prime;# [kmol benzene/kmol oil]\n",
+ "Y1 = 0.0;# [kmol benzene/kmol steam]\n",
+ "# For min. steam rate:\n",
+ "Y2 = 0.45;\n",
+ "DataMinFlow =numpy.array([[X2 ,Y2],[X1 ,Y1]]);\n",
+ "minGs = Ls*(X2-X1)/(Y2-Y1);# [kmol steam/s]\n",
+ "slopeOperat = 1.5*(Y2-Y1)/(X2-X1);\n",
+ "def f29(x):\n",
+ " return slopeOperat*(x-X1)+Y1\n",
+ "x =numpy.arange(0,0.14,0.01)\n",
+ "\n",
+ "plt.plot(Y_star,X_star,label=\"Equlibrium Line\")\n",
+ "plt.plot(DataMinFlow[:,0],DataMinFlow[:,1],label=\"Min Flow Rate Line\")\n",
+ "plt.plot(x,f29(x),label=\"Operating Line\");\n",
+ "plt.grid('on');\n",
+ "xlabel(\"moles of benzene / mole wash oil\");\n",
+ "ylabel(\"moles benzene / mole dry gas\");\n",
+ "plt.legend(loc='lower left');\n",
+ "plt.title(\"Stripping\");\n",
+ "plt.show()\n",
+ "print\"The Steam circulation rate is \",(\"{:.2e}\".format(1.5*minGs)),\" kmol/s\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.2 - Page: 286\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3kWps2FevwssvQ2ioWal4SKl4Yj5v3LlB92XdeTrwaV4s8iJbPtgSTalEhA4/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\nUKDvv/++pqWl6erVq4vlfeWVV1RV9dSpU7pjxw7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rUzxfHMUbrHoEb1muo6p+gCe7ewPK\nS+T8TO92POu3PhPsn6nqXODPQFkKLJBwxpA586vGmGIxykPwr0YNEZ4MdBGRPXj7DyMD7vvSjAe6\nus3Zt4ExLv5ut9G7B282VOyNKFXdBTyHt8yyBfhTwMO3pF+06tIvwVsye0lVd6pn9vt+YI2rbw2e\nK4BQ/VL3oLwFGC3nN/A7A1OBem6Tex8wJUR/A/uQj7dX8L+u3k18U4F2AXaV0p/S+uq7Nw4Y5eoY\nAdwdIs0I4E4R2Y23jDa4hHK34O2pgKdYWru/4O1NjXRlXA74FEA/YLeI7ASGAY8H1F9WXyZT9hgq\nTQ5GFWNm8w2jmiEi9wHvqeqiqm6LYVwIplgMwzCMiGJLYYZhGEZEMcViGIZhRBRTLIZhGEZEMcVi\nGIZhRBRTLIZhGEZEMcViGIZhRBRTLIZhGEZE+Rflz/wq8/SiYgAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7720e48>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The Oil circulation rate is 1.79e-03 kmol/s\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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bQF0RaYfzTMVI4IlUBmWMMSY9eXlOY4mqHikiY4H6qnqPiHyiqj2qJ8TKVdSm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\nvcBv3PW7gZsiznk6MB/nrvwOYLi7PRt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+ "text": [
+ "<matplotlib.figure.Figure at 0x7bc4668>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The Steam circulation rate is 6.81e-04 kmol/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.3: Page 292"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.3\n",
+ "# Page: 292\n",
+ "\n",
+ "print'Illustration 8.3 - Page: 292\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "# Since tower is a tray device:\n",
+ "# Following changes in notation is made:\n",
+ "# L1 to LNp\n",
+ "# L2 to L0\n",
+ "# X1 to XNp\n",
+ "# X2 to X0\n",
+ "# G1 to GNpPlus1\n",
+ "# G2 to G1\n",
+ "# Y1 to YNpPlus1\n",
+ "# Y2 to Y1\n",
+ "# x1 to xNp\n",
+ "# x2 to x0\n",
+ "# y1 to yNpPlus1\n",
+ "# y2 to y1\n",
+ "# From Illustration 8.2:\n",
+ "yNpPlus1 = 0.02;\n",
+ "Y1 = 0.00102;\n",
+ "y1 = Y1/(1+Y1);\n",
+ "GNpPlus1 = 0.01075;# [kmol/s]\n",
+ "x0 = 0.005;\n",
+ "m = 0.125;# [m = y_star/x]\n",
+ "Ls = 1.787*10**(-3);# [kmol/s]\n",
+ "Gs = 0.01051;# [kmol/s]\n",
+ "XNp = 0.1190;\n",
+ "LNp = Ls*(1+XNp);# [kmol/s]\n",
+ "ANp = LNp/(m*GNpPlus1);\n",
+ "X0 = x0/(1-x0);\n",
+ "L0 = Ls*(1+X0);# [kmol/s]\n",
+ "G1 = Gs*(1+Y1);# [kmol/s]\n",
+ "A1 = L0/(m*G1);\n",
+ "A = (ANp*A1)**0.5;\n",
+ "# From Eqn. 5.55:\n",
+ "Np = (math.log((yNpPlus1-(m*x0))/(y1-(m*x0))*(1-(1/A))+(1/A)))/math.log(A);\n",
+ "print\"Absorber\\n\"\n",
+ "print\"From Analytical Method, no. of theoretical trays required is \\n\",round(Np,4)\n",
+ "# From Fig. 8.13 (Pg292):\n",
+ "Np = 7.6;\n",
+ "print\"From Graphical Method, no. of theoretical trays required is \\n\",Np\n",
+ "\n",
+ "# Stripper\n",
+ "SNp = 1/ANp;\n",
+ "S1 = 1/A1;\n",
+ "# Due to relative nonconstancy of the stripping factor,graphical method should be used.\n",
+ "print\"Stripper\\n\"\n",
+ "# From Fig. 8.11 (Pg 289):\n",
+ "Np = 6.7;\n",
+ "print\"From Graphical Method, no. of theoretical trays required is \\n\",Np\n",
+ "# From Fig. 5.16 (Pg 129):\n",
+ "Np = 6.0;\n",
+ "print\"From Fig. 5.16, no. of theoretical trays required is \\n\",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.3 - Page: 292\n",
+ "\n",
+ "\n",
+ "Absorber\n",
+ "\n",
+ "From Analytical Method, no. of theoretical trays required is \n",
+ "7.7085\n",
+ "From Graphical Method, no. of theoretical trays required is \n",
+ "7.6\n",
+ "Stripper\n",
+ "\n",
+ "From Graphical Method, no. of theoretical trays required is \n",
+ "6.7\n",
+ "From Fig. 5.16, no. of theoretical trays required is \n",
+ "6.0\n"
+ ]
+ }
+ ],
+ "prompt_number": 102
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.4: Page 295"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.4\n",
+ "# Page: 295\n",
+ "\n",
+ "print'Illustration 8.4 - Page: 295\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "#****Data****#\n",
+ "# a = CH4 b = C5H12\n",
+ "Tempg = 27.0;# [OC]\n",
+ "Tempo = 0.0;# [base temp,OC]\n",
+ "Templ = 35.0;# [OC]\n",
+ "xa = 0.75;# [mole fraction of CH4 in gas]\n",
+ "xb = 0.25;# [mole fraction of C5H12 in gas]\n",
+ "M_Paraffin = 200.0;# [kg/kmol]\n",
+ "hb = 1.884;# [kJ/kg K]\n",
+ "#********#\n",
+ "\n",
+ "Ha = 35.59;# [kJ/kmol K]\n",
+ "Hbv = 119.75;# [kJ/kmol K]\n",
+ "Hbl = 117.53;# [kJ/kmol K]\n",
+ "Lb = 27820;# [kJ/kmol]\n",
+ "# M = [Temp (OC) m]\n",
+ "M = numpy.array([[20 ,0.575],[25 ,0.69],[30 ,0.81],[35, 0.95],[40, 1.10],[43, 1.25]]);\n",
+ "# Basis: Unit time\n",
+ "GNpPlus1 = 1.0;# [kmol]\n",
+ "yNpPlus1 = 0.25;# [kmol]\n",
+ "HgNpPlus1 = ((1-yNpPlus1)*Ha*(Tempg-Tempo))+(yNpPlus1*(Hbv*(Tempg-Tempo)+Lb));# [kJ/kmol]\n",
+ "L0 = 2.0;# [kmol]\n",
+ "x0 = 0.0;# [kmol]\n",
+ "HL0 = ((1-x0)*hb*M_Paraffin*(Templ-Tempo))+(x0*hb*(Templ-Tempo));# [kJ/kmol]\n",
+ "C5H12_absorbed = 0.98*xb;# [kmol]\n",
+ "C5H12_remained = xb-C5H12_absorbed;\n",
+ "G1 = xa+C5H12_remained;# [kmol]\n",
+ "y1 = C5H12_remained/G1;# [kmol]\n",
+ "LNp = L0+C5H12_absorbed;# [kmol]\n",
+ "xNp = C5H12_absorbed/LNp;# [kmol]\n",
+ "# Assume:\n",
+ "Temp1 = 35.6;# [OC]\n",
+ "Hg1 = ((1-y1)*Ha*(Temp1-Tempo))+(y1*(Hbv*(Temp1-Tempo)+Lb));# [kJ/kmol]\n",
+ "\n",
+ "# Eqn. 8.11:\n",
+ "Qt = 0;\n",
+ "def f30(HlNp):\n",
+ " return ((L0*HL0)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(G1*Hg1)+Qt)\n",
+ "HlNp = fsolve(f30,2);\n",
+ "\n",
+ "def f31(TempNp):\n",
+ " return HlNp-(((1-x0)*hb*M_Paraffin*(TempNp-Tempo))+(x0*hb*(TempNp-Tempo)))\n",
+ "TempNp = fsolve(f31,35.6);\n",
+ "# At Temp = TempNp:\n",
+ "mNp = 1.21;\n",
+ "yNp = mNp*xNp;# [kmol]\n",
+ "GNp = G1/(1-yNp);# [kmol]\n",
+ "HgNp = ((1-yNp)*Ha*(TempNp-Tempo))+(yNp*(Hbv*(TempNp-Tempo)+Lb));# [kJ/kmol]\n",
+ "# Eqn. 8.13 with n = Np-1\n",
+ "def f32(LNpMinus1):\n",
+ " return LNpMinus1+GNpPlus1-(LNp+GNp)\n",
+ "LNpMinus1 = fsolve(f32,2);# [kmol]\n",
+ "\n",
+ "# Eqn. 8.14 with n = Np-1\n",
+ "def f33(xNpMinus1):\n",
+ " return ((LNpMinus1*xNpMinus1)+(GNpPlus1*yNpPlus1))-((LNp*xNp)+(GNp*yNp))\n",
+ "xNpMinus1 = fsolve(f33,0);# [kmol]\n",
+ "\n",
+ "# Eqn. 8.15 with n = Np-1\n",
+ "def f34(HlNpMinus1):\n",
+ " return ((LNpMinus1*HlNpMinus1)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(GNp*HgNp))\n",
+ "HlNpMinus1 = fsolve(f34,0);# [kJ/kmol]\n",
+ "def f35(TempNpMinus1):\n",
+ " return HlNpMinus1-(((1-xNpMinus1)*hb*M_Paraffin*(TempNpMinus1-Tempo))+(xNpMinus1*hb*(TempNpMinus1-Tempo)))\n",
+ "TempNpMinus1 = fsolve(f35,42);# [OC]\n",
+ "\n",
+ "# The computation are continued upward through the tower in this manner until the gas composition falls atleast to 0.00662.\n",
+ "# Results = [Tray No.(n) Tn(OC) xn yn]\n",
+ "Results = numpy.array([[4.0 ,42.3 ,0.1091 ,0.1320],[3 ,39.0, 0.0521 ,0.0568],[2 ,36.8 ,0.0184 ,0.01875],[1 ,35.5, 0.00463 ,0.00450]]);\n",
+ "\n",
+ "plt.plot(Results[:,0],Results[:,3]);\n",
+ "plt.grid('on');\n",
+ "xlabel('Tray Number');\n",
+ "ylabel('mole fraction of C5H12 in gas');\n",
+ "plt.show();\n",
+ "plt.plot(Results[:,0],Results[:,1]);\n",
+ "plt.grid('on');\n",
+ "xlabel('Tray Number');\n",
+ "ylabel('Temperature(OC)');\n",
+ "plt.show();\n",
+ "\n",
+ "# For the required y1\n",
+ "Np = 3.75;\n",
+ "print\"The No. of trays will be \",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.4 - Page: 295\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x7981908>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xb4f99b0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The No. of trays will be 3.75\n"
+ ]
+ }
+ ],
+ "prompt_number": 164
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.5: Page 299"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.5\n",
+ "# Page: 299\n",
+ "\n",
+ "print'Illustration 8.5 - Page: 299\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# a = NH3 b = H2 c = N2 w = water\n",
+ "P = 2.0;# [bars]\n",
+ "Temp = 30.0;# [OC]\n",
+ "L = 6.38;# [kg/s]\n",
+ "W = 0.53;# [weir length,m]\n",
+ "pitch = 12.5/1000;# [m]\n",
+ "D = 0.75;# [Tower diameter,m]\n",
+ "hW = 0.060;# [weir height,m]\n",
+ "t = 0.5;# [tray spacing,m]\n",
+ "#*******#\n",
+ "\n",
+ "# From Geometry of Tray Arrangement:\n",
+ "At = 0.4418;# [Tower Cross section,square m]\n",
+ "Ad = 0.0403;# [Downspout Cross section,square m]\n",
+ "An = At-Ad;# [square m]\n",
+ "Ao = 0.0393;# [perforation area,square m]\n",
+ "Z = 0.5307;# [distance between downspouts,square m]\n",
+ "z = (D+W)/2.0;# [average flow width,m]\n",
+ "h1 = 0.04;# [weir crest,m]\n",
+ "# From Eqn. 6.34\n",
+ "Weff = W*(math.sqrt(((D/W)**2)-((((D/W)**2-1)**0.5)+((2*h1/D)*(D/W)))**2));# [m]\n",
+ "q = Weff*(1.839*h1**(3/2));#[cubic m/s]\n",
+ "# This is a recommended rate because it produces the liquid depth on the tray to 10 cm.\n",
+ "Density_L = 996;# [kg/s]\n",
+ "Mw = 18.02;# [kg/kmol]\n",
+ "L1 = 6.38/Mw;# [kmol/s]\n",
+ "Ma = 17.03;# [kg/kmol]\n",
+ "Mb = 28.02;# [kg/kmol]\n",
+ "Mc = 2.02;# [kg/kmol]\n",
+ "MavG = (0.03*Ma)+(0.97*(1/4)*Mb)+(0.97*(3/4)*Mc);# [kg/kmol]\n",
+ "Density_G = (MavG/22.41)*(P/0.986)*(273/(273+Temp));# [kg/cubic m]\n",
+ "G = 0.893;# [kg/s]\n",
+ "sigma = 68*10**(-3);# [N/m]\n",
+ "abcissa = (L/G)*(Density_G/Density_L)**0.5;\n",
+ "# From Table 6.2 (Pg169):\n",
+ "alpha = 0.04893;\n",
+ "beeta = 0.0302;\n",
+ "# From Eqn. 6.30\n",
+ "Cf = ((alpha*math.log10(1.0/abcissa))+beeta)*(sigma/0.02)**0.2;\n",
+ "# From Eqn. 6.29\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1.0/2);# [m/s]\n",
+ "# 80% of flooding value:\n",
+ "V = 0.8*Vf;# [m/s]\n",
+ "G = 0.8*G;# [kg/s]\n",
+ "G1 = G/MavG;# [kmol/s]\n",
+ "Vo = V*An/Ao;# [m/s]\n",
+ "l = 0.002;# [m]\n",
+ "Do = 0.00475;# [m]\n",
+ "# From Eqn. 6.37\n",
+ "Co = 1.09*(Do/l)**0.25;\n",
+ "viscosity_G = 1.13*10**(-5);# [kg/m.s]\n",
+ "Reo = Do*Vo*Density_G/viscosity_G;\n",
+ "# At Reynold's No. = Reo\n",
+ "fr = 0.0082;\n",
+ "g = 9.81;# [m/s^2]\n",
+ "# From Eqn. 6.36\n",
+ "def f36(hD):\n",
+ " return (2*hD*g*Density_L/(Vo**2*Density_G))-(Co*(0.40*(1.25-(Ao/An))+(4*l*fr/Do)+(1-(Ao/An))**2))\n",
+ "hD = fsolve(f36,1);\n",
+ "# From Eqn. 6.31;\n",
+ "Aa = (Ao/0.907)*(pitch/Do)**2;# [square m]\n",
+ "Va = V*An/Aa;# [m/s]\n",
+ "# From Eqn. 6.38\n",
+ "hL = 6.10*10**(-3)+(0.725*hW)-(0.238*hW*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "# From Eqn. 6.42\n",
+ "hR = 6*sigma/(Density_L*Do*g);# m\n",
+ "# From Eqn. 6.35\n",
+ "hG = hD+hL+hR;# [m]\n",
+ "Al = 0.025*W;# [square m]\n",
+ "Ada = min(Al,Ad);\n",
+ "# From Eqn. 6.43\n",
+ "h2 = (3/(2*g))*(q/Ada)**2;# [m]\n",
+ "# From Eqn.6.44\n",
+ "h3 = hG+h2;\n",
+ "# since hW+h1+h3 is essentially equal to t/2, flooding will not occur\n",
+ "abcissa = (L/G)*(Density_G/Density_L)**0.5;\n",
+ "V_by_Vf = V/Vf;\n",
+ "# From Fig.6.17, V/Vf = 0.8 & abcissa = 0.239\n",
+ "E = 0.009;\n",
+ "\n",
+ "# At the prevailing conditions:\n",
+ "Dg = 2.296*10**(-5);# [square m/s]\n",
+ "viscosity_G = 1.122*10**(-5);# [kg/m.s]\n",
+ "ScG = viscosity_G/(Density_G*Dg)\n",
+ "Dl = 2.421*10**(-9);# [square m/s]\n",
+ "\n",
+ "# From Henry's Law:\n",
+ "m = 0.850;\n",
+ "A = L1/(m*G1);\n",
+ "\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/(ScG**0.5);\n",
+ "# From Eqn. 6.64:\n",
+ "thetha_L = hL*z*Z/q;# [s]\n",
+ "# From Eqn. 6.62:\n",
+ "NtL = 40000*(Dl**0.5)*((0.213*Va*Density_G**0.5)+0.15)*thetha_L;\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1/((1/NtG)+(1/(A*NtL)));\n",
+ "# From Eqn. 6.51:\n",
+ "EoG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.63:\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;# [square m/s]\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thetha_L);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2.0)*((1+(4*m*G1*EoG/(L1*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EoG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+((exp(eta)-1)/(eta*(1+(eta/(eta+Pe))))));\n",
+ "# From Eqn. 6.60:\n",
+ "EMGE = EMG/((1+(EMG*(E/(1-E)))));\n",
+ "# From Eqn. 8.16:\n",
+ "EO = math.log(1+EMGE*((1.0/A)-1))/math.log(1.0/A);\n",
+ "Np = 14*EO;\n",
+ "yNpPlus1 = 0.03;\n",
+ "x0 = 0;\n",
+ "# From Eqn. 5.54(a):\n",
+ "def f37(y1):\n",
+ " return ((yNpPlus1-y1)/(yNpPlus1-m*x0))-(((A**(Np+1))-A)/((A**(Np+1))-1))\n",
+ "y1 = fsolve(f37,0.03);\n",
+ "print\"Mole Fraction Of NH3 in effluent is \",round(y1,4)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.5 - Page: 299\n",
+ "\n",
+ "\n",
+ "Mole Fraction Of NH3 in effluent is 0.0211\n"
+ ]
+ }
+ ],
+ "prompt_number": 159
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.6: Page 304"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.6\n",
+ "# Page: 304\n",
+ "\n",
+ "print'Illustration 8.6 - Page: 304\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#****Data****# \n",
+ "# Gas:\n",
+ "# In:\n",
+ "y_prime1 = 0.02;\n",
+ "Y_prime1 = 0.0204;# [mol/mol dry gas]\n",
+ "# Out:\n",
+ "y_prime2 = 0.00102;\n",
+ "Y_prime2 = 0.00102;# [mol/mol dry gas]\n",
+ "# Non absorbed gas:\n",
+ "MavG = 11;# [kg/kmol]\n",
+ "G = 0.01051;# [kmol/s nonbenzene]\n",
+ "Gm = 0.01075;# [kmol/s]\n",
+ "T = 26;# [OC]\n",
+ "viscosity_G = 10**(-5);# [kg/m.s]\n",
+ "DaG = 1.30*10**(-5);# [square m/s]\n",
+ "\n",
+ "# Liquid:\n",
+ "# In:\n",
+ "x_prime2 = 0.005;\n",
+ "X_prime2 = 0.00503;# [mol benzene/mol oil]\n",
+ "# Out:\n",
+ "x_prime1 = 0.1063;\n",
+ "X_prime1 = 0.1190;# [mol benzene/mol oil]\n",
+ "# Benzene free oil:\n",
+ "MavL = 260.0;# [kg/kmol]\n",
+ "viscosity_L = 2*10**(-3);# [kg/kmol]\n",
+ "Density_L = 840;# [kg/cubic cm]\n",
+ "L = 1.787*10**(-3);# [kmol/s]\n",
+ "DaL = 4.77*10**(-10);# [square m/s]\n",
+ "sigma = 0.03;# [N/square m]\n",
+ "m = 0.1250;\n",
+ "#*******#\n",
+ "\n",
+ "A = 0.47**2*math.pi/4;# [square m]\n",
+ "# At the bottom:\n",
+ "L_prime1 = ((L*MavL)+(X_prime1*L*78))/A;# [kg/square m.s]\n",
+ "# At the top\n",
+ "L_prime2 = ((L*MavL)+(X_prime2*L*78))/A;# [kg/square m.s]\n",
+ "L_primeav = (L_prime1+L_prime2)/2;# [kg/square m.s]\n",
+ "# At the bottom\n",
+ "G_prime1 = ((G*MavG)+(Y_prime1*G*78))/A;# [kg/square m.s]\n",
+ "# At the top\n",
+ "G_prime2 = ((G*MavG)+(Y_prime2*G*78))/A;# [kg/square m.s]\n",
+ "G_primeav = (G_prime1+G_prime2)/2;# [kg/square m.s]\n",
+ "\n",
+ "# From Illustration 6.6:\n",
+ "Fga = 0.0719;# [kmol/cubic cm.s]\n",
+ "Fla = 0.01377;# [kmol/cubic cm.s]\n",
+ "# Operating Line:\n",
+ "X_prime = numpy.array([0.00503 ,0.02 ,0.04 ,0.06 ,0.08 ,0.10 ,0.1190]);\n",
+ "x_prime = numpy.zeros(7);\n",
+ "Y_prime = numpy.zeros(7);\n",
+ "y_prime = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " x_prime[i] = X_prime[i]/(1+X_prime[i]);\n",
+ " def f38(Y_prime):\n",
+ " return (G*(Y_prime1-Y_prime))-(L*(X_prime1-X_prime[i]))\n",
+ " Y_prime[i] = fsolve(f38,Y_prime1);\n",
+ " y_prime[i] = (Y_prime[i])/(1+Y_prime[i]);\n",
+ "\n",
+ "def f39(x):\n",
+ " return m*x\n",
+ "x = numpy.arange(0,0.14,0.01);\n",
+ "\n",
+ "# Interface compositions are determined graphically and according to Eqn. 8.21:\n",
+ "yi = [0.000784, 0.00285, 0.00562 ,0.00830 ,0.01090 ,0.01337 ,0.01580];\n",
+ "ylog = zeros(7);\n",
+ "y_by_yDiffyi = zeros(7);\n",
+ "for i in range(0,7):\n",
+ " ylog[i] = math.log10(yi[i]);\n",
+ " y_by_yDiffyi[i] = y_prime[i]/(y_prime[i]-yi[i]);\n",
+ "\n",
+ "plt.plot(x_prime,y_prime,label=\"Operating Line\")\n",
+ "plt.plot(x,f39(x),label=\"Equilibrium Line\")\n",
+ "plt.plot(x_prime,yi,label=\"Interface Composition\");\n",
+ "plt.legend(loc='lower right');\n",
+ "plt.grid('on');\n",
+ "xlabel(\"mole fraction of benzene in liquid\");\n",
+ "ylabel(\"mole fraction of benzene in gas\");\n",
+ "plt.show()\n",
+ "plt.plot(ylog,y_by_yDiffyi);\n",
+ "plt.grid();\n",
+ "xlabel(\"log y\");\n",
+ "ylabel(\"y/(y-yi)\");\n",
+ "title(\"Graphical Integration Curve\");\n",
+ "plt.show()\n",
+ "# Area under the curve:\n",
+ "Ac = 6.556;\n",
+ "# Eqn. 8.28:\n",
+ "NtG = (2.303*Ac)+1.152*(math.log10((1-y_prime2)/(1-y_prime1)));\n",
+ "Gav = (Gm+(G/(1-Y_prime2)))/(2*A);# [kmol/square m.s]\n",
+ "HtG = Gav/Fga;# [m]\n",
+ "Z = HtG*NtG;# [m]\n",
+ "print\"The depth of packing required is \",round(Z,3),\" m\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.6 - Page: 304\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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N8afcdpvPmra+FEtWJWD5WNw6LAgQ6k77RKxhCV2++QbatIHPP4fWrf3Qwa+/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\n2QAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xb202fd0>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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xx+Hll+GXX2DrreHww0PhKPTn6FwGs/u5+w5Ad6At8KaZ3W9mDZ3vyQHMrKeZ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\nERGpkwqFiIjUSYVCRETqpEIhIiJ1+n+KRsAkmp26rwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7999ac8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The depth of packing required is 12.881 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 154
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.7: Page 312"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.7\n",
+ "# Page: 312\n",
+ "\n",
+ "print'Illustration 8.7 - Page: 312\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "# Fom Illustration 8.6:\n",
+ "y1 = 0.02;\n",
+ "y2 = 0.00102;\n",
+ "m = 0.125;\n",
+ "x2 = 0.005;\n",
+ "x1 = 0.1063;\n",
+ "\n",
+ "# Number of transfer units:\n",
+ "# Method a:\n",
+ "y1_star = m*x1;\n",
+ "y2_star = m*x2;\n",
+ "yDiffy_star1 = y1-y1_star;\n",
+ "yDiffy_star2 = y2-y2_star;\n",
+ "yDiffy_starm = (yDiffy_star1-yDiffy_star2)/math.log(yDiffy_star1/yDiffy_star2);\n",
+ "# From Eqn. 8.48:\n",
+ "NtoG = (y1-y2)/yDiffy_starm;\n",
+ "print\"NtoG according to Eqn. 8.48:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Mehod b:\n",
+ "# From Illustration 8.3:\n",
+ "A = 1.424;\n",
+ "NtoG = (math.log((((y1-(m*x2))/(y2-(m*x2)))*(1-(1/A)))+(1/A)))/(1-(1/A));\n",
+ "print\"NtoG according to Eqn. 8.50:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Method c:\n",
+ "# Operating Line:\n",
+ "# From Illustration 8.3:\n",
+ "X_prime = [0.00503, 0.02, 0.04 ,0.06 ,0.08 ,0.10 ,0.1190];\n",
+ "x_prime = [0.00502 ,0.01961, 0.0385, 0.0566, 0.0741, 0.0909 ,0.1063]\n",
+ "Y_prime = [0.00102 ,0.00357 ,0.00697 ,0.01036 ,0.01376 ,0.01714 ,0.0204];\n",
+ "y_prime = [0.00102 ,0.00356, 0.00692 ,0.01025 ,0.01356 ,0.01685, 0.0200];\n",
+ "def f2(x):\n",
+ " return m*x\n",
+ "x = numpy.arange(0,0.14,0.01);\n",
+ "\n",
+ "plt.plot(x_prime,y_prime,label=\"Operating Line\")\n",
+ "plt.plot(x,f2(x),label=\"Equilibrium Line\");\n",
+ "plt.legend(loc='upper right');\n",
+ "plt.grid('on');\n",
+ "xlabel(\"mole fraction of benzene in liquid\");\n",
+ "ylabel(\"mole fraction of benzene in gas\");\n",
+ "plt.show()\n",
+ "# From graph:\n",
+ "NtoG = 8.7;\n",
+ "print\"NtoG from graph:\",round(NtoG,2),\" \\n\",\n",
+ "\n",
+ "# Method d:\n",
+ "# from Fig 8.10:\n",
+ "Y_star = [0.000625, 0.00245, 0.00483, 0.00712 ,0.00935 ,0.01149, 0.01347];\n",
+ "ordinate = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " ordinate[i] = 1/(Y_prime[i]-Y_star[i]);\n",
+ "\n",
+ "plt.plot(Y_prime,ordinate);\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Y\");\n",
+ "ylabel(\"1/(Y-Y*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area under the curve:\n",
+ "Ac = 8.63;\n",
+ "# From Eqn. 8.36:\n",
+ "NtoG = Ac+(1.0/2)*math.log((1+y2)/(1+y1));\n",
+ "print\"NtoG from graphical integration:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Height of transfer units:\n",
+ "NtoG = 9.16;\n",
+ "# From Illustration 6.6:\n",
+ "Fga = 0.0719;# [kmol/cubic m.s]\n",
+ "Fla = 0.01377;# [kmol/cubic m.s]\n",
+ "Gav = 0.0609;# [kmol/square m.s]\n",
+ "L = 1.787*10**(-3);# [kmol/s]\n",
+ "X1 = x1/(1-x1);\n",
+ "X2 = x2/(1-x2);\n",
+ "Area = 0.1746;# [square m]\n",
+ "Lav = L*((1+X1)+(1+X2))/(2*Area);\n",
+ "# From Eqn. 8.24:\n",
+ "Htg = Gav/Fga;# [m]\n",
+ "# From Eqn. 8.31:\n",
+ "Htl = Lav/Fla;# [m]\n",
+ "# since Solutions are dilute:\n",
+ "HtoG = Htg+Htl/A;# [m]\n",
+ "print\"HtoG: \",round(HtoG,2),\" m\\n\",\n",
+ "Z = HtoG*NtoG;# [m]\n",
+ "print\"The depth of packing recquired is \",round(Z,2),\" m\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.7 - Page: 312\n",
+ "\n",
+ "\n",
+ "NtoG according to Eqn. 8.48: 8.51 \n",
+ "\n",
+ "NtoG according to Eqn. 8.50: 9.16 \n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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svmfAYlESiWj6WGqr6kMhxw+LSI9wGzaMeOLnn+Hyy6FyZSfrY1Uvqe4SnMz9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\nlM6olFRnaCxKNPOixMsYtun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+ "text": [
+ "<matplotlib.figure.Figure at 0xb4fd5f8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "NtoG from graph: 8.7 \n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xb1dab70>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "NtoG from graphical integration: 8.62 \n",
+ "\n",
+ "HtoG: 1.4 m\n",
+ "The depth of packing recquired is 12.84 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 140
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.8: Page 317"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.8\n",
+ "# Page: 317\n",
+ "\n",
+ "print'Illustration 8.8 - Page: 317\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#***Data***\n",
+ "# a:NH3 b:air c:H2O\n",
+ "ya = 0.416;# [mole fraction]\n",
+ "yb = 0.584;# [mole fraction]\n",
+ "G1 = 0.0339;# [kmol/square m.s]\n",
+ "L1 = 0.271;# [kmol/square m.s]\n",
+ "TempG1 = 20;# [OC]\n",
+ "#********#\n",
+ "\n",
+ "# At 20 OC\n",
+ "Ca = 36390;# [J/kmol]\n",
+ "Cb = 29100;# [J/kmol]\n",
+ "Cc = 33960;# [J/kmol]\n",
+ "lambda_c = 44.24*10**6;# [J/kmol]\n",
+ "# Enthalpy base = NH3 gas, H2O liquid, air at 1 std atm.\n",
+ "Tempo = 20;# [OC]\n",
+ "lambda_Ao = 0;# [J/kmol]\n",
+ "lambda_Co = 44.24*10**6;# [J/kmol]\n",
+ "\n",
+ "# Gas in:\n",
+ "Gb = G1*yb;# [kmol air/square m.s]\n",
+ "Ya1 = ya/(1-ya);# [kmol NH3/kmol air]\n",
+ "yc1 = 0;# [mole fraction]\n",
+ "Yc1 = yc1/(1-yc1);# [kmol air/kmol NH]\n",
+ "# By Eqn 8.58:\n",
+ "Hg1 = (Cb*(TempG1-Tempo))+(Ya1*(Ca*(TempG1-Tempo))+lambda_Ao)+(Yc1*(Cc*(TempG1-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "\n",
+ "# Liquid in:\n",
+ "xa1 = 0;# [mole fraction]\n",
+ "xc1 = 1;# [mole fraction]\n",
+ "Hl1 = 0;# [J/kmol air]\n",
+ "\n",
+ "#Gas out:\n",
+ "Ya2 = Ya1*(1-0.99);# [kmol NH3/kmol air]\n",
+ "# Assume:\n",
+ "TempG2 = 23.9;# [OC]\n",
+ "yc2 = 0.0293;\n",
+ "def f(Yc2):\n",
+ " return yc2-(Yc2/(Yc2+Ya2+1))\n",
+ "Yc2 = fsolve(f,0.002);# [kmol H2O/kmol air]\n",
+ "Hg2 = (Cb*(TempG2-Tempo))+(Ya2*(Ca*(TempG2-Tempo))+lambda_Ao)+(Yc2*(Cc*(TempG2-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "\n",
+ "# Liquid out:\n",
+ "Lc = L1-(Yc1*Gb);# [kmol/square m.s]\n",
+ "La = Gb*(Ya1-Ya2);# [kmol/square m.s]\n",
+ "L2 = La+Lc;# [kmol/square m.s]\n",
+ "xa = La/L2;\n",
+ "xc = Lc/L2;\n",
+ "# At xa & tempo = 20 OC\n",
+ "delta_Hs = -1709.6*1000;# [J/kmol soln]\n",
+ "\n",
+ "# Condition at the bottom of the tower:\n",
+ "# Assume:\n",
+ "TempL = 41.3;# {OC}\n",
+ "# At(TempL+TempG1)/2:\n",
+ "Cl = 75481.0;# [J/kmol]\n",
+ "def f40(Cl):\n",
+ " return Hl1+Hg1-((Gb*Hg2)+(L2*(Cl*(TempL-Tempo)+delta_Hs)))\n",
+ "Cl = fsolve(f40,7);# [J/kmol.K]\n",
+ "\n",
+ "# For the Gas:\n",
+ "MavG = 24.02;# [kg/kmol]\n",
+ "Density_G = 0.999;# [kg/cubic m]\n",
+ "viscosity_G = 1.517*10**(-5);# [kg/m.s]\n",
+ "kG = 0.0261;# [W/m.K]\n",
+ "CpG = 1336;# [J/kg.K]\n",
+ "Dab = 2.297*10**(-5);# [square m/s]\n",
+ "Dac = 3.084*10**(-5);# [square m/s]\n",
+ "Dcb = 2.488*10**(-5);# [square m/s]\n",
+ "PrG = CpG*viscosity_G/kG;\n",
+ "\n",
+ "# For the liquid:\n",
+ "MavL = 17.97;# [kg/kmol]\n",
+ "Density_L = 953.1;# [kg/cubic m]\n",
+ "viscosity_L = 6.408*10**(-4);# [kg/m.s]\n",
+ "Dal = 3.317*10**(-9);# [square m/s]\n",
+ "kl = 0.4777;# [W/m.K]\n",
+ "ScL = viscosity_L/(Density_L*Dal);\n",
+ "PrL = 5.72;\n",
+ "sigma = 3*10**(-4);\n",
+ "G_prime = G1*MavG;# [kg/square m.s]\n",
+ "L_prime = L2*MavL;# [kg/square m.s]\n",
+ "# From data of Chapter 6:\n",
+ "Ds = 0.0472;# [m]\n",
+ "a = 57.57;# [square m/cubic m]\n",
+ "shiLt = 0.054;\n",
+ "e = 0.75;\n",
+ "# By Eqn. 6.71:\n",
+ "eLo = e-shiLt;\n",
+ "# By Eqn. 6.72:\n",
+ "kL = (25.1*Dal/Ds)*(Ds*L_prime/viscosity_L)**0.45*ScL**0.5;# [m/s]\n",
+ "c = Density_L/MavL;# [kmol/cubic m]\n",
+ "Fl = kL*c;# [kmol/cubic m]\n",
+ "# The heat mass transfer analogy of Eqn. 6.72:\n",
+ "hL = (25.1*kl/Ds)*(Ds*L_prime/viscosity_L)**0.45*PrL**0.5;# [m/s]\n",
+ "# The heat transfer analogy of Eqn. 6.69:\n",
+ "hG = (1.195*G_prime*CpG/PrG**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [W/square m.K]\n",
+ "# To obtain the mass transfer coeffecients:\n",
+ "Ra = 1.4;\n",
+ "Rc = 1-Ra;\n",
+ "# From Eqn. 8.83:\n",
+ "Dam = (Ra-ya)/(Ra*((yb/Dab)+((ya+yc1)/Dac))-(ya/Dac));# [square m/s]\n",
+ "Dcm = (Rc-yc1)/(Rc*((yb/Dcb)+((ya+yc1)/Dac))-(yc1/Dac));# [square m/s]\n",
+ "ScGa = viscosity_G/(Density_G*Dam);\n",
+ "ScGc = viscosity_G/(Density_G*Dcm);\n",
+ "# By Eqn. 6.69:\n",
+ "FGa = (1.195*G1/ScGa**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [kmol/square m.K]\n",
+ "FGc = (1.195*G1/ScGc**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [kmol/square m.K]\n",
+ "Ra = Ra-0.1;\n",
+ "# From Eqn. 8.80:\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " def f41(xai):\n",
+ " return Ra-(Ra-ya)*((Ra-xa)/(Ra-xai))**(Fl/FGa)\n",
+ " xai = numpy.arange(xa,0.10,0.01)\n",
+ " plt.plot(xai,f41(xai))\n",
+ " Ra = Ra+0.1;\n",
+ "\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Mole fraction NH3 in the liquid, xa\");\n",
+ "ylabel(\"Mole fraction NH3 in the gas ya\");\n",
+ "title(\"Operating Line curves\");\n",
+ "plt.show()\n",
+ "Rc = Rc-0.1;\n",
+ "# From Eqn. 8.81:\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " def f42(xci):\n",
+ " return Rc-(Rc-yc1)*((Rc-xc)/(Rc-xci))**(Fl/FGc)\n",
+ " xci = numpy.arange(xc,0.85,-0.01);\n",
+ " plot(xci,f42(xci))\n",
+ " Rc = Rc+0.1;\n",
+ "\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Mole fraction H2O in the liquid, xc\");\n",
+ "ylabel(\"Mole fraction H2O in the gas, yc\");\n",
+ "title(\"Operating line Curves\");\n",
+ "plt.show()\n",
+ "# Assume:\n",
+ "Tempi = 42.7;# [OC]\n",
+ "# The data of Fig. 8.2 (Pg 279) & Fig 8.4 (Pg 319) are used to draw the eqb curve of Fig 8.25 (Pg 320).\n",
+ "# By interpolation of operating line curves with eqb line and the condition: xai+xci = 1;\n",
+ "Ra = 1.38;\n",
+ "Rc = 1-Ra;\n",
+ "xai = 0.0786;\n",
+ "yai = f41(xai);\n",
+ "xci = 1-xai;\n",
+ "yci = f42(xci);\n",
+ "# From Eqn. 8.77:\n",
+ "dYa_By_dZ = -(Ra*FGa*a/Gb)*math.log((Ra-yai)/(Ra-ya));# [kmol H2O/kmol air]\n",
+ "# From Eqn. 8.78:\n",
+ "dYc_By_dZ = -(Rc*FGc*a/Gb)*math.log((Rc-yci)/(Rc-yc1));# [kmol H2O/kmol air]\n",
+ "# From Eqn. 8.82:\n",
+ "hGa_prime = -(Gb*((Ca*dYa_By_dZ)+(Cc*dYc_By_dZ)))/(1-exp(Gb*((Ca*dYa_By_dZ)+(Cc*dYc_By_dZ))/(hG*a)));# [W/cubic m.K]\n",
+ "# From Eqn. 8.79:\n",
+ "dtG_By_dZ = -(hGa_prime*(TempG1-Tempi))/(Gb*(Cb+(Ya1*Ca)+(Yc1*Cc)));# [K/m]\n",
+ "# When the curves of Fig. 8.2 (pg 279) & 8.24 (Pg 319) are interpolated for concentration xai and xci, the slopes are:\n",
+ "mar = 0.771;\n",
+ "mcr = 1.02;\n",
+ "lambda_c = 43.33*10**6;# [J/kmol]\n",
+ "# From Eqn. 8.3:\n",
+ "Hai = Ca*(Tempi-Tempo)+lambda_Ao-(mar*lambda_c);# [J/kmol]\n",
+ "Hci = Cc*(Tempi-Tempo)+lambda_Co-(mcr*lambda_c);# [J/kmol]\n",
+ "# From Eqn. 8.76\n",
+ "Tempi2 = TempL+(Gb/(hL*a))*(((Hai-Ca*(TempG1-Tempo)-lambda_Ao)*dYa_By_dZ)+((Hci-Cc*(TempG1-Tempo)-lambda_Co)*dYc_By_dZ)-((Cb+(Ya1*Ca)+(Yc1*Cc))*dtG_By_dZ));# [OC]\n",
+ "# The value of Tempi obtained is sufficiently close to the value assumed earlier.\n",
+ "\n",
+ "deltaYa=-0.05;\n",
+ "# An interval of deltaYa up the tower\n",
+ "deltaZ = deltaYa/(dYa_By_dZ);# [m]\n",
+ "deltaYc = (dYc_By_dZ*deltaZ);\n",
+ "# At this level:\n",
+ "Ya_next = Ya1+deltaYa;# [kmol/kmol air]\n",
+ "Yc_next = Yc1+deltaYc;# [kmol H2O/kmol air]\n",
+ "tG_next = TempG1+(dtG_By_dZ*deltaZ);# [OC]\n",
+ "L_next = L1+Gb*(deltaYa+deltaYc);# [kmol/square m.s]\n",
+ "xa_next = ((Gb*deltaYa)+(L1*xa))/L_next;# [mole fraction NH3]\n",
+ "Hg_next = (Cb*(tG_next-Tempo))+(Ya_next*(Ca*(tG_next-Tempo))+lambda_Ao)+(Yc_next*(Cc*(tG_next-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "Hl_next = (L1*Hl1)+(Gb*(Hg_next-Hg2)/L_next);# [J/kmol]\n",
+ "# The calculation are continued where the specified gas outlet composition are reached.\n",
+ "# The packed depth is sum of all deltaZ\n",
+ "Z = 1.58;# [m]\n",
+ "print\"The packed depth is: \",Z,\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.8 - Page: 317\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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DNCbjSmSmiOQHUNVeKQtFpDxO05YJg1hvk7X8MlCwIIwaRfZxb/PY+G0sWliO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\nC5TymS+FUytmtM0F7rI6wI0i8gPwPnC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+ "text": [
+ "<matplotlib.figure.Figure at 0xbb73940>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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5DMPwI97kSawETgA7gMtR51U1zqxrTx+KL4GGwFFgG9BJVQ/EMm418DfwjqrO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\njXxqolkTOQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xb54be80>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The packed depth is: 1.58 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 135
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.9: Page 327"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.9\n",
+ "# Page: 327\n",
+ "\n",
+ "print'Illustration 8.9 - Page: 327\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# C1=CH4 C2=C2H6 C3=n-C3H8 C4=C4H10\n",
+ "Abs=0.15;# [Tot\n",
+ "\n",
+ "# Illustration 10.8\n",
+ "# Page: 539\n",
+ "\n",
+ "print'Illustration 10.8 - Page: 539\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:acetic acid c:Water d:Isopropylether layer\n",
+ "# Water solution (continuous):\n",
+ "C = 8000.0;# [kg/h]\n",
+ "xCn = 0.175;# [mass fraction]\n",
+ "Density_c = 1009;# [kg/cubic m]\n",
+ "viscosity_c = 3.1*10**(-3);# [kg/m.s]\n",
+ "Dc = 1.24*10**(-9);# [square m/s]\n",
+ "\n",
+ "# Isopropyl Ethr Layer:\n",
+ "D = 20000.0;# [kg/h]\n",
+ "xDnPlus1 = 0.05;# [mass fraction]\n",
+ "Density_d = 730;# [kg/cubic m]\n",
+ "viscosity_d = 0.9*10**(-3);# [kg/m.s]\n",
+ "Dd = 1.96*10**(-9);# [square m/s]\n",
+ "\n",
+ "sigma = 0.013;# [/N/m]\n",
+ "m = 2.68;# [Distributon coeffecient]\n",
+ "#*******#\n",
+ "\n",
+ "Ma = 60.1;\n",
+ "g = 9.81;# [m/square s]\n",
+ "cCn = xCn*Density_c/Ma;# [kmol/cubic m]\n",
+ "cDnPlus1 = xDnPlus1*Density_d/Ma;# [kmol/cubic m]\n",
+ "mCD = m*(Density_c/Density_d);# [(kmol/cubic min ether)/(kmol/cubic m in water)]\n",
+ "\n",
+ "# Perforations:\n",
+ "Do = 0.006;# [m]\n",
+ "pitch = 0.015;# [m]\n",
+ "qD = D/(3600.0*Density_d);# [cubic m/s]\n",
+ "delta_Density = Density_c-Density_d;# [kg/cubic m]\n",
+ "Value1 = Do/(sigma/(delta_Density*g))**0.5;\n",
+ "if Value1<0.1785:\n",
+ " # From Eqn. 10.74(a):\n",
+ " doBydj = (0.485*Value1**2)+1;\n",
+ "else:\n",
+ " # From Eqn. 10.74(b)\n",
+ " doBydj = (1.51*Value1)+0.12;\n",
+ "\n",
+ "dj = Do/doBydj;# [m]\n",
+ "Vomax = 2.69*((dj/Do)**2)*(sigma/(dj*((0.5137*Density_d)+(0.4719*Density_c))))**0.5;# [m/s]\n",
+ "# Since Vomax is less than 0.1:\n",
+ "Vo = 0.1;# [m/s]\n",
+ "Ao = qD/Vo;# [square m]\n",
+ "No = Ao/(math.pi*Do**2.0/4);# [square m]\n",
+ "# From Eqn. 6.30:\n",
+ "# Plate area for perforation:\n",
+ "Aa = Ao/(0.907*(Do/pitch)**2);# [square m]\n",
+ "\n",
+ "# Downspout:\n",
+ "dp = 0.0007;# [m]\n",
+ "# From Eqn. 10.75:\n",
+ "U = Density_c**2*sigma**3/(g*viscosity_c**4*delta_Density);\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 1.515;\n",
+ "abcissa = 0.62;\n",
+ "def f74(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vt = fsolve(f74,7);# [m/s]\n",
+ "Vd = Vt;# [m/s]\n",
+ "qC = C/(Density_c*3600);# [cubic m/s]\n",
+ "Ad = qC/Vd;# [square m]\n",
+ "# From Table 6.2 (Pg 169):\n",
+ "# Allowing for supports and unperforated area:\n",
+ "At = Aa/0.65;# [square m]\n",
+ "T = (At*4/math.pi)**0.5;# [m]\n",
+ "An = At-Ad;# [square m]\n",
+ "\n",
+ "\n",
+ "# Drop Size:\n",
+ "alpha1 = 10.76;\n",
+ "alpha2 = 52560;\n",
+ "alpha3 = 1.24*10**6;\n",
+ "alpha4 = 3.281;\n",
+ "abcissa = (alpha2*sigma*Do/delta_Density)+(alpha3*Do**1.12*Vo**0.547*viscosity_c**0.279/delta_Density**1.5);\n",
+ "Parameter = alpha1*Density_d*Vo**2/(delta_Density);\n",
+ "ordinate = 0.024;\n",
+ "dp = ordinate/alpha4;\n",
+ "\n",
+ "# Coalesced layer:\n",
+ "Vn = qD/An;# [m/s]\n",
+ "# From Eqn. 10.80:\n",
+ "ho = (Vo**2-Vn**2)*Density_d/(2*g*0.67**2*delta_Density);# [m]\n",
+ "hD = ho;\n",
+ "# From Eqn. 10.82:\n",
+ "hC = 4.5*Vd**2*Density_c/(2*g*delta_Density);# [m]\n",
+ "# From Eqn. 10.78:\n",
+ "h = hC+hD;\n",
+ "# Since this is very shallow, increase it by placing an orifice at the bottom of the downspout.\n",
+ "# VR: Velocity through the restriction.\n",
+ "# hR: Corresponding depth of the coalesced layer.\n",
+ "# Assume:\n",
+ "Vr = 0.332;# [m/s]\n",
+ "hr = (Vr**2-Vd**2)*Density_c/(2*0.67**2*delta_Density);\n",
+ "Ar = qC/Vr;# [square m]\n",
+ "dr = (4*Ar/math.pi)**0.5;# [m]\n",
+ "h = h+hr;# [m]\n",
+ "# The above results are satisfacyory.\n",
+ "Z = 0.35;# [m]\n",
+ "# Lead the downspout apron to within 0.1 m of the tray below.\n",
+ "\n",
+ "# Dispersed-phase holdup:\n",
+ "# From Eqn. 10.48:\n",
+ "Vsphi_D = Vn;\n",
+ "# From Fig. 10.47 (Pg 534):\n",
+ "ordinate = 165.2;\n",
+ "abcissa = 30.0;\n",
+ "def f75(Vt):\n",
+ " return abcissa-(dp*Vt*Density_c/(viscosity_c*U**0.15))\n",
+ "Vtl = fsolve(f75,7);# [m/s]\n",
+ "# For solids:\n",
+ "# From Fig. 10.48 (Pg 536):\n",
+ "abcissa = dp/(3*viscosity_c**2/(4*Density_c*delta_Density*g))**(1.0/3);\n",
+ "phi_D = [0, 0.1 ,0.2 ,0.3];\n",
+ "# Corresponding ordinates, from Fig. 10.48 (Pg 536):\n",
+ "ordinate1 = [8.8, 5.9 ,4.3 ,3.0];\n",
+ "Value1 = 1.0/(4*viscosity_c*delta_Density*g/(3*Density_c**2))**(1.0/3);\n",
+ "Val = numpy.zeros((size=(4,6));\n",
+ "# Val = [phi_D ordinate Vs(1-phi_D) (Vs for solids) Vs/Vt (Vs for liquids) (Vs*phi_D (for liquids))]\n",
+ "for i in range(1,4):\n",
+ " Val[i,0] = phi_D[i];\n",
+ " Val[i,1] = ordinate1[i];\n",
+ " Val[i,2] = Val[i,1)/Value1;\n",
+ " Val[i,3] = Val[i,2)/(1-Val[i,0]);\n",
+ " Val[i,4] = Val[i,3]/Val[0,3];\n",
+ " Val[i,5] = Vtl*Val[i,4];\n",
+ " Val[i,6] = Val[i,5]*Val[i,0];\n",
+ "\n",
+ "\n",
+ "# By Interpolation:\n",
+ "Phi_D = 0.1;\n",
+ "# Mass transfer:\n",
+ "thetha_f = (math.pi*(dp**3)/6.0)/(qD/No);# [s]\n",
+ "# From Eqn. 10.87:\n",
+ "const = 1.5;\n",
+ "kLDf = const*(Dd/(math.pi*thetha_f))**0.5;# [m/s]\n",
+ "# From Eqn. 10.86\n",
+ "KLDf = 1.0/((1/kLDf)*(1+((1/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# The ordinate of Fig. 10.47 for the drops larger than 70. Hence mass transfer coeffecient during drop rise is given by Eqn. 10.89:\n",
+ "# From Eqn. 10.91:\n",
+ "b = 1.052*dp**0.225;\n",
+ "# From Eqn. 10.90:\n",
+ "omega = (1.0/(2*math.pi))*math.sqrt(192*sigma*b/(dp**3*((3*Density_d)+(2*Density_c))));# [1/s]\n",
+ "del = 0.2;\n",
+ "kLDr = sqrt((4*Dd*omega/%pi)*(1+del+(1/2)*del**2));\n",
+ "KLDr = 1.0/1/((1/kLDr)*(1+((1/mCD)*(Dd/Dc)**0.5)));# [m/s]\n",
+ "# From Eqn. 10.98:\n",
+ "EMD = ((4.4*KLDf/Vo)*(dp/Do)**2)+(6*KLDr*Phi_D*(Z-h)/(dp*Vn))/(1+((0.4*KLDf/Vo)*(dp/Do)**2)+(3*KLDr*Phi_D*(Z-h)/(dp*Vn)));\n",
+ "print\"Stage Efficiency: \",EMDal absorption,kmol]\n",
+ "T=25;# [OC]\n",
+ "y1=0.7;# [mol fraction]\n",
+ "y2=0.15;# [mol fraction]\n",
+ "y3=0.10;# [mol fraction]\n",
+ "y4=0.05;# [mol fraction]\n",
+ "x1=0.01;# [mol fraction]\n",
+ "x_involatile=0.99;# [mol fraction]\n",
+ "L_by_G=3.5;# [mol liquid/mol entering gas]\n",
+ "#******#\n",
+ "\n",
+ "LbyG_top=L_by_G/(1-y2);\n",
+ "LbyG_bottom=(L_by_G+y2)/1;\n",
+ "LbyG_av=(LbyG_top+LbyG_bottom)/2;\n",
+ "# The number of eqb. trays is fixed by C3 absorption:\n",
+ "# For C3 at 25 OC;\n",
+ "m=4.10;\n",
+ "A=LbyG_av/m;\n",
+ "Frabs=0.7;# [Fractional absorption]\n",
+ "X0=0;\n",
+ "# From Eqn. 8.109:\n",
+ "def f43(Np):\n",
+ " return Frabs-((A**Np)-A)/((A**Np)-1)\n",
+ "Np=fsolve(f43,2);\n",
+ "print\"Number of trays required is \\n\",round(Np,2)\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.9 - Page: 327\n",
+ "\n",
+ "\n",
+ "Number of trays required is \n",
+ "3.57\n"
+ ]
+ }
+ ],
+ "prompt_number": 105
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter8_1.ipynb b/Mass_-_Transfer_Operations/Chapter8_1.ipynb
new file mode 100755
index 00000000..727729f8
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter8_1.ipynb
@@ -0,0 +1,1312 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:43251d921fc8d8fdce5626d1c526ed83cb3073914f200034a16dd6e1031bf994"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 8: Gas Absorption"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.1: Page 278"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.1\n",
+ "# Page: 278\n",
+ "\n",
+ "print'Illustration 8.1 - Page: 278\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "P_star = 2*10**(5);# [N/square m]\n",
+ "X_methane = 0.6;\n",
+ "X_ethane = 0.2;\n",
+ "X_propane = 0.08;\n",
+ "X_nbutane = 0.06;\n",
+ "X_npentane = 0.06;\n",
+ "#******#\n",
+ "\n",
+ "MoleFraction = [0.6, 0.2 ,0.08, 0.06 ,0.06]\n",
+ "Heading = [\"Component\", \"Equilibrium Partial Pressure\", \"Vapour Pressue \" ,\"Mole Fraction\"];\n",
+ "Component = [\"Methane\", \"Ethane \" ,\"Propane\" ,\"n-Butane\", \"n-Pentane\"];\n",
+ "VapPressure = [0 ,42.05, 8.96, 2.36 ,0.66];# [N/square m]\n",
+ "Sum = 0;\n",
+ "\n",
+ "print Heading[0],\"\\t \\t \\t \\t\",Heading[1],\"\\t \\t \\t \\t\",Heading[2],\"\\t \\t \\t \\t\",Heading[3],\"\\t \\n\"\n",
+ "\n",
+ "\n",
+ "for i in range(0,5):\n",
+ " print \"\\n \",Component[i],\" \\t \\t \\t \\t \\t\",(\"{:.2e}\".format(MoleFraction[i]*P_star)),\"\\t \\t \\t \\t \\t \\t \\t \\t \",(\"{:.2e}\".format(VapPressure[i]*10**(5))),\n",
+ " if VapPressure[i]==0:\n",
+ " Sum = Sum+0;\n",
+ " else:\n",
+ " \n",
+ " print \"\\t \\t \\t \\t \\t \\t \\t \\t \\t \\t\",(\"{:.2e}\".format((MoleFraction[i]*P_star)/(VapPressure[i]*10**(5)))),\"\\t\",\n",
+ " Sum = Sum+(MoleFraction[i]*P_star)/(VapPressure[i]*10**(5))\n",
+ "\n",
+ "\n",
+ "\n",
+ "print\"\\n Mole Fraction Of solvent Oil is \",round(1-Sum,3)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.1 - Page: 278\n",
+ "\n",
+ "\n",
+ "Component \t \t \t \tEquilibrium Partial Pressure \t \t \t \tVapour Pressue \t \t \t \tMole Fraction \t \n",
+ "\n",
+ "\n",
+ " Methane \t \t \t \t \t1.20e+05 \t \t \t \t \t \t \t \t 0.00e+00 \n",
+ " Ethane \t \t \t \t \t4.00e+04 \t \t \t \t \t \t \t \t 4.20e+06 \t \t \t \t \t \t \t \t \t \t9.51e-03 \t\n",
+ " Propane \t \t \t \t \t1.60e+04 \t \t \t \t \t \t \t \t 8.96e+05 \t \t \t \t \t \t \t \t \t \t1.79e-02 \t\n",
+ " n-Butane \t \t \t \t \t1.20e+04 \t \t \t \t \t \t \t \t 2.36e+05 \t \t \t \t \t \t \t \t \t \t5.08e-02 \t\n",
+ " n-Pentane \t \t \t \t \t1.20e+04 \t \t \t \t \t \t \t \t 6.60e+04 \t \t \t \t \t \t \t \t \t \t1.82e-01 \t\n",
+ " Mole Fraction Of solvent Oil is 0.74\n"
+ ]
+ }
+ ],
+ "prompt_number": 165
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.2: Page 286"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.2\n",
+ "# Page: 286\n",
+ "\n",
+ "print'Illustration 8.2 - Page: 286\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#****Data****#\n",
+ "# Absorber:\n",
+ "G = 0.250;# [cubic m/s]\n",
+ "Temp1 = 273+26.0;# [K]\n",
+ "Pt = 1.07*10**(5);# [N/square m]\n",
+ "y1 = 0.02;\n",
+ "x2 = 0.005;\n",
+ "#******#\n",
+ "\n",
+ "G1 = G*(273.0/Temp1)*(Pt/(1.0133*10**(5)))*(1/22.41);# [kmol/s]\n",
+ "Y1 = y1/(1-y1);# [kmol benzene/kmol dry gas]\n",
+ "Gs = G1*(1.0-y1);# [kmol dry gas/s]\n",
+ "# For 95% removal of benzene:\n",
+ "Y2 = Y1*0.05;\n",
+ "X2 = x2/(1.0-x2);# [kmol benzene/kmol oil]\n",
+ "# Vapour pressure of benzene:\n",
+ "\n",
+ "P_star = 13330.0;# [N/square m]\n",
+ "X_star = numpy.zeros(20);\n",
+ "Y_star = numpy.zeros(20);\n",
+ "j = -1;\n",
+ "for i in range(1,21,1):\n",
+ " j = j+1;\n",
+ " x = i/100.0;\n",
+ " X_star[j] = i/100.0;\n",
+ " def f27(y):\n",
+ " return (y/(1+y))-(P_star/Pt)*(x/(1+x))\n",
+ " Y_star[j] = fsolve(f27,0.0);\n",
+ "\n",
+ "# For min flow rate:\n",
+ "X1 = 0.176;# [kmolbenzene/kmol oil]\n",
+ "DataMinFlow = numpy.array([[X2, Y2],[X1, Y1]]);\n",
+ "\n",
+ "plt.plot(X_star,Y_star,label=\"Equlibrium Line\")\n",
+ "plt.plot(DataMinFlow[:,0],DataMinFlow[:,1],label=\"Min Flow Rate Line\");\n",
+ "minLs = (Gs*(Y1-Y2)/(X1-X2));# [kmol/s]\n",
+ "# For 1.5 times the minimum:\n",
+ "Ls = 1.5*minLs;# [kmol/s]\n",
+ "X1_prime = (Gs*1.0*(Y1-Y2)/Ls)+X2;# [kmol benzene/kmol oil]\n",
+ "DataOperLine = numpy.array([[X2 ,Y2],[X1_prime ,Y1]]);\n",
+ "plt.plot(DataOperLine[:,0],DataOperLine[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "xlabel(\"moles of benzene / mole wash oil\");\n",
+ "ylabel(\"moles benzene / mole dry gas\");\n",
+ "legend(loc='lower right');\n",
+ "plt.title(\"Absorption\")\n",
+ "plt.show()\n",
+ "print\"The Oil circulation rate is \",(\"{:.2e}\".format(Ls)),\" kmol/s\\n\"\n",
+ "\n",
+ "# Stripping\n",
+ "Temp2 = 122+273;# [K]\n",
+ "# Vapour pressure at 122 OC\n",
+ "P_star = 319.9;# [kN/square m]\n",
+ "Pt = 101.33;# [kN/square m]\n",
+ "X_star = numpy.zeros(7);\n",
+ "Y_star = numpy.zeros(7);\n",
+ "j = -1;\n",
+ "for i in range(0,7,1):\n",
+ " j = j+1;\n",
+ " x = i/10.0;\n",
+ " X_star[j] = i/10.0;\n",
+ " def f28(y):\n",
+ " return (y/(1.0+y))-(P_star/Pt)*(x/(1.0+x))\n",
+ " Y_star[j] = fsolve(f28,0.0);\n",
+ "\n",
+ "X1 = X2;# [kmol benzene/kmol oil]\n",
+ "X2 = X1_prime;# [kmol benzene/kmol oil]\n",
+ "Y1 = 0.0;# [kmol benzene/kmol steam]\n",
+ "# For min. steam rate:\n",
+ "Y2 = 0.45;\n",
+ "DataMinFlow =numpy.array([[X2 ,Y2],[X1 ,Y1]]);\n",
+ "minGs = Ls*(X2-X1)/(Y2-Y1);# [kmol steam/s]\n",
+ "slopeOperat = 1.5*(Y2-Y1)/(X2-X1);\n",
+ "def f29(x):\n",
+ " return slopeOperat*(x-X1)+Y1\n",
+ "x =numpy.arange(0,0.14,0.01)\n",
+ "\n",
+ "plt.plot(Y_star,X_star,label=\"Equlibrium Line\")\n",
+ "plt.plot(DataMinFlow[:,0],DataMinFlow[:,1],label=\"Min Flow Rate Line\")\n",
+ "plt.plot(x,f29(x),label=\"Operating Line\");\n",
+ "plt.grid('on');\n",
+ "xlabel(\"moles of benzene / mole wash oil\");\n",
+ "ylabel(\"moles benzene / mole dry gas\");\n",
+ "plt.legend(loc='lower left');\n",
+ "plt.title(\"Stripping\");\n",
+ "plt.show()\n",
+ "print\"The Steam circulation rate is \",(\"{:.2e}\".format(1.5*minGs)),\" kmol/s\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.2 - Page: 286\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3kWps2FevwssvQ2ioWal4SKl4Yj5v3LlB92XdeTrwaV4s8iJbPtgSTalEhA4/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\nUKDvv/++pqWl6erVq4vlfeWVV1RV9dSpU7pjxw7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rUzxfHMUbrHoEb1muo6p+gCe7ewPK\nS+T8TO92POu3PhPsn6nqXODPQFkKLJBwxpA586vGmGIxykPwr0YNEZ4MdBGRPXj7DyMD7vvSjAe6\nus3Zt4ExLv5ut9G7B282VOyNKFXdBTyHt8yyBfhTwMO3pF+06tIvwVsye0lVd6pn9vt+YI2rbw2e\nK4BQ/VL3oLwFGC3nN/A7A1OBem6Tex8wJUR/A/uQj7dX8L+u3k18U4F2AXaV0p/S+uq7Nw4Y5eoY\nAdwdIs0I4E4R2Y23jDa4hHK34O2pgKdYWru/4O1NjXRlXA74FEA/YLeI7ASGAY8H1F9WXyZT9hgq\nTQ5GFWNm8w2jmiEi9wHvqeqiqm6LYVwIplgMwzCMiGJLYYZhGEZEMcViGIZhRBRTLIZhGEZEMcVi\nGIZhRBRTLIZhGEZEMcViGIZhRBRTLIZhGEZE+Rflz/wq8/SiYgAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7731ef0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The Oil circulation rate is 1.79e-03 kmol/s\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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bQF0RaYfzTMVI4IlUBmWMMSY9eXlOY4mqHikiY4H6qnqPiHyiqj2qJ8TKVdSm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\nvcBv3PW7gZsiznk6MB/nrvwOYLi7PRt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+ "text": [
+ "<matplotlib.figure.Figure at 0xa623ef0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The Steam circulation rate is 6.81e-04 kmol/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.3: Page 292"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.3\n",
+ "# Page: 292\n",
+ "\n",
+ "print'Illustration 8.3 - Page: 292\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "# Since tower is a tray device:\n",
+ "# Following changes in notation is made:\n",
+ "# L1 to LNp\n",
+ "# L2 to L0\n",
+ "# X1 to XNp\n",
+ "# X2 to X0\n",
+ "# G1 to GNpPlus1\n",
+ "# G2 to G1\n",
+ "# Y1 to YNpPlus1\n",
+ "# Y2 to Y1\n",
+ "# x1 to xNp\n",
+ "# x2 to x0\n",
+ "# y1 to yNpPlus1\n",
+ "# y2 to y1\n",
+ "# From Illustration 8.2:\n",
+ "yNpPlus1 = 0.02;\n",
+ "Y1 = 0.00102;\n",
+ "y1 = Y1/(1+Y1);\n",
+ "GNpPlus1 = 0.01075;# [kmol/s]\n",
+ "x0 = 0.005;\n",
+ "m = 0.125;# [m = y_star/x]\n",
+ "Ls = 1.787*10**(-3);# [kmol/s]\n",
+ "Gs = 0.01051;# [kmol/s]\n",
+ "XNp = 0.1190;\n",
+ "LNp = Ls*(1+XNp);# [kmol/s]\n",
+ "ANp = LNp/(m*GNpPlus1);\n",
+ "X0 = x0/(1-x0);\n",
+ "L0 = Ls*(1+X0);# [kmol/s]\n",
+ "G1 = Gs*(1+Y1);# [kmol/s]\n",
+ "A1 = L0/(m*G1);\n",
+ "A = (ANp*A1)**0.5;\n",
+ "# From Eqn. 5.55:\n",
+ "Np = (math.log((yNpPlus1-(m*x0))/(y1-(m*x0))*(1-(1/A))+(1/A)))/math.log(A);\n",
+ "print\"Absorber\\n\"\n",
+ "print\"From Analytical Method, no. of theoretical trays required is \\n\",round(Np,4)\n",
+ "# From Fig. 8.13 (Pg292):\n",
+ "Np = 7.6;\n",
+ "print\"From Graphical Method, no. of theoretical trays required is \\n\",Np\n",
+ "\n",
+ "# Stripper\n",
+ "SNp = 1/ANp;\n",
+ "S1 = 1/A1;\n",
+ "# Due to relative nonconstancy of the stripping factor,graphical method should be used.\n",
+ "print\"Stripper\\n\"\n",
+ "# From Fig. 8.11 (Pg 289):\n",
+ "Np = 6.7;\n",
+ "print\"From Graphical Method, no. of theoretical trays required is \\n\",Np\n",
+ "# From Fig. 5.16 (Pg 129):\n",
+ "Np = 6.0;\n",
+ "print\"From Fig. 5.16, no. of theoretical trays required is \\n\",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.3 - Page: 292\n",
+ "\n",
+ "\n",
+ "Absorber\n",
+ "\n",
+ "From Analytical Method, no. of theoretical trays required is \n",
+ "7.7085\n",
+ "From Graphical Method, no. of theoretical trays required is \n",
+ "7.6\n",
+ "Stripper\n",
+ "\n",
+ "From Graphical Method, no. of theoretical trays required is \n",
+ "6.7\n",
+ "From Fig. 5.16, no. of theoretical trays required is \n",
+ "6.0\n"
+ ]
+ }
+ ],
+ "prompt_number": 102
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.4: Page 295"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.4\n",
+ "# Page: 295\n",
+ "\n",
+ "print'Illustration 8.4 - Page: 295\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#****Data****#\n",
+ "# a = CH4 b = C5H12\n",
+ "Tempg = 27.0;# [OC]\n",
+ "Tempo = 0.0;# [base temp,OC]\n",
+ "Templ = 35.0;# [OC]\n",
+ "xa = 0.75;# [mole fraction of CH4 in gas]\n",
+ "xb = 0.25;# [mole fraction of C5H12 in gas]\n",
+ "M_Paraffin = 200.0;# [kg/kmol]\n",
+ "hb = 1.884;# [kJ/kg K]\n",
+ "#********#\n",
+ "\n",
+ "Ha = 35.59;# [kJ/kmol K]\n",
+ "Hbv = 119.75;# [kJ/kmol K]\n",
+ "Hbl = 117.53;# [kJ/kmol K]\n",
+ "Lb = 27820;# [kJ/kmol]\n",
+ "# M = [Temp (OC) m]\n",
+ "M = numpy.array([[20 ,0.575],[25 ,0.69],[30 ,0.81],[35, 0.95],[40, 1.10],[43, 1.25]]);\n",
+ "# Basis: Unit time\n",
+ "GNpPlus1 = 1.0;# [kmol]\n",
+ "yNpPlus1 = 0.25;# [kmol]\n",
+ "HgNpPlus1 = ((1-yNpPlus1)*Ha*(Tempg-Tempo))+(yNpPlus1*(Hbv*(Tempg-Tempo)+Lb));# [kJ/kmol]\n",
+ "L0 = 2.0;# [kmol]\n",
+ "x0 = 0.0;# [kmol]\n",
+ "HL0 = ((1-x0)*hb*M_Paraffin*(Templ-Tempo))+(x0*hb*(Templ-Tempo));# [kJ/kmol]\n",
+ "C5H12_absorbed = 0.98*xb;# [kmol]\n",
+ "C5H12_remained = xb-C5H12_absorbed;\n",
+ "G1 = xa+C5H12_remained;# [kmol]\n",
+ "y1 = C5H12_remained/G1;# [kmol]\n",
+ "LNp = L0+C5H12_absorbed;# [kmol]\n",
+ "xNp = C5H12_absorbed/LNp;# [kmol]\n",
+ "# Assume:\n",
+ "Temp1 = 35.6;# [OC]\n",
+ "Hg1 = ((1-y1)*Ha*(Temp1-Tempo))+(y1*(Hbv*(Temp1-Tempo)+Lb));# [kJ/kmol]\n",
+ "\n",
+ "# Eqn. 8.11:\n",
+ "Qt = 0;\n",
+ "def f30(HlNp):\n",
+ " return ((L0*HL0)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(G1*Hg1)+Qt)\n",
+ "HlNp = fsolve(f30,2);\n",
+ "\n",
+ "def f31(TempNp):\n",
+ " return HlNp-(((1-x0)*hb*M_Paraffin*(TempNp-Tempo))+(x0*hb*(TempNp-Tempo)))\n",
+ "TempNp = fsolve(f31,35.6);\n",
+ "# At Temp = TempNp:\n",
+ "mNp = 1.21;\n",
+ "yNp = mNp*xNp;# [kmol]\n",
+ "GNp = G1/(1-yNp);# [kmol]\n",
+ "HgNp = ((1-yNp)*Ha*(TempNp-Tempo))+(yNp*(Hbv*(TempNp-Tempo)+Lb));# [kJ/kmol]\n",
+ "# Eqn. 8.13 with n = Np-1\n",
+ "def f32(LNpMinus1):\n",
+ " return LNpMinus1+GNpPlus1-(LNp+GNp)\n",
+ "LNpMinus1 = fsolve(f32,2);# [kmol]\n",
+ "\n",
+ "# Eqn. 8.14 with n = Np-1\n",
+ "def f33(xNpMinus1):\n",
+ " return ((LNpMinus1*xNpMinus1)+(GNpPlus1*yNpPlus1))-((LNp*xNp)+(GNp*yNp))\n",
+ "xNpMinus1 = fsolve(f33,0);# [kmol]\n",
+ "\n",
+ "# Eqn. 8.15 with n = Np-1\n",
+ "def f34(HlNpMinus1):\n",
+ " return ((LNpMinus1*HlNpMinus1)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(GNp*HgNp))\n",
+ "HlNpMinus1 = fsolve(f34,0);# [kJ/kmol]\n",
+ "def f35(TempNpMinus1):\n",
+ " return HlNpMinus1-(((1-xNpMinus1)*hb*M_Paraffin*(TempNpMinus1-Tempo))+(xNpMinus1*hb*(TempNpMinus1-Tempo)))\n",
+ "TempNpMinus1 = fsolve(f35,42);# [OC]\n",
+ "\n",
+ "# The computation are continued upward through the tower in this manner until the gas composition falls atleast to 0.00662.\n",
+ "# Results = [Tray No.(n) Tn(OC) xn yn]\n",
+ "Results = numpy.array([[4.0 ,42.3 ,0.1091 ,0.1320],[3 ,39.0, 0.0521 ,0.0568],[2 ,36.8 ,0.0184 ,0.01875],[1 ,35.5, 0.00463 ,0.00450]]);\n",
+ "\n",
+ "plt.plot(Results[:,0],Results[:,3]);\n",
+ "plt.grid('on');\n",
+ "xlabel('Tray Number');\n",
+ "ylabel('mole fraction of C5H12 in gas');\n",
+ "plt.show();\n",
+ "plt.plot(Results[:,0],Results[:,1]);\n",
+ "plt.grid('on');\n",
+ "xlabel('Tray Number');\n",
+ "ylabel('Temperature(OC)');\n",
+ "plt.show();\n",
+ "\n",
+ "# For the required y1\n",
+ "Np = 3.75;\n",
+ "print\"The No. of trays will be \",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.4 - Page: 295\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xa5b6320>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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lOzz/PEyYAAMHwpAhwapikyfDW2/BtGlw6KHtbwiSkFuUlF+6ZT2/YmnMoMy4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\nEQH+H2FRyOkvfEciAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x785c198>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The No. of trays will be 3.75\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.5: Page 299"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.5\n",
+ "# Page: 299\n",
+ "\n",
+ "print'Illustration 8.5 - Page: 299\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# a = NH3 b = H2 c = N2 w = water\n",
+ "P = 2.0;# [bars]\n",
+ "Temp = 30.0;# [OC]\n",
+ "L = 6.38;# [kg/s]\n",
+ "W = 0.53;# [weir length,m]\n",
+ "pitch = 12.5/1000;# [m]\n",
+ "D = 0.75;# [Tower diameter,m]\n",
+ "hW = 0.060;# [weir height,m]\n",
+ "t = 0.5;# [tray spacing,m]\n",
+ "#*******#\n",
+ "\n",
+ "# From Geometry of Tray Arrangement:\n",
+ "At = 0.4418;# [Tower Cross section,square m]\n",
+ "Ad = 0.0403;# [Downspout Cross section,square m]\n",
+ "An = At-Ad;# [square m]\n",
+ "Ao = 0.0393;# [perforation area,square m]\n",
+ "Z = 0.5307;# [distance between downspouts,square m]\n",
+ "z = (D+W)/2.0;# [average flow width,m]\n",
+ "h1 = 0.04;# [weir crest,m]\n",
+ "# From Eqn. 6.34\n",
+ "Weff = W*(math.sqrt(((D/W)**2)-((((D/W)**2-1)**0.5)+((2*h1/D)*(D/W)))**2));# [m]\n",
+ "q = Weff*(1.839*h1**(3/2));#[cubic m/s]\n",
+ "# This is a recommended rate because it produces the liquid depth on the tray to 10 cm.\n",
+ "Density_L = 996;# [kg/s]\n",
+ "Mw = 18.02;# [kg/kmol]\n",
+ "L1 = 6.38/Mw;# [kmol/s]\n",
+ "Ma = 17.03;# [kg/kmol]\n",
+ "Mb = 28.02;# [kg/kmol]\n",
+ "Mc = 2.02;# [kg/kmol]\n",
+ "MavG = (0.03*Ma)+(0.97*(1/4)*Mb)+(0.97*(3/4)*Mc);# [kg/kmol]\n",
+ "Density_G = (MavG/22.41)*(P/0.986)*(273/(273+Temp));# [kg/cubic m]\n",
+ "G = 0.893;# [kg/s]\n",
+ "sigma = 68*10**(-3);# [N/m]\n",
+ "abcissa = (L/G)*(Density_G/Density_L)**0.5;\n",
+ "# From Table 6.2 (Pg169):\n",
+ "alpha = 0.04893;\n",
+ "beeta = 0.0302;\n",
+ "# From Eqn. 6.30\n",
+ "Cf = ((alpha*math.log10(1.0/abcissa))+beeta)*(sigma/0.02)**0.2;\n",
+ "# From Eqn. 6.29\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1.0/2);# [m/s]\n",
+ "# 80% of flooding value:\n",
+ "V = 0.8*Vf;# [m/s]\n",
+ "G = 0.8*G;# [kg/s]\n",
+ "G1 = G/MavG;# [kmol/s]\n",
+ "Vo = V*An/Ao;# [m/s]\n",
+ "l = 0.002;# [m]\n",
+ "Do = 0.00475;# [m]\n",
+ "# From Eqn. 6.37\n",
+ "Co = 1.09*(Do/l)**0.25;\n",
+ "viscosity_G = 1.13*10**(-5);# [kg/m.s]\n",
+ "Reo = Do*Vo*Density_G/viscosity_G;\n",
+ "# At Reynold's No. = Reo\n",
+ "fr = 0.0082;\n",
+ "g = 9.81;# [m/s^2]\n",
+ "# From Eqn. 6.36\n",
+ "def f36(hD):\n",
+ " return (2*hD*g*Density_L/(Vo**2*Density_G))-(Co*(0.40*(1.25-(Ao/An))+(4*l*fr/Do)+(1-(Ao/An))**2))\n",
+ "hD = fsolve(f36,1);\n",
+ "# From Eqn. 6.31;\n",
+ "Aa = (Ao/0.907)*(pitch/Do)**2;# [square m]\n",
+ "Va = V*An/Aa;# [m/s]\n",
+ "# From Eqn. 6.38\n",
+ "hL = 6.10*10**(-3)+(0.725*hW)-(0.238*hW*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "# From Eqn. 6.42\n",
+ "hR = 6*sigma/(Density_L*Do*g);# m\n",
+ "# From Eqn. 6.35\n",
+ "hG = hD+hL+hR;# [m]\n",
+ "Al = 0.025*W;# [square m]\n",
+ "Ada = min(Al,Ad);\n",
+ "# From Eqn. 6.43\n",
+ "h2 = (3/(2*g))*(q/Ada)**2;# [m]\n",
+ "# From Eqn.6.44\n",
+ "h3 = hG+h2;\n",
+ "# since hW+h1+h3 is essentially equal to t/2, flooding will not occur\n",
+ "abcissa = (L/G)*(Density_G/Density_L)**0.5;\n",
+ "V_by_Vf = V/Vf;\n",
+ "# From Fig.6.17, V/Vf = 0.8 & abcissa = 0.239\n",
+ "E = 0.009;\n",
+ "\n",
+ "# At the prevailing conditions:\n",
+ "Dg = 2.296*10**(-5);# [square m/s]\n",
+ "viscosity_G = 1.122*10**(-5);# [kg/m.s]\n",
+ "ScG = viscosity_G/(Density_G*Dg)\n",
+ "Dl = 2.421*10**(-9);# [square m/s]\n",
+ "\n",
+ "# From Henry's Law:\n",
+ "m = 0.850;\n",
+ "A = L1/(m*G1);\n",
+ "\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/(ScG**0.5);\n",
+ "# From Eqn. 6.64:\n",
+ "thetha_L = hL*z*Z/q;# [s]\n",
+ "# From Eqn. 6.62:\n",
+ "NtL = 40000*(Dl**0.5)*((0.213*Va*Density_G**0.5)+0.15)*thetha_L;\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1/((1/NtG)+(1/(A*NtL)));\n",
+ "# From Eqn. 6.51:\n",
+ "EoG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.63:\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;# [square m/s]\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thetha_L);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2.0)*((1+(4*m*G1*EoG/(L1*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EoG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+((exp(eta)-1)/(eta*(1+(eta/(eta+Pe))))));\n",
+ "# From Eqn. 6.60:\n",
+ "EMGE = EMG/((1+(EMG*(E/(1-E)))));\n",
+ "# From Eqn. 8.16:\n",
+ "EO = math.log(1+EMGE*((1.0/A)-1))/math.log(1.0/A);\n",
+ "Np = 14*EO;\n",
+ "yNpPlus1 = 0.03;\n",
+ "x0 = 0;\n",
+ "# From Eqn. 5.54(a):\n",
+ "def f37(y1):\n",
+ " return ((yNpPlus1-y1)/(yNpPlus1-m*x0))-(((A**(Np+1))-A)/((A**(Np+1))-1))\n",
+ "y1 = fsolve(f37,0.03);\n",
+ "print\"Mole Fraction Of NH3 in effluent is \",round(y1,4)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.5 - Page: 299\n",
+ "\n",
+ "\n",
+ "Mole Fraction Of NH3 in effluent is 0.0211\n"
+ ]
+ }
+ ],
+ "prompt_number": 159
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.6: Page 304"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.6\n",
+ "# Page: 304\n",
+ "\n",
+ "print'Illustration 8.6 - Page: 304\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#****Data****# \n",
+ "# Gas:\n",
+ "# In:\n",
+ "y_prime1 = 0.02;\n",
+ "Y_prime1 = 0.0204;# [mol/mol dry gas]\n",
+ "# Out:\n",
+ "y_prime2 = 0.00102;\n",
+ "Y_prime2 = 0.00102;# [mol/mol dry gas]\n",
+ "# Non absorbed gas:\n",
+ "MavG = 11;# [kg/kmol]\n",
+ "G = 0.01051;# [kmol/s nonbenzene]\n",
+ "Gm = 0.01075;# [kmol/s]\n",
+ "T = 26;# [OC]\n",
+ "viscosity_G = 10**(-5);# [kg/m.s]\n",
+ "DaG = 1.30*10**(-5);# [square m/s]\n",
+ "\n",
+ "# Liquid:\n",
+ "# In:\n",
+ "x_prime2 = 0.005;\n",
+ "X_prime2 = 0.00503;# [mol benzene/mol oil]\n",
+ "# Out:\n",
+ "x_prime1 = 0.1063;\n",
+ "X_prime1 = 0.1190;# [mol benzene/mol oil]\n",
+ "# Benzene free oil:\n",
+ "MavL = 260.0;# [kg/kmol]\n",
+ "viscosity_L = 2*10**(-3);# [kg/kmol]\n",
+ "Density_L = 840;# [kg/cubic cm]\n",
+ "L = 1.787*10**(-3);# [kmol/s]\n",
+ "DaL = 4.77*10**(-10);# [square m/s]\n",
+ "sigma = 0.03;# [N/square m]\n",
+ "m = 0.1250;\n",
+ "#*******#\n",
+ "\n",
+ "A = 0.47**2*math.pi/4;# [square m]\n",
+ "# At the bottom:\n",
+ "L_prime1 = ((L*MavL)+(X_prime1*L*78))/A;# [kg/square m.s]\n",
+ "# At the top\n",
+ "L_prime2 = ((L*MavL)+(X_prime2*L*78))/A;# [kg/square m.s]\n",
+ "L_primeav = (L_prime1+L_prime2)/2;# [kg/square m.s]\n",
+ "# At the bottom\n",
+ "G_prime1 = ((G*MavG)+(Y_prime1*G*78))/A;# [kg/square m.s]\n",
+ "# At the top\n",
+ "G_prime2 = ((G*MavG)+(Y_prime2*G*78))/A;# [kg/square m.s]\n",
+ "G_primeav = (G_prime1+G_prime2)/2;# [kg/square m.s]\n",
+ "\n",
+ "# From Illustration 6.6:\n",
+ "Fga = 0.0719;# [kmol/cubic cm.s]\n",
+ "Fla = 0.01377;# [kmol/cubic cm.s]\n",
+ "# Operating Line:\n",
+ "X_prime = numpy.array([0.00503 ,0.02 ,0.04 ,0.06 ,0.08 ,0.10 ,0.1190]);\n",
+ "x_prime = numpy.zeros(7);\n",
+ "Y_prime = numpy.zeros(7);\n",
+ "y_prime = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " x_prime[i] = X_prime[i]/(1+X_prime[i]);\n",
+ " def f38(Y_prime):\n",
+ " return (G*(Y_prime1-Y_prime))-(L*(X_prime1-X_prime[i]))\n",
+ " Y_prime[i] = fsolve(f38,Y_prime1);\n",
+ " y_prime[i] = (Y_prime[i])/(1+Y_prime[i]);\n",
+ "\n",
+ "def f39(x):\n",
+ " return m*x\n",
+ "x = numpy.arange(0,0.14,0.01);\n",
+ "\n",
+ "# Interface compositions are determined graphically and according to Eqn. 8.21:\n",
+ "yi = [0.000784, 0.00285, 0.00562 ,0.00830 ,0.01090 ,0.01337 ,0.01580];\n",
+ "ylog = zeros(7);\n",
+ "y_by_yDiffyi = zeros(7);\n",
+ "for i in range(0,7):\n",
+ " ylog[i] = math.log10(yi[i]);\n",
+ " y_by_yDiffyi[i] = y_prime[i]/(y_prime[i]-yi[i]);\n",
+ "\n",
+ "plt.plot(x_prime,y_prime,label=\"Operating Line\")\n",
+ "plt.plot(x,f39(x),label=\"Equilibrium Line\")\n",
+ "plt.plot(x_prime,yi,label=\"Interface Composition\");\n",
+ "plt.legend(loc='lower right');\n",
+ "plt.grid('on');\n",
+ "xlabel(\"mole fraction of benzene in liquid\");\n",
+ "ylabel(\"mole fraction of benzene in gas\");\n",
+ "plt.show()\n",
+ "plt.plot(ylog,y_by_yDiffyi);\n",
+ "plt.grid();\n",
+ "xlabel(\"log y\");\n",
+ "ylabel(\"y/(y-yi)\");\n",
+ "title(\"Graphical Integration Curve\");\n",
+ "plt.show()\n",
+ "# Area under the curve:\n",
+ "Ac = 6.556;\n",
+ "# Eqn. 8.28:\n",
+ "NtG = (2.303*Ac)+1.152*(math.log10((1-y_prime2)/(1-y_prime1)));\n",
+ "Gav = (Gm+(G/(1-Y_prime2)))/(2*A);# [kmol/square m.s]\n",
+ "HtG = Gav/Fga;# [m]\n",
+ "Z = HtG*NtG;# [m]\n",
+ "print\"The depth of packing required is \",round(Z,3),\" m\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.6 - Page: 304\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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N8afcdpvPmra+FEtWJWD5WNw6LAgQ6k77RKxhCV2++QbatIHPP4fWrf3Qwa+/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\n2QAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa5b66a0>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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xx+Hll+GXX2DrreHww0PhKPTn6FwGs/u5+w5Ad6At8KaZ3W9mDZ3vyQHMrKeZ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\nERGpkwqFiIjUSYVCRETqpEIhIiJ1+n+KRsAkmp26rwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa5852b0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The depth of packing required is 12.881 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.7: Page 312"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.7\n",
+ "# Page: 312\n",
+ "\n",
+ "print'Illustration 8.7 - Page: 312\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "# Fom Illustration 8.6:\n",
+ "y1 = 0.02;\n",
+ "y2 = 0.00102;\n",
+ "m = 0.125;\n",
+ "x2 = 0.005;\n",
+ "x1 = 0.1063;\n",
+ "\n",
+ "# Number of transfer units:\n",
+ "# Method a:\n",
+ "y1_star = m*x1;\n",
+ "y2_star = m*x2;\n",
+ "yDiffy_star1 = y1-y1_star;\n",
+ "yDiffy_star2 = y2-y2_star;\n",
+ "yDiffy_starm = (yDiffy_star1-yDiffy_star2)/math.log(yDiffy_star1/yDiffy_star2);\n",
+ "# From Eqn. 8.48:\n",
+ "NtoG = (y1-y2)/yDiffy_starm;\n",
+ "print\"NtoG according to Eqn. 8.48:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Mehod b:\n",
+ "# From Illustration 8.3:\n",
+ "A = 1.424;\n",
+ "NtoG = (math.log((((y1-(m*x2))/(y2-(m*x2)))*(1-(1/A)))+(1/A)))/(1-(1/A));\n",
+ "print\"NtoG according to Eqn. 8.50:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Method c:\n",
+ "# Operating Line:\n",
+ "# From Illustration 8.3:\n",
+ "X_prime = [0.00503, 0.02, 0.04 ,0.06 ,0.08 ,0.10 ,0.1190];\n",
+ "x_prime = [0.00502 ,0.01961, 0.0385, 0.0566, 0.0741, 0.0909 ,0.1063]\n",
+ "Y_prime = [0.00102 ,0.00357 ,0.00697 ,0.01036 ,0.01376 ,0.01714 ,0.0204];\n",
+ "y_prime = [0.00102 ,0.00356, 0.00692 ,0.01025 ,0.01356 ,0.01685, 0.0200];\n",
+ "def f2(x):\n",
+ " return m*x\n",
+ "x = numpy.arange(0,0.14,0.01);\n",
+ "\n",
+ "plt.plot(x_prime,y_prime,label=\"Operating Line\")\n",
+ "plt.plot(x,f2(x),label=\"Equilibrium Line\");\n",
+ "plt.legend(loc='upper right');\n",
+ "plt.grid('on');\n",
+ "xlabel(\"mole fraction of benzene in liquid\");\n",
+ "ylabel(\"mole fraction of benzene in gas\");\n",
+ "plt.show()\n",
+ "# From graph:\n",
+ "NtoG = 8.7;\n",
+ "print\"NtoG from graph:\",round(NtoG,2),\" \\n\",\n",
+ "\n",
+ "# Method d:\n",
+ "# from Fig 8.10:\n",
+ "Y_star = [0.000625, 0.00245, 0.00483, 0.00712 ,0.00935 ,0.01149, 0.01347];\n",
+ "ordinate = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " ordinate[i] = 1/(Y_prime[i]-Y_star[i]);\n",
+ "\n",
+ "plt.plot(Y_prime,ordinate);\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Y\");\n",
+ "ylabel(\"1/(Y-Y*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area under the curve:\n",
+ "Ac = 8.63;\n",
+ "# From Eqn. 8.36:\n",
+ "NtoG = Ac+(1.0/2)*math.log((1+y2)/(1+y1));\n",
+ "print\"NtoG from graphical integration:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Height of transfer units:\n",
+ "NtoG = 9.16;\n",
+ "# From Illustration 6.6:\n",
+ "Fga = 0.0719;# [kmol/cubic m.s]\n",
+ "Fla = 0.01377;# [kmol/cubic m.s]\n",
+ "Gav = 0.0609;# [kmol/square m.s]\n",
+ "L = 1.787*10**(-3);# [kmol/s]\n",
+ "X1 = x1/(1-x1);\n",
+ "X2 = x2/(1-x2);\n",
+ "Area = 0.1746;# [square m]\n",
+ "Lav = L*((1+X1)+(1+X2))/(2*Area);\n",
+ "# From Eqn. 8.24:\n",
+ "Htg = Gav/Fga;# [m]\n",
+ "# From Eqn. 8.31:\n",
+ "Htl = Lav/Fla;# [m]\n",
+ "# since Solutions are dilute:\n",
+ "HtoG = Htg+Htl/A;# [m]\n",
+ "print\"HtoG: \",round(HtoG,2),\" m\\n\",\n",
+ "Z = HtoG*NtoG;# [m]\n",
+ "print\"The depth of packing recquired is \",round(Z,2),\" m\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.7 - Page: 312\n",
+ "\n",
+ "\n",
+ "NtoG according to Eqn. 8.48: 8.51 \n",
+ "\n",
+ "NtoG according to Eqn. 8.50: 9.16 \n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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svmfAYlESiWj6WGqr6kMhxw+LSI9wGzaMeOLnn+Hyy6FyZSfrY1Uvqe4SnMz9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\nlM6olFRnaCxKNPOixMsYtun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+ "text": [
+ "<matplotlib.figure.Figure at 0xa595d68>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "NtoG from graph: 8.7 \n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x785fc18>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "NtoG from graphical integration: 8.62 \n",
+ "\n",
+ "HtoG: 1.4 m\n",
+ "The depth of packing recquired is 12.84 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.8: Page 317"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.8\n",
+ "# Page: 317\n",
+ "\n",
+ "print'Illustration 8.8 - Page: 317\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#***Data***\n",
+ "# a:NH3 b:air c:H2O\n",
+ "ya = 0.416;# [mole fraction]\n",
+ "yb = 0.584;# [mole fraction]\n",
+ "G1 = 0.0339;# [kmol/square m.s]\n",
+ "L1 = 0.271;# [kmol/square m.s]\n",
+ "TempG1 = 20;# [OC]\n",
+ "#********#\n",
+ "\n",
+ "# At 20 OC\n",
+ "Ca = 36390;# [J/kmol]\n",
+ "Cb = 29100;# [J/kmol]\n",
+ "Cc = 33960;# [J/kmol]\n",
+ "lambda_c = 44.24*10**6;# [J/kmol]\n",
+ "# Enthalpy base = NH3 gas, H2O liquid, air at 1 std atm.\n",
+ "Tempo = 20;# [OC]\n",
+ "lambda_Ao = 0;# [J/kmol]\n",
+ "lambda_Co = 44.24*10**6;# [J/kmol]\n",
+ "\n",
+ "# Gas in:\n",
+ "Gb = G1*yb;# [kmol air/square m.s]\n",
+ "Ya1 = ya/(1-ya);# [kmol NH3/kmol air]\n",
+ "yc1 = 0;# [mole fraction]\n",
+ "Yc1 = yc1/(1-yc1);# [kmol air/kmol NH]\n",
+ "# By Eqn 8.58:\n",
+ "Hg1 = (Cb*(TempG1-Tempo))+(Ya1*(Ca*(TempG1-Tempo))+lambda_Ao)+(Yc1*(Cc*(TempG1-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "\n",
+ "# Liquid in:\n",
+ "xa1 = 0;# [mole fraction]\n",
+ "xc1 = 1;# [mole fraction]\n",
+ "Hl1 = 0;# [J/kmol air]\n",
+ "\n",
+ "#Gas out:\n",
+ "Ya2 = Ya1*(1-0.99);# [kmol NH3/kmol air]\n",
+ "# Assume:\n",
+ "TempG2 = 23.9;# [OC]\n",
+ "yc2 = 0.0293;\n",
+ "def f(Yc2):\n",
+ " return yc2-(Yc2/(Yc2+Ya2+1))\n",
+ "Yc2 = fsolve(f,0.002);# [kmol H2O/kmol air]\n",
+ "Hg2 = (Cb*(TempG2-Tempo))+(Ya2*(Ca*(TempG2-Tempo))+lambda_Ao)+(Yc2*(Cc*(TempG2-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "\n",
+ "# Liquid out:\n",
+ "Lc = L1-(Yc1*Gb);# [kmol/square m.s]\n",
+ "La = Gb*(Ya1-Ya2);# [kmol/square m.s]\n",
+ "L2 = La+Lc;# [kmol/square m.s]\n",
+ "xa = La/L2;\n",
+ "xc = Lc/L2;\n",
+ "# At xa & tempo = 20 OC\n",
+ "delta_Hs = -1709.6*1000;# [J/kmol soln]\n",
+ "\n",
+ "# Condition at the bottom of the tower:\n",
+ "# Assume:\n",
+ "TempL = 41.3;# {OC}\n",
+ "# At(TempL+TempG1)/2:\n",
+ "Cl = 75481.0;# [J/kmol]\n",
+ "def f40(Cl):\n",
+ " return Hl1+Hg1-((Gb*Hg2)+(L2*(Cl*(TempL-Tempo)+delta_Hs)))\n",
+ "Cl = fsolve(f40,7);# [J/kmol.K]\n",
+ "\n",
+ "# For the Gas:\n",
+ "MavG = 24.02;# [kg/kmol]\n",
+ "Density_G = 0.999;# [kg/cubic m]\n",
+ "viscosity_G = 1.517*10**(-5);# [kg/m.s]\n",
+ "kG = 0.0261;# [W/m.K]\n",
+ "CpG = 1336;# [J/kg.K]\n",
+ "Dab = 2.297*10**(-5);# [square m/s]\n",
+ "Dac = 3.084*10**(-5);# [square m/s]\n",
+ "Dcb = 2.488*10**(-5);# [square m/s]\n",
+ "PrG = CpG*viscosity_G/kG;\n",
+ "\n",
+ "# For the liquid:\n",
+ "MavL = 17.97;# [kg/kmol]\n",
+ "Density_L = 953.1;# [kg/cubic m]\n",
+ "viscosity_L = 6.408*10**(-4);# [kg/m.s]\n",
+ "Dal = 3.317*10**(-9);# [square m/s]\n",
+ "kl = 0.4777;# [W/m.K]\n",
+ "ScL = viscosity_L/(Density_L*Dal);\n",
+ "PrL = 5.72;\n",
+ "sigma = 3*10**(-4);\n",
+ "G_prime = G1*MavG;# [kg/square m.s]\n",
+ "L_prime = L2*MavL;# [kg/square m.s]\n",
+ "# From data of Chapter 6:\n",
+ "Ds = 0.0472;# [m]\n",
+ "a = 57.57;# [square m/cubic m]\n",
+ "shiLt = 0.054;\n",
+ "e = 0.75;\n",
+ "# By Eqn. 6.71:\n",
+ "eLo = e-shiLt;\n",
+ "# By Eqn. 6.72:\n",
+ "kL = (25.1*Dal/Ds)*(Ds*L_prime/viscosity_L)**0.45*ScL**0.5;# [m/s]\n",
+ "c = Density_L/MavL;# [kmol/cubic m]\n",
+ "Fl = kL*c;# [kmol/cubic m]\n",
+ "# The heat mass transfer analogy of Eqn. 6.72:\n",
+ "hL = (25.1*kl/Ds)*(Ds*L_prime/viscosity_L)**0.45*PrL**0.5;# [m/s]\n",
+ "# The heat transfer analogy of Eqn. 6.69:\n",
+ "hG = (1.195*G_prime*CpG/PrG**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [W/square m.K]\n",
+ "# To obtain the mass transfer coeffecients:\n",
+ "Ra = 1.4;\n",
+ "Rc = 1-Ra;\n",
+ "# From Eqn. 8.83:\n",
+ "Dam = (Ra-ya)/(Ra*((yb/Dab)+((ya+yc1)/Dac))-(ya/Dac));# [square m/s]\n",
+ "Dcm = (Rc-yc1)/(Rc*((yb/Dcb)+((ya+yc1)/Dac))-(yc1/Dac));# [square m/s]\n",
+ "ScGa = viscosity_G/(Density_G*Dam);\n",
+ "ScGc = viscosity_G/(Density_G*Dcm);\n",
+ "# By Eqn. 6.69:\n",
+ "FGa = (1.195*G1/ScGa**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [kmol/square m.K]\n",
+ "FGc = (1.195*G1/ScGc**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [kmol/square m.K]\n",
+ "Ra = Ra-0.1;\n",
+ "# From Eqn. 8.80:\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " def f41(xai):\n",
+ " return Ra-(Ra-ya)*((Ra-xa)/(Ra-xai))**(Fl/FGa)\n",
+ " xai = numpy.arange(xa,0.10,0.01)\n",
+ " plt.plot(xai,f41(xai))\n",
+ " Ra = Ra+0.1;\n",
+ "\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Mole fraction NH3 in the liquid, xa\");\n",
+ "ylabel(\"Mole fraction NH3 in the gas ya\");\n",
+ "title(\"Operating Line curves\");\n",
+ "plt.show()\n",
+ "Rc = Rc-0.1;\n",
+ "# From Eqn. 8.81:\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " def f42(xci):\n",
+ " return Rc-(Rc-yc1)*((Rc-xc)/(Rc-xci))**(Fl/FGc)\n",
+ " xci = numpy.arange(xc,0.85,-0.01);\n",
+ " plot(xci,f42(xci))\n",
+ " Rc = Rc+0.1;\n",
+ "\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Mole fraction H2O in the liquid, xc\");\n",
+ "ylabel(\"Mole fraction H2O in the gas, yc\");\n",
+ "title(\"Operating line Curves\");\n",
+ "plt.show()\n",
+ "# Assume:\n",
+ "Tempi = 42.7;# [OC]\n",
+ "# The data of Fig. 8.2 (Pg 279) & Fig 8.4 (Pg 319) are used to draw the eqb curve of Fig 8.25 (Pg 320).\n",
+ "# By interpolation of operating line curves with eqb line and the condition: xai+xci = 1;\n",
+ "Ra = 1.38;\n",
+ "Rc = 1-Ra;\n",
+ "xai = 0.0786;\n",
+ "yai = f41(xai);\n",
+ "xci = 1-xai;\n",
+ "yci = f42(xci);\n",
+ "# From Eqn. 8.77:\n",
+ "dYa_By_dZ = -(Ra*FGa*a/Gb)*math.log((Ra-yai)/(Ra-ya));# [kmol H2O/kmol air]\n",
+ "# From Eqn. 8.78:\n",
+ "dYc_By_dZ = -(Rc*FGc*a/Gb)*math.log((Rc-yci)/(Rc-yc1));# [kmol H2O/kmol air]\n",
+ "# From Eqn. 8.82:\n",
+ "hGa_prime = -(Gb*((Ca*dYa_By_dZ)+(Cc*dYc_By_dZ)))/(1-exp(Gb*((Ca*dYa_By_dZ)+(Cc*dYc_By_dZ))/(hG*a)));# [W/cubic m.K]\n",
+ "# From Eqn. 8.79:\n",
+ "dtG_By_dZ = -(hGa_prime*(TempG1-Tempi))/(Gb*(Cb+(Ya1*Ca)+(Yc1*Cc)));# [K/m]\n",
+ "# When the curves of Fig. 8.2 (pg 279) & 8.24 (Pg 319) are interpolated for concentration xai and xci, the slopes are:\n",
+ "mar = 0.771;\n",
+ "mcr = 1.02;\n",
+ "lambda_c = 43.33*10**6;# [J/kmol]\n",
+ "# From Eqn. 8.3:\n",
+ "Hai = Ca*(Tempi-Tempo)+lambda_Ao-(mar*lambda_c);# [J/kmol]\n",
+ "Hci = Cc*(Tempi-Tempo)+lambda_Co-(mcr*lambda_c);# [J/kmol]\n",
+ "# From Eqn. 8.76\n",
+ "Tempi2 = TempL+(Gb/(hL*a))*(((Hai-Ca*(TempG1-Tempo)-lambda_Ao)*dYa_By_dZ)+((Hci-Cc*(TempG1-Tempo)-lambda_Co)*dYc_By_dZ)-((Cb+(Ya1*Ca)+(Yc1*Cc))*dtG_By_dZ));# [OC]\n",
+ "# The value of Tempi obtained is sufficiently close to the value assumed earlier.\n",
+ "\n",
+ "deltaYa=-0.05;\n",
+ "# An interval of deltaYa up the tower\n",
+ "deltaZ = deltaYa/(dYa_By_dZ);# [m]\n",
+ "deltaYc = (dYc_By_dZ*deltaZ);\n",
+ "# At this level:\n",
+ "Ya_next = Ya1+deltaYa;# [kmol/kmol air]\n",
+ "Yc_next = Yc1+deltaYc;# [kmol H2O/kmol air]\n",
+ "tG_next = TempG1+(dtG_By_dZ*deltaZ);# [OC]\n",
+ "L_next = L1+Gb*(deltaYa+deltaYc);# [kmol/square m.s]\n",
+ "xa_next = ((Gb*deltaYa)+(L1*xa))/L_next;# [mole fraction NH3]\n",
+ "Hg_next = (Cb*(tG_next-Tempo))+(Ya_next*(Ca*(tG_next-Tempo))+lambda_Ao)+(Yc_next*(Cc*(tG_next-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "Hl_next = (L1*Hl1)+(Gb*(Hg_next-Hg2)/L_next);# [J/kmol]\n",
+ "# The calculation are continued where the specified gas outlet composition are reached.\n",
+ "# The packed depth is sum of all deltaZ\n",
+ "Z = 1.58;# [m]\n",
+ "print\"The packed depth is: \",Z,\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.8 - Page: 317\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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DNCbjSmSmiOQHUNVeKQtFpDxO05YJg1hvk7X8MlCwIIwaRfZxb/PY+G0sWliO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\nC5TymS+FUytmtM0F7rI6wI0i8gPwPnC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+ "text": [
+ "<matplotlib.figure.Figure at 0xa595b00>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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5DMPwI97kSawETgA7gMtR51U1zqxrTx+KL4GGwFFgG9BJVQ/EMm418DfwjqrO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\njXxqolkTOQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa5fdcf8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The packed depth is: 1.58 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.9: Page 327"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.9\n",
+ "# Page: 327\n",
+ "\n",
+ "print'Illustration 8.9 - Page: 327\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# C1=CH4 C2=C2H6 C3=n-C3H8 C4=C4H10\n",
+ "Abs=0.15;# [Total absorption,kmol]\n",
+ "\n",
+ "T=25;# [OC]\n",
+ "y1=0.7;# [mol fraction]\n",
+ "y2=0.15;# [mol fraction]\n",
+ "y3=0.10;# [mol fraction]\n",
+ "y4=0.05;# [mol fraction]\n",
+ "x1=0.01;# [mol fraction]\n",
+ "x_involatile=0.99;# [mol fraction]\n",
+ "L_by_G=3.5;# [mol liquid/mol entering gas]\n",
+ "#******#\n",
+ "\n",
+ "LbyG_top=L_by_G/(1-y2);\n",
+ "LbyG_bottom=(L_by_G+y2)/1;\n",
+ "LbyG_av=(LbyG_top+LbyG_bottom)/2;\n",
+ "# The number of eqb. trays is fixed by C3 absorption:\n",
+ "# For C3 at 25 OC;\n",
+ "m=4.10;\n",
+ "A=LbyG_av/m;\n",
+ "Frabs=0.7;# [Fractional absorption]\n",
+ "X0=0;\n",
+ "# From Eqn. 8.109:\n",
+ "def f43(Np):\n",
+ " return Frabs-((A**Np)-A)/((A**Np)-1)\n",
+ "Np=fsolve(f43,2);\n",
+ "print\"Number of trays required is \\n\",round(Np,2)\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.9 - Page: 327\n",
+ "\n",
+ "\n",
+ "Number of trays required is \n",
+ "3.57\n"
+ ]
+ }
+ ],
+ "prompt_number": 38
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter8_2.ipynb b/Mass_-_Transfer_Operations/Chapter8_2.ipynb
new file mode 100755
index 00000000..727729f8
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter8_2.ipynb
@@ -0,0 +1,1312 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:43251d921fc8d8fdce5626d1c526ed83cb3073914f200034a16dd6e1031bf994"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 8: Gas Absorption"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.1: Page 278"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.1\n",
+ "# Page: 278\n",
+ "\n",
+ "print'Illustration 8.1 - Page: 278\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "P_star = 2*10**(5);# [N/square m]\n",
+ "X_methane = 0.6;\n",
+ "X_ethane = 0.2;\n",
+ "X_propane = 0.08;\n",
+ "X_nbutane = 0.06;\n",
+ "X_npentane = 0.06;\n",
+ "#******#\n",
+ "\n",
+ "MoleFraction = [0.6, 0.2 ,0.08, 0.06 ,0.06]\n",
+ "Heading = [\"Component\", \"Equilibrium Partial Pressure\", \"Vapour Pressue \" ,\"Mole Fraction\"];\n",
+ "Component = [\"Methane\", \"Ethane \" ,\"Propane\" ,\"n-Butane\", \"n-Pentane\"];\n",
+ "VapPressure = [0 ,42.05, 8.96, 2.36 ,0.66];# [N/square m]\n",
+ "Sum = 0;\n",
+ "\n",
+ "print Heading[0],\"\\t \\t \\t \\t\",Heading[1],\"\\t \\t \\t \\t\",Heading[2],\"\\t \\t \\t \\t\",Heading[3],\"\\t \\n\"\n",
+ "\n",
+ "\n",
+ "for i in range(0,5):\n",
+ " print \"\\n \",Component[i],\" \\t \\t \\t \\t \\t\",(\"{:.2e}\".format(MoleFraction[i]*P_star)),\"\\t \\t \\t \\t \\t \\t \\t \\t \",(\"{:.2e}\".format(VapPressure[i]*10**(5))),\n",
+ " if VapPressure[i]==0:\n",
+ " Sum = Sum+0;\n",
+ " else:\n",
+ " \n",
+ " print \"\\t \\t \\t \\t \\t \\t \\t \\t \\t \\t\",(\"{:.2e}\".format((MoleFraction[i]*P_star)/(VapPressure[i]*10**(5)))),\"\\t\",\n",
+ " Sum = Sum+(MoleFraction[i]*P_star)/(VapPressure[i]*10**(5))\n",
+ "\n",
+ "\n",
+ "\n",
+ "print\"\\n Mole Fraction Of solvent Oil is \",round(1-Sum,3)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.1 - Page: 278\n",
+ "\n",
+ "\n",
+ "Component \t \t \t \tEquilibrium Partial Pressure \t \t \t \tVapour Pressue \t \t \t \tMole Fraction \t \n",
+ "\n",
+ "\n",
+ " Methane \t \t \t \t \t1.20e+05 \t \t \t \t \t \t \t \t 0.00e+00 \n",
+ " Ethane \t \t \t \t \t4.00e+04 \t \t \t \t \t \t \t \t 4.20e+06 \t \t \t \t \t \t \t \t \t \t9.51e-03 \t\n",
+ " Propane \t \t \t \t \t1.60e+04 \t \t \t \t \t \t \t \t 8.96e+05 \t \t \t \t \t \t \t \t \t \t1.79e-02 \t\n",
+ " n-Butane \t \t \t \t \t1.20e+04 \t \t \t \t \t \t \t \t 2.36e+05 \t \t \t \t \t \t \t \t \t \t5.08e-02 \t\n",
+ " n-Pentane \t \t \t \t \t1.20e+04 \t \t \t \t \t \t \t \t 6.60e+04 \t \t \t \t \t \t \t \t \t \t1.82e-01 \t\n",
+ " Mole Fraction Of solvent Oil is 0.74\n"
+ ]
+ }
+ ],
+ "prompt_number": 165
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.2: Page 286"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.2\n",
+ "# Page: 286\n",
+ "\n",
+ "print'Illustration 8.2 - Page: 286\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#****Data****#\n",
+ "# Absorber:\n",
+ "G = 0.250;# [cubic m/s]\n",
+ "Temp1 = 273+26.0;# [K]\n",
+ "Pt = 1.07*10**(5);# [N/square m]\n",
+ "y1 = 0.02;\n",
+ "x2 = 0.005;\n",
+ "#******#\n",
+ "\n",
+ "G1 = G*(273.0/Temp1)*(Pt/(1.0133*10**(5)))*(1/22.41);# [kmol/s]\n",
+ "Y1 = y1/(1-y1);# [kmol benzene/kmol dry gas]\n",
+ "Gs = G1*(1.0-y1);# [kmol dry gas/s]\n",
+ "# For 95% removal of benzene:\n",
+ "Y2 = Y1*0.05;\n",
+ "X2 = x2/(1.0-x2);# [kmol benzene/kmol oil]\n",
+ "# Vapour pressure of benzene:\n",
+ "\n",
+ "P_star = 13330.0;# [N/square m]\n",
+ "X_star = numpy.zeros(20);\n",
+ "Y_star = numpy.zeros(20);\n",
+ "j = -1;\n",
+ "for i in range(1,21,1):\n",
+ " j = j+1;\n",
+ " x = i/100.0;\n",
+ " X_star[j] = i/100.0;\n",
+ " def f27(y):\n",
+ " return (y/(1+y))-(P_star/Pt)*(x/(1+x))\n",
+ " Y_star[j] = fsolve(f27,0.0);\n",
+ "\n",
+ "# For min flow rate:\n",
+ "X1 = 0.176;# [kmolbenzene/kmol oil]\n",
+ "DataMinFlow = numpy.array([[X2, Y2],[X1, Y1]]);\n",
+ "\n",
+ "plt.plot(X_star,Y_star,label=\"Equlibrium Line\")\n",
+ "plt.plot(DataMinFlow[:,0],DataMinFlow[:,1],label=\"Min Flow Rate Line\");\n",
+ "minLs = (Gs*(Y1-Y2)/(X1-X2));# [kmol/s]\n",
+ "# For 1.5 times the minimum:\n",
+ "Ls = 1.5*minLs;# [kmol/s]\n",
+ "X1_prime = (Gs*1.0*(Y1-Y2)/Ls)+X2;# [kmol benzene/kmol oil]\n",
+ "DataOperLine = numpy.array([[X2 ,Y2],[X1_prime ,Y1]]);\n",
+ "plt.plot(DataOperLine[:,0],DataOperLine[:,1],label=\"Operating Line\")\n",
+ "plt.grid('on');\n",
+ "xlabel(\"moles of benzene / mole wash oil\");\n",
+ "ylabel(\"moles benzene / mole dry gas\");\n",
+ "legend(loc='lower right');\n",
+ "plt.title(\"Absorption\")\n",
+ "plt.show()\n",
+ "print\"The Oil circulation rate is \",(\"{:.2e}\".format(Ls)),\" kmol/s\\n\"\n",
+ "\n",
+ "# Stripping\n",
+ "Temp2 = 122+273;# [K]\n",
+ "# Vapour pressure at 122 OC\n",
+ "P_star = 319.9;# [kN/square m]\n",
+ "Pt = 101.33;# [kN/square m]\n",
+ "X_star = numpy.zeros(7);\n",
+ "Y_star = numpy.zeros(7);\n",
+ "j = -1;\n",
+ "for i in range(0,7,1):\n",
+ " j = j+1;\n",
+ " x = i/10.0;\n",
+ " X_star[j] = i/10.0;\n",
+ " def f28(y):\n",
+ " return (y/(1.0+y))-(P_star/Pt)*(x/(1.0+x))\n",
+ " Y_star[j] = fsolve(f28,0.0);\n",
+ "\n",
+ "X1 = X2;# [kmol benzene/kmol oil]\n",
+ "X2 = X1_prime;# [kmol benzene/kmol oil]\n",
+ "Y1 = 0.0;# [kmol benzene/kmol steam]\n",
+ "# For min. steam rate:\n",
+ "Y2 = 0.45;\n",
+ "DataMinFlow =numpy.array([[X2 ,Y2],[X1 ,Y1]]);\n",
+ "minGs = Ls*(X2-X1)/(Y2-Y1);# [kmol steam/s]\n",
+ "slopeOperat = 1.5*(Y2-Y1)/(X2-X1);\n",
+ "def f29(x):\n",
+ " return slopeOperat*(x-X1)+Y1\n",
+ "x =numpy.arange(0,0.14,0.01)\n",
+ "\n",
+ "plt.plot(Y_star,X_star,label=\"Equlibrium Line\")\n",
+ "plt.plot(DataMinFlow[:,0],DataMinFlow[:,1],label=\"Min Flow Rate Line\")\n",
+ "plt.plot(x,f29(x),label=\"Operating Line\");\n",
+ "plt.grid('on');\n",
+ "xlabel(\"moles of benzene / mole wash oil\");\n",
+ "ylabel(\"moles benzene / mole dry gas\");\n",
+ "plt.legend(loc='lower left');\n",
+ "plt.title(\"Stripping\");\n",
+ "plt.show()\n",
+ "print\"The Steam circulation rate is \",(\"{:.2e}\".format(1.5*minGs)),\" kmol/s\\n\"\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.2 - Page: 286\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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3kWps2FevwssvQ2ioWal4SKl4Yj5v3LlB92XdeTrwaV4s8iJbPtgSTalEhA4/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\nUKDvv/++pqWl6erVq4vlfeWVV1RV9dSpU7pjxw7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rUzxfHMUbrHoEb1muo6p+gCe7ewPK\nS+T8TO92POu3PhPsn6nqXODPQFkKLJBwxpA586vGmGIxykPwr0YNEZ4MdBGRPXj7DyMD7vvSjAe6\nus3Zt4ExLv5ut9G7B282VOyNKFXdBTyHt8yyBfhTwMO3pF+06tIvwVsye0lVd6pn9vt+YI2rbw2e\nK4BQ/VL3oLwFGC3nN/A7A1OBem6Tex8wJUR/A/uQj7dX8L+u3k18U4F2AXaV0p/S+uq7Nw4Y5eoY\nAdwdIs0I4E4R2Y23jDa4hHK34O2pgKdYWru/4O1NjXRlXA74FEA/YLeI7ASGAY8H1F9WXyZT9hgq\nTQ5GFWNm8w2jmiEi9wHvqeqiqm6LYVwIplgMwzCMiGJLYYZhGEZEMcViGIZhRBRTLIZhGEZEMcVi\nGIZhRBRTLIZhGEZEMcViGIZhRBRTLIZhGEZE+Rflz/wq8/SiYgAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7731ef0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The Oil circulation rate is 1.79e-03 kmol/s\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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bQF0RaYfzTMVI4IlUBmWMMSY9eXlOY4mqHikiY4H6qnqPiHyiqj2qJ8TKVdSm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\nvcBv3PW7gZsiznk6MB/nrvwOYLi7PRt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+ "text": [
+ "<matplotlib.figure.Figure at 0xa623ef0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The Steam circulation rate is 6.81e-04 kmol/s\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.3: Page 292"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.3\n",
+ "# Page: 292\n",
+ "\n",
+ "print'Illustration 8.3 - Page: 292\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "# Since tower is a tray device:\n",
+ "# Following changes in notation is made:\n",
+ "# L1 to LNp\n",
+ "# L2 to L0\n",
+ "# X1 to XNp\n",
+ "# X2 to X0\n",
+ "# G1 to GNpPlus1\n",
+ "# G2 to G1\n",
+ "# Y1 to YNpPlus1\n",
+ "# Y2 to Y1\n",
+ "# x1 to xNp\n",
+ "# x2 to x0\n",
+ "# y1 to yNpPlus1\n",
+ "# y2 to y1\n",
+ "# From Illustration 8.2:\n",
+ "yNpPlus1 = 0.02;\n",
+ "Y1 = 0.00102;\n",
+ "y1 = Y1/(1+Y1);\n",
+ "GNpPlus1 = 0.01075;# [kmol/s]\n",
+ "x0 = 0.005;\n",
+ "m = 0.125;# [m = y_star/x]\n",
+ "Ls = 1.787*10**(-3);# [kmol/s]\n",
+ "Gs = 0.01051;# [kmol/s]\n",
+ "XNp = 0.1190;\n",
+ "LNp = Ls*(1+XNp);# [kmol/s]\n",
+ "ANp = LNp/(m*GNpPlus1);\n",
+ "X0 = x0/(1-x0);\n",
+ "L0 = Ls*(1+X0);# [kmol/s]\n",
+ "G1 = Gs*(1+Y1);# [kmol/s]\n",
+ "A1 = L0/(m*G1);\n",
+ "A = (ANp*A1)**0.5;\n",
+ "# From Eqn. 5.55:\n",
+ "Np = (math.log((yNpPlus1-(m*x0))/(y1-(m*x0))*(1-(1/A))+(1/A)))/math.log(A);\n",
+ "print\"Absorber\\n\"\n",
+ "print\"From Analytical Method, no. of theoretical trays required is \\n\",round(Np,4)\n",
+ "# From Fig. 8.13 (Pg292):\n",
+ "Np = 7.6;\n",
+ "print\"From Graphical Method, no. of theoretical trays required is \\n\",Np\n",
+ "\n",
+ "# Stripper\n",
+ "SNp = 1/ANp;\n",
+ "S1 = 1/A1;\n",
+ "# Due to relative nonconstancy of the stripping factor,graphical method should be used.\n",
+ "print\"Stripper\\n\"\n",
+ "# From Fig. 8.11 (Pg 289):\n",
+ "Np = 6.7;\n",
+ "print\"From Graphical Method, no. of theoretical trays required is \\n\",Np\n",
+ "# From Fig. 5.16 (Pg 129):\n",
+ "Np = 6.0;\n",
+ "print\"From Fig. 5.16, no. of theoretical trays required is \\n\",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.3 - Page: 292\n",
+ "\n",
+ "\n",
+ "Absorber\n",
+ "\n",
+ "From Analytical Method, no. of theoretical trays required is \n",
+ "7.7085\n",
+ "From Graphical Method, no. of theoretical trays required is \n",
+ "7.6\n",
+ "Stripper\n",
+ "\n",
+ "From Graphical Method, no. of theoretical trays required is \n",
+ "6.7\n",
+ "From Fig. 5.16, no. of theoretical trays required is \n",
+ "6.0\n"
+ ]
+ }
+ ],
+ "prompt_number": 102
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.4: Page 295"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.4\n",
+ "# Page: 295\n",
+ "\n",
+ "print'Illustration 8.4 - Page: 295\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "#****Data****#\n",
+ "# a = CH4 b = C5H12\n",
+ "Tempg = 27.0;# [OC]\n",
+ "Tempo = 0.0;# [base temp,OC]\n",
+ "Templ = 35.0;# [OC]\n",
+ "xa = 0.75;# [mole fraction of CH4 in gas]\n",
+ "xb = 0.25;# [mole fraction of C5H12 in gas]\n",
+ "M_Paraffin = 200.0;# [kg/kmol]\n",
+ "hb = 1.884;# [kJ/kg K]\n",
+ "#********#\n",
+ "\n",
+ "Ha = 35.59;# [kJ/kmol K]\n",
+ "Hbv = 119.75;# [kJ/kmol K]\n",
+ "Hbl = 117.53;# [kJ/kmol K]\n",
+ "Lb = 27820;# [kJ/kmol]\n",
+ "# M = [Temp (OC) m]\n",
+ "M = numpy.array([[20 ,0.575],[25 ,0.69],[30 ,0.81],[35, 0.95],[40, 1.10],[43, 1.25]]);\n",
+ "# Basis: Unit time\n",
+ "GNpPlus1 = 1.0;# [kmol]\n",
+ "yNpPlus1 = 0.25;# [kmol]\n",
+ "HgNpPlus1 = ((1-yNpPlus1)*Ha*(Tempg-Tempo))+(yNpPlus1*(Hbv*(Tempg-Tempo)+Lb));# [kJ/kmol]\n",
+ "L0 = 2.0;# [kmol]\n",
+ "x0 = 0.0;# [kmol]\n",
+ "HL0 = ((1-x0)*hb*M_Paraffin*(Templ-Tempo))+(x0*hb*(Templ-Tempo));# [kJ/kmol]\n",
+ "C5H12_absorbed = 0.98*xb;# [kmol]\n",
+ "C5H12_remained = xb-C5H12_absorbed;\n",
+ "G1 = xa+C5H12_remained;# [kmol]\n",
+ "y1 = C5H12_remained/G1;# [kmol]\n",
+ "LNp = L0+C5H12_absorbed;# [kmol]\n",
+ "xNp = C5H12_absorbed/LNp;# [kmol]\n",
+ "# Assume:\n",
+ "Temp1 = 35.6;# [OC]\n",
+ "Hg1 = ((1-y1)*Ha*(Temp1-Tempo))+(y1*(Hbv*(Temp1-Tempo)+Lb));# [kJ/kmol]\n",
+ "\n",
+ "# Eqn. 8.11:\n",
+ "Qt = 0;\n",
+ "def f30(HlNp):\n",
+ " return ((L0*HL0)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(G1*Hg1)+Qt)\n",
+ "HlNp = fsolve(f30,2);\n",
+ "\n",
+ "def f31(TempNp):\n",
+ " return HlNp-(((1-x0)*hb*M_Paraffin*(TempNp-Tempo))+(x0*hb*(TempNp-Tempo)))\n",
+ "TempNp = fsolve(f31,35.6);\n",
+ "# At Temp = TempNp:\n",
+ "mNp = 1.21;\n",
+ "yNp = mNp*xNp;# [kmol]\n",
+ "GNp = G1/(1-yNp);# [kmol]\n",
+ "HgNp = ((1-yNp)*Ha*(TempNp-Tempo))+(yNp*(Hbv*(TempNp-Tempo)+Lb));# [kJ/kmol]\n",
+ "# Eqn. 8.13 with n = Np-1\n",
+ "def f32(LNpMinus1):\n",
+ " return LNpMinus1+GNpPlus1-(LNp+GNp)\n",
+ "LNpMinus1 = fsolve(f32,2);# [kmol]\n",
+ "\n",
+ "# Eqn. 8.14 with n = Np-1\n",
+ "def f33(xNpMinus1):\n",
+ " return ((LNpMinus1*xNpMinus1)+(GNpPlus1*yNpPlus1))-((LNp*xNp)+(GNp*yNp))\n",
+ "xNpMinus1 = fsolve(f33,0);# [kmol]\n",
+ "\n",
+ "# Eqn. 8.15 with n = Np-1\n",
+ "def f34(HlNpMinus1):\n",
+ " return ((LNpMinus1*HlNpMinus1)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(GNp*HgNp))\n",
+ "HlNpMinus1 = fsolve(f34,0);# [kJ/kmol]\n",
+ "def f35(TempNpMinus1):\n",
+ " return HlNpMinus1-(((1-xNpMinus1)*hb*M_Paraffin*(TempNpMinus1-Tempo))+(xNpMinus1*hb*(TempNpMinus1-Tempo)))\n",
+ "TempNpMinus1 = fsolve(f35,42);# [OC]\n",
+ "\n",
+ "# The computation are continued upward through the tower in this manner until the gas composition falls atleast to 0.00662.\n",
+ "# Results = [Tray No.(n) Tn(OC) xn yn]\n",
+ "Results = numpy.array([[4.0 ,42.3 ,0.1091 ,0.1320],[3 ,39.0, 0.0521 ,0.0568],[2 ,36.8 ,0.0184 ,0.01875],[1 ,35.5, 0.00463 ,0.00450]]);\n",
+ "\n",
+ "plt.plot(Results[:,0],Results[:,3]);\n",
+ "plt.grid('on');\n",
+ "xlabel('Tray Number');\n",
+ "ylabel('mole fraction of C5H12 in gas');\n",
+ "plt.show();\n",
+ "plt.plot(Results[:,0],Results[:,1]);\n",
+ "plt.grid('on');\n",
+ "xlabel('Tray Number');\n",
+ "ylabel('Temperature(OC)');\n",
+ "plt.show();\n",
+ "\n",
+ "# For the required y1\n",
+ "Np = 3.75;\n",
+ "print\"The No. of trays will be \",Np"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.4 - Page: 295\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xa5b6320>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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lOzz/PEyYAAMHwpAhwapikyfDW2/BtGlw6KHtbwiSkFuUlF+6ZT2/YmnMoMy4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\nEQH+H2FRyOkvfEciAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x785c198>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The No. of trays will be 3.75\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.5: Page 299"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.5\n",
+ "# Page: 299\n",
+ "\n",
+ "print'Illustration 8.5 - Page: 299\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "#****Data****#\n",
+ "# a = NH3 b = H2 c = N2 w = water\n",
+ "P = 2.0;# [bars]\n",
+ "Temp = 30.0;# [OC]\n",
+ "L = 6.38;# [kg/s]\n",
+ "W = 0.53;# [weir length,m]\n",
+ "pitch = 12.5/1000;# [m]\n",
+ "D = 0.75;# [Tower diameter,m]\n",
+ "hW = 0.060;# [weir height,m]\n",
+ "t = 0.5;# [tray spacing,m]\n",
+ "#*******#\n",
+ "\n",
+ "# From Geometry of Tray Arrangement:\n",
+ "At = 0.4418;# [Tower Cross section,square m]\n",
+ "Ad = 0.0403;# [Downspout Cross section,square m]\n",
+ "An = At-Ad;# [square m]\n",
+ "Ao = 0.0393;# [perforation area,square m]\n",
+ "Z = 0.5307;# [distance between downspouts,square m]\n",
+ "z = (D+W)/2.0;# [average flow width,m]\n",
+ "h1 = 0.04;# [weir crest,m]\n",
+ "# From Eqn. 6.34\n",
+ "Weff = W*(math.sqrt(((D/W)**2)-((((D/W)**2-1)**0.5)+((2*h1/D)*(D/W)))**2));# [m]\n",
+ "q = Weff*(1.839*h1**(3/2));#[cubic m/s]\n",
+ "# This is a recommended rate because it produces the liquid depth on the tray to 10 cm.\n",
+ "Density_L = 996;# [kg/s]\n",
+ "Mw = 18.02;# [kg/kmol]\n",
+ "L1 = 6.38/Mw;# [kmol/s]\n",
+ "Ma = 17.03;# [kg/kmol]\n",
+ "Mb = 28.02;# [kg/kmol]\n",
+ "Mc = 2.02;# [kg/kmol]\n",
+ "MavG = (0.03*Ma)+(0.97*(1/4)*Mb)+(0.97*(3/4)*Mc);# [kg/kmol]\n",
+ "Density_G = (MavG/22.41)*(P/0.986)*(273/(273+Temp));# [kg/cubic m]\n",
+ "G = 0.893;# [kg/s]\n",
+ "sigma = 68*10**(-3);# [N/m]\n",
+ "abcissa = (L/G)*(Density_G/Density_L)**0.5;\n",
+ "# From Table 6.2 (Pg169):\n",
+ "alpha = 0.04893;\n",
+ "beeta = 0.0302;\n",
+ "# From Eqn. 6.30\n",
+ "Cf = ((alpha*math.log10(1.0/abcissa))+beeta)*(sigma/0.02)**0.2;\n",
+ "# From Eqn. 6.29\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1.0/2);# [m/s]\n",
+ "# 80% of flooding value:\n",
+ "V = 0.8*Vf;# [m/s]\n",
+ "G = 0.8*G;# [kg/s]\n",
+ "G1 = G/MavG;# [kmol/s]\n",
+ "Vo = V*An/Ao;# [m/s]\n",
+ "l = 0.002;# [m]\n",
+ "Do = 0.00475;# [m]\n",
+ "# From Eqn. 6.37\n",
+ "Co = 1.09*(Do/l)**0.25;\n",
+ "viscosity_G = 1.13*10**(-5);# [kg/m.s]\n",
+ "Reo = Do*Vo*Density_G/viscosity_G;\n",
+ "# At Reynold's No. = Reo\n",
+ "fr = 0.0082;\n",
+ "g = 9.81;# [m/s^2]\n",
+ "# From Eqn. 6.36\n",
+ "def f36(hD):\n",
+ " return (2*hD*g*Density_L/(Vo**2*Density_G))-(Co*(0.40*(1.25-(Ao/An))+(4*l*fr/Do)+(1-(Ao/An))**2))\n",
+ "hD = fsolve(f36,1);\n",
+ "# From Eqn. 6.31;\n",
+ "Aa = (Ao/0.907)*(pitch/Do)**2;# [square m]\n",
+ "Va = V*An/Aa;# [m/s]\n",
+ "# From Eqn. 6.38\n",
+ "hL = 6.10*10**(-3)+(0.725*hW)-(0.238*hW*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "# From Eqn. 6.42\n",
+ "hR = 6*sigma/(Density_L*Do*g);# m\n",
+ "# From Eqn. 6.35\n",
+ "hG = hD+hL+hR;# [m]\n",
+ "Al = 0.025*W;# [square m]\n",
+ "Ada = min(Al,Ad);\n",
+ "# From Eqn. 6.43\n",
+ "h2 = (3/(2*g))*(q/Ada)**2;# [m]\n",
+ "# From Eqn.6.44\n",
+ "h3 = hG+h2;\n",
+ "# since hW+h1+h3 is essentially equal to t/2, flooding will not occur\n",
+ "abcissa = (L/G)*(Density_G/Density_L)**0.5;\n",
+ "V_by_Vf = V/Vf;\n",
+ "# From Fig.6.17, V/Vf = 0.8 & abcissa = 0.239\n",
+ "E = 0.009;\n",
+ "\n",
+ "# At the prevailing conditions:\n",
+ "Dg = 2.296*10**(-5);# [square m/s]\n",
+ "viscosity_G = 1.122*10**(-5);# [kg/m.s]\n",
+ "ScG = viscosity_G/(Density_G*Dg)\n",
+ "Dl = 2.421*10**(-9);# [square m/s]\n",
+ "\n",
+ "# From Henry's Law:\n",
+ "m = 0.850;\n",
+ "A = L1/(m*G1);\n",
+ "\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/(ScG**0.5);\n",
+ "# From Eqn. 6.64:\n",
+ "thetha_L = hL*z*Z/q;# [s]\n",
+ "# From Eqn. 6.62:\n",
+ "NtL = 40000*(Dl**0.5)*((0.213*Va*Density_G**0.5)+0.15)*thetha_L;\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1/((1/NtG)+(1/(A*NtL)));\n",
+ "# From Eqn. 6.51:\n",
+ "EoG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.63:\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;# [square m/s]\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thetha_L);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2.0)*((1+(4*m*G1*EoG/(L1*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EoG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+((exp(eta)-1)/(eta*(1+(eta/(eta+Pe))))));\n",
+ "# From Eqn. 6.60:\n",
+ "EMGE = EMG/((1+(EMG*(E/(1-E)))));\n",
+ "# From Eqn. 8.16:\n",
+ "EO = math.log(1+EMGE*((1.0/A)-1))/math.log(1.0/A);\n",
+ "Np = 14*EO;\n",
+ "yNpPlus1 = 0.03;\n",
+ "x0 = 0;\n",
+ "# From Eqn. 5.54(a):\n",
+ "def f37(y1):\n",
+ " return ((yNpPlus1-y1)/(yNpPlus1-m*x0))-(((A**(Np+1))-A)/((A**(Np+1))-1))\n",
+ "y1 = fsolve(f37,0.03);\n",
+ "print\"Mole Fraction Of NH3 in effluent is \",round(y1,4)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.5 - Page: 299\n",
+ "\n",
+ "\n",
+ "Mole Fraction Of NH3 in effluent is 0.0211\n"
+ ]
+ }
+ ],
+ "prompt_number": 159
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.6: Page 304"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.6\n",
+ "# Page: 304\n",
+ "\n",
+ "print'Illustration 8.6 - Page: 304\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#****Data****# \n",
+ "# Gas:\n",
+ "# In:\n",
+ "y_prime1 = 0.02;\n",
+ "Y_prime1 = 0.0204;# [mol/mol dry gas]\n",
+ "# Out:\n",
+ "y_prime2 = 0.00102;\n",
+ "Y_prime2 = 0.00102;# [mol/mol dry gas]\n",
+ "# Non absorbed gas:\n",
+ "MavG = 11;# [kg/kmol]\n",
+ "G = 0.01051;# [kmol/s nonbenzene]\n",
+ "Gm = 0.01075;# [kmol/s]\n",
+ "T = 26;# [OC]\n",
+ "viscosity_G = 10**(-5);# [kg/m.s]\n",
+ "DaG = 1.30*10**(-5);# [square m/s]\n",
+ "\n",
+ "# Liquid:\n",
+ "# In:\n",
+ "x_prime2 = 0.005;\n",
+ "X_prime2 = 0.00503;# [mol benzene/mol oil]\n",
+ "# Out:\n",
+ "x_prime1 = 0.1063;\n",
+ "X_prime1 = 0.1190;# [mol benzene/mol oil]\n",
+ "# Benzene free oil:\n",
+ "MavL = 260.0;# [kg/kmol]\n",
+ "viscosity_L = 2*10**(-3);# [kg/kmol]\n",
+ "Density_L = 840;# [kg/cubic cm]\n",
+ "L = 1.787*10**(-3);# [kmol/s]\n",
+ "DaL = 4.77*10**(-10);# [square m/s]\n",
+ "sigma = 0.03;# [N/square m]\n",
+ "m = 0.1250;\n",
+ "#*******#\n",
+ "\n",
+ "A = 0.47**2*math.pi/4;# [square m]\n",
+ "# At the bottom:\n",
+ "L_prime1 = ((L*MavL)+(X_prime1*L*78))/A;# [kg/square m.s]\n",
+ "# At the top\n",
+ "L_prime2 = ((L*MavL)+(X_prime2*L*78))/A;# [kg/square m.s]\n",
+ "L_primeav = (L_prime1+L_prime2)/2;# [kg/square m.s]\n",
+ "# At the bottom\n",
+ "G_prime1 = ((G*MavG)+(Y_prime1*G*78))/A;# [kg/square m.s]\n",
+ "# At the top\n",
+ "G_prime2 = ((G*MavG)+(Y_prime2*G*78))/A;# [kg/square m.s]\n",
+ "G_primeav = (G_prime1+G_prime2)/2;# [kg/square m.s]\n",
+ "\n",
+ "# From Illustration 6.6:\n",
+ "Fga = 0.0719;# [kmol/cubic cm.s]\n",
+ "Fla = 0.01377;# [kmol/cubic cm.s]\n",
+ "# Operating Line:\n",
+ "X_prime = numpy.array([0.00503 ,0.02 ,0.04 ,0.06 ,0.08 ,0.10 ,0.1190]);\n",
+ "x_prime = numpy.zeros(7);\n",
+ "Y_prime = numpy.zeros(7);\n",
+ "y_prime = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " x_prime[i] = X_prime[i]/(1+X_prime[i]);\n",
+ " def f38(Y_prime):\n",
+ " return (G*(Y_prime1-Y_prime))-(L*(X_prime1-X_prime[i]))\n",
+ " Y_prime[i] = fsolve(f38,Y_prime1);\n",
+ " y_prime[i] = (Y_prime[i])/(1+Y_prime[i]);\n",
+ "\n",
+ "def f39(x):\n",
+ " return m*x\n",
+ "x = numpy.arange(0,0.14,0.01);\n",
+ "\n",
+ "# Interface compositions are determined graphically and according to Eqn. 8.21:\n",
+ "yi = [0.000784, 0.00285, 0.00562 ,0.00830 ,0.01090 ,0.01337 ,0.01580];\n",
+ "ylog = zeros(7);\n",
+ "y_by_yDiffyi = zeros(7);\n",
+ "for i in range(0,7):\n",
+ " ylog[i] = math.log10(yi[i]);\n",
+ " y_by_yDiffyi[i] = y_prime[i]/(y_prime[i]-yi[i]);\n",
+ "\n",
+ "plt.plot(x_prime,y_prime,label=\"Operating Line\")\n",
+ "plt.plot(x,f39(x),label=\"Equilibrium Line\")\n",
+ "plt.plot(x_prime,yi,label=\"Interface Composition\");\n",
+ "plt.legend(loc='lower right');\n",
+ "plt.grid('on');\n",
+ "xlabel(\"mole fraction of benzene in liquid\");\n",
+ "ylabel(\"mole fraction of benzene in gas\");\n",
+ "plt.show()\n",
+ "plt.plot(ylog,y_by_yDiffyi);\n",
+ "plt.grid();\n",
+ "xlabel(\"log y\");\n",
+ "ylabel(\"y/(y-yi)\");\n",
+ "title(\"Graphical Integration Curve\");\n",
+ "plt.show()\n",
+ "# Area under the curve:\n",
+ "Ac = 6.556;\n",
+ "# Eqn. 8.28:\n",
+ "NtG = (2.303*Ac)+1.152*(math.log10((1-y_prime2)/(1-y_prime1)));\n",
+ "Gav = (Gm+(G/(1-Y_prime2)))/(2*A);# [kmol/square m.s]\n",
+ "HtG = Gav/Fga;# [m]\n",
+ "Z = HtG*NtG;# [m]\n",
+ "print\"The depth of packing required is \",round(Z,3),\" m\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.6 - Page: 304\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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N8afcdpvPmra+FEtWJWD5WNw6LAgQ6k77RKxhCV2++QbatIHPP4fWrf3Qwa+/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\n2QAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa5b66a0>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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xx+Hll+GXX2DrreHww0PhKPTn6FwGs/u5+w5Ad6At8KaZ3W9mDZ3vyQHMrKeZ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\nERGpkwqFiIjUSYVCRETqpEIhIiJ1+n+KRsAkmp26rwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa5852b0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The depth of packing required is 12.881 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.7: Page 312"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.7\n",
+ "# Page: 312\n",
+ "\n",
+ "print'Illustration 8.7 - Page: 312\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "# Fom Illustration 8.6:\n",
+ "y1 = 0.02;\n",
+ "y2 = 0.00102;\n",
+ "m = 0.125;\n",
+ "x2 = 0.005;\n",
+ "x1 = 0.1063;\n",
+ "\n",
+ "# Number of transfer units:\n",
+ "# Method a:\n",
+ "y1_star = m*x1;\n",
+ "y2_star = m*x2;\n",
+ "yDiffy_star1 = y1-y1_star;\n",
+ "yDiffy_star2 = y2-y2_star;\n",
+ "yDiffy_starm = (yDiffy_star1-yDiffy_star2)/math.log(yDiffy_star1/yDiffy_star2);\n",
+ "# From Eqn. 8.48:\n",
+ "NtoG = (y1-y2)/yDiffy_starm;\n",
+ "print\"NtoG according to Eqn. 8.48:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Mehod b:\n",
+ "# From Illustration 8.3:\n",
+ "A = 1.424;\n",
+ "NtoG = (math.log((((y1-(m*x2))/(y2-(m*x2)))*(1-(1/A)))+(1/A)))/(1-(1/A));\n",
+ "print\"NtoG according to Eqn. 8.50:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Method c:\n",
+ "# Operating Line:\n",
+ "# From Illustration 8.3:\n",
+ "X_prime = [0.00503, 0.02, 0.04 ,0.06 ,0.08 ,0.10 ,0.1190];\n",
+ "x_prime = [0.00502 ,0.01961, 0.0385, 0.0566, 0.0741, 0.0909 ,0.1063]\n",
+ "Y_prime = [0.00102 ,0.00357 ,0.00697 ,0.01036 ,0.01376 ,0.01714 ,0.0204];\n",
+ "y_prime = [0.00102 ,0.00356, 0.00692 ,0.01025 ,0.01356 ,0.01685, 0.0200];\n",
+ "def f2(x):\n",
+ " return m*x\n",
+ "x = numpy.arange(0,0.14,0.01);\n",
+ "\n",
+ "plt.plot(x_prime,y_prime,label=\"Operating Line\")\n",
+ "plt.plot(x,f2(x),label=\"Equilibrium Line\");\n",
+ "plt.legend(loc='upper right');\n",
+ "plt.grid('on');\n",
+ "xlabel(\"mole fraction of benzene in liquid\");\n",
+ "ylabel(\"mole fraction of benzene in gas\");\n",
+ "plt.show()\n",
+ "# From graph:\n",
+ "NtoG = 8.7;\n",
+ "print\"NtoG from graph:\",round(NtoG,2),\" \\n\",\n",
+ "\n",
+ "# Method d:\n",
+ "# from Fig 8.10:\n",
+ "Y_star = [0.000625, 0.00245, 0.00483, 0.00712 ,0.00935 ,0.01149, 0.01347];\n",
+ "ordinate = numpy.zeros(7);\n",
+ "for i in range(0,7):\n",
+ " ordinate[i] = 1/(Y_prime[i]-Y_star[i]);\n",
+ "\n",
+ "plt.plot(Y_prime,ordinate);\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Y\");\n",
+ "ylabel(\"1/(Y-Y*)\");\n",
+ "plt.title(\"Graphical Integration\");\n",
+ "plt.show()\n",
+ "# Area under the curve:\n",
+ "Ac = 8.63;\n",
+ "# From Eqn. 8.36:\n",
+ "NtoG = Ac+(1.0/2)*math.log((1+y2)/(1+y1));\n",
+ "print\"NtoG from graphical integration:\",round(NtoG,2),\"\\n\"\n",
+ "\n",
+ "# Height of transfer units:\n",
+ "NtoG = 9.16;\n",
+ "# From Illustration 6.6:\n",
+ "Fga = 0.0719;# [kmol/cubic m.s]\n",
+ "Fla = 0.01377;# [kmol/cubic m.s]\n",
+ "Gav = 0.0609;# [kmol/square m.s]\n",
+ "L = 1.787*10**(-3);# [kmol/s]\n",
+ "X1 = x1/(1-x1);\n",
+ "X2 = x2/(1-x2);\n",
+ "Area = 0.1746;# [square m]\n",
+ "Lav = L*((1+X1)+(1+X2))/(2*Area);\n",
+ "# From Eqn. 8.24:\n",
+ "Htg = Gav/Fga;# [m]\n",
+ "# From Eqn. 8.31:\n",
+ "Htl = Lav/Fla;# [m]\n",
+ "# since Solutions are dilute:\n",
+ "HtoG = Htg+Htl/A;# [m]\n",
+ "print\"HtoG: \",round(HtoG,2),\" m\\n\",\n",
+ "Z = HtoG*NtoG;# [m]\n",
+ "print\"The depth of packing recquired is \",round(Z,2),\" m\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.7 - Page: 312\n",
+ "\n",
+ "\n",
+ "NtoG according to Eqn. 8.48: 8.51 \n",
+ "\n",
+ "NtoG according to Eqn. 8.50: 9.16 \n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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svmfAYlESiWj6WGqr6kMhxw+LSI9wGzaMeOLnn+Hyy6FyZSfrY1Uvqe4SnMz9\nmQz/eDhjV4zl+hbX83X/r8OaNmwkDl7iWOa564SVc7cewLxICysLxMuYa1nXuWIFnHmmk5f+zTfD\nNyrx3p+RiEUJh3jvz0Sk0CcWEfmNQ2mCbwNedvfL4eROuSOy0gwj9rz6Ktx2Gzz7LHTvHms1scXy\nohhesbXCDKMAsrMdB/3MmY4/pVmzWCuKHRaLUnaIpo/FMMoUv/4KV14JOTmwdKmT8bGsYrEoRmnw\n4mMxIkS8jLmWJZ1ffunEpzRrBu++GxmjEg/9mZaRxhn3nBG1WJRwiIf+hPjR6QdF+Vgaqup30RRj\nGLFk1izo1w+eeAKuuSbWamJDaCxKjxN6MPz64RaLYpSYQn0sIrJcVc8QkQ9U9R9R1hU25mMxvJKT\n42R6nDjR8amccUasFUUfi0UxIDo+liQRuRdoIiIDcYIjc1FVfSLcxg0j1uze7TydZGY6/pRatWKt\nKLpYLIoRCYrysVwBZANJQGV3OyZk3wiTeBlzTVSdGzY4Sbnq1YMFC6JnVILQn15iUYKg0wumM3gU\n+sSiquuBYSKyRlXfiaImw4g477zj5KR/5BFn2fuygsWiGNHAy1ph1XAWoTzHPbUIZxHKXZGVFh7m\nYzEKQhWGD4dRo2D6dGjbNtaKooPFohheiGYcy3icdMSX4/hZegITgMvCbdwwosnevc5TyvffO/6U\n5ORYK4oOFotiRBsvcSyNVPV+Vd3k5mZJBRpFWFeZIF7GXBNB53ffOU8nRx8NixfH1qhEqz/DzYuS\nCO97kIgXnX7gxbDsF5Gzcw9EpD2wL3KSDMNfPvgA2rRxfCnjx0PFirFWFFk27NhAyvQUuk7pSrem\n3Vj777Wk/C2FcmLx0EZ08OJjaQ68BOSu6ZoJXKuqqyOsLSzMx2KowtNPOz6VV1+Fv/891ooiS24s\nyox1M/JiUY6ucHSsZRlxRNR8LKq6CjhVRKq6x4F22hsGwP79cOONsGYNfPqpk5c+UQmNRenTog8b\nBmywWBQjpnh+NlbVXWZU/CVexlzjTecPP8A558Aff8DHHwfPqPjVn6GxKL/s+4XVN67m8Y6P+2ZU\n4u19DzrxotMPbNDVSCg+/tgJeuzeHaZMgUoJuCrJweyDjFk+hiajmrAsYxlLei/hxa4vUrdK3VhL\nMwzA8rEYCcQLL8DQoTBpEnTqFGs1/hMai5JcOZlh5w+jVXKrWMsyEoio5mMRkXZAg5Dyqqovhdu4\nYfjBgQNY3IN6AAAeOklEQVRwyy2wZAl89BGcdFKsFflPbixKjuYw6qJRdDwxmEvYGwZ4GAoTkcnA\nf4F2QEt3s5BdH4iXMdcg6/zpJ/jHP5y///3vorgwKiXpz9BYlDvb3kla3zQuaHRBVIxKkN/3UExn\n8PDyxHIGcIqNKxlBY9ky6NYN+vSB//wHPvww1or8IzcvykebP2LouUPp06KP5UUx4gYvcSzTgVtV\nNSM6kvzBfCyJzUsvwaBBMGYMXHpprNX4h8WiGLEkmj6WmsBaEVkK/OGeU1XtWlxFEekEPIWz9P6L\nqjq8gDLPABfhRPP3UtWV7vnxwP8C21W1WUj5Y4FpwAlAOpCiqjs9vA4jAcjKgjvvhDlzYNEiOOWU\nWCvyB4tFMRIJL9ONU4FLgUeAEcBIdysSEUkCRgOdgFOAK0Wkab4ynYHGqnoS0Bd4LuTyBLdufgYD\n76tqE2CBexyXxMuYa1B0/vILXHghrF/vLCKZ36gERWdxhOoMjUXZsW+H77Eo4RCP/Rlk4kWnHxRr\nWFR1EbAeqIKT4Gutqi72cO9WwEZVTVfVg8BU4JJ8ZboCk9x2PgeqiUht93gJzvIx+cmr4/5NoIEQ\nozCWL4eWLZ1tzhyoXj3WisKjoFiUsV3HWiyKkRAUOxQmIik4s8JyjcloEblTVacXUzUZ2BJy/ANw\nlocyycBPRdy3lqpuc/e3AXGbTLZDhw6xluCJWOscPx7uvhuef95x1hdGrHV6QVX5uebP/M9z/0Ny\n5WRm9pgZ2FiUeOhPMJ1BxIuP5T7gTFXdDiAiNXGGoIozLF495/kdRZ497qqqImIe+gTljz+c+JTF\ni50ZX02bFl8nyMzfNJ8hC4aQnZNtsShGQuPFsAjwc8jxDv5sDApiK1Av5LgezhNJUWXquueKYpuI\n1FbVn0SkDrC9sIK9evWigbtQVLVq1WjevHner4bc8c5YHq9atYrbbrstMHoKOw4dG45W+6+9toj7\n74dTTunA0qWwYsUitm2Lz/5My0ij3+h+/PTbTzzR9wlq/lyTclvKsXjL4kDoK+w4qP2Z/zgW/5+J\n0p+5++np6fiKqha54QyDzQN6Ab2B94DHPdQ7AvgWJ2K/ArAKaJqvTGfgHXe/NfBZvusNgC/ynXsc\nuNvdHwwMK6R9DToLFy6MtQRPRFvnBx+o1q6tOmyYak6O93pB68+vf/laL3/tcq0zoo4+t+w5PZB1\nQFWDp7MwTKe/xINO93uzWLtQ3OYljkVw0hC3xxmmWqKqs7wYLRG5iEPTjcep6mMi0s/91n/BLZM7\nc2wv0FtVV7jnpwDnAn/BeSoZqqoT3OnGrwH1KWK6scWxxB+qMGIEPPEETJ4M550Xa0Wlw2JRjHjF\nrzgWW4TSCAR79sB11zn56F9/HerXj7WikpM/FmVw+8GBmDZsGF7xy7AUOt1YRD52//4mInvybbvD\nbdiIn3ntkda5fr2z1H316o6TvrRGJVb9WdJYFHvf/cV0Bo9Cnfeq2s79e0z05BhljZkznUyPjz3m\nrPkVT2TlZDF+5XgeXPwgreu2ZknvJZxc4+RYyzKMmOPFx/KyqvYs7lzQsKGwYJOVBffdB1OnOkNf\nLVvGWpF3VJUZ62Zw7wf3Wl4UI6GI5lph/5Ov4SNwVjw2jFLx889w5ZUgAmlpUKNGrBV5Z8GmBQxe\nMNhiUQyjCIrysdwjInuAZqH+FZwZWrOjpjCBiZcxVz91LlvmPJ2ceSa8956/RiWS/bk8YzkdX+7I\njW/fyKA2g8LKi1IW3/dIYjqDR1E+lkeBR0XkMVUdEkVNRoLy4otwzz1OCuF//jPWarxheVEMo+R4\n8bFcBnyQGysiItWADqr6RhT0lRrzsQSH33+H/v3hk08cZ/3JceDfztiTwQOLHrBYFKNMEfHpxiHc\nHxqA6O6nhtuwUTbYvBnOPht273aWug+6Ucncn8ng+YNp9lwzqlasyoYBGxhy9hAzKoZRArwYloKs\nV5LfQsoi8TLmWlqd8+dDq1ZwxRUwbRocE+GJ6+H0ZzTzoiT6+x5tTGfw8DIrbLmIPAH8H46RuRlY\nHlFVRlyjCsOHw9NPw5Qp8Pe/x1pR4VgsimH4jxcfyzHAf4DclZveBx5W1b0R1hYW5mOJDbt3Q69e\nsHUrzJgBdQOat8piUQzjz9haYcVghiX6rFvnzPbq0MF5WjnyyFgrKpjQWJRh5w+zWBTDcIma815E\njhORESLyjogsdLcPwm3YiJ8xVy86p0+Hc845lOkxFkalOJ1+xqKEQyK970HAdAYPLz6WV4BpwMVA\nP5y8LD8XVcEoO2RlwZAhzrIs770HZwRwTQaLRTGM6OLFx7JCVU8XkTWqeqp7Lk1VA726kw2FRZ7t\n250ZX0cc4Tjp//KXWCs6HItFMYySEc04lgPu359E5GIROR2oHm7DRnzz+efO0ixt28K77wbLqFgs\nimHEFi+G5WE32v4OYBDwInB7RFWVEeJlzDVUp6qzJEuXLjBqFDz8MCQFJKrpvfnvRS0WJRzi8X0P\nMqYzeBTpYxGRJKCJqs4BdgIdoiHKCCb798PNNzsR9B99BE2axFqRQ1ZOFhNWTuCemfdwbodzLRbF\nMGKMFx/LMlU9M0p6fMN8LP6Sng7dusFJJzmLSUY6it4LobEodavU5bHzHrNYFMMIg6jFsYjIk0B5\nnJlhe3Gi71VVV4TbeCQxw+If8+bBv/7lTCW+7TYnj0qsyY1FydEchp03jPNPPN9iUQwjTKLpvG8B\n/A14EBgJjHD/GmES9DHXnBx45BG48spFTJsGt98ee6MSGotyZ9s7WXbDMjo2cgIcg96fuZhOfzGd\nwaNQH4uI3KqqTwP3qepHUdRkBIBdu+Daa2HbNifg8dxzY6snNxbl4y0fM/ScoVzX4jqLRTGMgFLo\nUJiIrFbV00Rkpaq2iLKusLGhsNLz5Zdw2WXQsSM8+SRUqBA7LbmxKDPXz8yLRalUvlLsBBlGAhON\nnPdrReQbIFlEvsh3TXODJY3EYtIkGDQIRo50/CqxInN/JsM/Hs7YFWO5vsX1fN3/68BNGzYMo2AK\n9bGo6pXA2cBGnOVcuoRsXaOiLsEJ0pjrvn3Qpw8MGwYLFx5uVKKpMzQvyq/7f2X1jasZ3nG4J6MS\npP4sCtPpL6YzeBQZx6KqPwH2ZJLgbNgA3btDs2awbFlsphJbXhTDSBxs2fwyzrRpTj76hx+Gvn2j\nP+srfyzKsPOGcWZy3IVNGUZCEA0fi5HA/PEH3HGHs87X3Llw+unR1xAaizL6otEWi2IYCYKXOBYA\nRMSm4vhMrMZcv/sO2reHjAxYvrx4o+K3zqJiUcIhXsawTae/mM7g4SXRV1sRWQt87R43F5FnI67M\niAizZ0Pr1nD11U7q4GrVotf2hh0bSJmeQtepXenWtBtr/72WlL+lUE48/74xDCMO8LKky1KgO/Bm\nbjyLiHylqn+Lgr5SYz6Wwzl4EO691/GpTJ0KbdpEr22LRTGM+CCqPhZV3ZxvmCIr3IaN6PHDD05C\nripVnKGvGjWi025oLEqfFn0sFsUwyghexiA2i0g7ABGpICKDgHWRlVU2iMaY67x5cOaZ8L//C3Pm\nlM6olFRnaCxKNPOixMsYtun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+ "text": [
+ "<matplotlib.figure.Figure at 0xa595d68>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "NtoG from graph: 8.7 \n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x785fc18>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "NtoG from graphical integration: 8.62 \n",
+ "\n",
+ "HtoG: 1.4 m\n",
+ "The depth of packing recquired is 12.84 m\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.8: Page 317"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.8\n",
+ "# Page: 317\n",
+ "\n",
+ "print'Illustration 8.8 - Page: 317\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "#***Data***\n",
+ "# a:NH3 b:air c:H2O\n",
+ "ya = 0.416;# [mole fraction]\n",
+ "yb = 0.584;# [mole fraction]\n",
+ "G1 = 0.0339;# [kmol/square m.s]\n",
+ "L1 = 0.271;# [kmol/square m.s]\n",
+ "TempG1 = 20;# [OC]\n",
+ "#********#\n",
+ "\n",
+ "# At 20 OC\n",
+ "Ca = 36390;# [J/kmol]\n",
+ "Cb = 29100;# [J/kmol]\n",
+ "Cc = 33960;# [J/kmol]\n",
+ "lambda_c = 44.24*10**6;# [J/kmol]\n",
+ "# Enthalpy base = NH3 gas, H2O liquid, air at 1 std atm.\n",
+ "Tempo = 20;# [OC]\n",
+ "lambda_Ao = 0;# [J/kmol]\n",
+ "lambda_Co = 44.24*10**6;# [J/kmol]\n",
+ "\n",
+ "# Gas in:\n",
+ "Gb = G1*yb;# [kmol air/square m.s]\n",
+ "Ya1 = ya/(1-ya);# [kmol NH3/kmol air]\n",
+ "yc1 = 0;# [mole fraction]\n",
+ "Yc1 = yc1/(1-yc1);# [kmol air/kmol NH]\n",
+ "# By Eqn 8.58:\n",
+ "Hg1 = (Cb*(TempG1-Tempo))+(Ya1*(Ca*(TempG1-Tempo))+lambda_Ao)+(Yc1*(Cc*(TempG1-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "\n",
+ "# Liquid in:\n",
+ "xa1 = 0;# [mole fraction]\n",
+ "xc1 = 1;# [mole fraction]\n",
+ "Hl1 = 0;# [J/kmol air]\n",
+ "\n",
+ "#Gas out:\n",
+ "Ya2 = Ya1*(1-0.99);# [kmol NH3/kmol air]\n",
+ "# Assume:\n",
+ "TempG2 = 23.9;# [OC]\n",
+ "yc2 = 0.0293;\n",
+ "def f(Yc2):\n",
+ " return yc2-(Yc2/(Yc2+Ya2+1))\n",
+ "Yc2 = fsolve(f,0.002);# [kmol H2O/kmol air]\n",
+ "Hg2 = (Cb*(TempG2-Tempo))+(Ya2*(Ca*(TempG2-Tempo))+lambda_Ao)+(Yc2*(Cc*(TempG2-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "\n",
+ "# Liquid out:\n",
+ "Lc = L1-(Yc1*Gb);# [kmol/square m.s]\n",
+ "La = Gb*(Ya1-Ya2);# [kmol/square m.s]\n",
+ "L2 = La+Lc;# [kmol/square m.s]\n",
+ "xa = La/L2;\n",
+ "xc = Lc/L2;\n",
+ "# At xa & tempo = 20 OC\n",
+ "delta_Hs = -1709.6*1000;# [J/kmol soln]\n",
+ "\n",
+ "# Condition at the bottom of the tower:\n",
+ "# Assume:\n",
+ "TempL = 41.3;# {OC}\n",
+ "# At(TempL+TempG1)/2:\n",
+ "Cl = 75481.0;# [J/kmol]\n",
+ "def f40(Cl):\n",
+ " return Hl1+Hg1-((Gb*Hg2)+(L2*(Cl*(TempL-Tempo)+delta_Hs)))\n",
+ "Cl = fsolve(f40,7);# [J/kmol.K]\n",
+ "\n",
+ "# For the Gas:\n",
+ "MavG = 24.02;# [kg/kmol]\n",
+ "Density_G = 0.999;# [kg/cubic m]\n",
+ "viscosity_G = 1.517*10**(-5);# [kg/m.s]\n",
+ "kG = 0.0261;# [W/m.K]\n",
+ "CpG = 1336;# [J/kg.K]\n",
+ "Dab = 2.297*10**(-5);# [square m/s]\n",
+ "Dac = 3.084*10**(-5);# [square m/s]\n",
+ "Dcb = 2.488*10**(-5);# [square m/s]\n",
+ "PrG = CpG*viscosity_G/kG;\n",
+ "\n",
+ "# For the liquid:\n",
+ "MavL = 17.97;# [kg/kmol]\n",
+ "Density_L = 953.1;# [kg/cubic m]\n",
+ "viscosity_L = 6.408*10**(-4);# [kg/m.s]\n",
+ "Dal = 3.317*10**(-9);# [square m/s]\n",
+ "kl = 0.4777;# [W/m.K]\n",
+ "ScL = viscosity_L/(Density_L*Dal);\n",
+ "PrL = 5.72;\n",
+ "sigma = 3*10**(-4);\n",
+ "G_prime = G1*MavG;# [kg/square m.s]\n",
+ "L_prime = L2*MavL;# [kg/square m.s]\n",
+ "# From data of Chapter 6:\n",
+ "Ds = 0.0472;# [m]\n",
+ "a = 57.57;# [square m/cubic m]\n",
+ "shiLt = 0.054;\n",
+ "e = 0.75;\n",
+ "# By Eqn. 6.71:\n",
+ "eLo = e-shiLt;\n",
+ "# By Eqn. 6.72:\n",
+ "kL = (25.1*Dal/Ds)*(Ds*L_prime/viscosity_L)**0.45*ScL**0.5;# [m/s]\n",
+ "c = Density_L/MavL;# [kmol/cubic m]\n",
+ "Fl = kL*c;# [kmol/cubic m]\n",
+ "# The heat mass transfer analogy of Eqn. 6.72:\n",
+ "hL = (25.1*kl/Ds)*(Ds*L_prime/viscosity_L)**0.45*PrL**0.5;# [m/s]\n",
+ "# The heat transfer analogy of Eqn. 6.69:\n",
+ "hG = (1.195*G_prime*CpG/PrG**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [W/square m.K]\n",
+ "# To obtain the mass transfer coeffecients:\n",
+ "Ra = 1.4;\n",
+ "Rc = 1-Ra;\n",
+ "# From Eqn. 8.83:\n",
+ "Dam = (Ra-ya)/(Ra*((yb/Dab)+((ya+yc1)/Dac))-(ya/Dac));# [square m/s]\n",
+ "Dcm = (Rc-yc1)/(Rc*((yb/Dcb)+((ya+yc1)/Dac))-(yc1/Dac));# [square m/s]\n",
+ "ScGa = viscosity_G/(Density_G*Dam);\n",
+ "ScGc = viscosity_G/(Density_G*Dcm);\n",
+ "# By Eqn. 6.69:\n",
+ "FGa = (1.195*G1/ScGa**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [kmol/square m.K]\n",
+ "FGc = (1.195*G1/ScGc**(2/3))*(Ds*G_prime/(viscosity_G*(1-eLo)))**(-0.36);# [kmol/square m.K]\n",
+ "Ra = Ra-0.1;\n",
+ "# From Eqn. 8.80:\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " def f41(xai):\n",
+ " return Ra-(Ra-ya)*((Ra-xa)/(Ra-xai))**(Fl/FGa)\n",
+ " xai = numpy.arange(xa,0.10,0.01)\n",
+ " plt.plot(xai,f41(xai))\n",
+ " Ra = Ra+0.1;\n",
+ "\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Mole fraction NH3 in the liquid, xa\");\n",
+ "ylabel(\"Mole fraction NH3 in the gas ya\");\n",
+ "title(\"Operating Line curves\");\n",
+ "plt.show()\n",
+ "Rc = Rc-0.1;\n",
+ "# From Eqn. 8.81:\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " def f42(xci):\n",
+ " return Rc-(Rc-yc1)*((Rc-xc)/(Rc-xci))**(Fl/FGc)\n",
+ " xci = numpy.arange(xc,0.85,-0.01);\n",
+ " plot(xci,f42(xci))\n",
+ " Rc = Rc+0.1;\n",
+ "\n",
+ "plt.grid('on');\n",
+ "xlabel(\"Mole fraction H2O in the liquid, xc\");\n",
+ "ylabel(\"Mole fraction H2O in the gas, yc\");\n",
+ "title(\"Operating line Curves\");\n",
+ "plt.show()\n",
+ "# Assume:\n",
+ "Tempi = 42.7;# [OC]\n",
+ "# The data of Fig. 8.2 (Pg 279) & Fig 8.4 (Pg 319) are used to draw the eqb curve of Fig 8.25 (Pg 320).\n",
+ "# By interpolation of operating line curves with eqb line and the condition: xai+xci = 1;\n",
+ "Ra = 1.38;\n",
+ "Rc = 1-Ra;\n",
+ "xai = 0.0786;\n",
+ "yai = f41(xai);\n",
+ "xci = 1-xai;\n",
+ "yci = f42(xci);\n",
+ "# From Eqn. 8.77:\n",
+ "dYa_By_dZ = -(Ra*FGa*a/Gb)*math.log((Ra-yai)/(Ra-ya));# [kmol H2O/kmol air]\n",
+ "# From Eqn. 8.78:\n",
+ "dYc_By_dZ = -(Rc*FGc*a/Gb)*math.log((Rc-yci)/(Rc-yc1));# [kmol H2O/kmol air]\n",
+ "# From Eqn. 8.82:\n",
+ "hGa_prime = -(Gb*((Ca*dYa_By_dZ)+(Cc*dYc_By_dZ)))/(1-exp(Gb*((Ca*dYa_By_dZ)+(Cc*dYc_By_dZ))/(hG*a)));# [W/cubic m.K]\n",
+ "# From Eqn. 8.79:\n",
+ "dtG_By_dZ = -(hGa_prime*(TempG1-Tempi))/(Gb*(Cb+(Ya1*Ca)+(Yc1*Cc)));# [K/m]\n",
+ "# When the curves of Fig. 8.2 (pg 279) & 8.24 (Pg 319) are interpolated for concentration xai and xci, the slopes are:\n",
+ "mar = 0.771;\n",
+ "mcr = 1.02;\n",
+ "lambda_c = 43.33*10**6;# [J/kmol]\n",
+ "# From Eqn. 8.3:\n",
+ "Hai = Ca*(Tempi-Tempo)+lambda_Ao-(mar*lambda_c);# [J/kmol]\n",
+ "Hci = Cc*(Tempi-Tempo)+lambda_Co-(mcr*lambda_c);# [J/kmol]\n",
+ "# From Eqn. 8.76\n",
+ "Tempi2 = TempL+(Gb/(hL*a))*(((Hai-Ca*(TempG1-Tempo)-lambda_Ao)*dYa_By_dZ)+((Hci-Cc*(TempG1-Tempo)-lambda_Co)*dYc_By_dZ)-((Cb+(Ya1*Ca)+(Yc1*Cc))*dtG_By_dZ));# [OC]\n",
+ "# The value of Tempi obtained is sufficiently close to the value assumed earlier.\n",
+ "\n",
+ "deltaYa=-0.05;\n",
+ "# An interval of deltaYa up the tower\n",
+ "deltaZ = deltaYa/(dYa_By_dZ);# [m]\n",
+ "deltaYc = (dYc_By_dZ*deltaZ);\n",
+ "# At this level:\n",
+ "Ya_next = Ya1+deltaYa;# [kmol/kmol air]\n",
+ "Yc_next = Yc1+deltaYc;# [kmol H2O/kmol air]\n",
+ "tG_next = TempG1+(dtG_By_dZ*deltaZ);# [OC]\n",
+ "L_next = L1+Gb*(deltaYa+deltaYc);# [kmol/square m.s]\n",
+ "xa_next = ((Gb*deltaYa)+(L1*xa))/L_next;# [mole fraction NH3]\n",
+ "Hg_next = (Cb*(tG_next-Tempo))+(Ya_next*(Ca*(tG_next-Tempo))+lambda_Ao)+(Yc_next*(Cc*(tG_next-Tempo)+lambda_Co));# [J/kmol air]\n",
+ "Hl_next = (L1*Hl1)+(Gb*(Hg_next-Hg2)/L_next);# [J/kmol]\n",
+ "# The calculation are continued where the specified gas outlet composition are reached.\n",
+ "# The packed depth is sum of all deltaZ\n",
+ "Z = 1.58;# [m]\n",
+ "print\"The packed depth is: \",Z,\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.8 - Page: 317\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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DNCbjSmSmiOQHUNVeKQtFpDxO05YJg1hvk7X8MlCwIIwaRfZxb/PY+G0sWliO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\nC5TymS+FUytmtM0F7rI6wI0i8gPwPnC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+ "text": [
+ "<matplotlib.figure.Figure at 0xa595b00>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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5DMPwI97kSawETgA7gMtR51U1zqxrTx+KL4GGwFFgG9BJVQ/EMm418DfwjqrO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\njXxqolkTOQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xa5fdcf8>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The packed depth is: 1.58 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex8.9: Page 327"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 8.9\n",
+ "# Page: 327\n",
+ "\n",
+ "print'Illustration 8.9 - Page: 327\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# C1=CH4 C2=C2H6 C3=n-C3H8 C4=C4H10\n",
+ "Abs=0.15;# [Total absorption,kmol]\n",
+ "\n",
+ "T=25;# [OC]\n",
+ "y1=0.7;# [mol fraction]\n",
+ "y2=0.15;# [mol fraction]\n",
+ "y3=0.10;# [mol fraction]\n",
+ "y4=0.05;# [mol fraction]\n",
+ "x1=0.01;# [mol fraction]\n",
+ "x_involatile=0.99;# [mol fraction]\n",
+ "L_by_G=3.5;# [mol liquid/mol entering gas]\n",
+ "#******#\n",
+ "\n",
+ "LbyG_top=L_by_G/(1-y2);\n",
+ "LbyG_bottom=(L_by_G+y2)/1;\n",
+ "LbyG_av=(LbyG_top+LbyG_bottom)/2;\n",
+ "# The number of eqb. trays is fixed by C3 absorption:\n",
+ "# For C3 at 25 OC;\n",
+ "m=4.10;\n",
+ "A=LbyG_av/m;\n",
+ "Frabs=0.7;# [Fractional absorption]\n",
+ "X0=0;\n",
+ "# From Eqn. 8.109:\n",
+ "def f43(Np):\n",
+ " return Frabs-((A**Np)-A)/((A**Np)-1)\n",
+ "Np=fsolve(f43,2);\n",
+ "print\"Number of trays required is \\n\",round(Np,2)\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 8.9 - Page: 327\n",
+ "\n",
+ "\n",
+ "Number of trays required is \n",
+ "3.57\n"
+ ]
+ }
+ ],
+ "prompt_number": 38
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter9.ipynb b/Mass_-_Transfer_Operations/Chapter9.ipynb
new file mode 100755
index 00000000..66d006cb
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter9.ipynb
@@ -0,0 +1,2153 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:931dd4c1c9a0b7aa6178b7a72f77a062406642a8049e6a1d1b32f53a34e1b6d5"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 9: Distillation"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.1: Page 349"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.1\n",
+ "# Page: 349\n",
+ "\n",
+ "print'Illustration 9.1 - Page: 349\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "#****Data****#\n",
+ "# a:n-heptane b:n-octane\n",
+ "Pt = 760; # [mm Hg]\n",
+ "#*****#\n",
+ "\n",
+ "Tempa = 98.4;# [boiling point of A,OC]\n",
+ "Tempb = 125.6;# [boiling point of B,OC]\n",
+ "x = numpy.zeros(6);\n",
+ "y_star = numpy.zeros(6);\n",
+ "alpha = numpy.zeros(6);\n",
+ "# Data = [Temp Pa (mm Hg) Pb(mm Hg)]\n",
+ "Data = [(98.4, 760.0, 333.0),(105.0 ,940.0, 417.0),(110.0, 1050.0, 484.0),(115.0, 1200.0, 561.0),(120.0, 1350.0, 650.0),(125.6 ,1540.0, 760.0)];\n",
+ "for i in range(0,6): \n",
+ " x[i] = (Pt-Data[i][2])/(Data[i][1]-Data[i][2]);# [mole fraction of heptane in liquid]\n",
+ " y_star[i] = (Data[i][1]/Pt)*x[i];\n",
+ " alpha[i] = Data[i][1]/Data[i][2];\n",
+ "\n",
+ "print\"\\t\\t T(OC)\\t\\t\\t\\t Pa(mm Hg)\\t\\t\\t\\t\\t\\t\\t Pb(mm Hg)\\t\\t\\t\\t\\t\\t\\t\\t x\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t y*\\t\\t\\t\\t\\t\\t\\t\\t\\t alpha\\n\"\n",
+ "for i in range(0,6):\n",
+ " print \"\\t \\t \",Data[i][0],\"\\t \\t \\t \\t\",Data[i][1],\"\\t \\t \\t \\t\",Data[i][2],\"\\t \\t \\t \\t \",round(x[i],3),\"\\t \\t \\t \\t \",round(y_star[i],3),\"\\t\\t\\t\\t\\t\\t\\t\\t\",round(alpha[i],2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.1 - Page: 349\n",
+ "\n",
+ "\n",
+ "\t\t T(OC)\t\t\t\t Pa(mm Hg)\t\t\t\t\t\t\t Pb(mm Hg)\t\t\t\t\t\t\t\t x\t\t\t\t\t\t\t\t\t\t\t y*\t\t\t\t\t\t\t\t\t alpha\n",
+ "\n",
+ "\t \t 98.4 \t \t \t \t760.0 \t \t \t \t333.0 \t \t \t \t 1.0 \t \t \t \t 1.0 \t\t\t\t\t\t\t\t2.28\n",
+ "\t \t 105.0 \t \t \t \t940.0 \t \t \t \t417.0 \t \t \t \t 0.656 \t \t \t \t 0.811 \t\t\t\t\t\t\t\t2.25\n",
+ "\t \t 110.0 \t \t \t \t1050.0 \t \t \t \t484.0 \t \t \t \t 0.488 \t \t \t \t 0.674 \t\t\t\t\t\t\t\t2.17\n",
+ "\t \t 115.0 \t \t \t \t1200.0 \t \t \t \t561.0 \t \t \t \t 0.311 \t \t \t \t 0.492 \t\t\t\t\t\t\t\t2.14\n",
+ "\t \t 120.0 \t \t \t \t1350.0 \t \t \t \t650.0 \t \t \t \t 0.157 \t \t \t \t 0.279 \t\t\t\t\t\t\t\t2.08\n",
+ "\t \t 125.6 \t \t \t \t1540.0 \t \t \t \t760.0 \t \t \t \t 0.0 \t \t \t \t 0.0 \t\t\t\t\t\t\t\t2.03\n"
+ ]
+ }
+ ],
+ "prompt_number": 70
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.2: Page 354"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.2\n",
+ "# Page: 354\n",
+ "\n",
+ "print'Illustration 9.2 - Page: 354\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:ethylaniline\n",
+ "Pt = 760.0; # [mm Hg]\n",
+ "ma1 = 50.0;# [g]\n",
+ "mb1 = 50.0;# [g]\n",
+ "#*******#\n",
+ "\n",
+ "# Data = [Temp Pa(mm Hg) Pb(mm Hg)]\n",
+ "Data = [(38.5, 51.1 ,1.0),(64.4 ,199.7, 5.0),(80.6 ,363.9 ,10.0),(96.0, 657.6, 20.0),(99.15 ,737.2 ,22.8),(113.2, 1225.0, 40.0)];\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Mb = 121.1;# [kg/kmol]\n",
+ "\n",
+ "for i in range(0,6):\n",
+ " p = Data[i][1]+Data[i][2];\n",
+ " if p==Pt:\n",
+ " pa = Data[4][1];# [mm Hg]\n",
+ " pb = Data[i][2];# [mm Hg]\n",
+ " T = Data[i][0];# [OC]\n",
+ " \n",
+ "\n",
+ "ya_star = pa/Pt;\n",
+ "yb_star = pb/Pt;\n",
+ "ya1 = ma1/Ma;# [g mol water]\n",
+ "yb1 = mb1/Mb;# [g mol ethylalinine]\n",
+ "Y = ya1*(yb_star/ya_star);# [g mol ethylalinine]\n",
+ "print\"The original mixture contained\",round(ya1,2),\"g mol water and \",round(yb1,2),\" g mol ethylanaline\\n\"\n",
+ "print\"The mixture will continue to boil at \",T,\" degree C, where the equilibrium vapour of the indicated composition,until all the water evaporated together with \",round(Y,3),\"g mol ethylaniline\\n\"\n",
+ "print\"The temparature will then rise to 204 degree C, and the equilibrium vapour will be of pure ethylanaline\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.2 - Page: 354\n",
+ "\n",
+ "\n",
+ "The original mixture contained 2.77 g mol water and 0.41 g mol ethylanaline\n",
+ "\n",
+ "The mixture will continue to boil at 99.15 degree C, where the equilibrium vapour of the indicated composition,until all the water evaporated together with 0.086 g mol ethylaniline\n",
+ "\n",
+ "The temparature will then rise to 204 degree C, and the equilibrium vapour will be of pure ethylanaline\n"
+ ]
+ }
+ ],
+ "prompt_number": 71
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.3: Page 362"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.3\n",
+ "# Page: 362\n",
+ "\n",
+ "print'Illustration 9.3 - Page: 362\\n\\n'\n",
+ "import numpy\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:n-C3H8 b:n-C4H10 c:n-C5H12 d:n-C6H14\n",
+ "# Bubble Point Calculation\n",
+ "xa = 0.05;\n",
+ "xb = 0.30;\n",
+ "xc = 0.40;\n",
+ "xd = 0.25;\n",
+ "P = 350;# [kN/square m]\n",
+ "#******#\n",
+ "\n",
+ "# Assume:\n",
+ "Temp = 60;# [OC]\n",
+ "x = [0.05, 0.30, 0.40, 0.25];\n",
+ "m = [4.70, 1.70 ,0.62 ,0.25];# [At 60 OC]\n",
+ "# Reference: C5H12\n",
+ "mref = m[3];\n",
+ "Sum = 0;\n",
+ "alpha = numpy.zeros(4)\n",
+ "alpha_x = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[3];\n",
+ " alpha_x[i] = alpha[i]*x[i];\n",
+ " Sum = Sum+alpha_x[i];\n",
+ "\n",
+ "# From Eqn. 9.23:\n",
+ "SumF = Sum;\n",
+ "Sum = 0;\n",
+ "mref = 1/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 56.8;# [OC]\n",
+ "m = [4.60, 1.60, 0.588, 0.235];# [At 56.8 OC]\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[2];\n",
+ " alpha_x[i] = alpha[i]*x[i];\n",
+ " Sum = Sum+alpha_x[i];\n",
+ "\n",
+ "SumF = Sum;\n",
+ "mref = 1/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 56.7;# [OC]\n",
+ "Bt = 56.8;# [OC]\n",
+ "yi = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " yi[i] = alpha_x[i]/Sum;\n",
+ "\n",
+ "print\"The Bubble Point is \",Bt,\" degree C\\n\"\n",
+ "print\"Bubble point vapour composition \\n\"\n",
+ "print\"\\t yi\\n\";\n",
+ "print\"\\n n-C3\\t \",round(yi[0],3)\n",
+ "print\"\\n n-C4\\t \",round(yi[1],3)\n",
+ "print\"\\n n-C5\\t \",round(yi[2],3)\n",
+ "print\"\\n n-C6\\t \",round(yi[3],3)\n",
+ "\n",
+ "print\"\\n \\n \\n\"\n",
+ "\n",
+ "# Dew Point Calculation\n",
+ "# Asume:\n",
+ "ya = 0.05;\n",
+ "yb = 0.30;\n",
+ "yc = 0.40;\n",
+ "yd = 0.25;\n",
+ "Temp = 80;# [OC]\n",
+ "y = [0.05, 0.30 ,0.40, 0.25];\n",
+ "m = [6.30 ,2.50 ,0.96 ,0.43];# [At 60 OC]\n",
+ "# Reference: C5H12\n",
+ "mref = m[3];\n",
+ "Sum = 0;\n",
+ "alpha = numpy.zeros(4)\n",
+ "alpha_y = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[3];\n",
+ " alpha_y[i] = y[i]/alpha[i];\n",
+ " Sum = Sum+alpha_y[i];\n",
+ "\n",
+ "\n",
+ "# From Eqn. 9.29:\n",
+ "SumF = Sum;\n",
+ "Sum = 0;\n",
+ "mref = SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 83.7;# [OC]\n",
+ "m = [6.60, 2.70, 1.08, 0.47];# [At 56.8 OC]\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[2];\n",
+ " alpha_y[i] = y[i]/alpha[i];\n",
+ " Sum = Sum+alpha_y[i];\n",
+ "\n",
+ "SumF = Sum;\n",
+ "mref = 1.0/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 84.0;# [OC]\n",
+ "Dt = 84.0;# [OC]\n",
+ "xi = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " xi[i] = alpha_y[i]/Sum;\n",
+ "\n",
+ "print\"The Dew Point is \",Dt,\" degree C\\n\"\n",
+ "print\"Dew point liquid composition \\n\"\n",
+ "print\"\\t xi\\n\"\n",
+ "print\"\\n n-C3\\t \",round(xi[0],3)\n",
+ "print\"\\n n-C4\\t \",round(xi[1],3)\n",
+ "print\"\\n n-C5\\t \",round(xi[2],3)\n",
+ "print\"\\n n-C6\\t \",round(xi[3],3)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.3 - Page: 362\n",
+ "\n",
+ "\n",
+ "The Bubble Point is 56.8 degree C\n",
+ "\n",
+ "Bubble point vapour composition \n",
+ "\n",
+ "\t yi\n",
+ "\n",
+ "\n",
+ " n-C3\t 0.229\n",
+ "\n",
+ " n-C4\t 0.478\n",
+ "\n",
+ " n-C5\t 0.234\n",
+ "\n",
+ " n-C6\t 0.059\n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ "The Dew Point is 84.0 degree C\n",
+ "\n",
+ "Dew point liquid composition \n",
+ "\n",
+ "\t xi\n",
+ "\n",
+ "\n",
+ " n-C3\t 0.007\n",
+ "\n",
+ " n-C4\t 0.109\n",
+ "\n",
+ " n-C5\t 0.363\n",
+ "\n",
+ " n-C6\t 0.521\n"
+ ]
+ }
+ ],
+ "prompt_number": 72
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.4: Page 365"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.4\n",
+ "# Page: 365\n",
+ "\n",
+ "print'Illustration 9.4 - Page: 365\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol feed]\n",
+ "zF = 0.5;\n",
+ "D = 60.0;# [mol]\n",
+ "W = 40.0;# [mol]\n",
+ "#*******#\n",
+ "\n",
+ "# From Illustration 9.1, Equilibrium data:\n",
+ "Data = numpy.array([[1.0 ,1.0],[0.655, 0.810],[0.487 ,0.674],[0.312, 0.492],[0.1571 ,0.279],[0, 0]]);\n",
+ "Feed = numpy.array([[0 ,0],[1.0 ,1.0]]);\n",
+ "# The operating line is drawn with a slope -(W/D) to cut the equilibrium line.\n",
+ "def f44(x):\n",
+ " return -((W/D)*(x-zF))+zF\n",
+ "x = numpy.arange(0.2,0.7,0.1);\n",
+ "plt.plot(Data[:,0],Data[:,1],label=\"Equilibrium Line\")\n",
+ "plt.plot(Feed[:,0],Feed[:,1],label=\"Feed Line\")\n",
+ "plt.plot(x,f44(x),label=\"Operating Line\");\n",
+ "plt.grid('on')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Mole fraction of heptane in liquid\")\n",
+ "ax.set_ylabel(\"Mole fraction of heptane in vapour\")\n",
+ "plt.legend(loc='lower right');\n",
+ "plt.show()\n",
+ "# The point at which the operating line cuts the equilibrium line has the following composition* temparature:\n",
+ "yd = 0.575;# [mole fraction heptane in vapour phase]\n",
+ "xW = 0.387;# [mole fraction heptane in liquid phase]\n",
+ "Temp = 113;# [OC]\n",
+ "print\"mole fraction of heptane in vapour phase %f \\n\",yd\n",
+ "print\"mole fraction of heptane in liquid phase %f\\n\",xW\n",
+ "print\"Temperature is %d degree C\\n\",Temp"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.4 - Page: 365\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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FmC7Kj5cRAwCqujPCe7cHVqnqWgARmQxcBCwLaXcLbpX1KRHe34iQ4BQXqbKa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\nzM+VsnViXQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xafecc50>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "mole fraction of heptane in vapour phase %f \n",
+ "0.575\n",
+ "mole fraction of heptane in liquid phase %f\n",
+ "0.387\n",
+ "Temperature is %d degree C\n",
+ "113\n"
+ ]
+ }
+ ],
+ "prompt_number": 73
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.5: Page 366"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.5\n",
+ "# Page: 366\n",
+ "\n",
+ "print'Illustration 9.5 - Page: 366\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "import pylab\n",
+ "import numpy.linalg as lin\n",
+ "#****Data****#\n",
+ "Pt = 760.0;# [mm Hg]\n",
+ "zFa = 0.5;# [mol fraction benzene]\n",
+ "zFb = 0.25;# [mol fraction toulene]\n",
+ "zFc = 0.25;# [mol fraction o-xylene]\n",
+ "#********#\n",
+ "\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol feed]\n",
+ "# For Summtion of Yd_star to be unity, W/D = 2.08 \n",
+ "# The Eqn.are \n",
+ "# (1): W+D = F \n",
+ "# (2): W-2.08D = 0\n",
+ "a =numpy.array([[1.0 ,1.0],[1.0 ,-2.08]]);\n",
+ "b = numpy.array([[F*1.0],[0]]);\n",
+ "soln = lin.solve(a,b)\n",
+ "W = soln[0];\n",
+ "D = soln[1];\n",
+ "Sub = ['A','B','C'];\n",
+ "p =numpy.array([1370 ,550, 200]);# [mm Hg]\n",
+ "m = numpy.zeros(3);\n",
+ "zF = [zFa ,zFb, zFc];# [Given]\n",
+ "yd_star = numpy.array([0,0,0]);\n",
+ "xW = numpy.zeros(3);\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " m[i] = p[i]/Pt;\n",
+ " yd_star[i]=(zF[i])*((W/D)+1)#/(1+(W/(D*m[i])));\n",
+ " xW[i] = yd_star[i]/m[i];\n",
+ "\n",
+ "print\"\\t \\t \\t \\t \\t \\t \\t \\t At W/D = 2.08\\n\\n\\n\"\n",
+ "print\"Substance \\t \\t p(mm Hg)\\t \\t m\\t \\t \\t \\t \\t \\t \\t \\t \\t \\t zF\\t \\t \\t \\t \\t \\t \\t yd*\\t\\t\\t\\t\\t\\txW\\n\"\n",
+ "for i in range(0,3):\n",
+ " print \"\\n\",Sub[i],\" \\t \\t \\t \\t \",p[i],\"\\t \\t \\t \\t \",m[i],\"\\t \\t \\t\",m[i],\"\\t \\t \\t\",zF[i],\" \\t \\t \\t\",yd_star[i],\"\\t\",xW[i]\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.5 - Page: 366\n",
+ "\n",
+ "\n",
+ "\t \t \t \t \t \t \t \t At W/D = 2.08\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\n",
+ "Substance \t \t p(mm Hg)\t \t m\t \t \t \t \t \t \t \t \t \t zF\t \t \t \t \t \t \t yd*\t\t\t\t\t\txW\n",
+ "\n",
+ "\n",
+ "A \t \t \t \t 1370 \t \t \t \t 1.80263157895 \t \t \t1.80263157895 \t \t \t0.5 \t \t \t1 \t0.554744525547\n",
+ "\n",
+ "B \t \t \t \t 550 \t \t \t \t 0.723684210526 \t \t \t0.723684210526 \t \t \t0.25 \t \t \t0 \t0.0\n",
+ "\n",
+ "C \t \t \t \t 200 \t \t \t \t 0.263157894737 \t \t \t0.263157894737 \t \t \t0.25 \t \t \t0 \t0.0\n"
+ ]
+ }
+ ],
+ "prompt_number": 74
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.6: Page 370"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.6\n",
+ "# Page: 370\n",
+ "\n",
+ "print'Illustration 9.6 - Page: 370\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol]\n",
+ "xF = 0.5;\n",
+ "D = 0.6*100;# [mol]\n",
+ "#******#\n",
+ "\n",
+ "W = F-D;# [mol]\n",
+ "# From Illustration 9.1:\n",
+ "alpha = 2.16;# [average value of alpha]\n",
+ "# From Eqn.9.46;\n",
+ "def f45(xW):\n",
+ " return math.log(F*xF/(W*xW))-(alpha*math.log(F*(1-xF)/(W*(1-xW))))\n",
+ "xW = fsolve(f45,0.5);# [mole fraction heptane]\n",
+ "def f46(yD):\n",
+ " return F*xF-((D*yD)+(W*xW))\n",
+ "yD = fsolve(f46,100);# [mole fraction heptane]\n",
+ "print\"Mole Fraction of heptane in the distillate is \",round(yD,3),\"\\n\"\n",
+ "print\"Mole Fraction of heptane in the residue is \",round(xW,3),\" \\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.6 - Page: 370\n",
+ "\n",
+ "\n",
+ "Mole Fraction of heptane in the distillate is 0.615 \n",
+ "\n",
+ "Mole Fraction of heptane in the residue is 0.328 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 75
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.7: Page 371"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.7\n",
+ "# Page: 371\n",
+ "from scipy.optimize import fsolve\n",
+ "print'Illustration 9.7 - Page: 371\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:benzene b:toulene c:o-xylene\n",
+ "# Assume:\n",
+ "Bt = 100.0;#[OC]\n",
+ "pa = 1370.0;# [mm Hg]\n",
+ "pb = 550.0;# [mm Hg]\n",
+ "pc = 200.0;# [mm Hg]\n",
+ "xFa = 0.5;# [mole fraction]\n",
+ "xFb = 0.25;# [mole fraction]\n",
+ "xFc = 0.25;# [mole fraction]\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol]\n",
+ "D = 32.5;# [mol]\n",
+ "#*******#\n",
+ "\n",
+ "ref = pb;\n",
+ "alpha_a = pa/ref;\n",
+ "alpha_b = pb/ref;\n",
+ "alpha_c = pc/ref;\n",
+ "W = F-D;# [mol]\n",
+ "xbW = 0.3;# [mol]\n",
+ "xaW = 0.4;# [mol]\n",
+ "xcW = 0.3;# [mol]\n",
+ "err = 1.0;\n",
+ "while(err>(10**(-1))):\n",
+ " # From Eqn. 9.47:\n",
+ " def f47(xaW):\n",
+ " return math.log(F*xFa/(W*xaW))-(alpha_a*math.log(F*xFb/(W*xbW)))\n",
+ " xaW = fsolve(f47,xbW);\n",
+ " def f48(xcW):\n",
+ " return math.log(F*xFc/(W*xcW))-(alpha_c*math.log(F*xFb/(W*xbW)))\n",
+ " xcW = fsolve(f48,xbW);\n",
+ " xbW_n = 1-(xaW+xcW);\n",
+ " err = abs(xbW-xbW_n);\n",
+ " xbw = xbW_n;\n",
+ "\n",
+ "# Material balance:\n",
+ "# for A:\n",
+ "def f49(yaD):\n",
+ " return F*xFa-((D*yaD)+(W*xaW))\n",
+ "yaD = fsolve(f49,100);# [mole fraction benzene]\n",
+ "# For B:\n",
+ "def f50(ybD):\n",
+ " return F*xFb-((D*ybD)+(W*xbW))\n",
+ "ybD = fsolve(f50,100);# [mole fraction toulene]\n",
+ "# For C:\n",
+ "def f51(ycD):\n",
+ " return F*xFc-((D*ycD)+(W*xcW))\n",
+ "ycD = fsolve(f51,100);# [mole fraction o-xylene]\n",
+ "print\"The residual compositions are:\\n\"\n",
+ "print\"Benzene:\\n\",round(xaW,3)\n",
+ "print\"Toulene:\\n\",round(xbW,3)\n",
+ "print\"o-xylene:\\n\",round(xcW,3)\n",
+ "print\"\\n The composited distillate compositions are:\\n\"\n",
+ "print\"Benzene:\\n\",round(yaD,3)\n",
+ "print\"Toulene:\\n\",round(ybD,3)\n",
+ "print\"o-xylene:\\n\",round(ycD,3)\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.7 - Page: 371\n",
+ "\n",
+ "\n",
+ "The residual compositions are:\n",
+ "\n",
+ "Benzene:\n",
+ "0.438\n",
+ "Toulene:\n",
+ "0.3\n",
+ "o-xylene:\n",
+ "0.343\n",
+ "\n",
+ " The composited distillate compositions are:\n",
+ "\n",
+ "Benzene:\n",
+ "0.628\n",
+ "Toulene:\n",
+ "0.146\n",
+ "o-xylene:\n",
+ "0.057\n"
+ ]
+ }
+ ],
+ "prompt_number": 76
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.8: Page 388"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.8\n",
+ "# Page: 388\n",
+ "\n",
+ "print'Illustration 9.8 - Page: 388\\n\\n'\n",
+ "import numpy.linalg as lin\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a:methanol b:water\n",
+ "Xa = 0.5;# [Wt fraction]\n",
+ "Temp1 = 26.7;# [OC]\n",
+ "Temp2 = 37.8;# [OC]\n",
+ "F1 = 5000.0;# [kg/hr]\n",
+ "#******#\n",
+ "\n",
+ "#(a)\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "Xa = 0.5;# [Wt fraction]\n",
+ "Xb = 1-Xa;# [Wt fraction]\n",
+ "Temp1 = 26.7;# [OC]\n",
+ "Temp2 = 37.8;# [OC]\n",
+ "F1 = 5000.0;# [kg/hr];\n",
+ "# Basis: 1hr\n",
+ "F = (F1*Xa/Ma)+(F1*Xb/Mb);# [kmol/hr]\n",
+ "# For feed:\n",
+ "zF = (F1*Xa/Ma)/F;# [mole fracton methanol]\n",
+ "MavF = F1/F;# [kg/kmol]\n",
+ "# For distillate:\n",
+ "xD = (95/Ma)/((95/Ma)+(5/Mb));# [mole fraction methanol]\n",
+ "MavD = 100.0/((95/Ma)+(5/Mb));# [kg/kmol]\n",
+ "# For residue:\n",
+ "xW = (1/Ma)/((1/Ma)+(99/Mb));# [mole fraction methanol]\n",
+ "MavR = 100/((1/Ma)+(99/Mb));# [kg/kmol]\n",
+ "# (1): D+W = F [Eqn.9.75]\n",
+ "# (2): D*xD+W*xW = F*zF [Eqn. 9.76]\n",
+ "# Solvving simultaneously:\n",
+ "a = numpy.array([[1.0 ,1.0],[xD ,xW]]);\n",
+ "b = numpy.array([F,F*zF]);\n",
+ "soln = lin.solve(a,b);\n",
+ "D = soln[0];# [kmol/h]\n",
+ "W = soln[1];# [kmol/h]\n",
+ "print\"Quantity of Distillate is\", round(D*MavD),\" kg/hr\\n\"\n",
+ "print\"Quantity of Residue is \",round(W*MavR),\" kg/hr\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (b)\n",
+ "# For the vapour-liquid equilibria:\n",
+ "Tempo = 19.69;# [Base Temp. according to \"International Critical Tables\"]\n",
+ "BtR = 99.0;# [Bubble point of the residue, OC]\n",
+ "hR = 4179.0;# [J/kg K]\n",
+ "hF = 3852.0;# [J/kg K]\n",
+ "def f52(tF):\n",
+ " return (F1*hF*(tF-Temp1))-((W*MavR)*hR*(BtR-Temp2))\n",
+ "tF = fsolve(f52,Temp1);# [OC]\n",
+ "BtF = 76.0;# [Bubble point of feed, OC]\n",
+ "# For the feed:\n",
+ "delta_Hs = -902.5;# [kJ/kmol]\n",
+ "Hf = ((hF/1000.0)*MavF*(tF-Tempo))+delta_Hs;# [kJ/kmol]\n",
+ "# From Fig 9.27:\n",
+ "HD = 6000.0;# [kJ/kmol]\n",
+ "HLo = 3640.0;# [kJ/kmol]\n",
+ "HW = 6000.0;# [kJ/kmol]\n",
+ "print\"The enthalpy of feed is \",round(Hf),\" kJ/kmol\\n\"\n",
+ "print\"The enthalpy of the residue is \",round(HW),\" kJ/kmol\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (c)\n",
+ "# From Fig.9.27:\n",
+ "# The miium reflux ratio is established by the tie line (x = 0.37 y = 0.71), which extended pass through F,the feed.\n",
+ "# At Dm:\n",
+ "Qm = 62570.0;# [kJ/kmol]\n",
+ "Hg1 = 38610.0;# [kJ/kmol]\n",
+ "# From Eqn. 9.65:\n",
+ "Rm = (Qm-Hg1)/(Hg1-HLo);\n",
+ "print\"The minimum reflux ratio is \",round(Rm,4),\"\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (d)\n",
+ "# From Fig. 9.28:\n",
+ "Np = 4.9;\n",
+ "# But it include the reboiler.\n",
+ "Nm = Np-1;\n",
+ "print\"The minimum number of theoretical trays required is \",round(Nm),\" \\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (e)\n",
+ "R = 1.5*Rm;\n",
+ "# Eqn. 9.65:\n",
+ "def f53(Q_prime):\n",
+ " return R-((Q_prime-Hg1)/(Hg1-HLo))\n",
+ "Q_prime = fsolve(f53,2);# [kJ/kmol]\n",
+ "def f54(Qc):\n",
+ " return Q_prime-(HD+(Qc/D))\n",
+ "Qc = fsolve(f54,2);# [kJ/hr]\n",
+ "Qc = Qc/3600.0;# [kW]\n",
+ "print\"The Condensor heat load is \",round(Qc),\" kW\\n\"\n",
+ "# From Eqn. 9.77:\n",
+ "def f55(Q_dprime):\n",
+ " return (F*Hf)-((D*Q_prime)+(W*Q_dprime))\n",
+ "Q_dprime = fsolve(f55,2);\n",
+ "def f56(Qb):\n",
+ " return Q_dprime-(HW-(Qb/W))\n",
+ "Qb = fsolve(f56,2);# [kJ/hr]\n",
+ "Qb = Qb/3600.0;# [kW]\n",
+ "print\"The Reboiler heat load is \",round(Qb),\" kW\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (f)\n",
+ "# From Fig: 9.28\n",
+ "Np = 9.0;\n",
+ "# But it is including the reboiler\n",
+ "print\"No. of theoretical trays in tower is\",Np-1,\"\\n\",\n",
+ "G1 = D*(R+1);# [kmol/hr]\n",
+ "Lo = D*R;# [kmol/hr]\n",
+ "# From Fig. 9.28:\n",
+ "# At the feed tray:\n",
+ "x4 = 0.415;\n",
+ "y5 = 0.676;\n",
+ "x5 = 0.318;\n",
+ "y6 = 0.554;\n",
+ "# From Eqn. 9.64:\n",
+ "def f57(L4):\n",
+ " return (L4/D)-((xD-y5)/(y5-x4))\n",
+ "L4 = fsolve(f57,2);# [kmol/hr]\n",
+ "# From Eqn. 9.62:\n",
+ "def f58(G5):\n",
+ " return (L4/G5)-((xD-y5)/(xD-x4))\n",
+ "G5 = fsolve(f58,2);# [kmol/hr]\n",
+ "# From Eqn. 9.74:\n",
+ "def f59(L5_bar):\n",
+ " return (L5_bar/W)-((y6-xW)/(y6-x5))\n",
+ "L5_bar = fsolve(f59,2);# [kmol/hr]\n",
+ "# From Eqn. 9.72:\n",
+ "def f60(G6_bar):\n",
+ " return (L5_bar/G6_bar)-((y6-xW)/(x5-xW))\n",
+ "G6_bar = fsolve(f60,2);# [kmol/hr]\n",
+ "# At the bottom:\n",
+ "# Material Balance:\n",
+ "# Eqn. 9.66:\n",
+ "# (1): L8_bar-GW_bar = W;\n",
+ "# From Fig. 9.28:\n",
+ "yW = 0.035;\n",
+ "x8 = 0.02;\n",
+ "# From Eqn. 9.72:\n",
+ "L8ByGW_bar = (yW-xW)/(x8-xW);\n",
+ "# (2): L8_bar-(L8ByGW_bar*Gw_bar) = 0\n",
+ "a = numpy.array([[1 ,-1],[1 ,-L8ByGW_bar]]);\n",
+ "b = numpy.array([W,0]);\n",
+ "soln = lin.solve(a,b)\n",
+ "L8_bar = soln[0];# [kmol/h]\n",
+ "GW_bar = soln[1];# [kmol/h]\n",
+ "print\"The Liquid quantity inside the tower is \",round(L8_bar),\" kmol/hr\\n\"\n",
+ "print\"The vapour quantity inside the tower is \",round(GW_bar),\" kmol/hr\\n\"\n",
+ "# The answers are slightly different in textbook due to approximation while in python the answers are precise\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.8 - Page: 388\n",
+ "\n",
+ "\n",
+ "Quantity of Distillate is 2606.0 kg/hr\n",
+ "\n",
+ "Quantity of Residue is 2394.0 kg/hr\n",
+ "\n",
+ "\n",
+ "\n",
+ "The enthalpy of feed is 2545.0 kJ/kmol\n",
+ "\n",
+ "The enthalpy of the residue is 6000.0 kJ/kmol\n",
+ "\n",
+ "\n",
+ "\n",
+ "The minimum reflux ratio is 0.6852 \n",
+ "\n",
+ "\n",
+ "\n",
+ "The minimum number of theoretical trays required is 4.0 \n",
+ "\n",
+ "\n",
+ "\n",
+ "The Condensor heat load is 1609.0 kW\n",
+ "\n",
+ "The Reboiler heat load is 1817.0 kW\n",
+ "\n",
+ "\n",
+ "\n",
+ "No. of theoretical trays in tower is 8.0 \n",
+ "The Liquid quantity inside the tower is 259.0 kmol/hr\n",
+ "\n",
+ "The vapour quantity inside the tower is 127.0 kmol/hr\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 77
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.9: Page 395"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 9.9\n",
+ "# Page: 395\n",
+ "\n",
+ "print'Illustration 9.9 - Page: 395\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import scipy\n",
+ "import numpy\n",
+ "import numpy.linalg as lin\n",
+ "\n",
+ "#****Data****#\n",
+ "P = 695.0;# [kN/square m]\n",
+ "#********#\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "# From Illustration 9.8:\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "F = 216.8;# [kmol/h]\n",
+ "Tempo = 19.7;# [OC]\n",
+ "zF = 0.360;# [mole fraction methanol]\n",
+ "HF = 2533;# [kJ/kmol]\n",
+ "D = 84.4;# [kkmol/h]\n",
+ "zD = 0.915;# [mole fraction methanol]\n",
+ "HD = 3640.0;# [kJ/kmol]\n",
+ "Qc = 5990000.0;# [kJ/h]\n",
+ "# Since the bottom will essentially be pure water:\n",
+ "HW = 6094.0;# [kJ/kmol]\n",
+ "# From Steam tables:\n",
+ "Hs = 2699.0;# [enthalpy of saturated steam, kJ/kg]\n",
+ "hW = 4.2*(Tempo-0);# [enthalpy of water, kJ/kg]\n",
+ "HgNpPlus1 = (Hs-hW)*Mb;# [kJ/kmol]\n",
+ "# (1): GNpPlus1-W = D-F [From Eqn. 9.86]\n",
+ "# (2): (GNpPlus1*HgNpPlus1)-(W*HW) = (D*HD)+Qc-(F*HF) [From Eqn. 9.88]\n",
+ "a = numpy.array([[1 ,-1],[HgNpPlus1 ,-HW]]);\n",
+ "b = numpy.array([[D-F],[(D*HD)+Qc-(F*HF)]]);\n",
+ "soln=lin.solve(a,b)\n",
+ "GNpPlus1 = soln[0];# [kmol/h]\n",
+ "W = soln[1];# [kmol/h]\n",
+ "# From Eqn. 9.87:\n",
+ "def f61(xW):\n",
+ " return (F*zF)-((D*zD)+(W*xW))\n",
+ "xW = fsolve(f61,2);\n",
+ "# The enthalpy of the solution at its bubble point is 6048 kJ/kmol, sufficiently closed to 6094 assumed earlier.\n",
+ "# For delta_w:\n",
+ "xdelta_w = W*xW/(W-GNpPlus1);\n",
+ "Q_dprime = ((W*HW)-(GNpPlus1*HgNpPlus1))/(W-GNpPlus1);# [kJ/kmol]\n",
+ "# From Fig 9.27 ad Fig. 9.28, and for the stripping section:\n",
+ "Np = 9.5;\n",
+ "print\"Steam Rate: \",round(GNpPlus1,1),\"kmol/h\\n\"\n",
+ "print\"Bottom Composition: xW:\",round(xW,5),\"\\n\"\n",
+ "print\"Number of theoretical stages: \",Np,\"\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.9 - Page: 395\n",
+ "\n",
+ "\n",
+ "Steam Rate: 159.7 kmol/h\n",
+ "\n",
+ "Bottom Composition: xW: 0.00281 \n",
+ "\n",
+ "Number of theoretical stages: 9.5 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 78
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.10: Page 412"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.10\n",
+ "# Page: 412\n",
+ "\n",
+ "print'Illustration 9.10 - Page: 412\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# Feed:\n",
+ "F1 = 5000;# [kg/h]\n",
+ "F = 216.8;# [kmol/h]\n",
+ "Tempo = 19.7;# [OC]\n",
+ "zF = 0.360;# [mole fraction methanol]\n",
+ "MavF = 23.1;# [kg/kmol]\n",
+ "Tempf = 58.3;# [OC]\n",
+ "# Distillate:\n",
+ "D1 = 2620;# [kg/h]\n",
+ "D = 84.4;# [kkmol/h]\n",
+ "xD = 0.915;# [mole fraction methanol]\n",
+ "# Residue:\n",
+ "R1 = 2380;# [kg/h]\n",
+ "R = 132.4;# [kmol/h]\n",
+ "xW = 0.00565;# [mole fraction methanol]\n",
+ "\n",
+ "# From Fig. 9.42 (Pg 413):\n",
+ "BtF = 76.0;# [Bubble point if the feed, OC]\n",
+ "DtF = 89.7;# [Dew point of the feed, OC]\n",
+ "# Latent heat of vaporisation at 76 OC\n",
+ "lambda_a = 1046.7;# [kJ/kg]\n",
+ "lambda_b = 2284;# [kJ/kg]\n",
+ "ha = 2.721;# [kJ/kg K]\n",
+ "hb = 4.187;# [kJ/kg K]\n",
+ "hF = 3.852;# [kJ/kg K]\n",
+ "# If heats of solution is ignaored:\n",
+ "# Enthalpy of the feed at the bubble point referred to the feed temp.\n",
+ "HF = hF*MavF*(BtF-Tempf);# [kJ/kmol]\n",
+ "# enthalpy of the saturated vapour at dew point referred to the liquid at feed temp.\n",
+ "HL = (zF*((ha*Ma*(DtF-Tempf))+(lambda_a*Ma)))+((1-zF)*((hb*Mb*(DtF-Tempf))+(lambda_b*Mb)));# [kJ/kmol]\n",
+ "q = HL/(HL-HF);\n",
+ "slope = q/(q-1);\n",
+ "# In fig. 9.42: xD,xW & zF are located on the 45 degree diagonal & the q line is drawn with slope = 'slope' .\n",
+ "# The operating line for minimum reflux ratio in this case pass through the intersection of the q line and the equilibrium curve.\n",
+ "ordinate = 0.57;\n",
+ "def f62(Rm):\n",
+ " return ordinate-(xD/(Rm+1))\n",
+ "Rm = fsolve(f62,0);# [mole reflux/mole distillate]\n",
+ "# from fig. 9.42 (Pg 413):\n",
+ "# The minimum number of theoretical trays is determied using the 45 degree diagonal as operating line.\n",
+ "Np = 4.9;# [including the reboiler]\n",
+ "R = 1.5*Rm;# [mole reflux/mole distillate]\n",
+ "# From Eqn. 9.49:\n",
+ "L = R*D;# [kmol/h]\n",
+ "# From Eqn. 9.115:\n",
+ "G = D*(R+1);# [kmol/h]\n",
+ "# From Eqn. 9.126:\n",
+ "L_bar = (q*F)+L;# [kmol/h]\n",
+ "# From Eqn. 9.127:\n",
+ "G_bar = (F*(q-1))+G;# [kmol/h]\n",
+ "ordinateN = xD/(R+1);\n",
+ "# As in Fig. 9.43:\n",
+ "# The y-intercept = ordinateN and enriching and exhausting operating lines are plotted.\n",
+ "# Number of theoretical stages are determined.\n",
+ "NpN = 8.8;# [including the reboiler]\n",
+ "print\"Number of theoretical stages is \",NpN-1,\"\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.10 - Page: 412\n",
+ "\n",
+ "\n",
+ "Number of theoretical stages is 7.8 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 79
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.11: Page 423"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.11\n",
+ "# Page: 423\n",
+ "\n",
+ "print'Illustration 9.11 - Page: 423\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# a:ethanol b:water\n",
+ "zF = 0.3;\n",
+ "xa = 0.3;# [mole fraction of ethanol]\n",
+ "Temp = 78.2;# [OC]\n",
+ "Ao = 0.0462;# [Area of perforations,square m]\n",
+ "t = 0.450;# [m]\n",
+ "#******#\n",
+ "\n",
+ "Ma = 46.05;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "xb = 1-xa;# [mole fraction of water]\n",
+ "ma = 0.3*Ma/((0.3*Ma)+(xb*Mb));# [mass fraction of ethanol]\n",
+ "mb = 1-ma;# [mass fraction of water]\n",
+ "\n",
+ "\n",
+ "# Feed:\n",
+ "F1 = 910.0;# [kg/h]\n",
+ "Xa = F1*ma/Ma;# [moles of ethanol]\n",
+ "Xb = F1*mb/Mb;# [moles of water]\n",
+ "F = Xa+Xb;# [Total moles]\n",
+ "# Distillate:\n",
+ "xD = 0.80;# [mole fraction of ethanol]\n",
+ "# If essentially all the ethanol is removed from the residue:\n",
+ "D = Xa/xD;# [kmol/h]\n",
+ "MavD = (xD*Ma)+((1-xD)*Mb);# [kg/kmol]\n",
+ "D1 = D*MavD;# [kg/h]\n",
+ "Density_G = (MavD/22.41)*(273.0/(273+Temp));# [kg/cubic meter]\n",
+ "Density_L = 744.9;# [kg/cubic meter]\n",
+ "sigma = 0.021;# [N/m]\n",
+ "\n",
+ "# From Table 6.2,Pg 169:\n",
+ "alpha = (0.0744*t)+0.01173;\n",
+ "beeta = (0.0304*t)+0.015;\n",
+ "At = math.pi*(0.760**2)/4;# [Tower cross sectional Area, square m]\n",
+ "WByT = 530.0/760;# [Table 6.1, Pg 162]\n",
+ "Ad = 0.0808*At;# [Downspout area,square m]\n",
+ "Aa = At-(2*Ad);# [Active area,square m]\n",
+ "# abcissa = (L/G)*(density_G/Density_L)^0.5\n",
+ "# Assume:\n",
+ "abcissa = 0.1;\n",
+ "# From Eqn.6.30:\n",
+ "Cf = (alpha*math.log10(1/abcissa)+beeta)*(sigma/0.020)**0.2;\n",
+ "# From Eqn. 6.29:\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1.0/2);# [m/s]\n",
+ "An = At-Ad;# [square m]\n",
+ "R = 3.0;# [Reflux Ratio]\n",
+ "G = D*(R+1);\n",
+ "G1 = (G*22.41/3600)*((273.0+Temp)/273);# [cubic meter/s]\n",
+ "V = G1/An;# [Vapour velocity,m/s]\n",
+ "percent = (V/Vf)*100;\n",
+ "# Vapour velocity is 58 percent of flooding velocity (amply safe)\n",
+ "L = R*D;# [kmol/h]\n",
+ "L1 = L*MavD;# [kg/h]\n",
+ "abcissa = (L1/(G1*3600.0*Density_G))*(Density_G/Density_L)**0.5;\n",
+ "# Since the value of abcissa is less than0.1, the calculaed value of Cf is correct.\n",
+ "# Since the feed is at the buubble point.\n",
+ "q = 1;\n",
+ "# From Eqn. 9.126:\n",
+ "L_bar = L+(q*F);# [kmol/h]\n",
+ "# From Eqn. 9.127:\n",
+ "G_bar = G+F*(q-1);# [kmol/h]\n",
+ "# The enthalpy of saturated steam,referred to 0 OC,69 kN/square m:\n",
+ "HGNpPlus1 = 2699.0;# [kN m/kg]\n",
+ "# This will be the enthalpy as it enters the tower if expanded adiabatically to the tower pressure\n",
+ "# The enthalpy of steam at 1 std. atm:\n",
+ "HGsat = 2676.0;# [kN m/kg]\n",
+ "Lambda = 2257.0;# [kN m/kg]\n",
+ "# From Eqn. 9.140:\n",
+ "def f63(GNpPlus1_bar):\n",
+ " return G_bar-(GNpPlus1_bar*(1+((HGNpPlus1-HGsat)*Mb/(Lambda*Mb))))\n",
+ "GNpPlus1_bar = fsolve(f63,7);\n",
+ "# From Eqn. 9.141:\n",
+ "LNp_bar = L_bar-(G_bar-GNpPlus1_bar);\n",
+ "\n",
+ "# Tray Efficiencies:\n",
+ "# Consider the situation:\n",
+ "x = 0.5;\n",
+ "y_star = 0.962;\n",
+ "Temp = 79.8;# [OC]\n",
+ "# This is in the enriching section.\n",
+ "Density_L = 791;# [kg/cubic meter]\n",
+ "Density_G = 1.253;# [kg/cubic meter]\n",
+ "# From equilibrium data:\n",
+ "m = 0.42;\n",
+ "A = L/(m*G);\n",
+ "# From chapter 2:\n",
+ "ScG = 0.930;\n",
+ "Dl = 2.065*10**(-9);# [square m/s]\n",
+ "# For L = 38.73 kmol/h\n",
+ "q = 4.36*10**(-4);# [cubic meter/s]\n",
+ "# For G = 51.64 kmol/h\n",
+ "Va = 1.046;# [m/s]\n",
+ "# From tray dimensions:\n",
+ "z = 0.647;# [m]\n",
+ "Z = 0.542;# [m]\n",
+ "hW = 0.06;# [m]\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/(ScG**0.5);\n",
+ "# From Eqn. 6.38\n",
+ "hL = 6.10*10**(-3)+(0.725*hW)-(0.238*hW*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "# From Eqn. 6.64:\n",
+ "thetha_L = hL*z*Z/q;# [s]\n",
+ "# From Eqn. 6.62:\n",
+ "NtL = 40000*(Dl**0.5)*((0.213*Va*Density_G**0.5)+0.15)*thetha_L;\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1/((1/NtG)+(1/(A*NtL)));\n",
+ "# From Eqn. 6.51:\n",
+ "EoG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.63:\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thetha_L);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2)*((1+(4*m*G1*EoG/(L1*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EoG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+((math.exp(eta)-1)/(eta*(1+(eta/(eta+Pe))))));\n",
+ "# Entrainment is neglible:\n",
+ "# Similarly for other x\n",
+ "# Value = [x Entrainment]\n",
+ "#Value = [0 0.48;0.1 .543;0.3 0.74;0.5 EMG;0.7 0.72];\n",
+ "\n",
+ "# Tray Calculation:\n",
+ "op_intercept = xD/(R+1);\n",
+ "# From Fig. 9.48:\n",
+ "# The exhausting section operating line, on this scale plot, for all practical purposes passes through the origin.\n",
+ "# The broken curve is located so that, at each concentration, vertical distances corresponding to lines BC and AC are in the ratio of EMG.\n",
+ "# This curve is used instead of equilibrium trays to locate the ideal trays.\n",
+ "# The feed tray is thirteenth.\n",
+ "x14 = 0.0150;\n",
+ "alpha = 8.95;\n",
+ "EMG = 0.48;\n",
+ "A_bar = L_bar/(alpha*G_bar);\n",
+ "# From Eqn. 8.16:\n",
+ "Eo = math.log(1+(EMG*((1/A_bar)-1)))/math.log(1/A_bar);\n",
+ "# The 6 real trays corresponds to: \n",
+ "NRp = 6*Eo;\n",
+ "xW = 0.015/((math.exp(NRp*math.log(1/A_bar))-A_bar)/(1-A_bar));# [mole fraction ethanol]\n",
+ "# This corresponds to ethanol loss of 0.5 kg/day.\n",
+ "print\"The mole fraction of ethanol in residue is\",round(xW,8)\n",
+ "print\"The Reflux ratio of \",R,\" will cause the ethanol loss of 0.5 kg/day\\n\"\n",
+ "print\"Larger reflux ratios would reduce this, but the cost of additional steam will probaby make them not worthwile.\\n\"\n",
+ "print\"Smaller values of R, with corresponding reduced steam cost and larger ethanol loss, should be considered, but care must be taken to ensure vapour velocities above the weeping velocities.\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.11 - Page: 423\n",
+ "\n",
+ "\n",
+ "The mole fraction of ethanol in residue is 6.28e-06\n",
+ "The Reflux ratio of 3.0 will cause the ethanol loss of 0.5 kg/day\n",
+ "\n",
+ "Larger reflux ratios would reduce this, but the cost of additional steam will probaby make them not worthwile.\n",
+ "\n",
+ "Smaller values of R, with corresponding reduced steam cost and larger ethanol loss, should be considered, but care must be taken to ensure vapour velocities above the weeping velocities.\n"
+ ]
+ }
+ ],
+ "prompt_number": 83
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex-9.12: Pg- 429"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 9.12\n",
+ "# Page: 429\n",
+ "\n",
+ "print'Illustration 9.12 - Page: 429\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "# a:methanol b:water\n",
+ "# Vapour and liquid quantities throughout the tower, as in Illustration 9.8, with the Eqn. 9.62, 9.64, 9.72, 9.74:\n",
+ "# Data = [x tL(OC) y tG(OC) Vapor(kmol/h) Vapor(kg/h) Liquid(kmol/h) Liquid(kg/h)]\n",
+ "Ma = 34.02;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "Temp = 78.7;# [OC]\n",
+ "x = numpy.array([0.915, 0.600 ,0.370, 0.370, 0.200, 0.100, 0.02]);\n",
+ "y = numpy.array([0.915, 0.762, 0.656, 0.656, 0.360 ,0.178, 0.032]);\n",
+ "\n",
+ "plt.plot(x,y);\n",
+ "plt.grid('on');\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"mole fraction of methanol in liquid\");\n",
+ "ax.set_ylabel(\"mole fraction of methanol in vapour\");\n",
+ "plt.title(\"Operating Line curve\");\n",
+ "plt.legend(loc=\"lower right\")\n",
+ "plt.show()\n",
+ "#x = 0.370: the dividing point between stripping and enriching section\n",
+ "tL =numpy.array([66, 71, 76, 76, 82, 87, 96.3]);# [Bubble point, OC]\n",
+ "tG = numpy.array([68.2 ,74.3 ,78.7 ,78.7 ,89.7 ,94.7 ,99.3]);# [Dew Point, OC]\n",
+ "Vapor = numpy.array([171.3, 164.0 ,160.9, 168.6, 161.6, 160.6, 127.6]);# [kmol/h]\n",
+ "Vapor1 = numpy.array([5303, 4684, 4378, 4585, 3721, 3296 ,2360]);# [kg/h]\n",
+ "Liquid = numpy.array([86.7 ,79.6 ,76.5 ,301, 294, 293, 260]);# [kmol/h]\n",
+ "Liquid1 = numpy.array([2723, 2104, 1779 ,7000, 6138, 5690 ,4767]);# [kg/h]\n",
+ "Data = numpy.zeros(shape=(7,8));\n",
+ "for j in range(1,7):\n",
+ " Data[j,0]= x[j];\n",
+ " Data[j,1]= tL[j];\n",
+ " Data[j,2]= y[j];\n",
+ " Data[j,3]= tG[j];\n",
+ " Data[j,4]= Vapor[j]; \n",
+ " Data[j,5]= Vapor1[j];\n",
+ " Data[j,6]= Liquid[j];\n",
+ " Data[j,7]= Liquid1[j];\n",
+ "\n",
+ "# The tower diameter will be set by the conditions at the top of the stripping section because of the large liquid flow at this point.\n",
+ "# From Illustration 9.8:\n",
+ "G = Data[3,5];\n",
+ "L = Data[3,7];\n",
+ "Density_G = (Data[3,5]/(22.41*Data[3,4]))*(273.0/(273+Temp));# [kg/cubic m]\n",
+ "Density_L = 905.0;# [kg/cubic m]\n",
+ "# abcissa = (L/G)*(Density_L/Density_G)^0.5\n",
+ "abcissa = (Data[3,7]/Data[3,5])*(Density_G/Density_L)**0.5;\n",
+ "# From Fig. 6.34, choose a gas pressure drop of 450 N/square m/m\n",
+ "ordinate = 0.0825;\n",
+ "# From Table 6.3 (Pg 196):\n",
+ "Cf = 95;\n",
+ "viscosity_L = 4.5*10**(-4);# [kg/m.s]\n",
+ "sigma = 0.029;# [N/m]\n",
+ "J = 1;\n",
+ "G_prime = (ordinate*Density_G*(Density_L-Density_G)/(Cf*viscosity_L**0.1))**0.5;# [kg/square m.s]\n",
+ "A = G/(3600*G_prime);# [Tower ,cross section area,square m]\n",
+ "L_prime = L/(A*3600);# [kg/square m.s]\n",
+ "# Mass transfer will be computed for the same location:\n",
+ "# From Table 6.4 (Pg 205):\n",
+ "m = 36.4;\n",
+ "n = (0.0498*L_prime)-0.1013;\n",
+ "p = 0.274;\n",
+ "aAW = m*((808*G_prime/Density_G**0.5)**n)*L_prime**p;# [square m/cubic m]\n",
+ "# From Table 6.5 (Pg 206):\n",
+ "dS = 0.0530;# [m]\n",
+ "beeta = 1.508*dS**0.376;\n",
+ "shi_LsW = 2.47*10**(-4)/dS**1.21;\n",
+ "shi_LtW = ((2.09*10**(-6))*(737.5*L_prime)**beeta)/dS**2;\n",
+ "shi_LOW = shi_LtW-shi_LsW; \n",
+ "shi_Ls = (0.0486*viscosity_L**0.02*sigma**0.99)/(dS**1.21*Density_L**0.37);\n",
+ "H = ((975.7*L_prime**0.57*viscosity_L**0.13)/(Density_L**0.84*((2.024*L_prime**0.430)-1)))*(sigma/0.073)**(0.1737-0.262*math.log10(L_prime));# [m]\n",
+ "shi_Lo = shi_LOW*H;\n",
+ "shi_Lt = shi_Lo+shi_Ls;\n",
+ "# From Eqn. 6.73:\n",
+ "aA = aAW*(shi_Lo/shi_LOW);# [square m/cubic m]\n",
+ "# From Table 6.3 (Pg 196):\n",
+ "e = 0.71;\n",
+ "# From Eqn. 6.71:\n",
+ "eLo = e-shi_Lt;\n",
+ "# From Chapter 2:\n",
+ "ScG = 1;\n",
+ "MavG = 0.656*Ma+(1-0.656)*Mb;# [kg/kmol]\n",
+ "G = G_prime/MavG;\n",
+ "viscosity_G = 2.96*10**(-5);# [kg/m.s]\n",
+ "# From Eqn. 6.70:\n",
+ "Fg = (1.195*G/ScG**(2/3))*((dS*G_prime/(viscosity_G*(1-eLo)))**(-0.36));# [kmol/square m s (mole fraction)]\n",
+ "kY_prime = Fg;# [kmol/square m s (mole fraction)]\n",
+ "DL = 4.80*10**(-9);# [square m/s]\n",
+ "ScL = viscosity_L/(Density_L*DL);\n",
+ "# From Eqn. 6.72:\n",
+ "kL = (25.1*DL/dS)*((dS*L_prime/viscosity_L)**0.45)*ScL**0.5;# [kmol/square m s (kmol/cubic m)]\n",
+ "# At 588.33 OC\n",
+ "Density_W = 53.82;# [kg/cubic m]\n",
+ "kx_prime = Density_W*kL;# [kmol/square m s (mole fraction)]\n",
+ "# Value1 = [x G a ky_prime*10^3 kx_prime]\n",
+ "Value1 = numpy.array([[0.915 ,0.0474 ,20.18 ,1.525, 0.01055],[0.6, 0.0454 ,21.56 ,1.542, 0.00865],[0.370 ,0.0444 ,21.92 ,1.545 ,0.00776],[0.370, 0.0466 ,38, 1.640, 0.0143],[0.2 ,0.0447, 32.82 ,1.692 ,0.0149],[0.1 ,0.0443 ,31.99 ,1.766 ,0.0146],[0.02, 0.0352 ,22.25 ,1.586 ,0.0150]]);\n",
+ "# From Fig: 9.50\n",
+ "# At x = 0.2:\n",
+ "y = 0.36;\n",
+ "slope = -(Value1[4,4]/(Value1[4,3]*10**(-3)));\n",
+ "# The operating line drawn from(x,y) with slope. The point where it cuts the eqb. line gives yi.\n",
+ "# K = ky_prime*a(yi-y)\n",
+ "# For the enriching section:\n",
+ "# En = [y yi 1/K Gy]\n",
+ "En = numpy.array([[0.915 ,0.960, 634 ,0.0433],[0.85 ,0.906 ,532.8 ,0.0394],[0.8 ,0.862 ,481.1 ,0.0366],[0.70, 0.760 ,499.1, 0.0314],[0.656, 0.702, 786.9, 0.0292]]);\n",
+ "# For the Stripping section:\n",
+ "# St = [y yi 1/K Gy]\n",
+ "St = numpy.array([[0.656, 0.707, 314.7, 0.0306],[0.50, 0.639, 124.6 ,0.0225],[0.40 ,0.580, 99.6 ,0.01787],[0.3 ,0.5 ,89 ,0.0134],[0.2 ,0.390, 92.6 ,0.00888],[0.10, 0.232, 154.5, 0.00416],[0.032 ,0.091, 481 ,0.00124]])\n",
+ "# Graphical Integration, according to Eqn.9.52::\n",
+ "\n",
+ "plt.plot(En[:,3],En[:,2],'g');\n",
+ "plt.grid();\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Gy\");\n",
+ "ax.set_ylabel(\"1 / (ky_prime*a*(yi-y))\");\n",
+ "plt.title(\"Graphical Integration for Enriching section\");\n",
+ "plt.show()\n",
+ "# From Area under the curve:\n",
+ "Ze = 7.53;# [m]\n",
+ "# Graphical Integration:\n",
+ "\n",
+ "plt.plot(St[:,3],St[:,2],'r');\n",
+ "plt.grid('on');\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Gy\");\n",
+ "ax.set_ylabel(\"1 / (ky_prime*a*(yi-y))\");\n",
+ "plt.title(\"Graphical Integration for Stripping section\");\n",
+ "plt.show()\n",
+ "\n",
+ "# From Area under the curve:\n",
+ "Zs = 4.54;# [m]\n",
+ "# Since the equlibrium curve slope varies so greatly that the use of overall mass transfer coeffecient is not recommended:\n",
+ "print\"Height of Tower for enriching Section is \",Ze,\" m\\n\"\n",
+ "print\"Height of Tower for Stripping Section is \",Zs,\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.12 - Page: 429\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xab95208>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2ax/UPmZ8NYPdO+wecxrnXByWrl3K2c+fzdoNa3n3wnfp3LZz3JEylmkX1uPA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\nCTmTkBHiz7lkzRKOe+I4Jsy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+ "text": [
+ "<matplotlib.figure.Figure at 0xc471d68>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xc3ed390>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Height of Tower for enriching Section is 7.53 m\n",
+ "\n",
+ "Height of Tower for Stripping Section is 4.54 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 84
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.13: Page 436"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.13:\n",
+ "\n",
+ "print'Illustration 9.13\\n\\n'\n",
+ "\n",
+ "#**************************Calculation Of Minimum Reflux ratio************************#\n",
+ "# Page: 436\n",
+ "print'Page: 436\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy import interp\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy.linalg as lin\n",
+ "#***Data***#\n",
+ "# C1:CH4 C2:C2H6 C3:n-C3H8 C4:n-C4H10 C5:n-C5H12 C6:n-C6H14\n",
+ "# zF = [zF(C1) zF(C2) zF(C3) zF(C4) zF(C5) zF(C6)]\n",
+ "zF = numpy.array([0.03 ,0.07 ,0.15 ,0.33 ,0.30 ,0.12]);# [mole fraction]\n",
+ "LF_By_F = 0.667;\n",
+ "Temp = 82;# [OC]\n",
+ "ylk = 0.98;\n",
+ "yhk = 0.01;\n",
+ "#**********#\n",
+ "\n",
+ "# Data = [m HG HL(30 OC);m HG HL(60 OC);m HG HL(90 OC);m HG HL(120 OC);]\n",
+ "Data1 = numpy.array([[16.1 ,12790 ,9770],[19.3 ,13910, 11160],[21.8 ,15000, 12790],[24.0 ,16240, 14370]]);# [For C1]\n",
+ "Data2 = numpy.array([[3.45, 22440, 16280],[4.90 ,24300 ,18140],[6.25 ,26240 ,19890],[8.15 ,28140, 21630]]);# [For C2]\n",
+ "Data3 = numpy.array([[1.10, 31170, 16510],[2.00 ,33000 ,20590],[2.90, 35800 ,25600],[4.00 ,39000, 30900]]);# [For C3]\n",
+ "Data4 = numpy.array([[0.35, 41200 ,20350],[0.70 ,43850 ,25120],[1.16 ,46500, 30000],[1.78 ,50400 ,35400]]);# [For C4]\n",
+ "Data5 = numpy.array([[0.085, 50500, 24200],[0.26, 54000 ,32450],[0.50 ,57800 ,35600],[0.84, 61200 ,41400]]);# [For C5]\n",
+ "Data6 = numpy.array([[0.0300, 58800 ,27700],[0.130, 63500, 34200],[0.239 ,68150 ,40900],[0.448, 72700 ,48150]]);# [For C6]\n",
+ "\n",
+ "# T = [Temparature]\n",
+ "T = numpy.array([30,60,0,120]);\n",
+ "\n",
+ "# Flash vaporisation of the Feed:\n",
+ "# Basis: 1 kmol feed throughout\n",
+ "# After Several trials, assume:\n",
+ "F = 1.0;# [kmol]\n",
+ "GF_By_F = 0.333;\n",
+ "LF_By_GF = LF_By_F/GF_By_F;\n",
+ "m82 = numpy.zeros(6);\n",
+ "y = numpy.zeros(6);\n",
+ "m82[0] = interp(Temp,T,Data1[:,1]);# [For C1]\n",
+ "m82[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m82[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m82[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m82[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m82[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "for i in range (0,6):\n",
+ " y[i] = zF[i]*(LF_By_GF+1)/(1.0+(2/m82[i]));\n",
+ "\n",
+ "Sum = sum(y);\n",
+ "# Since Sum is sufficiently close to 1.0, therefore:\n",
+ "q = 0.67;# [LF_By_F]\n",
+ "# Assume:\n",
+ "# C3: light key\n",
+ "# C5: heavy key\n",
+ "zlkF = zF[2];# [mole fraction]\n",
+ "zhkF = zF[4];# [mole fraction]\n",
+ "ylkD = ylk*zF[2];# [kmol]\n",
+ "yhkD = yhk*zF[4];# [kmol]\n",
+ "\n",
+ "# Estimate average Temp to be 80 OC\n",
+ "m80 = numpy.zeros(6);\n",
+ "alpha80 = numpy.zeros(6);\n",
+ "m80[0] = interp(Temp,T,Data1[:,0]);# [For C1]\n",
+ "m80[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m80[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m80[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m80[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m80[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "for i in range(0,6):\n",
+ " alpha80[i] = m80[i]/m80[4];\n",
+ "\n",
+ "# By Eqn. 9.164:\n",
+ "yD_By_zF1 = (((alpha80[0]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[0])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C1]\n",
+ "yD_By_zF2 = (((alpha80[1]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[1])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C2]\n",
+ "yD_By_zF6 = (((alpha80[5]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[5])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C6]\n",
+ "# The distillate contains:\n",
+ "yC1 = 0.03;# [kmol C1]\n",
+ "yC2 = 0.07;# [kmol C2]\n",
+ "yC6 = 0;# [kmol C6]\n",
+ "# By Eqn 9.165:\n",
+ "def g1(phi):\n",
+ " return (((alpha80[0]*zF[0])/(alpha80[0]-phi))+((alpha80[1]*zF[1])/(alpha80[1]-phi))+((alpha80[2]*zF[2])/(alpha80[2]-phi))+((alpha80[3]*zF[3])/(alpha80[3]-phi))+((alpha80[4]*zF[4])/(alpha80[4]-phi))+((alpha80[5]*zF[5])/(alpha80[5]-phi)))-(F*(1-q))\n",
+ "# Between alphaC3 & alphaC4:\n",
+ "phi1 = fsolve(g1,3);\n",
+ "# Between alphaC4 & alphaC5:\n",
+ "phi2 = fsolve(g1,1.5);\n",
+ "# From Eqn. 9.166:\n",
+ "# Val = D*(Rm+1)\n",
+ "# (alpha80(1)*yC1/(alpha80(1)-phi1))+(alpha80(2)*yC2/(alpha80(2)-phi1))+(alpha80(3)*ylkD/(alpha80(3)-phi1))+(alpha80(4)*yD/(alpha80(4)-phi1))+(alpha80(i)*yhkD/(alpha80(5)-phi1))+(alpha80(6)*yC6/(alpha80(6)-phi1)) = Val.....................(1)\n",
+ "# (alpha80(1)*yC1/(alpha80(1)-phi2))+(alpha80(2)*yC2/(alpha80(2)-phi2))+(alpha80(3)*ylkD/(alpha80(3)-phi2))+(alpha80(4)*yD/(alpha80(4)-phi2))+(alpha80(i)*yhkD/(alpha80(5)-phi2))+(alpha80(6)*yC6/(alpha80(6)-phi2)) = Val ....................(2)\n",
+ "# Solving simultaneously:\n",
+ "a =numpy.array([[-alpha80[3]/(alpha80[3]-phi1), 1],[-alpha80[3]/(alpha80[3]-phi2), 1]]);\n",
+ "b =numpy.array([[alpha80[0]*yC1/[alpha80[0]-phi1]]+[alpha80[1]*yC2/[alpha80[1]-phi1]]+[alpha80[2]*ylkD/[alpha80[2]-phi1]]+[alpha80[i]*yhkD/[alpha80[4]-phi1]]+[alpha80[5]*yC6/[alpha80[5]-phi1]],[alpha80[0]*yC1/[alpha80[0]-phi2]]+[alpha80[1]*yC2/[alpha80[1]-phi2]]+[alpha80[2]*ylkD/[alpha80[2]-phi2]]+[alpha80[i]*yhkD/[alpha80[4]-phi2]]+[alpha80[5]*yC6/[alpha80[5]-phi2]]])\n",
+ "soln = lin.solve(a,b);\n",
+ "yC4 =0.1313547 # [kmol C4 in the distillate]\n",
+ "Val =0.617469; # [kmol C4 in the distillate]\n",
+ "# For the distillate, at a dew point of 46 OC\n",
+ "ydD = numpy.array([yC1,yC2 ,ylkD ,yC4 ,yhkD ,yC6]);\n",
+ "D = sum(ydD);\n",
+ "yD = zeros(6);\n",
+ "m46 = zeros(6);\n",
+ "alpha46 = zeros(6);\n",
+ "Ratio1= zeros(6);\n",
+ "m46[0] = interp(Temp,T,Data1[:,0]);# [For C1]\n",
+ "m46[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m46[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m46[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m46[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m46[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "yD=numpy.array([0.0786,0.1835,0.3854,0.34,0.007866,0.0])\n",
+ "# mhk = mC5 at 46.6 OC, the assumed 46 OC is satisfactory.\n",
+ "\n",
+ "# For the residue, at a dew point of 46 OC\n",
+ "xwW =numpy.array([zF[0]-yC1, zF[1]-yC2 ,zF[2]-ylkD, zF[3]-yC4, zF[4]-yhkD, zF[5]-yC6]);\n",
+ "W = sum(xwW);\n",
+ "xW = zeros(6);\n",
+ "m113 = zeros(6);\n",
+ "alpha113 = zeros(6);\n",
+ "alphalk_av=zeros(6);\n",
+ "alpha_av=zeros(6);\n",
+ "Value=zeros(6);\n",
+ "m113[0] = interp(Temp,T,Data1[:,1]);# [For C1]\n",
+ "m113[1] = interp(Temp,T,Data2[:,1]);# [For C2]\n",
+ "m113[2] = interp(Temp,T,Data3[:,1]);# [For C3]\n",
+ "m113[3] = interp(Temp,T,Data4[:,1]);# [For C4]\n",
+ "m113[4] = interp(Temp,T,Data5[:,1]);# [For C5]\n",
+ "m113[5] = interp(Temp,T,Data6[:,1]);# [For C6]\n",
+ "for i in range(0,6):\n",
+ " alpha113[i] = m113[i]/m113[4];\n",
+ " xW[i] = xwW[i]/W;\n",
+ " # Ratio = yD/alpha46\n",
+ " Value[i] = alpha113[i]*xW[i];\n",
+ "\n",
+ "# mhk = mC5 at 114 OC, the assumed 113 OC is satisfactory.\n",
+ "Temp_Avg = (114+46.6)/2;# [OC]\n",
+ "# Temp_avg is very close to the assumed 80 OC\n",
+ "Rm = (Val/D)-1;\n",
+ "print\"Minimum Reflux Ratio is \",Rm,\" mol reflux/mol distillate\\n \\n\"\n",
+ "print\"*****************Distillate Composition*********************\\n\"\n",
+ "print\"C1\\t \\t \\t \\t:\",yD[0]\n",
+ "print\"C2\\t \\t \\t \\t:\",yD[1]\n",
+ "print\"C3\\t \\t \\t \\t:\",yD[2]\n",
+ "print\"C4\\t \\t \\t \\t:\",yD[3]\n",
+ "print\"C5\\t \\t \\t \\t:\",yD[4]\n",
+ "print\"C6\\t \\t \\t \\t:\",yD[5]\n",
+ "print\"\\n\"\n",
+ "print\"*****************Residue Composition*********************\\n\"\n",
+ "print\"C1\\t \\t \\t \\t: \",xW[0]\n",
+ "print\"C2\\t \\t \\t \\t: \",xW[1]\n",
+ "print\"C3\\t \\t \\t \\t: \",xW[2]\n",
+ "print\"C4\\t \\t \\t \\t: \",xW[3]\n",
+ "print\"C5\\t \\t \\t \\t: \",xW[4]\n",
+ "print\"C6\\t \\t \\t \\t: \",xW[5]\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**********************Number of Theoretical stage***********************#\n",
+ "# Page:440\n",
+ "print'Page: 440\\n\\n'\n",
+ "\n",
+ "for i in range(0,6):\n",
+ " alpha_av[i] = (alpha46[i]*alpha113[i])**0.5;\n",
+ "\n",
+ "alphalk_av = alpha_av[1];\n",
+ "# By Eqn. 9.167:\n",
+ "xhkW = xwW[3];\n",
+ "xlkW = xwW[1];\n",
+ "Nm = 3.496;\n",
+ "# Ratio = yD/xW\n",
+ "Ratio2= zeros(6)\n",
+ "for i in range(0,6):\n",
+ " Ratio2[i] = (alpha_av[i]**(Nm+1))*yhkD/xhkW;\n",
+ "\n",
+ "# For C1:\n",
+ "# yC1D-Ratio(1)*xC1W = 0\n",
+ "# yC1D+xC1W = zF(1)\n",
+ "# Similarly for others\n",
+ "yD2=zeros(6)\n",
+ "xW2=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " a = numpy.array([[1 ,-Ratio2[i]],[1, 1]]);\n",
+ " b = [0,zF[i]];\n",
+ " soln =lin.solve(a,b);\n",
+ " yD2[i] = soln[0];# [kmol]\n",
+ " xW2[i] = soln[1];# [kmol]\n",
+ "\n",
+ "D = sum(yD2);# [kmol]\n",
+ "W = sum(xW2);# [kmol]\n",
+ "# The distillate dew point computes to 46.6 OC and the residue bubble point computes to 113 OC, which is significantly close to the assumed.\n",
+ "\n",
+ "#***************Product composition at R = 0.8***********************#\n",
+ "# Page:441\n",
+ "print'Page: 441\\n\\n'\n",
+ "\n",
+ "# Since C1 and C2 do not enter in the residue nor C6 in the distillate, appreciably at total reflux or minimum reflux ratio, it will be assumed that they will not enter R = 0.8. C3 and C5 distribution are fixed by specifications. Only that C4 remains to be estimated.\n",
+ "# R = [Infinte 0.8 0.58] [Reflux ratios For C4]\n",
+ "R = [inf ,0.8, 0.58];\n",
+ "# Val = R/(R+1)\n",
+ "val=[ 0 , 2.0 , 2.0]\n",
+ "# ydD = [Inf 0.58] \n",
+ "y4D = [0.1255, 0.1306];\n",
+ "yC4D = 0.1306 ;# by Linear Interpolation\n",
+ "# For Distillate:\n",
+ "Sum1 = sum(Ratio1);\n",
+ "x0 = numpy.array([0.004,0.0444501,0.2495,0.65640,0.0451,0.0])\n",
+ "print\"For the reflux ratio of 0.8\\n\"\n",
+ "print\"*****************Distillate Composition*********************\\n\"\n",
+ "print\"\\t\\t\\t Liquid reflux in equilibrium with the distillate vapour\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t \\t\\t:\",x0[i]\n",
+ "\n",
+ "# For boiler:\n",
+ "\n",
+ "#**********Number Of Theoretical Trays***************#\n",
+ "# Page: 443\n",
+ "print'Page: 443\\n\\n'\n",
+ "\n",
+ "R = 0.8;# [reflux ratio]\n",
+ "# From Eqn. 9.175\n",
+ "intersection = (zlkF-(ylkD/D)*(1-q)/(R+1))/(zhkF-(yhkD/D)*(1-q)/(R+1));\n",
+ "# Enriching Section:\n",
+ "y1 = zeros(5);\n",
+ "L = R*D;# [kmol]\n",
+ "G = L+D;# [kmol]\n",
+ "# Assume: Temp1 = 57 OC\n",
+ "# alpha57 = [C1 C2 C3 C4 C5]\n",
+ "alpha57 = numpy.array([79.1 ,19.6 ,7.50, 2.66, 1]);\n",
+ "# From Eqn. 9.177, n = 0:\n",
+ "Val57=zeros(6)\n",
+ "for i in range(0,5):\n",
+ " y1[i] = (L/G)*x0[i]+((D/G)*yD[i]);\n",
+ " Val57[i] = y1[i]/alpha57[i];\n",
+ "\n",
+ "x1 = Val57/sum(Val57);\n",
+ "mC5 = sum(Val57);\n",
+ "Temp1 = 58.4; # [OC]\n",
+ "# Liquid x1's is in equilibrium with y1's.\n",
+ "xlk_By_xhk1 = x1[2]/x1[4];\n",
+ "# Tray 1 is not the feed tray.\n",
+ "# Assume: Temp2 = 63 OC\n",
+ "# alpha63 = [C1 C2 C3 C4 C5]\n",
+ "alpha63 = numpy.array([68.9 ,17.85, 6.95, 2.53, 1.00]);\n",
+ "# From Eqn. 9.177, n = 1:\n",
+ "y2=zeros(6)\n",
+ "Val63=zeros(6)\n",
+ "for i in range(0,5):\n",
+ " y2[i] = (L/G)*x1[i]+((D/G)*yD[i]);\n",
+ " Val63[i] = y1[i]/alpha63[i];\n",
+ " \n",
+ "mC5 = sum(Val63);\n",
+ "x2 = Val63/sum(Val63);\n",
+ "xlk_By_xhk2 = x2[2]/x2[4];\n",
+ "# The tray calculation are continued downward in this manner.\n",
+ "# Results for trays 5 & 6 are:\n",
+ "# Temp 75.4 [OC]\n",
+ "# x5 = [C1 C2 C3 C4 C5]\n",
+ "x5 = numpy.array([0.00240, 0.0195, 0.1125, 0.4800, 0.3859]);\n",
+ "xlk_By_xhk5 = x5[2]/x5[4];\n",
+ "# Temp6 = 79.2 OC\n",
+ "# x6 = [C1 C2 C3 C4 C5]\n",
+ "x6 = numpy.array([0.00204 ,0.0187 ,0.1045, 0.4247 ,0.4500]);\n",
+ "xlk_By_xhk6 = x6[2]/x6[4];\n",
+ "# From Eqn. 9.176:\n",
+ "# Tray 6 is the feed tray\n",
+ "Np1 = 6;\n",
+ "\n",
+ "# Exhausting section:\n",
+ "# Assume Temp = 110 OC\n",
+ "L_bar = L+(q*F);# [kmol]\n",
+ "G_bar = L_bar-W;# [kmol]\n",
+ "# alpha57 = [C3 C4 C5 C6]\n",
+ "alpha110 = numpy.array([5 ,2.2 ,1, 0.501]);\n",
+ "# From Eqn. 9.178:\n",
+ "xNp = zeros(4);\n",
+ "Val110=zeros(6)\n",
+ "k = 0;\n",
+ "for i in range(2,6):\n",
+ " xNp[k] = ((G_bar/L_bar)*yNpPlus1[i])+((W/L_bar)*xW[i]);\n",
+ " Val110[k] = alpha110[k]*xNp[k];\n",
+ " k = k+1;\n",
+ "\n",
+ "yNp = Val110/sum(Val110);\n",
+ "mC5 = 1/sum(Val110);\n",
+ "# yNp is in Eqb. with xNp:\n",
+ "xlk_By_xhkNp = xNp[0]/xNp[3];\n",
+ "# Results for Np-7 to Np-9 trays:\n",
+ "# For Np-7\n",
+ "# Temp = 95.7 OC\n",
+ "# xNpMinus7 = [C3 C4 C5 C6]\n",
+ "xNpMinus7 = numpy.array([0.0790 ,0.3944 ,0.3850, 0.1366]);\n",
+ "xlk_By_xhkNpMinus7 = xNpMinus7[0]/xNpMinus7[2];\n",
+ "# For Np-8\n",
+ "# Temp = 94.1 OC\n",
+ "# xNpMinus8 = [C3 C4 C5 C6]\n",
+ "xNpMinus8 = numpy.array([0.0915, 0.3897 ,0.3826, 0.1362]);\n",
+ "xlk_By_xhkNpMinus8 = xNpMinus8[0]/xNpMinus8[2];\n",
+ "# For Np-9\n",
+ "# Temp = 93.6 OC\n",
+ "# xNpMinus9 = [C3 C4 C5 C6]\n",
+ "xNpMinus9 = numpy.array([0.1032, 0.3812, 0.3801 ,0.1355]);\n",
+ "xlk_By_xhkNpMinus9 = xNpMinus9[0]/xNpMinus9[2];\n",
+ "# From Eqn. 9.176:\n",
+ "# Np-8 is the feed tray.\n",
+ "def g2(Np):\n",
+ " return Np-8-Np1\n",
+ "Np = fsolve(g2,7);\n",
+ "print\"Number of theoretical Trays required for R = 0.8: \",Np[0]\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**************Composition Correction*****************#\n",
+ "# Page: 446\n",
+ "print'Page: 446\\n\\n'\n",
+ "\n",
+ "# New Bubble Point:\n",
+ "# Temp = 86.4 OC\n",
+ "x6_new = x6*(1-xNpMinus8[3]);\n",
+ "x6_new[4] = xNpMinus8[3];\n",
+ "# alpha86 = [C1 C2 C3 C4 C5 C6]\n",
+ "alpha86 =numpy.array([46.5, 13.5, 5.87, 2.39, 1.00, 0.467]);\n",
+ "# From Eqn. 9.181:\n",
+ "xhkn = x5[3];\n",
+ "xhknPlus1 = x6_new[3];\n",
+ "xC65 = alpha86[5]*x6_new[4]*xhkn/xhknPlus1;\n",
+ "x5_new = x5*(1-xC65);\n",
+ "x5_new[4] = 1-sum(x5_new);\n",
+ "# Tray 5 has a bubble point of 80 OC\n",
+ "# Similarly , the calculations are continued upward:\n",
+ "# x2_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x2_new = numpy.array([0.0021, 0.0214 ,0.1418, 0.6786, 0.1553, 0.00262]);\n",
+ "# y2_new = [C1 C2 C3 C4 C5 C6]\n",
+ "y2_new = numpy.array([0.0444, 0.111 ,0.2885, 0.5099, 0.0458 ,0.00034]);\n",
+ "# x1_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x1_new = numpy.array([0.00226, 0.0241, 0.1697 ,0.7100, 0.0932, 0.00079]);\n",
+ "# y1_new = [C1 C2 C3 C4 C5 C6]\n",
+ "y1_new = numpy.array([0.0451 ,0.1209 ,0.3259 ,0.4840 ,0.0239 ,0.000090]);\n",
+ "# x0_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x0_new = numpy.array([0.00425 ,0.0425 ,0.2495, 0.6611 ,0.0425 ,0.00015]);\n",
+ "# yD_new = [C1 C2 C3 C4 C5 C6]\n",
+ "yD_new = numpy.array([0.0789 ,0.1842 ,0.3870 ,0.3420 ,0.0079, 0.00001]);\n",
+ "# From Eqn. 9.184:\n",
+ "# For C1 & C2\n",
+ "alphalkm = alpha86[2];\n",
+ "xlkmPlus1 = xNpMinus7[0];\n",
+ "xlkm = x6_new[2];\n",
+ "xC17 = x6_new[0]*alpha86[2]*xlkmPlus1/(alpha86[0]*xlkm);\n",
+ "xC27 = x6_new[1]*alpha86[2]*xlkmPlus1/(alpha86[1]*xlkm);\n",
+ "# Since xC17 = 1-xC27\n",
+ "# The adjusted value above constitute x7's.\n",
+ "# The new bubbl point is 94 OC\n",
+ "# The calculations are continued down in the same fashion.\n",
+ "# The new tray 6 has:\n",
+ "# xC1 = 0.000023 & xC2 = 0.00236\n",
+ "# It is clear that the conc. of these components are reducing so rapidly that there is no need to go an further.\n",
+ "print\"******Corrected Composition***********\\n\"\n",
+ "print\"Component\\t \\tx2\\t \\t \\t y2\\t \\t \\t x1\\t \\t \\t y1\\t \\t \\tx0\\t \\t \\tyD\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t\",x2_new[i],\"\\t \\t \\t \\t \",y2_new[i],\"\\t \\t \\t \\t \",x1_new[i],\"\\t \\t \\t \\t\",y1_new[i],\"\\t \\t \\t \\t \\t\",x0_new[i],\"\\t \\t \\t \\t\",yD_new[i]\n",
+ "\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#*************Heat Load of Condensor & Boiler & L/G ratio**********#\n",
+ "# Page 448\n",
+ "print'Page: 448\\n\\n'\n",
+ "\n",
+ "# Values of x0, yD & y1 are taken from the corrected concentration.\n",
+ "# HD46 = [C1 C2 C3 C4 C5 C6]\n",
+ "HD46 = numpy.array([13490, 23380, 32100, 42330, 52570, 61480]);# [kJ/kmol]\n",
+ "yDHD= zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yDHD[i] = yD_new[i]*HD46[i];\n",
+ "\n",
+ "HD = sum(yDHD);# [kJ]\n",
+ "# HL46 = [C1 C2 C3 C4 C5 C6]\n",
+ "HL46 = numpy.array([10470, 17210, 18610, 22790, 27100, 31050]);# [kJ/kmol]\n",
+ "xHL=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " xHL[i] = x0_new[i]*HL46[i];\n",
+ "\n",
+ "HL0 = sum(xHL);# [kJ]\n",
+ "# HG58 = [C1 C2 C3 C4 C5 C6]\n",
+ "HG58 = numpy.array([13960, 24190, 37260, 43500, 53900, 63500]);# [kJ/kmol]\n",
+ "yHG1=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yHG1[i] = y1_new[i]*HG58[i];\n",
+ "\n",
+ "HG1 = sum(yHG1);# [kJ]\n",
+ "# From Eqn. 9.54:\n",
+ "Qc = D*((R+1)*HG1-(R*HL0)-HD);# [kJ/kmol feed]\n",
+ "# Similarly:\n",
+ "HW = 39220;# [kJ]\n",
+ "HF = 34260;# [kJ]\n",
+ "# From Eqn. 9.55:\n",
+ "Qb = (D*HD)+(W*HW)+Qc-(F*HF);# [kJ/kmol feed]\n",
+ "# For tray n = 1\n",
+ "G1 = D*(R+1);# [kmol]\n",
+ "# With x1 & y2 from corrected composition;\n",
+ "# HG66 = [C1 C2 C3 C4 C5 C6]\n",
+ "HG66 = numpy.array([14070, 24610, 33800, 44100, 54780, 64430]);# [kJ/kmol feed]\n",
+ "yHG2=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yHG2[i] = y2_new[i]*HG66[i];\n",
+ "\n",
+ "HG2 = sum(yHG2);# [kJ]\n",
+ "# HL58 = [C1 C2 C3 C4 C5 C6]\n",
+ "HL58 =numpy.array([11610 ,17910 ,20470, 24900, 29500, 33840]);# [kJ/kmol feed]\n",
+ "xHL1=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " xHL1[i] = x1_new[i]*HL58[i];\n",
+ "\n",
+ "HL1 = sum(xHL1);# [kJ]\n",
+ "# From Eqn. 9.185:\n",
+ "G2 = (Qc+D*(HD-HL1))/(HG2-HL1);# [kmol]\n",
+ "L2 = G2-D;# [kmol]\n",
+ "L2_By_G2 = L2/G2;\n",
+ "# Similarly, the calculations are made for other trays in enriching section.\n",
+ "# For tray, Np = 14:\n",
+ "# C1 & C2 are absent.\n",
+ "# HG113 = [C3 C4 C5 C6]\n",
+ "HG113 = numpy.array([38260, 49310 ,60240, 71640]);# [kJ/kmol feed]\n",
+ "k = 2;\n",
+ "yHG15=zeros(6)\n",
+ "for i in range(0,4):\n",
+ " yHG15[i] = yNpPlus1[k]*HG113[i];\n",
+ " k = k+1;\n",
+ "\n",
+ "HG15 = sum(yHG15);\n",
+ "# HL107 = [C3 C4 C5 C6]\n",
+ "HL107 = numpy.array([29310 ,31870, 37680 ,43500]);# [kJ/kmol feed]\n",
+ "xHL14=zeros(6)\n",
+ "for i in range(0,4):\n",
+ " xHL14[i] = xNp[i]*HL107[i];\n",
+ "\n",
+ "HL14 = sum(xHL14);# [kJ]\n",
+ "# Similarly:\n",
+ "HL13 = 36790;# [kJ]\n",
+ "HG14 = 52610;# [kJ]\n",
+ "# From Eqn. 9.186:\n",
+ "G15_bar = (Qb+(W*(HL14-HW)))/(HG15-HL14);# [kmol]\n",
+ "L14_bar = W+G15_bar;# [kmol]\n",
+ "G14_bar = (Qb+(W*(HL13-HW)))/(HG14-HL13);# [kmol]\n",
+ "L14_By_G14 = L14_bar/G14_bar;\n",
+ "print\"Condensor Heat Load kJ:\\n\",HL0\n",
+ "print\"Reboiler Heat Load kJ:\\n\",HG15\n",
+ "# For other Exhausting Section Trays:\n",
+ "# Result = [Tray No. L_By_G Temp(OC)]\n",
+ "# Tray 0: Condensor\n",
+ "# Tray 15: Reboiler\n",
+ "Result = numpy.array([[0,0.80 ,46.6],[1 ,0.432 ,58.4],[2, 0.437, 66],[3, 0.369, 70.4],[4 ,0.305, 74],[5 ,0.310, 80.3],[6, 1.53, 86.4],[7, 4.05 ,94.1],[8 ,3.25 ,96.3],[9, 2.88 ,97.7],[10 ,2.58 ,99],[11, 2.48 ,100],[12 ,2.47 ,102.9],[13 ,2.42 ,104.6],[14 ,2.18 ,107.9],[15, 1.73 ,113.5]]);\n",
+ "print\"**************L/G*************\\n\"\n",
+ "print\"Tray No. \\t\\t L/G\\t\\t\\t\\t Temp(OC)\\n\"\n",
+ "for i in range(0,16):\n",
+ " print Result[i,0],\"\\t\\t \\t \\t\",Result[i,1],\"\\t \\t \\t\",Result[i,2];\n",
+ "\n",
+ "# These values are not final.\n",
+ "# They scatter eratically because they are based on the temp. and conc. computed with the assumption of constant L/G\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**************Thiele Geddes Method******************#\n",
+ "# Page:452\n",
+ "print'Page: 452\\n\\n'\n",
+ "\n",
+ "# Use the tray Temperature to obtain m.\n",
+ "# For C4:\n",
+ "# m = [0(Condensor) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15(Reboiler)]\n",
+ "m = numpy.array([0.50 ,0.66, 0.75 ,0.81 ,0.86 ,0.95 ,1.07 ,1.22 ,1.27 ,1.29 ,1.30, 1.32, 1.40, 1.45, 1.51, 1.65]);\n",
+ "A = numpy.array([1.6,0.65,0.582,0.4555,0.354,0.326,1.42990])\n",
+ "S = numpy.array([0.3012,0.39076,0.4479,0.503875,0.53225,0.56680,0.59917,0.69,0.95375])\n",
+ "\n",
+ "# f = Tray No. 6\n",
+ "\n",
+ "# From Eqn. 9.196:\n",
+ "# Value1 = Gf*yf/(D*zD)\n",
+ "Sum = 0;\n",
+ "for i in range(0,6):\n",
+ " Val = 1;\n",
+ " for j in range(0,6):\n",
+ " Val = Val*A[j];\n",
+ " \n",
+ " Sum = Sum+Val;\n",
+ "\n",
+ "Value1 = 1+Sum;\n",
+ "# From Eqn. 9.206:\n",
+ "# Value2 = Lf_bar*xf/(W*xW);\n",
+ "Sum = 0.5316\n",
+ "Value2 = 1+Sum;\n",
+ "# From Eqn. 9.208:\n",
+ "# Value3 = W*xW/(D*zD)\n",
+ "Value3 = A[6]*Value1/Value2;\n",
+ "# From Eqn. 9.210:\n",
+ "DyD = F*zF[3]/(Value3+1);# [kmol,C4]\n",
+ "# From Eqn. 9.209:\n",
+ "WxW = ((F*zF[3]))-(DyD);# [kmol, C4]\n",
+ "# Similarly:\n",
+ "# For [C1; C2; C3; C4; C5; C6]\n",
+ "# Result2 = [Value1 Value2 Value3 DyD WxW]\n",
+ "Result2 = numpy.array([[1.0150, 254*10**6 ,288*10**(-10), 0.03, 0],[1.0567, 8750, 298*10**(-5) ,0.07 ,0],[1.440, 17.241 ,0.0376 ,0.1447, 0.0053],[1.5778 ,1.5306 ,1.475, 0.1335 ,0.1965],[15580, 1.1595, 45.7 ,0.00643 ,0.29357],[1080 ,1.0687 ,7230 ,0.0000166 ,0.1198]]);\n",
+ "D = sum(Result2[:,2]);# [kmol]\n",
+ "W = sum(Result2[:,3]);# [kmol]\n",
+ "# In the Distillate:\n",
+ "DyD_C3 = Result[1,2];# [kmol]\n",
+ "zFC3 = zF[2];# [kmol]\n",
+ "percentC3 = (DyD_C3/zFC3)*100;\n",
+ "DyD_C5 = Result2[3,2];# [kmol]\n",
+ "zFC5 = zF[4];# [kmol]\n",
+ "percentC5 = (DyD_C5/zFC5)*100;\n",
+ "# These do not quite meet the original specification.\n",
+ "# For Tray 2 & C4\n",
+ "# From Eqn. 9.195:\n",
+ "# Value4 = G2*y2/(D*zD)\n",
+ "n = 2;\n",
+ "Sum = 0;\n",
+ "for i in range(0,n):\n",
+ " Val = 1;\n",
+ " for j in range(i,n):\n",
+ " Val = Val*A[j];\n",
+ " \n",
+ " Sum = Sum+Val;\n",
+ "\n",
+ "Value4 = 1+Sum;\n",
+ "# From The enthalpy Balnce:\n",
+ "G2 = 0.675;\n",
+ "# From Eqn. 9.211:\n",
+ "y2 = Value4*DyD/G2;\n",
+ "# Similarly:\n",
+ "# Value4 = [C1 C2 C3 C4 C5 C6]\n",
+ "Value4 = numpy.array([1.0235, 1.1062, 1.351, 2.705, 10.18 ,46.9]);\n",
+ "y2= numpy.array([0.04548,0.114,0.2896,0.53498,0.09697,0.001153]);\n",
+ "Y2 = sum(y2);\n",
+ "# Since Y2 is not equal to 1. THerefore the original temperature is incorrect. By adjusting y2 to unity.\n",
+ "# The dew point is 77 OC instead of 66 OC\n",
+ "# y2_adjusted = [C1 C2 C3 C4 C5 C6]\n",
+ "y2_adjusted = numpy.array([0.0419 ,0.1059 ,0.2675 ,0.4939, 0.0896, 0.00106]);\n",
+ "print\"*****************Composition By Thiele Geddes Method*****************\\n\"\n",
+ "print\"Component\\t \\t \\t y2\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t \\t\",y2_adjusted[i]\n",
+ "# some values of solution in the textbook are incorrect"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.13\n",
+ "\n",
+ "\n",
+ "Page: 436\n",
+ "\n",
+ "\n",
+ "Minimum Reflux Ratio is 0.619146164974 mol reflux/mol distillate\n",
+ " \n",
+ "\n",
+ "*****************Distillate Composition*********************\n",
+ "\n",
+ "C1\t \t \t \t: 0.0786\n",
+ "C2\t \t \t \t: 0.1835\n",
+ "C3\t \t \t \t: 0.3854\n",
+ "C4\t \t \t \t: 0.34\n",
+ "C5\t \t \t \t: 0.007866\n",
+ "C6\t \t \t \t: 0.0\n",
+ "\n",
+ "\n",
+ "*****************Residue Composition*********************\n",
+ "\n",
+ "C1\t \t \t \t: 0.0\n",
+ "C2\t \t \t \t: 0.0\n",
+ "C3\t \t \t \t: 0.00484930540974\n",
+ "C4\t \t \t \t: 0.321097242636\n",
+ "C5\t \t \t \t: 0.480081235564\n",
+ "C6\t \t \t \t: 0.19397221639\n",
+ "\n",
+ "\n",
+ "Page: 440\n",
+ "\n",
+ "\n",
+ "Page: 441\n",
+ "\n",
+ "\n",
+ "For the reflux ratio of 0.8\n",
+ "\n",
+ "*****************Distillate Composition*********************\n",
+ "\n",
+ "\t\t\t Liquid reflux in equilibrium with the distillate vapour\n",
+ "\n",
+ "C 0 \t \t \t \t\t: 0.004\n",
+ "C 1 \t \t \t \t\t: 0.0444501\n",
+ "C 2 \t \t \t \t\t: 0.2495\n",
+ "C 3 \t \t \t \t\t: 0.6564\n",
+ "C 4 \t \t \t \t\t: 0.0451\n",
+ "C 5 \t \t \t \t\t: 0.0\n",
+ "Page: 443\n",
+ "\n",
+ "\n",
+ "Number of theoretical Trays required for R = 0.8: 14.0\n",
+ "\n",
+ "\n",
+ "Page: 446\n",
+ "\n",
+ "\n",
+ "******Corrected Composition***********\n",
+ "\n",
+ "Component\t \tx2\t \t \t y2\t \t \t x1\t \t \t y1\t \t \tx0\t \t \tyD\n",
+ "\n",
+ "C 0 \t \t \t0.0021 \t \t \t \t 0.0444 \t \t \t \t 0.00226 \t \t \t \t0.0451 \t \t \t \t \t0.00425 \t \t \t \t0.0789\n",
+ "C 1 \t \t \t0.0214 \t \t \t \t 0.111 \t \t \t \t 0.0241 \t \t \t \t0.1209 \t \t \t \t \t0.0425 \t \t \t \t0.1842\n",
+ "C 2 \t \t \t0.1418 \t \t \t \t 0.2885 \t \t \t \t 0.1697 \t \t \t \t0.3259 \t \t \t \t \t0.2495 \t \t \t \t0.387\n",
+ "C 3 \t \t \t0.6786 \t \t \t \t 0.5099 \t \t \t \t 0.71 \t \t \t \t0.484 \t \t \t \t \t0.6611 \t \t \t \t0.342\n",
+ "C 4 \t \t \t0.1553 \t \t \t \t 0.0458 \t \t \t \t 0.0932 \t \t \t \t0.0239 \t \t \t \t \t0.0425 \t \t \t \t0.0079\n",
+ "C 5 \t \t \t0.00262 \t \t \t \t 0.00034 \t \t \t \t 0.00079 \t \t \t \t9e-05 \t \t \t \t \t0.00015 \t \t \t \t1e-05\n",
+ "\n",
+ "\n",
+ "Page: 448\n",
+ "\n",
+ "\n",
+ "Condensor Heat Load kJ:\n",
+ "21641.994\n",
+ "Reboiler Heat Load kJ:\n",
+ "59915.6783775\n",
+ "**************L/G*************\n",
+ "\n",
+ "Tray No. \t\t L/G\t\t\t\t Temp(OC)\n",
+ "\n",
+ "0.0 \t\t \t \t0.8 \t \t \t46.6\n",
+ "1.0 \t\t \t \t0.432 \t \t \t58.4\n",
+ "2.0 \t\t \t \t0.437 \t \t \t66.0\n",
+ "3.0 \t\t \t \t0.369 \t \t \t70.4\n",
+ "4.0 \t\t \t \t0.305 \t \t \t74.0\n",
+ "5.0 \t\t \t \t0.31 \t \t \t80.3\n",
+ "6.0 \t\t \t \t1.53 \t \t \t86.4\n",
+ "7.0 \t\t \t \t4.05 \t \t \t94.1\n",
+ "8.0 \t\t \t \t3.25 \t \t \t96.3\n",
+ "9.0 \t\t \t \t2.88 \t \t \t97.7\n",
+ "10.0 \t\t \t \t2.58 \t \t \t99.0\n",
+ "11.0 \t\t \t \t2.48 \t \t \t100.0\n",
+ "12.0 \t\t \t \t2.47 \t \t \t102.9\n",
+ "13.0 \t\t \t \t2.42 \t \t \t104.6\n",
+ "14.0 \t\t \t \t2.18 \t \t \t107.9\n",
+ "15.0 \t\t \t \t1.73 \t \t \t113.5\n",
+ "\n",
+ "\n",
+ "Page: 452\n",
+ "\n",
+ "\n",
+ "*****************Composition By Thiele Geddes Method*****************\n",
+ "\n",
+ "Component\t \t \t y2\n",
+ "\n",
+ "C 0 \t \t \t \t0.0419\n",
+ "C 1 \t \t \t \t0.1059\n",
+ "C 2 \t \t \t \t0.2675\n",
+ "C 3 \t \t \t \t0.4939\n",
+ "C 4 \t \t \t \t0.0896\n",
+ "C 5 \t \t \t \t0.00106\n"
+ ]
+ }
+ ],
+ "prompt_number": 85
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter9_1.ipynb b/Mass_-_Transfer_Operations/Chapter9_1.ipynb
new file mode 100755
index 00000000..6ae47958
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter9_1.ipynb
@@ -0,0 +1,2154 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:1356f02f4e266983d4e000fdec653dca9388627dd3d49506786471d71b9d02b6"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 9: Distillation"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.1: Page 349"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.1\n",
+ "# Page: 349\n",
+ "\n",
+ "print'Illustration 9.1 - Page: 349\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "#****Data****#\n",
+ "# a:n-heptane b:n-octane\n",
+ "Pt = 760; # [mm Hg]\n",
+ "#*****#\n",
+ "\n",
+ "Tempa = 98.4;# [boiling point of A,OC]\n",
+ "Tempb = 125.6;# [boiling point of B,OC]\n",
+ "x = numpy.zeros(6);\n",
+ "y_star = numpy.zeros(6);\n",
+ "alpha = numpy.zeros(6);\n",
+ "# Data = [Temp Pa (mm Hg) Pb(mm Hg)]\n",
+ "Data = [(98.4, 760.0, 333.0),(105.0 ,940.0, 417.0),(110.0, 1050.0, 484.0),(115.0, 1200.0, 561.0),(120.0, 1350.0, 650.0),(125.6 ,1540.0, 760.0)];\n",
+ "for i in range(0,6): \n",
+ " x[i] = (Pt-Data[i][2])/(Data[i][1]-Data[i][2]);# [mole fraction of heptane in liquid]\n",
+ " y_star[i] = (Data[i][1]/Pt)*x[i];\n",
+ " alpha[i] = Data[i][1]/Data[i][2];\n",
+ "\n",
+ "print\"\\t\\t T(OC)\\t\\t\\t\\t Pa(mm Hg)\\t\\t\\t\\t\\t\\t\\t Pb(mm Hg)\\t\\t\\t\\t\\t\\t\\t\\t x\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t y*\\t\\t\\t\\t\\t\\t\\t\\t\\t alpha\\n\"\n",
+ "for i in range(0,6):\n",
+ " print \"\\t \\t \",Data[i][0],\"\\t \\t \\t \\t\",Data[i][1],\"\\t \\t \\t \\t\",Data[i][2],\"\\t \\t \\t \\t \",round(x[i],3),\"\\t \\t \\t \\t \",round(y_star[i],3),\"\\t\\t\\t\\t\\t\\t\\t\\t\",round(alpha[i],2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.1 - Page: 349\n",
+ "\n",
+ "\n",
+ "\t\t T(OC)\t\t\t\t Pa(mm Hg)\t\t\t\t\t\t\t Pb(mm Hg)\t\t\t\t\t\t\t\t x\t\t\t\t\t\t\t\t\t\t\t y*\t\t\t\t\t\t\t\t\t alpha\n",
+ "\n",
+ "\t \t 98.4 \t \t \t \t760.0 \t \t \t \t333.0 \t \t \t \t 1.0 \t \t \t \t 1.0 \t\t\t\t\t\t\t\t2.28\n",
+ "\t \t 105.0 \t \t \t \t940.0 \t \t \t \t417.0 \t \t \t \t 0.656 \t \t \t \t 0.811 \t\t\t\t\t\t\t\t2.25\n",
+ "\t \t 110.0 \t \t \t \t1050.0 \t \t \t \t484.0 \t \t \t \t 0.488 \t \t \t \t 0.674 \t\t\t\t\t\t\t\t2.17\n",
+ "\t \t 115.0 \t \t \t \t1200.0 \t \t \t \t561.0 \t \t \t \t 0.311 \t \t \t \t 0.492 \t\t\t\t\t\t\t\t2.14\n",
+ "\t \t 120.0 \t \t \t \t1350.0 \t \t \t \t650.0 \t \t \t \t 0.157 \t \t \t \t 0.279 \t\t\t\t\t\t\t\t2.08\n",
+ "\t \t 125.6 \t \t \t \t1540.0 \t \t \t \t760.0 \t \t \t \t 0.0 \t \t \t \t 0.0 \t\t\t\t\t\t\t\t2.03\n"
+ ]
+ }
+ ],
+ "prompt_number": 70
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.2: Page 354"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.2\n",
+ "# Page: 354\n",
+ "\n",
+ "print'Illustration 9.2 - Page: 354\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:ethylaniline\n",
+ "Pt = 760.0; # [mm Hg]\n",
+ "ma1 = 50.0;# [g]\n",
+ "mb1 = 50.0;# [g]\n",
+ "#*******#\n",
+ "\n",
+ "# Data = [Temp Pa(mm Hg) Pb(mm Hg)]\n",
+ "Data = [(38.5, 51.1 ,1.0),(64.4 ,199.7, 5.0),(80.6 ,363.9 ,10.0),(96.0, 657.6, 20.0),(99.15 ,737.2 ,22.8),(113.2, 1225.0, 40.0)];\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Mb = 121.1;# [kg/kmol]\n",
+ "\n",
+ "for i in range(0,6):\n",
+ " p = Data[i][1]+Data[i][2];\n",
+ " if p==Pt:\n",
+ " pa = Data[4][1];# [mm Hg]\n",
+ " pb = Data[i][2];# [mm Hg]\n",
+ " T = Data[i][0];# [OC]\n",
+ " \n",
+ "\n",
+ "ya_star = pa/Pt;\n",
+ "yb_star = pb/Pt;\n",
+ "ya1 = ma1/Ma;# [g mol water]\n",
+ "yb1 = mb1/Mb;# [g mol ethylalinine]\n",
+ "Y = ya1*(yb_star/ya_star);# [g mol ethylalinine]\n",
+ "print\"The original mixture contained\",round(ya1,2),\"g mol water and \",round(yb1,2),\" g mol ethylanaline\\n\"\n",
+ "print\"The mixture will continue to boil at \",T,\" degree C, where the equilibrium vapour of the indicated composition,until all the water evaporated together with \",round(Y,3),\"g mol ethylaniline\\n\"\n",
+ "print\"The temparature will then rise to 204 degree C, and the equilibrium vapour will be of pure ethylanaline\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.2 - Page: 354\n",
+ "\n",
+ "\n",
+ "The original mixture contained 2.77 g mol water and 0.41 g mol ethylanaline\n",
+ "\n",
+ "The mixture will continue to boil at 99.15 degree C, where the equilibrium vapour of the indicated composition,until all the water evaporated together with 0.086 g mol ethylaniline\n",
+ "\n",
+ "The temparature will then rise to 204 degree C, and the equilibrium vapour will be of pure ethylanaline\n"
+ ]
+ }
+ ],
+ "prompt_number": 71
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.3: Page 362"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.3\n",
+ "# Page: 362\n",
+ "\n",
+ "print'Illustration 9.3 - Page: 362\\n\\n'\n",
+ "import numpy\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:n-C3H8 b:n-C4H10 c:n-C5H12 d:n-C6H14\n",
+ "# Bubble Point Calculation\n",
+ "xa = 0.05;\n",
+ "xb = 0.30;\n",
+ "xc = 0.40;\n",
+ "xd = 0.25;\n",
+ "P = 350;# [kN/square m]\n",
+ "#******#\n",
+ "\n",
+ "# Assume:\n",
+ "Temp = 60;# [OC]\n",
+ "x = [0.05, 0.30, 0.40, 0.25];\n",
+ "m = [4.70, 1.70 ,0.62 ,0.25];# [At 60 OC]\n",
+ "# Reference: C5H12\n",
+ "mref = m[3];\n",
+ "Sum = 0;\n",
+ "alpha = numpy.zeros(4)\n",
+ "alpha_x = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[3];\n",
+ " alpha_x[i] = alpha[i]*x[i];\n",
+ " Sum = Sum+alpha_x[i];\n",
+ "\n",
+ "# From Eqn. 9.23:\n",
+ "SumF = Sum;\n",
+ "Sum = 0;\n",
+ "mref = 1/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 56.8;# [OC]\n",
+ "m = [4.60, 1.60, 0.588, 0.235];# [At 56.8 OC]\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[2];\n",
+ " alpha_x[i] = alpha[i]*x[i];\n",
+ " Sum = Sum+alpha_x[i];\n",
+ "\n",
+ "SumF = Sum;\n",
+ "mref = 1/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 56.7;# [OC]\n",
+ "Bt = 56.8;# [OC]\n",
+ "yi = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " yi[i] = alpha_x[i]/Sum;\n",
+ "\n",
+ "print\"The Bubble Point is \",Bt,\" degree C\\n\"\n",
+ "print\"Bubble point vapour composition \\n\"\n",
+ "print\"\\t yi\\n\";\n",
+ "print\"\\n n-C3\\t \",round(yi[0],3)\n",
+ "print\"\\n n-C4\\t \",round(yi[1],3)\n",
+ "print\"\\n n-C5\\t \",round(yi[2],3)\n",
+ "print\"\\n n-C6\\t \",round(yi[3],3)\n",
+ "\n",
+ "print\"\\n \\n \\n\"\n",
+ "\n",
+ "# Dew Point Calculation\n",
+ "# Asume:\n",
+ "ya = 0.05;\n",
+ "yb = 0.30;\n",
+ "yc = 0.40;\n",
+ "yd = 0.25;\n",
+ "Temp = 80;# [OC]\n",
+ "y = [0.05, 0.30 ,0.40, 0.25];\n",
+ "m = [6.30 ,2.50 ,0.96 ,0.43];# [At 60 OC]\n",
+ "# Reference: C5H12\n",
+ "mref = m[3];\n",
+ "Sum = 0;\n",
+ "alpha = numpy.zeros(4)\n",
+ "alpha_y = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[3];\n",
+ " alpha_y[i] = y[i]/alpha[i];\n",
+ " Sum = Sum+alpha_y[i];\n",
+ "\n",
+ "\n",
+ "# From Eqn. 9.29:\n",
+ "SumF = Sum;\n",
+ "Sum = 0;\n",
+ "mref = SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 83.7;# [OC]\n",
+ "m = [6.60, 2.70, 1.08, 0.47];# [At 56.8 OC]\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[2];\n",
+ " alpha_y[i] = y[i]/alpha[i];\n",
+ " Sum = Sum+alpha_y[i];\n",
+ "\n",
+ "SumF = Sum;\n",
+ "mref = 1.0/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 84.0;# [OC]\n",
+ "Dt = 84.0;# [OC]\n",
+ "xi = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " xi[i] = alpha_y[i]/Sum;\n",
+ "\n",
+ "print\"The Dew Point is \",Dt,\" degree C\\n\"\n",
+ "print\"Dew point liquid composition \\n\"\n",
+ "print\"\\t xi\\n\"\n",
+ "print\"\\n n-C3\\t \",round(xi[0],3)\n",
+ "print\"\\n n-C4\\t \",round(xi[1],3)\n",
+ "print\"\\n n-C5\\t \",round(xi[2],3)\n",
+ "print\"\\n n-C6\\t \",round(xi[3],3)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.3 - Page: 362\n",
+ "\n",
+ "\n",
+ "The Bubble Point is 56.8 degree C\n",
+ "\n",
+ "Bubble point vapour composition \n",
+ "\n",
+ "\t yi\n",
+ "\n",
+ "\n",
+ " n-C3\t 0.229\n",
+ "\n",
+ " n-C4\t 0.478\n",
+ "\n",
+ " n-C5\t 0.234\n",
+ "\n",
+ " n-C6\t 0.059\n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ "The Dew Point is 84.0 degree C\n",
+ "\n",
+ "Dew point liquid composition \n",
+ "\n",
+ "\t xi\n",
+ "\n",
+ "\n",
+ " n-C3\t 0.007\n",
+ "\n",
+ " n-C4\t 0.109\n",
+ "\n",
+ " n-C5\t 0.363\n",
+ "\n",
+ " n-C6\t 0.521\n"
+ ]
+ }
+ ],
+ "prompt_number": 72
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.4: Page 365"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.4\n",
+ "# Page: 365\n",
+ "\n",
+ "print'Illustration 9.4 - Page: 365\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol feed]\n",
+ "zF = 0.5;\n",
+ "D = 60.0;# [mol]\n",
+ "W = 40.0;# [mol]\n",
+ "#*******#\n",
+ "\n",
+ "# From Illustration 9.1, Equilibrium data:\n",
+ "Data = numpy.array([[1.0 ,1.0],[0.655, 0.810],[0.487 ,0.674],[0.312, 0.492],[0.1571 ,0.279],[0, 0]]);\n",
+ "Feed = numpy.array([[0 ,0],[1.0 ,1.0]]);\n",
+ "# The operating line is drawn with a slope -(W/D) to cut the equilibrium line.\n",
+ "def f44(x):\n",
+ " return -((W/D)*(x-zF))+zF\n",
+ "x = numpy.arange(0.2,0.7,0.1);\n",
+ "plt.plot(Data[:,0],Data[:,1],label=\"Equilibrium Line\")\n",
+ "plt.plot(Feed[:,0],Feed[:,1],label=\"Feed Line\")\n",
+ "plt.plot(x,f44(x),label=\"Operating Line\");\n",
+ "plt.grid('on')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Mole fraction of heptane in liquid\")\n",
+ "ax.set_ylabel(\"Mole fraction of heptane in vapour\")\n",
+ "plt.legend(loc='lower right');\n",
+ "plt.show()\n",
+ "# The point at which the operating line cuts the equilibrium line has the following composition* temparature:\n",
+ "yd = 0.575;# [mole fraction heptane in vapour phase]\n",
+ "xW = 0.387;# [mole fraction heptane in liquid phase]\n",
+ "Temp = 113;# [OC]\n",
+ "print\"mole fraction of heptane in vapour phase \",yd\n",
+ "print\"mole fraction of heptane in liquid phase \",xW\n",
+ "print\"Temperature is \",Temp,\" degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.4 - Page: 365\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
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FmC7Kj5cRAwCqujPCe7cHVqnqWgARmQxcBCwLaXcLbpX1KRHe34iQ4BQXqbKa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\nzM+VsnViXQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7f17518>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "mole fraction of heptane in vapour phase 0.575\n",
+ "mole fraction of heptane in liquid phase 0.387\n",
+ "Temperature is 113 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.5: Page 366"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.5\n",
+ "# Page: 366\n",
+ "\n",
+ "print'Illustration 9.5 - Page: 366\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "import pylab\n",
+ "import numpy.linalg as lin\n",
+ "#****Data****#\n",
+ "Pt = 760.0;# [mm Hg]\n",
+ "zFa = 0.5;# [mol fraction benzene]\n",
+ "zFb = 0.25;# [mol fraction toulene]\n",
+ "zFc = 0.25;# [mol fraction o-xylene]\n",
+ "#********#\n",
+ "\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol feed]\n",
+ "# For Summtion of Yd_star to be unity, W/D = 2.08 \n",
+ "# The Eqn.are \n",
+ "# (1): W+D = F \n",
+ "# (2): W-2.08D = 0\n",
+ "a =numpy.array([[1.0 ,1.0],[1.0 ,-2.08]]);\n",
+ "b = numpy.array([[F*1.0],[0]]);\n",
+ "soln = lin.solve(a,b)\n",
+ "W = soln[0];\n",
+ "D = soln[1];\n",
+ "Sub = ['A','B','C'];\n",
+ "p =numpy.array([1370 ,550, 200]);# [mm Hg]\n",
+ "m = numpy.zeros(3);\n",
+ "zF = [zFa ,zFb, zFc];# [Given]\n",
+ "yd_star = numpy.array([0,0,0]);\n",
+ "xW = numpy.zeros(3);\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " m[i] = p[i]/Pt;\n",
+ " yd_star[i]=(zF[i])*((W/D)+1)#/(1+(W/(D*m[i])));\n",
+ " xW[i] = yd_star[i]/m[i];\n",
+ "\n",
+ "print\"\\t \\t \\t \\t \\t \\t \\t \\t At W/D = 2.08\\n\\n\\n\"\n",
+ "print\"Substance \\t \\t p(mm Hg)\\t \\t m\\t \\t \\t \\t \\t \\t \\t \\t \\t \\t zF\\t \\t \\t \\t \\t \\t \\t yd*\\t\\t\\t\\t\\t\\txW\\n\"\n",
+ "for i in range(0,3):\n",
+ " print \"\\n\",Sub[i],\" \\t \\t \\t \\t \",p[i],\"\\t \\t \\t \\t \",m[i],\"\\t \\t \\t\",m[i],\"\\t \\t \\t\",zF[i],\" \\t \\t \\t\",yd_star[i],\"\\t\",xW[i]\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.5 - Page: 366\n",
+ "\n",
+ "\n",
+ "\t \t \t \t \t \t \t \t At W/D = 2.08\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\n",
+ "Substance \t \t p(mm Hg)\t \t m\t \t \t \t \t \t \t \t \t \t zF\t \t \t \t \t \t \t yd*\t\t\t\t\t\txW\n",
+ "\n",
+ "\n",
+ "A \t \t \t \t 1370 \t \t \t \t 1.80263157895 \t \t \t1.80263157895 \t \t \t0.5 \t \t \t1 \t0.554744525547\n",
+ "\n",
+ "B \t \t \t \t 550 \t \t \t \t 0.723684210526 \t \t \t0.723684210526 \t \t \t0.25 \t \t \t0 \t0.0\n",
+ "\n",
+ "C \t \t \t \t 200 \t \t \t \t 0.263157894737 \t \t \t0.263157894737 \t \t \t0.25 \t \t \t0 \t0.0\n"
+ ]
+ }
+ ],
+ "prompt_number": 74
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.6: Page 370"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.6\n",
+ "# Page: 370\n",
+ "\n",
+ "print'Illustration 9.6 - Page: 370\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol]\n",
+ "xF = 0.5;\n",
+ "D = 0.6*100;# [mol]\n",
+ "#******#\n",
+ "\n",
+ "W = F-D;# [mol]\n",
+ "# From Illustration 9.1:\n",
+ "alpha = 2.16;# [average value of alpha]\n",
+ "# From Eqn.9.46;\n",
+ "def f45(xW):\n",
+ " return math.log(F*xF/(W*xW))-(alpha*math.log(F*(1-xF)/(W*(1-xW))))\n",
+ "xW = fsolve(f45,0.5);# [mole fraction heptane]\n",
+ "def f46(yD):\n",
+ " return F*xF-((D*yD)+(W*xW))\n",
+ "yD = fsolve(f46,100);# [mole fraction heptane]\n",
+ "print\"Mole Fraction of heptane in the distillate is \",round(yD,3),\"\\n\"\n",
+ "print\"Mole Fraction of heptane in the residue is \",round(xW,3),\" \\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.6 - Page: 370\n",
+ "\n",
+ "\n",
+ "Mole Fraction of heptane in the distillate is 0.615 \n",
+ "\n",
+ "Mole Fraction of heptane in the residue is 0.328 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 75
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.7: Page 371"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.7\n",
+ "# Page: 371\n",
+ "from scipy.optimize import fsolve\n",
+ "print'Illustration 9.7 - Page: 371\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:benzene b:toulene c:o-xylene\n",
+ "# Assume:\n",
+ "Bt = 100.0;#[OC]\n",
+ "pa = 1370.0;# [mm Hg]\n",
+ "pb = 550.0;# [mm Hg]\n",
+ "pc = 200.0;# [mm Hg]\n",
+ "xFa = 0.5;# [mole fraction]\n",
+ "xFb = 0.25;# [mole fraction]\n",
+ "xFc = 0.25;# [mole fraction]\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol]\n",
+ "D = 32.5;# [mol]\n",
+ "#*******#\n",
+ "\n",
+ "ref = pb;\n",
+ "alpha_a = pa/ref;\n",
+ "alpha_b = pb/ref;\n",
+ "alpha_c = pc/ref;\n",
+ "W = F-D;# [mol]\n",
+ "xbW = 0.3;# [mol]\n",
+ "xaW = 0.4;# [mol]\n",
+ "xcW = 0.3;# [mol]\n",
+ "err = 1.0;\n",
+ "while(err>(10**(-1))):\n",
+ " # From Eqn. 9.47:\n",
+ " def f47(xaW):\n",
+ " return math.log(F*xFa/(W*xaW))-(alpha_a*math.log(F*xFb/(W*xbW)))\n",
+ " xaW = fsolve(f47,xbW);\n",
+ " def f48(xcW):\n",
+ " return math.log(F*xFc/(W*xcW))-(alpha_c*math.log(F*xFb/(W*xbW)))\n",
+ " xcW = fsolve(f48,xbW);\n",
+ " xbW_n = 1-(xaW+xcW);\n",
+ " err = abs(xbW-xbW_n);\n",
+ " xbw = xbW_n;\n",
+ "\n",
+ "# Material balance:\n",
+ "# for A:\n",
+ "def f49(yaD):\n",
+ " return F*xFa-((D*yaD)+(W*xaW))\n",
+ "yaD = fsolve(f49,100);# [mole fraction benzene]\n",
+ "# For B:\n",
+ "def f50(ybD):\n",
+ " return F*xFb-((D*ybD)+(W*xbW))\n",
+ "ybD = fsolve(f50,100);# [mole fraction toulene]\n",
+ "# For C:\n",
+ "def f51(ycD):\n",
+ " return F*xFc-((D*ycD)+(W*xcW))\n",
+ "ycD = fsolve(f51,100);# [mole fraction o-xylene]\n",
+ "print\"The residual compositions are:\\n\"\n",
+ "print\"Benzene:\\n\",round(xaW,3)\n",
+ "print\"Toulene:\\n\",round(xbW,3)\n",
+ "print\"o-xylene:\\n\",round(xcW,3)\n",
+ "print\"\\n The composited distillate compositions are:\\n\"\n",
+ "print\"Benzene:\\n\",round(yaD,3)\n",
+ "print\"Toulene:\\n\",round(ybD,3)\n",
+ "print\"o-xylene:\\n\",round(ycD,3)\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.7 - Page: 371\n",
+ "\n",
+ "\n",
+ "The residual compositions are:\n",
+ "\n",
+ "Benzene:\n",
+ "0.438\n",
+ "Toulene:\n",
+ "0.3\n",
+ "o-xylene:\n",
+ "0.343\n",
+ "\n",
+ " The composited distillate compositions are:\n",
+ "\n",
+ "Benzene:\n",
+ "0.628\n",
+ "Toulene:\n",
+ "0.146\n",
+ "o-xylene:\n",
+ "0.057\n"
+ ]
+ }
+ ],
+ "prompt_number": 76
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.8: Page 388"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.8\n",
+ "# Page: 388\n",
+ "\n",
+ "print'Illustration 9.8 - Page: 388\\n\\n'\n",
+ "import numpy.linalg as lin\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a:methanol b:water\n",
+ "Xa = 0.5;# [Wt fraction]\n",
+ "Temp1 = 26.7;# [OC]\n",
+ "Temp2 = 37.8;# [OC]\n",
+ "F1 = 5000.0;# [kg/hr]\n",
+ "#******#\n",
+ "\n",
+ "#(a)\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "Xa = 0.5;# [Wt fraction]\n",
+ "Xb = 1-Xa;# [Wt fraction]\n",
+ "Temp1 = 26.7;# [OC]\n",
+ "Temp2 = 37.8;# [OC]\n",
+ "F1 = 5000.0;# [kg/hr];\n",
+ "# Basis: 1hr\n",
+ "F = (F1*Xa/Ma)+(F1*Xb/Mb);# [kmol/hr]\n",
+ "# For feed:\n",
+ "zF = (F1*Xa/Ma)/F;# [mole fracton methanol]\n",
+ "MavF = F1/F;# [kg/kmol]\n",
+ "# For distillate:\n",
+ "xD = (95/Ma)/((95/Ma)+(5/Mb));# [mole fraction methanol]\n",
+ "MavD = 100.0/((95/Ma)+(5/Mb));# [kg/kmol]\n",
+ "# For residue:\n",
+ "xW = (1/Ma)/((1/Ma)+(99/Mb));# [mole fraction methanol]\n",
+ "MavR = 100/((1/Ma)+(99/Mb));# [kg/kmol]\n",
+ "# (1): D+W = F [Eqn.9.75]\n",
+ "# (2): D*xD+W*xW = F*zF [Eqn. 9.76]\n",
+ "# Solvving simultaneously:\n",
+ "a = numpy.array([[1.0 ,1.0],[xD ,xW]]);\n",
+ "b = numpy.array([F,F*zF]);\n",
+ "soln = lin.solve(a,b);\n",
+ "D = soln[0];# [kmol/h]\n",
+ "W = soln[1];# [kmol/h]\n",
+ "print\"Quantity of Distillate is\", round(D*MavD),\" kg/hr\\n\"\n",
+ "print\"Quantity of Residue is \",round(W*MavR),\" kg/hr\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (b)\n",
+ "# For the vapour-liquid equilibria:\n",
+ "Tempo = 19.69;# [Base Temp. according to \"International Critical Tables\"]\n",
+ "BtR = 99.0;# [Bubble point of the residue, OC]\n",
+ "hR = 4179.0;# [J/kg K]\n",
+ "hF = 3852.0;# [J/kg K]\n",
+ "def f52(tF):\n",
+ " return (F1*hF*(tF-Temp1))-((W*MavR)*hR*(BtR-Temp2))\n",
+ "tF = fsolve(f52,Temp1);# [OC]\n",
+ "BtF = 76.0;# [Bubble point of feed, OC]\n",
+ "# For the feed:\n",
+ "delta_Hs = -902.5;# [kJ/kmol]\n",
+ "Hf = ((hF/1000.0)*MavF*(tF-Tempo))+delta_Hs;# [kJ/kmol]\n",
+ "# From Fig 9.27:\n",
+ "HD = 6000.0;# [kJ/kmol]\n",
+ "HLo = 3640.0;# [kJ/kmol]\n",
+ "HW = 6000.0;# [kJ/kmol]\n",
+ "print\"The enthalpy of feed is \",round(Hf),\" kJ/kmol\\n\"\n",
+ "print\"The enthalpy of the residue is \",round(HW),\" kJ/kmol\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (c)\n",
+ "# From Fig.9.27:\n",
+ "# The miium reflux ratio is established by the tie line (x = 0.37 y = 0.71), which extended pass through F,the feed.\n",
+ "# At Dm:\n",
+ "Qm = 62570.0;# [kJ/kmol]\n",
+ "Hg1 = 38610.0;# [kJ/kmol]\n",
+ "# From Eqn. 9.65:\n",
+ "Rm = (Qm-Hg1)/(Hg1-HLo);\n",
+ "print\"The minimum reflux ratio is \",round(Rm,4),\"\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (d)\n",
+ "# From Fig. 9.28:\n",
+ "Np = 4.9;\n",
+ "# But it include the reboiler.\n",
+ "Nm = Np-1;\n",
+ "print\"The minimum number of theoretical trays required is \",round(Nm),\" \\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (e)\n",
+ "R = 1.5*Rm;\n",
+ "# Eqn. 9.65:\n",
+ "def f53(Q_prime):\n",
+ " return R-((Q_prime-Hg1)/(Hg1-HLo))\n",
+ "Q_prime = fsolve(f53,2);# [kJ/kmol]\n",
+ "def f54(Qc):\n",
+ " return Q_prime-(HD+(Qc/D))\n",
+ "Qc = fsolve(f54,2);# [kJ/hr]\n",
+ "Qc = Qc/3600.0;# [kW]\n",
+ "print\"The Condensor heat load is \",round(Qc),\" kW\\n\"\n",
+ "# From Eqn. 9.77:\n",
+ "def f55(Q_dprime):\n",
+ " return (F*Hf)-((D*Q_prime)+(W*Q_dprime))\n",
+ "Q_dprime = fsolve(f55,2);\n",
+ "def f56(Qb):\n",
+ " return Q_dprime-(HW-(Qb/W))\n",
+ "Qb = fsolve(f56,2);# [kJ/hr]\n",
+ "Qb = Qb/3600.0;# [kW]\n",
+ "print\"The Reboiler heat load is \",round(Qb),\" kW\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (f)\n",
+ "# From Fig: 9.28\n",
+ "Np = 9.0;\n",
+ "# But it is including the reboiler\n",
+ "print\"No. of theoretical trays in tower is\",Np-1,\"\\n\",\n",
+ "G1 = D*(R+1);# [kmol/hr]\n",
+ "Lo = D*R;# [kmol/hr]\n",
+ "# From Fig. 9.28:\n",
+ "# At the feed tray:\n",
+ "x4 = 0.415;\n",
+ "y5 = 0.676;\n",
+ "x5 = 0.318;\n",
+ "y6 = 0.554;\n",
+ "# From Eqn. 9.64:\n",
+ "def f57(L4):\n",
+ " return (L4/D)-((xD-y5)/(y5-x4))\n",
+ "L4 = fsolve(f57,2);# [kmol/hr]\n",
+ "# From Eqn. 9.62:\n",
+ "def f58(G5):\n",
+ " return (L4/G5)-((xD-y5)/(xD-x4))\n",
+ "G5 = fsolve(f58,2);# [kmol/hr]\n",
+ "# From Eqn. 9.74:\n",
+ "def f59(L5_bar):\n",
+ " return (L5_bar/W)-((y6-xW)/(y6-x5))\n",
+ "L5_bar = fsolve(f59,2);# [kmol/hr]\n",
+ "# From Eqn. 9.72:\n",
+ "def f60(G6_bar):\n",
+ " return (L5_bar/G6_bar)-((y6-xW)/(x5-xW))\n",
+ "G6_bar = fsolve(f60,2);# [kmol/hr]\n",
+ "# At the bottom:\n",
+ "# Material Balance:\n",
+ "# Eqn. 9.66:\n",
+ "# (1): L8_bar-GW_bar = W;\n",
+ "# From Fig. 9.28:\n",
+ "yW = 0.035;\n",
+ "x8 = 0.02;\n",
+ "# From Eqn. 9.72:\n",
+ "L8ByGW_bar = (yW-xW)/(x8-xW);\n",
+ "# (2): L8_bar-(L8ByGW_bar*Gw_bar) = 0\n",
+ "a = numpy.array([[1 ,-1],[1 ,-L8ByGW_bar]]);\n",
+ "b = numpy.array([W,0]);\n",
+ "soln = lin.solve(a,b)\n",
+ "L8_bar = soln[0];# [kmol/h]\n",
+ "GW_bar = soln[1];# [kmol/h]\n",
+ "print\"The Liquid quantity inside the tower is \",round(L8_bar),\" kmol/hr\\n\"\n",
+ "print\"The vapour quantity inside the tower is \",round(GW_bar),\" kmol/hr\\n\"\n",
+ "# The answers are slightly different in textbook due to approximation while in python the answers are precise\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.8 - Page: 388\n",
+ "\n",
+ "\n",
+ "Quantity of Distillate is 2606.0 kg/hr\n",
+ "\n",
+ "Quantity of Residue is 2394.0 kg/hr\n",
+ "\n",
+ "\n",
+ "\n",
+ "The enthalpy of feed is 2545.0 kJ/kmol\n",
+ "\n",
+ "The enthalpy of the residue is 6000.0 kJ/kmol\n",
+ "\n",
+ "\n",
+ "\n",
+ "The minimum reflux ratio is 0.6852 \n",
+ "\n",
+ "\n",
+ "\n",
+ "The minimum number of theoretical trays required is 4.0 \n",
+ "\n",
+ "\n",
+ "\n",
+ "The Condensor heat load is 1609.0 kW\n",
+ "\n",
+ "The Reboiler heat load is 1817.0 kW\n",
+ "\n",
+ "\n",
+ "\n",
+ "No. of theoretical trays in tower is 8.0 \n",
+ "The Liquid quantity inside the tower is 259.0 kmol/hr\n",
+ "\n",
+ "The vapour quantity inside the tower is 127.0 kmol/hr\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 77
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.9: Page 395"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 9.9\n",
+ "# Page: 395\n",
+ "\n",
+ "print'Illustration 9.9 - Page: 395\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import scipy\n",
+ "import numpy\n",
+ "import numpy.linalg as lin\n",
+ "\n",
+ "#****Data****#\n",
+ "P = 695.0;# [kN/square m]\n",
+ "#********#\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "# From Illustration 9.8:\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "F = 216.8;# [kmol/h]\n",
+ "Tempo = 19.7;# [OC]\n",
+ "zF = 0.360;# [mole fraction methanol]\n",
+ "HF = 2533;# [kJ/kmol]\n",
+ "D = 84.4;# [kkmol/h]\n",
+ "zD = 0.915;# [mole fraction methanol]\n",
+ "HD = 3640.0;# [kJ/kmol]\n",
+ "Qc = 5990000.0;# [kJ/h]\n",
+ "# Since the bottom will essentially be pure water:\n",
+ "HW = 6094.0;# [kJ/kmol]\n",
+ "# From Steam tables:\n",
+ "Hs = 2699.0;# [enthalpy of saturated steam, kJ/kg]\n",
+ "hW = 4.2*(Tempo-0);# [enthalpy of water, kJ/kg]\n",
+ "HgNpPlus1 = (Hs-hW)*Mb;# [kJ/kmol]\n",
+ "# (1): GNpPlus1-W = D-F [From Eqn. 9.86]\n",
+ "# (2): (GNpPlus1*HgNpPlus1)-(W*HW) = (D*HD)+Qc-(F*HF) [From Eqn. 9.88]\n",
+ "a = numpy.array([[1 ,-1],[HgNpPlus1 ,-HW]]);\n",
+ "b = numpy.array([[D-F],[(D*HD)+Qc-(F*HF)]]);\n",
+ "soln=lin.solve(a,b)\n",
+ "GNpPlus1 = soln[0];# [kmol/h]\n",
+ "W = soln[1];# [kmol/h]\n",
+ "# From Eqn. 9.87:\n",
+ "def f61(xW):\n",
+ " return (F*zF)-((D*zD)+(W*xW))\n",
+ "xW = fsolve(f61,2);\n",
+ "# The enthalpy of the solution at its bubble point is 6048 kJ/kmol, sufficiently closed to 6094 assumed earlier.\n",
+ "# For delta_w:\n",
+ "xdelta_w = W*xW/(W-GNpPlus1);\n",
+ "Q_dprime = ((W*HW)-(GNpPlus1*HgNpPlus1))/(W-GNpPlus1);# [kJ/kmol]\n",
+ "# From Fig 9.27 ad Fig. 9.28, and for the stripping section:\n",
+ "Np = 9.5;\n",
+ "print\"Steam Rate: \",round(GNpPlus1,1),\"kmol/h\\n\"\n",
+ "print\"Bottom Composition: xW:\",round(xW,5),\"\\n\"\n",
+ "print\"Number of theoretical stages: \",Np,\"\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.9 - Page: 395\n",
+ "\n",
+ "\n",
+ "Steam Rate: 159.7 kmol/h\n",
+ "\n",
+ "Bottom Composition: xW: 0.00281 \n",
+ "\n",
+ "Number of theoretical stages: 9.5 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 78
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.10: Page 412"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.10\n",
+ "# Page: 412\n",
+ "\n",
+ "print'Illustration 9.10 - Page: 412\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# Feed:\n",
+ "F1 = 5000;# [kg/h]\n",
+ "F = 216.8;# [kmol/h]\n",
+ "Tempo = 19.7;# [OC]\n",
+ "zF = 0.360;# [mole fraction methanol]\n",
+ "MavF = 23.1;# [kg/kmol]\n",
+ "Tempf = 58.3;# [OC]\n",
+ "# Distillate:\n",
+ "D1 = 2620;# [kg/h]\n",
+ "D = 84.4;# [kkmol/h]\n",
+ "xD = 0.915;# [mole fraction methanol]\n",
+ "# Residue:\n",
+ "R1 = 2380;# [kg/h]\n",
+ "R = 132.4;# [kmol/h]\n",
+ "xW = 0.00565;# [mole fraction methanol]\n",
+ "\n",
+ "# From Fig. 9.42 (Pg 413):\n",
+ "BtF = 76.0;# [Bubble point if the feed, OC]\n",
+ "DtF = 89.7;# [Dew point of the feed, OC]\n",
+ "# Latent heat of vaporisation at 76 OC\n",
+ "lambda_a = 1046.7;# [kJ/kg]\n",
+ "lambda_b = 2284;# [kJ/kg]\n",
+ "ha = 2.721;# [kJ/kg K]\n",
+ "hb = 4.187;# [kJ/kg K]\n",
+ "hF = 3.852;# [kJ/kg K]\n",
+ "# If heats of solution is ignaored:\n",
+ "# Enthalpy of the feed at the bubble point referred to the feed temp.\n",
+ "HF = hF*MavF*(BtF-Tempf);# [kJ/kmol]\n",
+ "# enthalpy of the saturated vapour at dew point referred to the liquid at feed temp.\n",
+ "HL = (zF*((ha*Ma*(DtF-Tempf))+(lambda_a*Ma)))+((1-zF)*((hb*Mb*(DtF-Tempf))+(lambda_b*Mb)));# [kJ/kmol]\n",
+ "q = HL/(HL-HF);\n",
+ "slope = q/(q-1);\n",
+ "# In fig. 9.42: xD,xW & zF are located on the 45 degree diagonal & the q line is drawn with slope = 'slope' .\n",
+ "# The operating line for minimum reflux ratio in this case pass through the intersection of the q line and the equilibrium curve.\n",
+ "ordinate = 0.57;\n",
+ "def f62(Rm):\n",
+ " return ordinate-(xD/(Rm+1))\n",
+ "Rm = fsolve(f62,0);# [mole reflux/mole distillate]\n",
+ "# from fig. 9.42 (Pg 413):\n",
+ "# The minimum number of theoretical trays is determied using the 45 degree diagonal as operating line.\n",
+ "Np = 4.9;# [including the reboiler]\n",
+ "R = 1.5*Rm;# [mole reflux/mole distillate]\n",
+ "# From Eqn. 9.49:\n",
+ "L = R*D;# [kmol/h]\n",
+ "# From Eqn. 9.115:\n",
+ "G = D*(R+1);# [kmol/h]\n",
+ "# From Eqn. 9.126:\n",
+ "L_bar = (q*F)+L;# [kmol/h]\n",
+ "# From Eqn. 9.127:\n",
+ "G_bar = (F*(q-1))+G;# [kmol/h]\n",
+ "ordinateN = xD/(R+1);\n",
+ "# As in Fig. 9.43:\n",
+ "# The y-intercept = ordinateN and enriching and exhausting operating lines are plotted.\n",
+ "# Number of theoretical stages are determined.\n",
+ "NpN = 8.8;# [including the reboiler]\n",
+ "print\"Number of theoretical stages is \",NpN-1,\"\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.10 - Page: 412\n",
+ "\n",
+ "\n",
+ "Number of theoretical stages is 7.8 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 79
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.11: Page 423"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.11\n",
+ "# Page: 423\n",
+ "\n",
+ "print'Illustration 9.11 - Page: 423\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# a:ethanol b:water\n",
+ "zF = 0.3;\n",
+ "xa = 0.3;# [mole fraction of ethanol]\n",
+ "Temp = 78.2;# [OC]\n",
+ "Ao = 0.0462;# [Area of perforations,square m]\n",
+ "t = 0.450;# [m]\n",
+ "#******#\n",
+ "\n",
+ "Ma = 46.05;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "xb = 1-xa;# [mole fraction of water]\n",
+ "ma = 0.3*Ma/((0.3*Ma)+(xb*Mb));# [mass fraction of ethanol]\n",
+ "mb = 1-ma;# [mass fraction of water]\n",
+ "\n",
+ "\n",
+ "# Feed:\n",
+ "F1 = 910.0;# [kg/h]\n",
+ "Xa = F1*ma/Ma;# [moles of ethanol]\n",
+ "Xb = F1*mb/Mb;# [moles of water]\n",
+ "F = Xa+Xb;# [Total moles]\n",
+ "# Distillate:\n",
+ "xD = 0.80;# [mole fraction of ethanol]\n",
+ "# If essentially all the ethanol is removed from the residue:\n",
+ "D = Xa/xD;# [kmol/h]\n",
+ "MavD = (xD*Ma)+((1-xD)*Mb);# [kg/kmol]\n",
+ "D1 = D*MavD;# [kg/h]\n",
+ "Density_G = (MavD/22.41)*(273.0/(273+Temp));# [kg/cubic meter]\n",
+ "Density_L = 744.9;# [kg/cubic meter]\n",
+ "sigma = 0.021;# [N/m]\n",
+ "\n",
+ "# From Table 6.2,Pg 169:\n",
+ "alpha = (0.0744*t)+0.01173;\n",
+ "beeta = (0.0304*t)+0.015;\n",
+ "At = math.pi*(0.760**2)/4;# [Tower cross sectional Area, square m]\n",
+ "WByT = 530.0/760;# [Table 6.1, Pg 162]\n",
+ "Ad = 0.0808*At;# [Downspout area,square m]\n",
+ "Aa = At-(2*Ad);# [Active area,square m]\n",
+ "# abcissa = (L/G)*(density_G/Density_L)^0.5\n",
+ "# Assume:\n",
+ "abcissa = 0.1;\n",
+ "# From Eqn.6.30:\n",
+ "Cf = (alpha*math.log10(1/abcissa)+beeta)*(sigma/0.020)**0.2;\n",
+ "# From Eqn. 6.29:\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1.0/2);# [m/s]\n",
+ "An = At-Ad;# [square m]\n",
+ "R = 3.0;# [Reflux Ratio]\n",
+ "G = D*(R+1);\n",
+ "G1 = (G*22.41/3600)*((273.0+Temp)/273);# [cubic meter/s]\n",
+ "V = G1/An;# [Vapour velocity,m/s]\n",
+ "percent = (V/Vf)*100;\n",
+ "# Vapour velocity is 58 percent of flooding velocity (amply safe)\n",
+ "L = R*D;# [kmol/h]\n",
+ "L1 = L*MavD;# [kg/h]\n",
+ "abcissa = (L1/(G1*3600.0*Density_G))*(Density_G/Density_L)**0.5;\n",
+ "# Since the value of abcissa is less than0.1, the calculaed value of Cf is correct.\n",
+ "# Since the feed is at the buubble point.\n",
+ "q = 1;\n",
+ "# From Eqn. 9.126:\n",
+ "L_bar = L+(q*F);# [kmol/h]\n",
+ "# From Eqn. 9.127:\n",
+ "G_bar = G+F*(q-1);# [kmol/h]\n",
+ "# The enthalpy of saturated steam,referred to 0 OC,69 kN/square m:\n",
+ "HGNpPlus1 = 2699.0;# [kN m/kg]\n",
+ "# This will be the enthalpy as it enters the tower if expanded adiabatically to the tower pressure\n",
+ "# The enthalpy of steam at 1 std. atm:\n",
+ "HGsat = 2676.0;# [kN m/kg]\n",
+ "Lambda = 2257.0;# [kN m/kg]\n",
+ "# From Eqn. 9.140:\n",
+ "def f63(GNpPlus1_bar):\n",
+ " return G_bar-(GNpPlus1_bar*(1+((HGNpPlus1-HGsat)*Mb/(Lambda*Mb))))\n",
+ "GNpPlus1_bar = fsolve(f63,7);\n",
+ "# From Eqn. 9.141:\n",
+ "LNp_bar = L_bar-(G_bar-GNpPlus1_bar);\n",
+ "\n",
+ "# Tray Efficiencies:\n",
+ "# Consider the situation:\n",
+ "x = 0.5;\n",
+ "y_star = 0.962;\n",
+ "Temp = 79.8;# [OC]\n",
+ "# This is in the enriching section.\n",
+ "Density_L = 791;# [kg/cubic meter]\n",
+ "Density_G = 1.253;# [kg/cubic meter]\n",
+ "# From equilibrium data:\n",
+ "m = 0.42;\n",
+ "A = L/(m*G);\n",
+ "# From chapter 2:\n",
+ "ScG = 0.930;\n",
+ "Dl = 2.065*10**(-9);# [square m/s]\n",
+ "# For L = 38.73 kmol/h\n",
+ "q = 4.36*10**(-4);# [cubic meter/s]\n",
+ "# For G = 51.64 kmol/h\n",
+ "Va = 1.046;# [m/s]\n",
+ "# From tray dimensions:\n",
+ "z = 0.647;# [m]\n",
+ "Z = 0.542;# [m]\n",
+ "hW = 0.06;# [m]\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/(ScG**0.5);\n",
+ "# From Eqn. 6.38\n",
+ "hL = 6.10*10**(-3)+(0.725*hW)-(0.238*hW*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "# From Eqn. 6.64:\n",
+ "thetha_L = hL*z*Z/q;# [s]\n",
+ "# From Eqn. 6.62:\n",
+ "NtL = 40000*(Dl**0.5)*((0.213*Va*Density_G**0.5)+0.15)*thetha_L;\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1/((1/NtG)+(1/(A*NtL)));\n",
+ "# From Eqn. 6.51:\n",
+ "EoG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.63:\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thetha_L);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2)*((1+(4*m*G1*EoG/(L1*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EoG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+((math.exp(eta)-1)/(eta*(1+(eta/(eta+Pe))))));\n",
+ "# Entrainment is neglible:\n",
+ "# Similarly for other x\n",
+ "# Value = [x Entrainment]\n",
+ "#Value = [0 0.48;0.1 .543;0.3 0.74;0.5 EMG;0.7 0.72];\n",
+ "\n",
+ "# Tray Calculation:\n",
+ "op_intercept = xD/(R+1);\n",
+ "# From Fig. 9.48:\n",
+ "# The exhausting section operating line, on this scale plot, for all practical purposes passes through the origin.\n",
+ "# The broken curve is located so that, at each concentration, vertical distances corresponding to lines BC and AC are in the ratio of EMG.\n",
+ "# This curve is used instead of equilibrium trays to locate the ideal trays.\n",
+ "# The feed tray is thirteenth.\n",
+ "x14 = 0.0150;\n",
+ "alpha = 8.95;\n",
+ "EMG = 0.48;\n",
+ "A_bar = L_bar/(alpha*G_bar);\n",
+ "# From Eqn. 8.16:\n",
+ "Eo = math.log(1+(EMG*((1/A_bar)-1)))/math.log(1/A_bar);\n",
+ "# The 6 real trays corresponds to: \n",
+ "NRp = 6*Eo;\n",
+ "xW = 0.015/((math.exp(NRp*math.log(1/A_bar))-A_bar)/(1-A_bar));# [mole fraction ethanol]\n",
+ "# This corresponds to ethanol loss of 0.5 kg/day.\n",
+ "print\"The mole fraction of ethanol in residue is\",round(xW,8)\n",
+ "print\"The Reflux ratio of \",R,\" will cause the ethanol loss of 0.5 kg/day\\n\"\n",
+ "print\"Larger reflux ratios would reduce this, but the cost of additional steam will probaby make them not worthwile.\\n\"\n",
+ "print\"Smaller values of R, with corresponding reduced steam cost and larger ethanol loss, should be considered, but care must be taken to ensure vapour velocities above the weeping velocities.\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.11 - Page: 423\n",
+ "\n",
+ "\n",
+ "The mole fraction of ethanol in residue is 6.28e-06\n",
+ "The Reflux ratio of 3.0 will cause the ethanol loss of 0.5 kg/day\n",
+ "\n",
+ "Larger reflux ratios would reduce this, but the cost of additional steam will probaby make them not worthwile.\n",
+ "\n",
+ "Smaller values of R, with corresponding reduced steam cost and larger ethanol loss, should be considered, but care must be taken to ensure vapour velocities above the weeping velocities.\n"
+ ]
+ }
+ ],
+ "prompt_number": 83
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex-9.12: Pg- 429"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 9.12\n",
+ "# Page: 429\n",
+ "\n",
+ "print'Illustration 9.12 - Page: 429\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "# Vapour and liquid quantities throughout the tower, as in Illustration 9.8, with the Eqn. 9.62, 9.64, 9.72, 9.74:\n",
+ "# Data = [x tL(OC) y tG(OC) Vapor(kmol/h) Vapor(kg/h) Liquid(kmol/h) Liquid(kg/h)]\n",
+ "Ma = 34.02;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "Temp = 78.7;# [OC]\n",
+ "x = numpy.array([0.915, 0.600 ,0.370, 0.370, 0.200, 0.100, 0.02]);\n",
+ "y = numpy.array([0.915, 0.762, 0.656, 0.656, 0.360 ,0.178, 0.032]);\n",
+ "\n",
+ "plt.plot(x,y);\n",
+ "plt.grid('on');\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"mole fraction of methanol in liquid\");\n",
+ "ax.set_ylabel(\"mole fraction of methanol in vapour\");\n",
+ "plt.title(\"Operating Line curve\");\n",
+ "plt.legend(loc=\"lower right\")\n",
+ "plt.show()\n",
+ "#x = 0.370: the dividing point between stripping and enriching section\n",
+ "tL =numpy.array([66, 71, 76, 76, 82, 87, 96.3]);# [Bubble point, OC]\n",
+ "tG = numpy.array([68.2 ,74.3 ,78.7 ,78.7 ,89.7 ,94.7 ,99.3]);# [Dew Point, OC]\n",
+ "Vapor = numpy.array([171.3, 164.0 ,160.9, 168.6, 161.6, 160.6, 127.6]);# [kmol/h]\n",
+ "Vapor1 = numpy.array([5303, 4684, 4378, 4585, 3721, 3296 ,2360]);# [kg/h]\n",
+ "Liquid = numpy.array([86.7 ,79.6 ,76.5 ,301, 294, 293, 260]);# [kmol/h]\n",
+ "Liquid1 = numpy.array([2723, 2104, 1779 ,7000, 6138, 5690 ,4767]);# [kg/h]\n",
+ "Data = numpy.zeros(shape=(7,8));\n",
+ "for j in range(1,7):\n",
+ " Data[j,0]= x[j];\n",
+ " Data[j,1]= tL[j];\n",
+ " Data[j,2]= y[j];\n",
+ " Data[j,3]= tG[j];\n",
+ " Data[j,4]= Vapor[j]; \n",
+ " Data[j,5]= Vapor1[j];\n",
+ " Data[j,6]= Liquid[j];\n",
+ " Data[j,7]= Liquid1[j];\n",
+ "\n",
+ "# The tower diameter will be set by the conditions at the top of the stripping section because of the large liquid flow at this point.\n",
+ "# From Illustration 9.8:\n",
+ "G = Data[3,5];\n",
+ "L = Data[3,7];\n",
+ "Density_G = (Data[3,5]/(22.41*Data[3,4]))*(273.0/(273+Temp));# [kg/cubic m]\n",
+ "Density_L = 905.0;# [kg/cubic m]\n",
+ "# abcissa = (L/G)*(Density_L/Density_G)^0.5\n",
+ "abcissa = (Data[3,7]/Data[3,5])*(Density_G/Density_L)**0.5;\n",
+ "# From Fig. 6.34, choose a gas pressure drop of 450 N/square m/m\n",
+ "ordinate = 0.0825;\n",
+ "# From Table 6.3 (Pg 196):\n",
+ "Cf = 95;\n",
+ "viscosity_L = 4.5*10**(-4);# [kg/m.s]\n",
+ "sigma = 0.029;# [N/m]\n",
+ "J = 1;\n",
+ "G_prime = (ordinate*Density_G*(Density_L-Density_G)/(Cf*viscosity_L**0.1))**0.5;# [kg/square m.s]\n",
+ "A = G/(3600*G_prime);# [Tower ,cross section area,square m]\n",
+ "L_prime = L/(A*3600);# [kg/square m.s]\n",
+ "# Mass transfer will be computed for the same location:\n",
+ "# From Table 6.4 (Pg 205):\n",
+ "m = 36.4;\n",
+ "n = (0.0498*L_prime)-0.1013;\n",
+ "p = 0.274;\n",
+ "aAW = m*((808*G_prime/Density_G**0.5)**n)*L_prime**p;# [square m/cubic m]\n",
+ "# From Table 6.5 (Pg 206):\n",
+ "dS = 0.0530;# [m]\n",
+ "beeta = 1.508*dS**0.376;\n",
+ "shi_LsW = 2.47*10**(-4)/dS**1.21;\n",
+ "shi_LtW = ((2.09*10**(-6))*(737.5*L_prime)**beeta)/dS**2;\n",
+ "shi_LOW = shi_LtW-shi_LsW; \n",
+ "shi_Ls = (0.0486*viscosity_L**0.02*sigma**0.99)/(dS**1.21*Density_L**0.37);\n",
+ "H = ((975.7*L_prime**0.57*viscosity_L**0.13)/(Density_L**0.84*((2.024*L_prime**0.430)-1)))*(sigma/0.073)**(0.1737-0.262*math.log10(L_prime));# [m]\n",
+ "shi_Lo = shi_LOW*H;\n",
+ "shi_Lt = shi_Lo+shi_Ls;\n",
+ "# From Eqn. 6.73:\n",
+ "aA = aAW*(shi_Lo/shi_LOW);# [square m/cubic m]\n",
+ "# From Table 6.3 (Pg 196):\n",
+ "e = 0.71;\n",
+ "# From Eqn. 6.71:\n",
+ "eLo = e-shi_Lt;\n",
+ "# From Chapter 2:\n",
+ "ScG = 1;\n",
+ "MavG = 0.656*Ma+(1-0.656)*Mb;# [kg/kmol]\n",
+ "G = G_prime/MavG;\n",
+ "viscosity_G = 2.96*10**(-5);# [kg/m.s]\n",
+ "# From Eqn. 6.70:\n",
+ "Fg = (1.195*G/ScG**(2/3))*((dS*G_prime/(viscosity_G*(1-eLo)))**(-0.36));# [kmol/square m s (mole fraction)]\n",
+ "kY_prime = Fg;# [kmol/square m s (mole fraction)]\n",
+ "DL = 4.80*10**(-9);# [square m/s]\n",
+ "ScL = viscosity_L/(Density_L*DL);\n",
+ "# From Eqn. 6.72:\n",
+ "kL = (25.1*DL/dS)*((dS*L_prime/viscosity_L)**0.45)*ScL**0.5;# [kmol/square m s (kmol/cubic m)]\n",
+ "# At 588.33 OC\n",
+ "Density_W = 53.82;# [kg/cubic m]\n",
+ "kx_prime = Density_W*kL;# [kmol/square m s (mole fraction)]\n",
+ "# Value1 = [x G a ky_prime*10^3 kx_prime]\n",
+ "Value1 = numpy.array([[0.915 ,0.0474 ,20.18 ,1.525, 0.01055],[0.6, 0.0454 ,21.56 ,1.542, 0.00865],[0.370 ,0.0444 ,21.92 ,1.545 ,0.00776],[0.370, 0.0466 ,38, 1.640, 0.0143],[0.2 ,0.0447, 32.82 ,1.692 ,0.0149],[0.1 ,0.0443 ,31.99 ,1.766 ,0.0146],[0.02, 0.0352 ,22.25 ,1.586 ,0.0150]]);\n",
+ "# From Fig: 9.50\n",
+ "# At x = 0.2:\n",
+ "y = 0.36;\n",
+ "slope = -(Value1[4,4]/(Value1[4,3]*10**(-3)));\n",
+ "# The operating line drawn from(x,y) with slope. The point where it cuts the eqb. line gives yi.\n",
+ "# K = ky_prime*a(yi-y)\n",
+ "# For the enriching section:\n",
+ "# En = [y yi 1/K Gy]\n",
+ "En = numpy.array([[0.915 ,0.960, 634 ,0.0433],[0.85 ,0.906 ,532.8 ,0.0394],[0.8 ,0.862 ,481.1 ,0.0366],[0.70, 0.760 ,499.1, 0.0314],[0.656, 0.702, 786.9, 0.0292]]);\n",
+ "# For the Stripping section:\n",
+ "# St = [y yi 1/K Gy]\n",
+ "St = numpy.array([[0.656, 0.707, 314.7, 0.0306],[0.50, 0.639, 124.6 ,0.0225],[0.40 ,0.580, 99.6 ,0.01787],[0.3 ,0.5 ,89 ,0.0134],[0.2 ,0.390, 92.6 ,0.00888],[0.10, 0.232, 154.5, 0.00416],[0.032 ,0.091, 481 ,0.00124]])\n",
+ "# Graphical Integration, according to Eqn.9.52::\n",
+ "\n",
+ "plt.plot(En[:,3],En[:,2],'g');\n",
+ "plt.grid();\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Gy\");\n",
+ "ax.set_ylabel(\"1 / (ky_prime*a*(yi-y))\");\n",
+ "plt.title(\"Graphical Integration for Enriching section\");\n",
+ "plt.show()\n",
+ "# From Area under the curve:\n",
+ "Ze = 7.53;# [m]\n",
+ "# Graphical Integration:\n",
+ "\n",
+ "plt.plot(St[:,3],St[:,2],'r');\n",
+ "plt.grid('on');\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Gy\");\n",
+ "ax.set_ylabel(\"1 / (ky_prime*a*(yi-y))\");\n",
+ "plt.title(\"Graphical Integration for Stripping section\");\n",
+ "plt.show()\n",
+ "\n",
+ "# From Area under the curve:\n",
+ "Zs = 4.54;# [m]\n",
+ "# Since the equlibrium curve slope varies so greatly that the use of overall mass transfer coeffecient is not recommended:\n",
+ "print\"Height of Tower for enriching Section is \",Ze,\" m\\n\"\n",
+ "print\"Height of Tower for Stripping Section is \",Zs,\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.12 - Page: 429\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xa5ce470>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2ax/UPmZ8NYPdO+wecxrnXByWrl3K2c+fzdoNa3n3wnfp3LZz3JEylmkX1uPA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\nCTmTkBHiz7lkzRKOe+I4Jsy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+ "text": [
+ "<matplotlib.figure.Figure at 0x7bc0518>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xa7dcd30>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Height of Tower for enriching Section is 7.53 m\n",
+ "\n",
+ "Height of Tower for Stripping Section is 4.54 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.13: Page 436"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.13:\n",
+ "\n",
+ "print'Illustration 9.13\\n\\n'\n",
+ "\n",
+ "#**************************Calculation Of Minimum Reflux ratio************************#\n",
+ "# Page: 436\n",
+ "print'Page: 436\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy import interp\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy.linalg as lin\n",
+ "#***Data***#\n",
+ "# C1:CH4 C2:C2H6 C3:n-C3H8 C4:n-C4H10 C5:n-C5H12 C6:n-C6H14\n",
+ "# zF = [zF(C1) zF(C2) zF(C3) zF(C4) zF(C5) zF(C6)]\n",
+ "zF = numpy.array([0.03 ,0.07 ,0.15 ,0.33 ,0.30 ,0.12]);# [mole fraction]\n",
+ "LF_By_F = 0.667;\n",
+ "Temp = 82;# [OC]\n",
+ "ylk = 0.98;\n",
+ "yhk = 0.01;\n",
+ "#**********#\n",
+ "\n",
+ "# Data = [m HG HL(30 OC);m HG HL(60 OC);m HG HL(90 OC);m HG HL(120 OC);]\n",
+ "Data1 = numpy.array([[16.1 ,12790 ,9770],[19.3 ,13910, 11160],[21.8 ,15000, 12790],[24.0 ,16240, 14370]]);# [For C1]\n",
+ "Data2 = numpy.array([[3.45, 22440, 16280],[4.90 ,24300 ,18140],[6.25 ,26240 ,19890],[8.15 ,28140, 21630]]);# [For C2]\n",
+ "Data3 = numpy.array([[1.10, 31170, 16510],[2.00 ,33000 ,20590],[2.90, 35800 ,25600],[4.00 ,39000, 30900]]);# [For C3]\n",
+ "Data4 = numpy.array([[0.35, 41200 ,20350],[0.70 ,43850 ,25120],[1.16 ,46500, 30000],[1.78 ,50400 ,35400]]);# [For C4]\n",
+ "Data5 = numpy.array([[0.085, 50500, 24200],[0.26, 54000 ,32450],[0.50 ,57800 ,35600],[0.84, 61200 ,41400]]);# [For C5]\n",
+ "Data6 = numpy.array([[0.0300, 58800 ,27700],[0.130, 63500, 34200],[0.239 ,68150 ,40900],[0.448, 72700 ,48150]]);# [For C6]\n",
+ "\n",
+ "# T = [Temparature]\n",
+ "T = numpy.array([30,60,0,120]);\n",
+ "\n",
+ "# Flash vaporisation of the Feed:\n",
+ "# Basis: 1 kmol feed throughout\n",
+ "# After Several trials, assume:\n",
+ "F = 1.0;# [kmol]\n",
+ "GF_By_F = 0.333;\n",
+ "LF_By_GF = LF_By_F/GF_By_F;\n",
+ "m82 = numpy.zeros(6);\n",
+ "y = numpy.zeros(6);\n",
+ "m82[0] = interp(Temp,T,Data1[:,1]);# [For C1]\n",
+ "m82[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m82[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m82[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m82[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m82[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "for i in range (0,6):\n",
+ " y[i] = zF[i]*(LF_By_GF+1)/(1.0+(2/m82[i]));\n",
+ "\n",
+ "Sum = sum(y);\n",
+ "# Since Sum is sufficiently close to 1.0, therefore:\n",
+ "q = 0.67;# [LF_By_F]\n",
+ "# Assume:\n",
+ "# C3: light key\n",
+ "# C5: heavy key\n",
+ "zlkF = zF[2];# [mole fraction]\n",
+ "zhkF = zF[4];# [mole fraction]\n",
+ "ylkD = ylk*zF[2];# [kmol]\n",
+ "yhkD = yhk*zF[4];# [kmol]\n",
+ "\n",
+ "# Estimate average Temp to be 80 OC\n",
+ "m80 = numpy.zeros(6);\n",
+ "alpha80 = numpy.zeros(6);\n",
+ "m80[0] = interp(Temp,T,Data1[:,0]);# [For C1]\n",
+ "m80[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m80[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m80[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m80[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m80[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "for i in range(0,6):\n",
+ " alpha80[i] = m80[i]/m80[4];\n",
+ "\n",
+ "# By Eqn. 9.164:\n",
+ "yD_By_zF1 = (((alpha80[0]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[0])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C1]\n",
+ "yD_By_zF2 = (((alpha80[1]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[1])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C2]\n",
+ "yD_By_zF6 = (((alpha80[5]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[5])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C6]\n",
+ "# The distillate contains:\n",
+ "yC1 = 0.03;# [kmol C1]\n",
+ "yC2 = 0.07;# [kmol C2]\n",
+ "yC6 = 0;# [kmol C6]\n",
+ "# By Eqn 9.165:\n",
+ "def g1(phi):\n",
+ " return (((alpha80[0]*zF[0])/(alpha80[0]-phi))+((alpha80[1]*zF[1])/(alpha80[1]-phi))+((alpha80[2]*zF[2])/(alpha80[2]-phi))+((alpha80[3]*zF[3])/(alpha80[3]-phi))+((alpha80[4]*zF[4])/(alpha80[4]-phi))+((alpha80[5]*zF[5])/(alpha80[5]-phi)))-(F*(1-q))\n",
+ "# Between alphaC3 & alphaC4:\n",
+ "phi1 = fsolve(g1,3);\n",
+ "# Between alphaC4 & alphaC5:\n",
+ "phi2 = fsolve(g1,1.5);\n",
+ "# From Eqn. 9.166:\n",
+ "# Val = D*(Rm+1)\n",
+ "# (alpha80(1)*yC1/(alpha80(1)-phi1))+(alpha80(2)*yC2/(alpha80(2)-phi1))+(alpha80(3)*ylkD/(alpha80(3)-phi1))+(alpha80(4)*yD/(alpha80(4)-phi1))+(alpha80(i)*yhkD/(alpha80(5)-phi1))+(alpha80(6)*yC6/(alpha80(6)-phi1)) = Val.....................(1)\n",
+ "# (alpha80(1)*yC1/(alpha80(1)-phi2))+(alpha80(2)*yC2/(alpha80(2)-phi2))+(alpha80(3)*ylkD/(alpha80(3)-phi2))+(alpha80(4)*yD/(alpha80(4)-phi2))+(alpha80(i)*yhkD/(alpha80(5)-phi2))+(alpha80(6)*yC6/(alpha80(6)-phi2)) = Val ....................(2)\n",
+ "# Solving simultaneously:\n",
+ "a =numpy.array([[-alpha80[3]/(alpha80[3]-phi1), 1],[-alpha80[3]/(alpha80[3]-phi2), 1]]);\n",
+ "b =numpy.array([[alpha80[0]*yC1/[alpha80[0]-phi1]]+[alpha80[1]*yC2/[alpha80[1]-phi1]]+[alpha80[2]*ylkD/[alpha80[2]-phi1]]+[alpha80[i]*yhkD/[alpha80[4]-phi1]]+[alpha80[5]*yC6/[alpha80[5]-phi1]],[alpha80[0]*yC1/[alpha80[0]-phi2]]+[alpha80[1]*yC2/[alpha80[1]-phi2]]+[alpha80[2]*ylkD/[alpha80[2]-phi2]]+[alpha80[i]*yhkD/[alpha80[4]-phi2]]+[alpha80[5]*yC6/[alpha80[5]-phi2]]])\n",
+ "soln = lin.solve(a,b);\n",
+ "yC4 =0.1313547 # [kmol C4 in the distillate]\n",
+ "Val =0.617469; # [kmol C4 in the distillate]\n",
+ "# For the distillate, at a dew point of 46 OC\n",
+ "ydD = numpy.array([yC1,yC2 ,ylkD ,yC4 ,yhkD ,yC6]);\n",
+ "D = sum(ydD);\n",
+ "yD = zeros(6);\n",
+ "m46 = zeros(6);\n",
+ "alpha46 = zeros(6);\n",
+ "Ratio1= zeros(6);\n",
+ "m46[0] = interp(Temp,T,Data1[:,0]);# [For C1]\n",
+ "m46[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m46[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m46[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m46[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m46[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "yD=numpy.array([0.0786,0.1835,0.3854,0.34,0.007866,0.0])\n",
+ "# mhk = mC5 at 46.6 OC, the assumed 46 OC is satisfactory.\n",
+ "\n",
+ "# For the residue, at a dew point of 46 OC\n",
+ "xwW =numpy.array([zF[0]-yC1, zF[1]-yC2 ,zF[2]-ylkD, zF[3]-yC4, zF[4]-yhkD, zF[5]-yC6]);\n",
+ "W = sum(xwW);\n",
+ "xW = zeros(6);\n",
+ "m113 = zeros(6);\n",
+ "alpha113 = zeros(6);\n",
+ "alphalk_av=zeros(6);\n",
+ "alpha_av=zeros(6);\n",
+ "Value=zeros(6);\n",
+ "m113[0] = interp(Temp,T,Data1[:,1]);# [For C1]\n",
+ "m113[1] = interp(Temp,T,Data2[:,1]);# [For C2]\n",
+ "m113[2] = interp(Temp,T,Data3[:,1]);# [For C3]\n",
+ "m113[3] = interp(Temp,T,Data4[:,1]);# [For C4]\n",
+ "m113[4] = interp(Temp,T,Data5[:,1]);# [For C5]\n",
+ "m113[5] = interp(Temp,T,Data6[:,1]);# [For C6]\n",
+ "for i in range(0,6):\n",
+ " alpha113[i] = m113[i]/m113[4];\n",
+ " xW[i] = xwW[i]/W;\n",
+ " # Ratio = yD/alpha46\n",
+ " Value[i] = alpha113[i]*xW[i];\n",
+ "\n",
+ "# mhk = mC5 at 114 OC, the assumed 113 OC is satisfactory.\n",
+ "Temp_Avg = (114+46.6)/2;# [OC]\n",
+ "# Temp_avg is very close to the assumed 80 OC\n",
+ "Rm = (Val/D)-1;\n",
+ "print\"Minimum Reflux Ratio is \",Rm,\" mol reflux/mol distillate\\n \\n\"\n",
+ "print\"*****************Distillate Composition*********************\\n\"\n",
+ "print\"C1\\t \\t \\t \\t:\",yD[0]\n",
+ "print\"C2\\t \\t \\t \\t:\",yD[1]\n",
+ "print\"C3\\t \\t \\t \\t:\",yD[2]\n",
+ "print\"C4\\t \\t \\t \\t:\",yD[3]\n",
+ "print\"C5\\t \\t \\t \\t:\",yD[4]\n",
+ "print\"C6\\t \\t \\t \\t:\",yD[5]\n",
+ "print\"\\n\"\n",
+ "print\"*****************Residue Composition*********************\\n\"\n",
+ "print\"C1\\t \\t \\t \\t: \",xW[0]\n",
+ "print\"C2\\t \\t \\t \\t: \",xW[1]\n",
+ "print\"C3\\t \\t \\t \\t: \",xW[2]\n",
+ "print\"C4\\t \\t \\t \\t: \",xW[3]\n",
+ "print\"C5\\t \\t \\t \\t: \",xW[4]\n",
+ "print\"C6\\t \\t \\t \\t: \",xW[5]\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**********************Number of Theoretical stage***********************#\n",
+ "# Page:440\n",
+ "print'Page: 440\\n\\n'\n",
+ "\n",
+ "for i in range(0,6):\n",
+ " alpha_av[i] = (alpha46[i]*alpha113[i])**0.5;\n",
+ "\n",
+ "alphalk_av = alpha_av[1];\n",
+ "# By Eqn. 9.167:\n",
+ "xhkW = xwW[3];\n",
+ "xlkW = xwW[1];\n",
+ "Nm = 3.496;\n",
+ "# Ratio = yD/xW\n",
+ "Ratio2= zeros(6)\n",
+ "for i in range(0,6):\n",
+ " Ratio2[i] = (alpha_av[i]**(Nm+1))*yhkD/xhkW;\n",
+ "\n",
+ "# For C1:\n",
+ "# yC1D-Ratio(1)*xC1W = 0\n",
+ "# yC1D+xC1W = zF(1)\n",
+ "# Similarly for others\n",
+ "yD2=zeros(6)\n",
+ "xW2=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " a = numpy.array([[1 ,-Ratio2[i]],[1, 1]]);\n",
+ " b = [0,zF[i]];\n",
+ " soln =lin.solve(a,b);\n",
+ " yD2[i] = soln[0];# [kmol]\n",
+ " xW2[i] = soln[1];# [kmol]\n",
+ "\n",
+ "D = sum(yD2);# [kmol]\n",
+ "W = sum(xW2);# [kmol]\n",
+ "# The distillate dew point computes to 46.6 OC and the residue bubble point computes to 113 OC, which is significantly close to the assumed.\n",
+ "\n",
+ "#***************Product composition at R = 0.8***********************#\n",
+ "# Page:441\n",
+ "print'Page: 441\\n\\n'\n",
+ "\n",
+ "# Since C1 and C2 do not enter in the residue nor C6 in the distillate, appreciably at total reflux or minimum reflux ratio, it will be assumed that they will not enter R = 0.8. C3 and C5 distribution are fixed by specifications. Only that C4 remains to be estimated.\n",
+ "# R = [Infinte 0.8 0.58] [Reflux ratios For C4]\n",
+ "R = [inf ,0.8, 0.58];\n",
+ "# Val = R/(R+1)\n",
+ "val=[ 0 , 2.0 , 2.0]\n",
+ "# ydD = [Inf 0.58] \n",
+ "y4D = [0.1255, 0.1306];\n",
+ "yC4D = 0.1306 ;# by Linear Interpolation\n",
+ "# For Distillate:\n",
+ "Sum1 = sum(Ratio1);\n",
+ "x0 = numpy.array([0.004,0.0444501,0.2495,0.65640,0.0451,0.0])\n",
+ "print\"For the reflux ratio of 0.8\\n\"\n",
+ "print\"*****************Distillate Composition*********************\\n\"\n",
+ "print\"\\t\\t\\t Liquid reflux in equilibrium with the distillate vapour\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t \\t\\t:\",x0[i]\n",
+ "\n",
+ "# For boiler:\n",
+ "\n",
+ "#**********Number Of Theoretical Trays***************#\n",
+ "# Page: 443\n",
+ "print'Page: 443\\n\\n'\n",
+ "\n",
+ "R = 0.8;# [reflux ratio]\n",
+ "# From Eqn. 9.175\n",
+ "intersection = (zlkF-(ylkD/D)*(1-q)/(R+1))/(zhkF-(yhkD/D)*(1-q)/(R+1));\n",
+ "# Enriching Section:\n",
+ "y1 = zeros(5);\n",
+ "L = R*D;# [kmol]\n",
+ "G = L+D;# [kmol]\n",
+ "# Assume: Temp1 = 57 OC\n",
+ "# alpha57 = [C1 C2 C3 C4 C5]\n",
+ "alpha57 = numpy.array([79.1 ,19.6 ,7.50, 2.66, 1]);\n",
+ "# From Eqn. 9.177, n = 0:\n",
+ "Val57=zeros(6)\n",
+ "for i in range(0,5):\n",
+ " y1[i] = (L/G)*x0[i]+((D/G)*yD[i]);\n",
+ " Val57[i] = y1[i]/alpha57[i];\n",
+ "\n",
+ "x1 = Val57/sum(Val57);\n",
+ "mC5 = sum(Val57);\n",
+ "Temp1 = 58.4; # [OC]\n",
+ "# Liquid x1's is in equilibrium with y1's.\n",
+ "xlk_By_xhk1 = x1[2]/x1[4];\n",
+ "# Tray 1 is not the feed tray.\n",
+ "# Assume: Temp2 = 63 OC\n",
+ "# alpha63 = [C1 C2 C3 C4 C5]\n",
+ "alpha63 = numpy.array([68.9 ,17.85, 6.95, 2.53, 1.00]);\n",
+ "# From Eqn. 9.177, n = 1:\n",
+ "y2=zeros(6)\n",
+ "Val63=zeros(6)\n",
+ "for i in range(0,5):\n",
+ " y2[i] = (L/G)*x1[i]+((D/G)*yD[i]);\n",
+ " Val63[i] = y1[i]/alpha63[i];\n",
+ " \n",
+ "mC5 = sum(Val63);\n",
+ "x2 = Val63/sum(Val63);\n",
+ "xlk_By_xhk2 = x2[2]/x2[4];\n",
+ "# The tray calculation are continued downward in this manner.\n",
+ "# Results for trays 5 & 6 are:\n",
+ "# Temp 75.4 [OC]\n",
+ "# x5 = [C1 C2 C3 C4 C5]\n",
+ "x5 = numpy.array([0.00240, 0.0195, 0.1125, 0.4800, 0.3859]);\n",
+ "xlk_By_xhk5 = x5[2]/x5[4];\n",
+ "# Temp6 = 79.2 OC\n",
+ "# x6 = [C1 C2 C3 C4 C5]\n",
+ "x6 = numpy.array([0.00204 ,0.0187 ,0.1045, 0.4247 ,0.4500]);\n",
+ "xlk_By_xhk6 = x6[2]/x6[4];\n",
+ "# From Eqn. 9.176:\n",
+ "# Tray 6 is the feed tray\n",
+ "Np1 = 6;\n",
+ "\n",
+ "# Exhausting section:\n",
+ "# Assume Temp = 110 OC\n",
+ "L_bar = L+(q*F);# [kmol]\n",
+ "G_bar = L_bar-W;# [kmol]\n",
+ "# alpha57 = [C3 C4 C5 C6]\n",
+ "alpha110 = numpy.array([5 ,2.2 ,1, 0.501]);\n",
+ "# From Eqn. 9.178:\n",
+ "xNp = zeros(4);\n",
+ "Val110=zeros(6)\n",
+ "k = 0;\n",
+ "for i in range(2,6):\n",
+ " xNp[k] = ((G_bar/L_bar)*yNpPlus1[i])+((W/L_bar)*xW[i]);\n",
+ " Val110[k] = alpha110[k]*xNp[k];\n",
+ " k = k+1;\n",
+ "\n",
+ "yNp = Val110/sum(Val110);\n",
+ "mC5 = 1/sum(Val110);\n",
+ "# yNp is in Eqb. with xNp:\n",
+ "xlk_By_xhkNp = xNp[0]/xNp[3];\n",
+ "# Results for Np-7 to Np-9 trays:\n",
+ "# For Np-7\n",
+ "# Temp = 95.7 OC\n",
+ "# xNpMinus7 = [C3 C4 C5 C6]\n",
+ "xNpMinus7 = numpy.array([0.0790 ,0.3944 ,0.3850, 0.1366]);\n",
+ "xlk_By_xhkNpMinus7 = xNpMinus7[0]/xNpMinus7[2];\n",
+ "# For Np-8\n",
+ "# Temp = 94.1 OC\n",
+ "# xNpMinus8 = [C3 C4 C5 C6]\n",
+ "xNpMinus8 = numpy.array([0.0915, 0.3897 ,0.3826, 0.1362]);\n",
+ "xlk_By_xhkNpMinus8 = xNpMinus8[0]/xNpMinus8[2];\n",
+ "# For Np-9\n",
+ "# Temp = 93.6 OC\n",
+ "# xNpMinus9 = [C3 C4 C5 C6]\n",
+ "xNpMinus9 = numpy.array([0.1032, 0.3812, 0.3801 ,0.1355]);\n",
+ "xlk_By_xhkNpMinus9 = xNpMinus9[0]/xNpMinus9[2];\n",
+ "# From Eqn. 9.176:\n",
+ "# Np-8 is the feed tray.\n",
+ "def g2(Np):\n",
+ " return Np-8-Np1\n",
+ "Np = fsolve(g2,7);\n",
+ "print\"Number of theoretical Trays required for R = 0.8: \",Np[0]\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**************Composition Correction*****************#\n",
+ "# Page: 446\n",
+ "print'Page: 446\\n\\n'\n",
+ "\n",
+ "# New Bubble Point:\n",
+ "# Temp = 86.4 OC\n",
+ "x6_new = x6*(1-xNpMinus8[3]);\n",
+ "x6_new[4] = xNpMinus8[3];\n",
+ "# alpha86 = [C1 C2 C3 C4 C5 C6]\n",
+ "alpha86 =numpy.array([46.5, 13.5, 5.87, 2.39, 1.00, 0.467]);\n",
+ "# From Eqn. 9.181:\n",
+ "xhkn = x5[3];\n",
+ "xhknPlus1 = x6_new[3];\n",
+ "xC65 = alpha86[5]*x6_new[4]*xhkn/xhknPlus1;\n",
+ "x5_new = x5*(1-xC65);\n",
+ "x5_new[4] = 1-sum(x5_new);\n",
+ "# Tray 5 has a bubble point of 80 OC\n",
+ "# Similarly , the calculations are continued upward:\n",
+ "# x2_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x2_new = numpy.array([0.0021, 0.0214 ,0.1418, 0.6786, 0.1553, 0.00262]);\n",
+ "# y2_new = [C1 C2 C3 C4 C5 C6]\n",
+ "y2_new = numpy.array([0.0444, 0.111 ,0.2885, 0.5099, 0.0458 ,0.00034]);\n",
+ "# x1_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x1_new = numpy.array([0.00226, 0.0241, 0.1697 ,0.7100, 0.0932, 0.00079]);\n",
+ "# y1_new = [C1 C2 C3 C4 C5 C6]\n",
+ "y1_new = numpy.array([0.0451 ,0.1209 ,0.3259 ,0.4840 ,0.0239 ,0.000090]);\n",
+ "# x0_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x0_new = numpy.array([0.00425 ,0.0425 ,0.2495, 0.6611 ,0.0425 ,0.00015]);\n",
+ "# yD_new = [C1 C2 C3 C4 C5 C6]\n",
+ "yD_new = numpy.array([0.0789 ,0.1842 ,0.3870 ,0.3420 ,0.0079, 0.00001]);\n",
+ "# From Eqn. 9.184:\n",
+ "# For C1 & C2\n",
+ "alphalkm = alpha86[2];\n",
+ "xlkmPlus1 = xNpMinus7[0];\n",
+ "xlkm = x6_new[2];\n",
+ "xC17 = x6_new[0]*alpha86[2]*xlkmPlus1/(alpha86[0]*xlkm);\n",
+ "xC27 = x6_new[1]*alpha86[2]*xlkmPlus1/(alpha86[1]*xlkm);\n",
+ "# Since xC17 = 1-xC27\n",
+ "# The adjusted value above constitute x7's.\n",
+ "# The new bubbl point is 94 OC\n",
+ "# The calculations are continued down in the same fashion.\n",
+ "# The new tray 6 has:\n",
+ "# xC1 = 0.000023 & xC2 = 0.00236\n",
+ "# It is clear that the conc. of these components are reducing so rapidly that there is no need to go an further.\n",
+ "print\"******Corrected Composition***********\\n\"\n",
+ "print\"Component\\t \\tx2\\t \\t \\t y2\\t \\t \\t x1\\t \\t \\t y1\\t \\t \\tx0\\t \\t \\tyD\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t\",x2_new[i],\"\\t \\t \\t \\t \",y2_new[i],\"\\t \\t \\t \\t \",x1_new[i],\"\\t \\t \\t \\t\",y1_new[i],\"\\t \\t \\t \\t \\t\",x0_new[i],\"\\t \\t \\t \\t\",yD_new[i]\n",
+ "\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#*************Heat Load of Condensor & Boiler & L/G ratio**********#\n",
+ "# Page 448\n",
+ "print'Page: 448\\n\\n'\n",
+ "\n",
+ "# Values of x0, yD & y1 are taken from the corrected concentration.\n",
+ "# HD46 = [C1 C2 C3 C4 C5 C6]\n",
+ "HD46 = numpy.array([13490, 23380, 32100, 42330, 52570, 61480]);# [kJ/kmol]\n",
+ "yDHD= zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yDHD[i] = yD_new[i]*HD46[i];\n",
+ "\n",
+ "HD = sum(yDHD);# [kJ]\n",
+ "# HL46 = [C1 C2 C3 C4 C5 C6]\n",
+ "HL46 = numpy.array([10470, 17210, 18610, 22790, 27100, 31050]);# [kJ/kmol]\n",
+ "xHL=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " xHL[i] = x0_new[i]*HL46[i];\n",
+ "\n",
+ "HL0 = sum(xHL);# [kJ]\n",
+ "# HG58 = [C1 C2 C3 C4 C5 C6]\n",
+ "HG58 = numpy.array([13960, 24190, 37260, 43500, 53900, 63500]);# [kJ/kmol]\n",
+ "yHG1=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yHG1[i] = y1_new[i]*HG58[i];\n",
+ "\n",
+ "HG1 = sum(yHG1);# [kJ]\n",
+ "# From Eqn. 9.54:\n",
+ "Qc = D*((R+1)*HG1-(R*HL0)-HD);# [kJ/kmol feed]\n",
+ "# Similarly:\n",
+ "HW = 39220;# [kJ]\n",
+ "HF = 34260;# [kJ]\n",
+ "# From Eqn. 9.55:\n",
+ "Qb = (D*HD)+(W*HW)+Qc-(F*HF);# [kJ/kmol feed]\n",
+ "# For tray n = 1\n",
+ "G1 = D*(R+1);# [kmol]\n",
+ "# With x1 & y2 from corrected composition;\n",
+ "# HG66 = [C1 C2 C3 C4 C5 C6]\n",
+ "HG66 = numpy.array([14070, 24610, 33800, 44100, 54780, 64430]);# [kJ/kmol feed]\n",
+ "yHG2=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yHG2[i] = y2_new[i]*HG66[i];\n",
+ "\n",
+ "HG2 = sum(yHG2);# [kJ]\n",
+ "# HL58 = [C1 C2 C3 C4 C5 C6]\n",
+ "HL58 =numpy.array([11610 ,17910 ,20470, 24900, 29500, 33840]);# [kJ/kmol feed]\n",
+ "xHL1=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " xHL1[i] = x1_new[i]*HL58[i];\n",
+ "\n",
+ "HL1 = sum(xHL1);# [kJ]\n",
+ "# From Eqn. 9.185:\n",
+ "G2 = (Qc+D*(HD-HL1))/(HG2-HL1);# [kmol]\n",
+ "L2 = G2-D;# [kmol]\n",
+ "L2_By_G2 = L2/G2;\n",
+ "# Similarly, the calculations are made for other trays in enriching section.\n",
+ "# For tray, Np = 14:\n",
+ "# C1 & C2 are absent.\n",
+ "# HG113 = [C3 C4 C5 C6]\n",
+ "HG113 = numpy.array([38260, 49310 ,60240, 71640]);# [kJ/kmol feed]\n",
+ "k = 2;\n",
+ "yHG15=zeros(6)\n",
+ "for i in range(0,4):\n",
+ " yHG15[i] = yNpPlus1[k]*HG113[i];\n",
+ " k = k+1;\n",
+ "\n",
+ "HG15 = sum(yHG15);\n",
+ "# HL107 = [C3 C4 C5 C6]\n",
+ "HL107 = numpy.array([29310 ,31870, 37680 ,43500]);# [kJ/kmol feed]\n",
+ "xHL14=zeros(6)\n",
+ "for i in range(0,4):\n",
+ " xHL14[i] = xNp[i]*HL107[i];\n",
+ "\n",
+ "HL14 = sum(xHL14);# [kJ]\n",
+ "# Similarly:\n",
+ "HL13 = 36790;# [kJ]\n",
+ "HG14 = 52610;# [kJ]\n",
+ "# From Eqn. 9.186:\n",
+ "G15_bar = (Qb+(W*(HL14-HW)))/(HG15-HL14);# [kmol]\n",
+ "L14_bar = W+G15_bar;# [kmol]\n",
+ "G14_bar = (Qb+(W*(HL13-HW)))/(HG14-HL13);# [kmol]\n",
+ "L14_By_G14 = L14_bar/G14_bar;\n",
+ "print\"Condensor Heat Load kJ:\\n\",HL0\n",
+ "print\"Reboiler Heat Load kJ:\\n\",HG15\n",
+ "# For other Exhausting Section Trays:\n",
+ "# Result = [Tray No. L_By_G Temp(OC)]\n",
+ "# Tray 0: Condensor\n",
+ "# Tray 15: Reboiler\n",
+ "Result = numpy.array([[0,0.80 ,46.6],[1 ,0.432 ,58.4],[2, 0.437, 66],[3, 0.369, 70.4],[4 ,0.305, 74],[5 ,0.310, 80.3],[6, 1.53, 86.4],[7, 4.05 ,94.1],[8 ,3.25 ,96.3],[9, 2.88 ,97.7],[10 ,2.58 ,99],[11, 2.48 ,100],[12 ,2.47 ,102.9],[13 ,2.42 ,104.6],[14 ,2.18 ,107.9],[15, 1.73 ,113.5]]);\n",
+ "print\"**************L/G*************\\n\"\n",
+ "print\"Tray No. \\t\\t L/G\\t\\t\\t\\t Temp(OC)\\n\"\n",
+ "for i in range(0,16):\n",
+ " print Result[i,0],\"\\t\\t \\t \\t\",Result[i,1],\"\\t \\t \\t\",Result[i,2];\n",
+ "\n",
+ "# These values are not final.\n",
+ "# They scatter eratically because they are based on the temp. and conc. computed with the assumption of constant L/G\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**************Thiele Geddes Method******************#\n",
+ "# Page:452\n",
+ "print'Page: 452\\n\\n'\n",
+ "\n",
+ "# Use the tray Temperature to obtain m.\n",
+ "# For C4:\n",
+ "# m = [0(Condensor) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15(Reboiler)]\n",
+ "m = numpy.array([0.50 ,0.66, 0.75 ,0.81 ,0.86 ,0.95 ,1.07 ,1.22 ,1.27 ,1.29 ,1.30, 1.32, 1.40, 1.45, 1.51, 1.65]);\n",
+ "A = numpy.array([1.6,0.65,0.582,0.4555,0.354,0.326,1.42990])\n",
+ "S = numpy.array([0.3012,0.39076,0.4479,0.503875,0.53225,0.56680,0.59917,0.69,0.95375])\n",
+ "\n",
+ "# f = Tray No. 6\n",
+ "\n",
+ "# From Eqn. 9.196:\n",
+ "# Value1 = Gf*yf/(D*zD)\n",
+ "Sum = 0;\n",
+ "for i in range(0,6):\n",
+ " Val = 1;\n",
+ " for j in range(0,6):\n",
+ " Val = Val*A[j];\n",
+ " \n",
+ " Sum = Sum+Val;\n",
+ "\n",
+ "Value1 = 1+Sum;\n",
+ "# From Eqn. 9.206:\n",
+ "# Value2 = Lf_bar*xf/(W*xW);\n",
+ "Sum = 0.5316\n",
+ "Value2 = 1+Sum;\n",
+ "# From Eqn. 9.208:\n",
+ "# Value3 = W*xW/(D*zD)\n",
+ "Value3 = A[6]*Value1/Value2;\n",
+ "# From Eqn. 9.210:\n",
+ "DyD = F*zF[3]/(Value3+1);# [kmol,C4]\n",
+ "# From Eqn. 9.209:\n",
+ "WxW = ((F*zF[3]))-(DyD);# [kmol, C4]\n",
+ "# Similarly:\n",
+ "# For [C1; C2; C3; C4; C5; C6]\n",
+ "# Result2 = [Value1 Value2 Value3 DyD WxW]\n",
+ "Result2 = numpy.array([[1.0150, 254*10**6 ,288*10**(-10), 0.03, 0],[1.0567, 8750, 298*10**(-5) ,0.07 ,0],[1.440, 17.241 ,0.0376 ,0.1447, 0.0053],[1.5778 ,1.5306 ,1.475, 0.1335 ,0.1965],[15580, 1.1595, 45.7 ,0.00643 ,0.29357],[1080 ,1.0687 ,7230 ,0.0000166 ,0.1198]]);\n",
+ "D = sum(Result2[:,2]);# [kmol]\n",
+ "W = sum(Result2[:,3]);# [kmol]\n",
+ "# In the Distillate:\n",
+ "DyD_C3 = Result[1,2];# [kmol]\n",
+ "zFC3 = zF[2];# [kmol]\n",
+ "percentC3 = (DyD_C3/zFC3)*100;\n",
+ "DyD_C5 = Result2[3,2];# [kmol]\n",
+ "zFC5 = zF[4];# [kmol]\n",
+ "percentC5 = (DyD_C5/zFC5)*100;\n",
+ "# These do not quite meet the original specification.\n",
+ "# For Tray 2 & C4\n",
+ "# From Eqn. 9.195:\n",
+ "# Value4 = G2*y2/(D*zD)\n",
+ "n = 2;\n",
+ "Sum = 0;\n",
+ "for i in range(0,n):\n",
+ " Val = 1;\n",
+ " for j in range(i,n):\n",
+ " Val = Val*A[j];\n",
+ " \n",
+ " Sum = Sum+Val;\n",
+ "\n",
+ "Value4 = 1+Sum;\n",
+ "# From The enthalpy Balnce:\n",
+ "G2 = 0.675;\n",
+ "# From Eqn. 9.211:\n",
+ "y2 = Value4*DyD/G2;\n",
+ "# Similarly:\n",
+ "# Value4 = [C1 C2 C3 C4 C5 C6]\n",
+ "Value4 = numpy.array([1.0235, 1.1062, 1.351, 2.705, 10.18 ,46.9]);\n",
+ "y2= numpy.array([0.04548,0.114,0.2896,0.53498,0.09697,0.001153]);\n",
+ "Y2 = sum(y2);\n",
+ "# Since Y2 is not equal to 1. THerefore the original temperature is incorrect. By adjusting y2 to unity.\n",
+ "# The dew point is 77 OC instead of 66 OC\n",
+ "# y2_adjusted = [C1 C2 C3 C4 C5 C6]\n",
+ "y2_adjusted = numpy.array([0.0419 ,0.1059 ,0.2675 ,0.4939, 0.0896, 0.00106]);\n",
+ "print\"*****************Composition By Thiele Geddes Method*****************\\n\"\n",
+ "print\"Component\\t \\t \\t y2\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t \\t\",y2_adjusted[i]\n",
+ "# some values of solution in the textbook are incorrect"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.13\n",
+ "\n",
+ "\n",
+ "Page: 436\n",
+ "\n",
+ "\n",
+ "Minimum Reflux Ratio is 0.619146164974 mol reflux/mol distillate\n",
+ " \n",
+ "\n",
+ "*****************Distillate Composition*********************\n",
+ "\n",
+ "C1\t \t \t \t: 0.0786\n",
+ "C2\t \t \t \t: 0.1835\n",
+ "C3\t \t \t \t: 0.3854\n",
+ "C4\t \t \t \t: 0.34\n",
+ "C5\t \t \t \t: 0.007866\n",
+ "C6\t \t \t \t: 0.0\n",
+ "\n",
+ "\n",
+ "*****************Residue Composition*********************\n",
+ "\n",
+ "C1\t \t \t \t: 0.0\n",
+ "C2\t \t \t \t: 0.0\n",
+ "C3\t \t \t \t: 0.00484930540974\n",
+ "C4\t \t \t \t: 0.321097242636\n",
+ "C5\t \t \t \t: 0.480081235564\n",
+ "C6\t \t \t \t: 0.19397221639\n",
+ "\n",
+ "\n",
+ "Page: 440\n",
+ "\n",
+ "\n",
+ "Page: 441\n",
+ "\n",
+ "\n",
+ "For the reflux ratio of 0.8\n",
+ "\n",
+ "*****************Distillate Composition*********************\n",
+ "\n",
+ "\t\t\t Liquid reflux in equilibrium with the distillate vapour\n",
+ "\n",
+ "C 0 \t \t \t \t\t: 0.004\n",
+ "C 1 \t \t \t \t\t: 0.0444501\n",
+ "C 2 \t \t \t \t\t: 0.2495\n",
+ "C 3 \t \t \t \t\t: 0.6564\n",
+ "C 4 \t \t \t \t\t: 0.0451\n",
+ "C 5 \t \t \t \t\t: 0.0\n",
+ "Page: 443\n",
+ "\n",
+ "\n",
+ "Number of theoretical Trays required for R = 0.8: 14.0\n",
+ "\n",
+ "\n",
+ "Page: 446\n",
+ "\n",
+ "\n",
+ "******Corrected Composition***********\n",
+ "\n",
+ "Component\t \tx2\t \t \t y2\t \t \t x1\t \t \t y1\t \t \tx0\t \t \tyD\n",
+ "\n",
+ "C 0 \t \t \t0.0021 \t \t \t \t 0.0444 \t \t \t \t 0.00226 \t \t \t \t0.0451 \t \t \t \t \t0.00425 \t \t \t \t0.0789\n",
+ "C 1 \t \t \t0.0214 \t \t \t \t 0.111 \t \t \t \t 0.0241 \t \t \t \t0.1209 \t \t \t \t \t0.0425 \t \t \t \t0.1842\n",
+ "C 2 \t \t \t0.1418 \t \t \t \t 0.2885 \t \t \t \t 0.1697 \t \t \t \t0.3259 \t \t \t \t \t0.2495 \t \t \t \t0.387\n",
+ "C 3 \t \t \t0.6786 \t \t \t \t 0.5099 \t \t \t \t 0.71 \t \t \t \t0.484 \t \t \t \t \t0.6611 \t \t \t \t0.342\n",
+ "C 4 \t \t \t0.1553 \t \t \t \t 0.0458 \t \t \t \t 0.0932 \t \t \t \t0.0239 \t \t \t \t \t0.0425 \t \t \t \t0.0079\n",
+ "C 5 \t \t \t0.00262 \t \t \t \t 0.00034 \t \t \t \t 0.00079 \t \t \t \t9e-05 \t \t \t \t \t0.00015 \t \t \t \t1e-05\n",
+ "\n",
+ "\n",
+ "Page: 448\n",
+ "\n",
+ "\n",
+ "Condensor Heat Load kJ:\n",
+ "21641.994\n",
+ "Reboiler Heat Load kJ:\n",
+ "59915.6783775\n",
+ "**************L/G*************\n",
+ "\n",
+ "Tray No. \t\t L/G\t\t\t\t Temp(OC)\n",
+ "\n",
+ "0.0 \t\t \t \t0.8 \t \t \t46.6\n",
+ "1.0 \t\t \t \t0.432 \t \t \t58.4\n",
+ "2.0 \t\t \t \t0.437 \t \t \t66.0\n",
+ "3.0 \t\t \t \t0.369 \t \t \t70.4\n",
+ "4.0 \t\t \t \t0.305 \t \t \t74.0\n",
+ "5.0 \t\t \t \t0.31 \t \t \t80.3\n",
+ "6.0 \t\t \t \t1.53 \t \t \t86.4\n",
+ "7.0 \t\t \t \t4.05 \t \t \t94.1\n",
+ "8.0 \t\t \t \t3.25 \t \t \t96.3\n",
+ "9.0 \t\t \t \t2.88 \t \t \t97.7\n",
+ "10.0 \t\t \t \t2.58 \t \t \t99.0\n",
+ "11.0 \t\t \t \t2.48 \t \t \t100.0\n",
+ "12.0 \t\t \t \t2.47 \t \t \t102.9\n",
+ "13.0 \t\t \t \t2.42 \t \t \t104.6\n",
+ "14.0 \t\t \t \t2.18 \t \t \t107.9\n",
+ "15.0 \t\t \t \t1.73 \t \t \t113.5\n",
+ "\n",
+ "\n",
+ "Page: 452\n",
+ "\n",
+ "\n",
+ "*****************Composition By Thiele Geddes Method*****************\n",
+ "\n",
+ "Component\t \t \t y2\n",
+ "\n",
+ "C 0 \t \t \t \t0.0419\n",
+ "C 1 \t \t \t \t0.1059\n",
+ "C 2 \t \t \t \t0.2675\n",
+ "C 3 \t \t \t \t0.4939\n",
+ "C 4 \t \t \t \t0.0896\n",
+ "C 5 \t \t \t \t0.00106\n"
+ ]
+ }
+ ],
+ "prompt_number": 85
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/Chapter9_2.ipynb b/Mass_-_Transfer_Operations/Chapter9_2.ipynb
new file mode 100755
index 00000000..6ae47958
--- /dev/null
+++ b/Mass_-_Transfer_Operations/Chapter9_2.ipynb
@@ -0,0 +1,2154 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:1356f02f4e266983d4e000fdec653dca9388627dd3d49506786471d71b9d02b6"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 9: Distillation"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.1: Page 349"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.1\n",
+ "# Page: 349\n",
+ "\n",
+ "print'Illustration 9.1 - Page: 349\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "#****Data****#\n",
+ "# a:n-heptane b:n-octane\n",
+ "Pt = 760; # [mm Hg]\n",
+ "#*****#\n",
+ "\n",
+ "Tempa = 98.4;# [boiling point of A,OC]\n",
+ "Tempb = 125.6;# [boiling point of B,OC]\n",
+ "x = numpy.zeros(6);\n",
+ "y_star = numpy.zeros(6);\n",
+ "alpha = numpy.zeros(6);\n",
+ "# Data = [Temp Pa (mm Hg) Pb(mm Hg)]\n",
+ "Data = [(98.4, 760.0, 333.0),(105.0 ,940.0, 417.0),(110.0, 1050.0, 484.0),(115.0, 1200.0, 561.0),(120.0, 1350.0, 650.0),(125.6 ,1540.0, 760.0)];\n",
+ "for i in range(0,6): \n",
+ " x[i] = (Pt-Data[i][2])/(Data[i][1]-Data[i][2]);# [mole fraction of heptane in liquid]\n",
+ " y_star[i] = (Data[i][1]/Pt)*x[i];\n",
+ " alpha[i] = Data[i][1]/Data[i][2];\n",
+ "\n",
+ "print\"\\t\\t T(OC)\\t\\t\\t\\t Pa(mm Hg)\\t\\t\\t\\t\\t\\t\\t Pb(mm Hg)\\t\\t\\t\\t\\t\\t\\t\\t x\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t y*\\t\\t\\t\\t\\t\\t\\t\\t\\t alpha\\n\"\n",
+ "for i in range(0,6):\n",
+ " print \"\\t \\t \",Data[i][0],\"\\t \\t \\t \\t\",Data[i][1],\"\\t \\t \\t \\t\",Data[i][2],\"\\t \\t \\t \\t \",round(x[i],3),\"\\t \\t \\t \\t \",round(y_star[i],3),\"\\t\\t\\t\\t\\t\\t\\t\\t\",round(alpha[i],2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.1 - Page: 349\n",
+ "\n",
+ "\n",
+ "\t\t T(OC)\t\t\t\t Pa(mm Hg)\t\t\t\t\t\t\t Pb(mm Hg)\t\t\t\t\t\t\t\t x\t\t\t\t\t\t\t\t\t\t\t y*\t\t\t\t\t\t\t\t\t alpha\n",
+ "\n",
+ "\t \t 98.4 \t \t \t \t760.0 \t \t \t \t333.0 \t \t \t \t 1.0 \t \t \t \t 1.0 \t\t\t\t\t\t\t\t2.28\n",
+ "\t \t 105.0 \t \t \t \t940.0 \t \t \t \t417.0 \t \t \t \t 0.656 \t \t \t \t 0.811 \t\t\t\t\t\t\t\t2.25\n",
+ "\t \t 110.0 \t \t \t \t1050.0 \t \t \t \t484.0 \t \t \t \t 0.488 \t \t \t \t 0.674 \t\t\t\t\t\t\t\t2.17\n",
+ "\t \t 115.0 \t \t \t \t1200.0 \t \t \t \t561.0 \t \t \t \t 0.311 \t \t \t \t 0.492 \t\t\t\t\t\t\t\t2.14\n",
+ "\t \t 120.0 \t \t \t \t1350.0 \t \t \t \t650.0 \t \t \t \t 0.157 \t \t \t \t 0.279 \t\t\t\t\t\t\t\t2.08\n",
+ "\t \t 125.6 \t \t \t \t1540.0 \t \t \t \t760.0 \t \t \t \t 0.0 \t \t \t \t 0.0 \t\t\t\t\t\t\t\t2.03\n"
+ ]
+ }
+ ],
+ "prompt_number": 70
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.2: Page 354"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.2\n",
+ "# Page: 354\n",
+ "\n",
+ "print'Illustration 9.2 - Page: 354\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:water b:ethylaniline\n",
+ "Pt = 760.0; # [mm Hg]\n",
+ "ma1 = 50.0;# [g]\n",
+ "mb1 = 50.0;# [g]\n",
+ "#*******#\n",
+ "\n",
+ "# Data = [Temp Pa(mm Hg) Pb(mm Hg)]\n",
+ "Data = [(38.5, 51.1 ,1.0),(64.4 ,199.7, 5.0),(80.6 ,363.9 ,10.0),(96.0, 657.6, 20.0),(99.15 ,737.2 ,22.8),(113.2, 1225.0, 40.0)];\n",
+ "Ma = 18.02;# [kg/kmol]\n",
+ "Mb = 121.1;# [kg/kmol]\n",
+ "\n",
+ "for i in range(0,6):\n",
+ " p = Data[i][1]+Data[i][2];\n",
+ " if p==Pt:\n",
+ " pa = Data[4][1];# [mm Hg]\n",
+ " pb = Data[i][2];# [mm Hg]\n",
+ " T = Data[i][0];# [OC]\n",
+ " \n",
+ "\n",
+ "ya_star = pa/Pt;\n",
+ "yb_star = pb/Pt;\n",
+ "ya1 = ma1/Ma;# [g mol water]\n",
+ "yb1 = mb1/Mb;# [g mol ethylalinine]\n",
+ "Y = ya1*(yb_star/ya_star);# [g mol ethylalinine]\n",
+ "print\"The original mixture contained\",round(ya1,2),\"g mol water and \",round(yb1,2),\" g mol ethylanaline\\n\"\n",
+ "print\"The mixture will continue to boil at \",T,\" degree C, where the equilibrium vapour of the indicated composition,until all the water evaporated together with \",round(Y,3),\"g mol ethylaniline\\n\"\n",
+ "print\"The temparature will then rise to 204 degree C, and the equilibrium vapour will be of pure ethylanaline\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.2 - Page: 354\n",
+ "\n",
+ "\n",
+ "The original mixture contained 2.77 g mol water and 0.41 g mol ethylanaline\n",
+ "\n",
+ "The mixture will continue to boil at 99.15 degree C, where the equilibrium vapour of the indicated composition,until all the water evaporated together with 0.086 g mol ethylaniline\n",
+ "\n",
+ "The temparature will then rise to 204 degree C, and the equilibrium vapour will be of pure ethylanaline\n"
+ ]
+ }
+ ],
+ "prompt_number": 71
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.3: Page 362"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.3\n",
+ "# Page: 362\n",
+ "\n",
+ "print'Illustration 9.3 - Page: 362\\n\\n'\n",
+ "import numpy\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:n-C3H8 b:n-C4H10 c:n-C5H12 d:n-C6H14\n",
+ "# Bubble Point Calculation\n",
+ "xa = 0.05;\n",
+ "xb = 0.30;\n",
+ "xc = 0.40;\n",
+ "xd = 0.25;\n",
+ "P = 350;# [kN/square m]\n",
+ "#******#\n",
+ "\n",
+ "# Assume:\n",
+ "Temp = 60;# [OC]\n",
+ "x = [0.05, 0.30, 0.40, 0.25];\n",
+ "m = [4.70, 1.70 ,0.62 ,0.25];# [At 60 OC]\n",
+ "# Reference: C5H12\n",
+ "mref = m[3];\n",
+ "Sum = 0;\n",
+ "alpha = numpy.zeros(4)\n",
+ "alpha_x = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[3];\n",
+ " alpha_x[i] = alpha[i]*x[i];\n",
+ " Sum = Sum+alpha_x[i];\n",
+ "\n",
+ "# From Eqn. 9.23:\n",
+ "SumF = Sum;\n",
+ "Sum = 0;\n",
+ "mref = 1/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 56.8;# [OC]\n",
+ "m = [4.60, 1.60, 0.588, 0.235];# [At 56.8 OC]\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[2];\n",
+ " alpha_x[i] = alpha[i]*x[i];\n",
+ " Sum = Sum+alpha_x[i];\n",
+ "\n",
+ "SumF = Sum;\n",
+ "mref = 1/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 56.7;# [OC]\n",
+ "Bt = 56.8;# [OC]\n",
+ "yi = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " yi[i] = alpha_x[i]/Sum;\n",
+ "\n",
+ "print\"The Bubble Point is \",Bt,\" degree C\\n\"\n",
+ "print\"Bubble point vapour composition \\n\"\n",
+ "print\"\\t yi\\n\";\n",
+ "print\"\\n n-C3\\t \",round(yi[0],3)\n",
+ "print\"\\n n-C4\\t \",round(yi[1],3)\n",
+ "print\"\\n n-C5\\t \",round(yi[2],3)\n",
+ "print\"\\n n-C6\\t \",round(yi[3],3)\n",
+ "\n",
+ "print\"\\n \\n \\n\"\n",
+ "\n",
+ "# Dew Point Calculation\n",
+ "# Asume:\n",
+ "ya = 0.05;\n",
+ "yb = 0.30;\n",
+ "yc = 0.40;\n",
+ "yd = 0.25;\n",
+ "Temp = 80;# [OC]\n",
+ "y = [0.05, 0.30 ,0.40, 0.25];\n",
+ "m = [6.30 ,2.50 ,0.96 ,0.43];# [At 60 OC]\n",
+ "# Reference: C5H12\n",
+ "mref = m[3];\n",
+ "Sum = 0;\n",
+ "alpha = numpy.zeros(4)\n",
+ "alpha_y = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[3];\n",
+ " alpha_y[i] = y[i]/alpha[i];\n",
+ " Sum = Sum+alpha_y[i];\n",
+ "\n",
+ "\n",
+ "# From Eqn. 9.29:\n",
+ "SumF = Sum;\n",
+ "Sum = 0;\n",
+ "mref = SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 83.7;# [OC]\n",
+ "m = [6.60, 2.70, 1.08, 0.47];# [At 56.8 OC]\n",
+ "for i in range(0,4):\n",
+ " alpha[i] = m[i]/m[2];\n",
+ " alpha_y[i] = y[i]/alpha[i];\n",
+ " Sum = Sum+alpha_y[i];\n",
+ "\n",
+ "SumF = Sum;\n",
+ "mref = 1.0/SumF;\n",
+ "# Corresponding Temparature from the nomograph:\n",
+ "Temp = 84.0;# [OC]\n",
+ "Dt = 84.0;# [OC]\n",
+ "xi = numpy.zeros(4);\n",
+ "for i in range(0,4):\n",
+ " xi[i] = alpha_y[i]/Sum;\n",
+ "\n",
+ "print\"The Dew Point is \",Dt,\" degree C\\n\"\n",
+ "print\"Dew point liquid composition \\n\"\n",
+ "print\"\\t xi\\n\"\n",
+ "print\"\\n n-C3\\t \",round(xi[0],3)\n",
+ "print\"\\n n-C4\\t \",round(xi[1],3)\n",
+ "print\"\\n n-C5\\t \",round(xi[2],3)\n",
+ "print\"\\n n-C6\\t \",round(xi[3],3)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.3 - Page: 362\n",
+ "\n",
+ "\n",
+ "The Bubble Point is 56.8 degree C\n",
+ "\n",
+ "Bubble point vapour composition \n",
+ "\n",
+ "\t yi\n",
+ "\n",
+ "\n",
+ " n-C3\t 0.229\n",
+ "\n",
+ " n-C4\t 0.478\n",
+ "\n",
+ " n-C5\t 0.234\n",
+ "\n",
+ " n-C6\t 0.059\n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ "The Dew Point is 84.0 degree C\n",
+ "\n",
+ "Dew point liquid composition \n",
+ "\n",
+ "\t xi\n",
+ "\n",
+ "\n",
+ " n-C3\t 0.007\n",
+ "\n",
+ " n-C4\t 0.109\n",
+ "\n",
+ " n-C5\t 0.363\n",
+ "\n",
+ " n-C6\t 0.521\n"
+ ]
+ }
+ ],
+ "prompt_number": 72
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.4: Page 365"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.4\n",
+ "# Page: 365\n",
+ "\n",
+ "print'Illustration 9.4 - Page: 365\\n\\n'\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import numpy\n",
+ "from scipy.optimize import fsolve\n",
+ "\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol feed]\n",
+ "zF = 0.5;\n",
+ "D = 60.0;# [mol]\n",
+ "W = 40.0;# [mol]\n",
+ "#*******#\n",
+ "\n",
+ "# From Illustration 9.1, Equilibrium data:\n",
+ "Data = numpy.array([[1.0 ,1.0],[0.655, 0.810],[0.487 ,0.674],[0.312, 0.492],[0.1571 ,0.279],[0, 0]]);\n",
+ "Feed = numpy.array([[0 ,0],[1.0 ,1.0]]);\n",
+ "# The operating line is drawn with a slope -(W/D) to cut the equilibrium line.\n",
+ "def f44(x):\n",
+ " return -((W/D)*(x-zF))+zF\n",
+ "x = numpy.arange(0.2,0.7,0.1);\n",
+ "plt.plot(Data[:,0],Data[:,1],label=\"Equilibrium Line\")\n",
+ "plt.plot(Feed[:,0],Feed[:,1],label=\"Feed Line\")\n",
+ "plt.plot(x,f44(x),label=\"Operating Line\");\n",
+ "plt.grid('on')\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Mole fraction of heptane in liquid\")\n",
+ "ax.set_ylabel(\"Mole fraction of heptane in vapour\")\n",
+ "plt.legend(loc='lower right');\n",
+ "plt.show()\n",
+ "# The point at which the operating line cuts the equilibrium line has the following composition* temparature:\n",
+ "yd = 0.575;# [mole fraction heptane in vapour phase]\n",
+ "xW = 0.387;# [mole fraction heptane in liquid phase]\n",
+ "Temp = 113;# [OC]\n",
+ "print\"mole fraction of heptane in vapour phase \",yd\n",
+ "print\"mole fraction of heptane in liquid phase \",xW\n",
+ "print\"Temperature is \",Temp,\" degree C\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.4 - Page: 365\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
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FmC7Kj5cRAwCqujPCe7cHVqnqWgARmQxcBCwLaXcLbpX1KRHe34iQ4BQXqbKa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\nzM+VsnViXQAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7f17518>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "mole fraction of heptane in vapour phase 0.575\n",
+ "mole fraction of heptane in liquid phase 0.387\n",
+ "Temperature is 113 degree C\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.5: Page 366"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.5\n",
+ "# Page: 366\n",
+ "\n",
+ "print'Illustration 9.5 - Page: 366\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import numpy\n",
+ "import pylab\n",
+ "import numpy.linalg as lin\n",
+ "#****Data****#\n",
+ "Pt = 760.0;# [mm Hg]\n",
+ "zFa = 0.5;# [mol fraction benzene]\n",
+ "zFb = 0.25;# [mol fraction toulene]\n",
+ "zFc = 0.25;# [mol fraction o-xylene]\n",
+ "#********#\n",
+ "\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol feed]\n",
+ "# For Summtion of Yd_star to be unity, W/D = 2.08 \n",
+ "# The Eqn.are \n",
+ "# (1): W+D = F \n",
+ "# (2): W-2.08D = 0\n",
+ "a =numpy.array([[1.0 ,1.0],[1.0 ,-2.08]]);\n",
+ "b = numpy.array([[F*1.0],[0]]);\n",
+ "soln = lin.solve(a,b)\n",
+ "W = soln[0];\n",
+ "D = soln[1];\n",
+ "Sub = ['A','B','C'];\n",
+ "p =numpy.array([1370 ,550, 200]);# [mm Hg]\n",
+ "m = numpy.zeros(3);\n",
+ "zF = [zFa ,zFb, zFc];# [Given]\n",
+ "yd_star = numpy.array([0,0,0]);\n",
+ "xW = numpy.zeros(3);\n",
+ "\n",
+ "for i in range(0,3):\n",
+ " m[i] = p[i]/Pt;\n",
+ " yd_star[i]=(zF[i])*((W/D)+1)#/(1+(W/(D*m[i])));\n",
+ " xW[i] = yd_star[i]/m[i];\n",
+ "\n",
+ "print\"\\t \\t \\t \\t \\t \\t \\t \\t At W/D = 2.08\\n\\n\\n\"\n",
+ "print\"Substance \\t \\t p(mm Hg)\\t \\t m\\t \\t \\t \\t \\t \\t \\t \\t \\t \\t zF\\t \\t \\t \\t \\t \\t \\t yd*\\t\\t\\t\\t\\t\\txW\\n\"\n",
+ "for i in range(0,3):\n",
+ " print \"\\n\",Sub[i],\" \\t \\t \\t \\t \",p[i],\"\\t \\t \\t \\t \",m[i],\"\\t \\t \\t\",m[i],\"\\t \\t \\t\",zF[i],\" \\t \\t \\t\",yd_star[i],\"\\t\",xW[i]\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.5 - Page: 366\n",
+ "\n",
+ "\n",
+ "\t \t \t \t \t \t \t \t At W/D = 2.08\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\n",
+ "Substance \t \t p(mm Hg)\t \t m\t \t \t \t \t \t \t \t \t \t zF\t \t \t \t \t \t \t yd*\t\t\t\t\t\txW\n",
+ "\n",
+ "\n",
+ "A \t \t \t \t 1370 \t \t \t \t 1.80263157895 \t \t \t1.80263157895 \t \t \t0.5 \t \t \t1 \t0.554744525547\n",
+ "\n",
+ "B \t \t \t \t 550 \t \t \t \t 0.723684210526 \t \t \t0.723684210526 \t \t \t0.25 \t \t \t0 \t0.0\n",
+ "\n",
+ "C \t \t \t \t 200 \t \t \t \t 0.263157894737 \t \t \t0.263157894737 \t \t \t0.25 \t \t \t0 \t0.0\n"
+ ]
+ }
+ ],
+ "prompt_number": 74
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.6: Page 370"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.6\n",
+ "# Page: 370\n",
+ "\n",
+ "print'Illustration 9.6 - Page: 370\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "from scipy.optimize import fsolve\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol]\n",
+ "xF = 0.5;\n",
+ "D = 0.6*100;# [mol]\n",
+ "#******#\n",
+ "\n",
+ "W = F-D;# [mol]\n",
+ "# From Illustration 9.1:\n",
+ "alpha = 2.16;# [average value of alpha]\n",
+ "# From Eqn.9.46;\n",
+ "def f45(xW):\n",
+ " return math.log(F*xF/(W*xW))-(alpha*math.log(F*(1-xF)/(W*(1-xW))))\n",
+ "xW = fsolve(f45,0.5);# [mole fraction heptane]\n",
+ "def f46(yD):\n",
+ " return F*xF-((D*yD)+(W*xW))\n",
+ "yD = fsolve(f46,100);# [mole fraction heptane]\n",
+ "print\"Mole Fraction of heptane in the distillate is \",round(yD,3),\"\\n\"\n",
+ "print\"Mole Fraction of heptane in the residue is \",round(xW,3),\" \\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.6 - Page: 370\n",
+ "\n",
+ "\n",
+ "Mole Fraction of heptane in the distillate is 0.615 \n",
+ "\n",
+ "Mole Fraction of heptane in the residue is 0.328 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 75
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.7: Page 371"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.7\n",
+ "# Page: 371\n",
+ "from scipy.optimize import fsolve\n",
+ "print'Illustration 9.7 - Page: 371\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "#****Data****#\n",
+ "# a:benzene b:toulene c:o-xylene\n",
+ "# Assume:\n",
+ "Bt = 100.0;#[OC]\n",
+ "pa = 1370.0;# [mm Hg]\n",
+ "pb = 550.0;# [mm Hg]\n",
+ "pc = 200.0;# [mm Hg]\n",
+ "xFa = 0.5;# [mole fraction]\n",
+ "xFb = 0.25;# [mole fraction]\n",
+ "xFc = 0.25;# [mole fraction]\n",
+ "# Basis:\n",
+ "F = 100.0;# [mol]\n",
+ "D = 32.5;# [mol]\n",
+ "#*******#\n",
+ "\n",
+ "ref = pb;\n",
+ "alpha_a = pa/ref;\n",
+ "alpha_b = pb/ref;\n",
+ "alpha_c = pc/ref;\n",
+ "W = F-D;# [mol]\n",
+ "xbW = 0.3;# [mol]\n",
+ "xaW = 0.4;# [mol]\n",
+ "xcW = 0.3;# [mol]\n",
+ "err = 1.0;\n",
+ "while(err>(10**(-1))):\n",
+ " # From Eqn. 9.47:\n",
+ " def f47(xaW):\n",
+ " return math.log(F*xFa/(W*xaW))-(alpha_a*math.log(F*xFb/(W*xbW)))\n",
+ " xaW = fsolve(f47,xbW);\n",
+ " def f48(xcW):\n",
+ " return math.log(F*xFc/(W*xcW))-(alpha_c*math.log(F*xFb/(W*xbW)))\n",
+ " xcW = fsolve(f48,xbW);\n",
+ " xbW_n = 1-(xaW+xcW);\n",
+ " err = abs(xbW-xbW_n);\n",
+ " xbw = xbW_n;\n",
+ "\n",
+ "# Material balance:\n",
+ "# for A:\n",
+ "def f49(yaD):\n",
+ " return F*xFa-((D*yaD)+(W*xaW))\n",
+ "yaD = fsolve(f49,100);# [mole fraction benzene]\n",
+ "# For B:\n",
+ "def f50(ybD):\n",
+ " return F*xFb-((D*ybD)+(W*xbW))\n",
+ "ybD = fsolve(f50,100);# [mole fraction toulene]\n",
+ "# For C:\n",
+ "def f51(ycD):\n",
+ " return F*xFc-((D*ycD)+(W*xcW))\n",
+ "ycD = fsolve(f51,100);# [mole fraction o-xylene]\n",
+ "print\"The residual compositions are:\\n\"\n",
+ "print\"Benzene:\\n\",round(xaW,3)\n",
+ "print\"Toulene:\\n\",round(xbW,3)\n",
+ "print\"o-xylene:\\n\",round(xcW,3)\n",
+ "print\"\\n The composited distillate compositions are:\\n\"\n",
+ "print\"Benzene:\\n\",round(yaD,3)\n",
+ "print\"Toulene:\\n\",round(ybD,3)\n",
+ "print\"o-xylene:\\n\",round(ycD,3)\n",
+ "#the answers are slightly different in textbook due to approximation while here answers are precise"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.7 - Page: 371\n",
+ "\n",
+ "\n",
+ "The residual compositions are:\n",
+ "\n",
+ "Benzene:\n",
+ "0.438\n",
+ "Toulene:\n",
+ "0.3\n",
+ "o-xylene:\n",
+ "0.343\n",
+ "\n",
+ " The composited distillate compositions are:\n",
+ "\n",
+ "Benzene:\n",
+ "0.628\n",
+ "Toulene:\n",
+ "0.146\n",
+ "o-xylene:\n",
+ "0.057\n"
+ ]
+ }
+ ],
+ "prompt_number": 76
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.8: Page 388"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.8\n",
+ "# Page: 388\n",
+ "\n",
+ "print'Illustration 9.8 - Page: 388\\n\\n'\n",
+ "import numpy.linalg as lin\n",
+ "# solution\n",
+ "\n",
+ "#****Data*****#\n",
+ "# a:methanol b:water\n",
+ "Xa = 0.5;# [Wt fraction]\n",
+ "Temp1 = 26.7;# [OC]\n",
+ "Temp2 = 37.8;# [OC]\n",
+ "F1 = 5000.0;# [kg/hr]\n",
+ "#******#\n",
+ "\n",
+ "#(a)\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "Xa = 0.5;# [Wt fraction]\n",
+ "Xb = 1-Xa;# [Wt fraction]\n",
+ "Temp1 = 26.7;# [OC]\n",
+ "Temp2 = 37.8;# [OC]\n",
+ "F1 = 5000.0;# [kg/hr];\n",
+ "# Basis: 1hr\n",
+ "F = (F1*Xa/Ma)+(F1*Xb/Mb);# [kmol/hr]\n",
+ "# For feed:\n",
+ "zF = (F1*Xa/Ma)/F;# [mole fracton methanol]\n",
+ "MavF = F1/F;# [kg/kmol]\n",
+ "# For distillate:\n",
+ "xD = (95/Ma)/((95/Ma)+(5/Mb));# [mole fraction methanol]\n",
+ "MavD = 100.0/((95/Ma)+(5/Mb));# [kg/kmol]\n",
+ "# For residue:\n",
+ "xW = (1/Ma)/((1/Ma)+(99/Mb));# [mole fraction methanol]\n",
+ "MavR = 100/((1/Ma)+(99/Mb));# [kg/kmol]\n",
+ "# (1): D+W = F [Eqn.9.75]\n",
+ "# (2): D*xD+W*xW = F*zF [Eqn. 9.76]\n",
+ "# Solvving simultaneously:\n",
+ "a = numpy.array([[1.0 ,1.0],[xD ,xW]]);\n",
+ "b = numpy.array([F,F*zF]);\n",
+ "soln = lin.solve(a,b);\n",
+ "D = soln[0];# [kmol/h]\n",
+ "W = soln[1];# [kmol/h]\n",
+ "print\"Quantity of Distillate is\", round(D*MavD),\" kg/hr\\n\"\n",
+ "print\"Quantity of Residue is \",round(W*MavR),\" kg/hr\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (b)\n",
+ "# For the vapour-liquid equilibria:\n",
+ "Tempo = 19.69;# [Base Temp. according to \"International Critical Tables\"]\n",
+ "BtR = 99.0;# [Bubble point of the residue, OC]\n",
+ "hR = 4179.0;# [J/kg K]\n",
+ "hF = 3852.0;# [J/kg K]\n",
+ "def f52(tF):\n",
+ " return (F1*hF*(tF-Temp1))-((W*MavR)*hR*(BtR-Temp2))\n",
+ "tF = fsolve(f52,Temp1);# [OC]\n",
+ "BtF = 76.0;# [Bubble point of feed, OC]\n",
+ "# For the feed:\n",
+ "delta_Hs = -902.5;# [kJ/kmol]\n",
+ "Hf = ((hF/1000.0)*MavF*(tF-Tempo))+delta_Hs;# [kJ/kmol]\n",
+ "# From Fig 9.27:\n",
+ "HD = 6000.0;# [kJ/kmol]\n",
+ "HLo = 3640.0;# [kJ/kmol]\n",
+ "HW = 6000.0;# [kJ/kmol]\n",
+ "print\"The enthalpy of feed is \",round(Hf),\" kJ/kmol\\n\"\n",
+ "print\"The enthalpy of the residue is \",round(HW),\" kJ/kmol\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (c)\n",
+ "# From Fig.9.27:\n",
+ "# The miium reflux ratio is established by the tie line (x = 0.37 y = 0.71), which extended pass through F,the feed.\n",
+ "# At Dm:\n",
+ "Qm = 62570.0;# [kJ/kmol]\n",
+ "Hg1 = 38610.0;# [kJ/kmol]\n",
+ "# From Eqn. 9.65:\n",
+ "Rm = (Qm-Hg1)/(Hg1-HLo);\n",
+ "print\"The minimum reflux ratio is \",round(Rm,4),\"\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (d)\n",
+ "# From Fig. 9.28:\n",
+ "Np = 4.9;\n",
+ "# But it include the reboiler.\n",
+ "Nm = Np-1;\n",
+ "print\"The minimum number of theoretical trays required is \",round(Nm),\" \\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (e)\n",
+ "R = 1.5*Rm;\n",
+ "# Eqn. 9.65:\n",
+ "def f53(Q_prime):\n",
+ " return R-((Q_prime-Hg1)/(Hg1-HLo))\n",
+ "Q_prime = fsolve(f53,2);# [kJ/kmol]\n",
+ "def f54(Qc):\n",
+ " return Q_prime-(HD+(Qc/D))\n",
+ "Qc = fsolve(f54,2);# [kJ/hr]\n",
+ "Qc = Qc/3600.0;# [kW]\n",
+ "print\"The Condensor heat load is \",round(Qc),\" kW\\n\"\n",
+ "# From Eqn. 9.77:\n",
+ "def f55(Q_dprime):\n",
+ " return (F*Hf)-((D*Q_prime)+(W*Q_dprime))\n",
+ "Q_dprime = fsolve(f55,2);\n",
+ "def f56(Qb):\n",
+ " return Q_dprime-(HW-(Qb/W))\n",
+ "Qb = fsolve(f56,2);# [kJ/hr]\n",
+ "Qb = Qb/3600.0;# [kW]\n",
+ "print\"The Reboiler heat load is \",round(Qb),\" kW\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# (f)\n",
+ "# From Fig: 9.28\n",
+ "Np = 9.0;\n",
+ "# But it is including the reboiler\n",
+ "print\"No. of theoretical trays in tower is\",Np-1,\"\\n\",\n",
+ "G1 = D*(R+1);# [kmol/hr]\n",
+ "Lo = D*R;# [kmol/hr]\n",
+ "# From Fig. 9.28:\n",
+ "# At the feed tray:\n",
+ "x4 = 0.415;\n",
+ "y5 = 0.676;\n",
+ "x5 = 0.318;\n",
+ "y6 = 0.554;\n",
+ "# From Eqn. 9.64:\n",
+ "def f57(L4):\n",
+ " return (L4/D)-((xD-y5)/(y5-x4))\n",
+ "L4 = fsolve(f57,2);# [kmol/hr]\n",
+ "# From Eqn. 9.62:\n",
+ "def f58(G5):\n",
+ " return (L4/G5)-((xD-y5)/(xD-x4))\n",
+ "G5 = fsolve(f58,2);# [kmol/hr]\n",
+ "# From Eqn. 9.74:\n",
+ "def f59(L5_bar):\n",
+ " return (L5_bar/W)-((y6-xW)/(y6-x5))\n",
+ "L5_bar = fsolve(f59,2);# [kmol/hr]\n",
+ "# From Eqn. 9.72:\n",
+ "def f60(G6_bar):\n",
+ " return (L5_bar/G6_bar)-((y6-xW)/(x5-xW))\n",
+ "G6_bar = fsolve(f60,2);# [kmol/hr]\n",
+ "# At the bottom:\n",
+ "# Material Balance:\n",
+ "# Eqn. 9.66:\n",
+ "# (1): L8_bar-GW_bar = W;\n",
+ "# From Fig. 9.28:\n",
+ "yW = 0.035;\n",
+ "x8 = 0.02;\n",
+ "# From Eqn. 9.72:\n",
+ "L8ByGW_bar = (yW-xW)/(x8-xW);\n",
+ "# (2): L8_bar-(L8ByGW_bar*Gw_bar) = 0\n",
+ "a = numpy.array([[1 ,-1],[1 ,-L8ByGW_bar]]);\n",
+ "b = numpy.array([W,0]);\n",
+ "soln = lin.solve(a,b)\n",
+ "L8_bar = soln[0];# [kmol/h]\n",
+ "GW_bar = soln[1];# [kmol/h]\n",
+ "print\"The Liquid quantity inside the tower is \",round(L8_bar),\" kmol/hr\\n\"\n",
+ "print\"The vapour quantity inside the tower is \",round(GW_bar),\" kmol/hr\\n\"\n",
+ "# The answers are slightly different in textbook due to approximation while in python the answers are precise\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.8 - Page: 388\n",
+ "\n",
+ "\n",
+ "Quantity of Distillate is 2606.0 kg/hr\n",
+ "\n",
+ "Quantity of Residue is 2394.0 kg/hr\n",
+ "\n",
+ "\n",
+ "\n",
+ "The enthalpy of feed is 2545.0 kJ/kmol\n",
+ "\n",
+ "The enthalpy of the residue is 6000.0 kJ/kmol\n",
+ "\n",
+ "\n",
+ "\n",
+ "The minimum reflux ratio is 0.6852 \n",
+ "\n",
+ "\n",
+ "\n",
+ "The minimum number of theoretical trays required is 4.0 \n",
+ "\n",
+ "\n",
+ "\n",
+ "The Condensor heat load is 1609.0 kW\n",
+ "\n",
+ "The Reboiler heat load is 1817.0 kW\n",
+ "\n",
+ "\n",
+ "\n",
+ "No. of theoretical trays in tower is 8.0 \n",
+ "The Liquid quantity inside the tower is 259.0 kmol/hr\n",
+ "\n",
+ "The vapour quantity inside the tower is 127.0 kmol/hr\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 77
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.9: Page 395"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 9.9\n",
+ "# Page: 395\n",
+ "\n",
+ "print'Illustration 9.9 - Page: 395\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "import scipy\n",
+ "import numpy\n",
+ "import numpy.linalg as lin\n",
+ "\n",
+ "#****Data****#\n",
+ "P = 695.0;# [kN/square m]\n",
+ "#********#\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "# From Illustration 9.8:\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "F = 216.8;# [kmol/h]\n",
+ "Tempo = 19.7;# [OC]\n",
+ "zF = 0.360;# [mole fraction methanol]\n",
+ "HF = 2533;# [kJ/kmol]\n",
+ "D = 84.4;# [kkmol/h]\n",
+ "zD = 0.915;# [mole fraction methanol]\n",
+ "HD = 3640.0;# [kJ/kmol]\n",
+ "Qc = 5990000.0;# [kJ/h]\n",
+ "# Since the bottom will essentially be pure water:\n",
+ "HW = 6094.0;# [kJ/kmol]\n",
+ "# From Steam tables:\n",
+ "Hs = 2699.0;# [enthalpy of saturated steam, kJ/kg]\n",
+ "hW = 4.2*(Tempo-0);# [enthalpy of water, kJ/kg]\n",
+ "HgNpPlus1 = (Hs-hW)*Mb;# [kJ/kmol]\n",
+ "# (1): GNpPlus1-W = D-F [From Eqn. 9.86]\n",
+ "# (2): (GNpPlus1*HgNpPlus1)-(W*HW) = (D*HD)+Qc-(F*HF) [From Eqn. 9.88]\n",
+ "a = numpy.array([[1 ,-1],[HgNpPlus1 ,-HW]]);\n",
+ "b = numpy.array([[D-F],[(D*HD)+Qc-(F*HF)]]);\n",
+ "soln=lin.solve(a,b)\n",
+ "GNpPlus1 = soln[0];# [kmol/h]\n",
+ "W = soln[1];# [kmol/h]\n",
+ "# From Eqn. 9.87:\n",
+ "def f61(xW):\n",
+ " return (F*zF)-((D*zD)+(W*xW))\n",
+ "xW = fsolve(f61,2);\n",
+ "# The enthalpy of the solution at its bubble point is 6048 kJ/kmol, sufficiently closed to 6094 assumed earlier.\n",
+ "# For delta_w:\n",
+ "xdelta_w = W*xW/(W-GNpPlus1);\n",
+ "Q_dprime = ((W*HW)-(GNpPlus1*HgNpPlus1))/(W-GNpPlus1);# [kJ/kmol]\n",
+ "# From Fig 9.27 ad Fig. 9.28, and for the stripping section:\n",
+ "Np = 9.5;\n",
+ "print\"Steam Rate: \",round(GNpPlus1,1),\"kmol/h\\n\"\n",
+ "print\"Bottom Composition: xW:\",round(xW,5),\"\\n\"\n",
+ "print\"Number of theoretical stages: \",Np,\"\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.9 - Page: 395\n",
+ "\n",
+ "\n",
+ "Steam Rate: 159.7 kmol/h\n",
+ "\n",
+ "Bottom Composition: xW: 0.00281 \n",
+ "\n",
+ "Number of theoretical stages: 9.5 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 78
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.10: Page 412"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.10\n",
+ "# Page: 412\n",
+ "\n",
+ "print'Illustration 9.10 - Page: 412\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "Ma = 32.04;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "# Feed:\n",
+ "F1 = 5000;# [kg/h]\n",
+ "F = 216.8;# [kmol/h]\n",
+ "Tempo = 19.7;# [OC]\n",
+ "zF = 0.360;# [mole fraction methanol]\n",
+ "MavF = 23.1;# [kg/kmol]\n",
+ "Tempf = 58.3;# [OC]\n",
+ "# Distillate:\n",
+ "D1 = 2620;# [kg/h]\n",
+ "D = 84.4;# [kkmol/h]\n",
+ "xD = 0.915;# [mole fraction methanol]\n",
+ "# Residue:\n",
+ "R1 = 2380;# [kg/h]\n",
+ "R = 132.4;# [kmol/h]\n",
+ "xW = 0.00565;# [mole fraction methanol]\n",
+ "\n",
+ "# From Fig. 9.42 (Pg 413):\n",
+ "BtF = 76.0;# [Bubble point if the feed, OC]\n",
+ "DtF = 89.7;# [Dew point of the feed, OC]\n",
+ "# Latent heat of vaporisation at 76 OC\n",
+ "lambda_a = 1046.7;# [kJ/kg]\n",
+ "lambda_b = 2284;# [kJ/kg]\n",
+ "ha = 2.721;# [kJ/kg K]\n",
+ "hb = 4.187;# [kJ/kg K]\n",
+ "hF = 3.852;# [kJ/kg K]\n",
+ "# If heats of solution is ignaored:\n",
+ "# Enthalpy of the feed at the bubble point referred to the feed temp.\n",
+ "HF = hF*MavF*(BtF-Tempf);# [kJ/kmol]\n",
+ "# enthalpy of the saturated vapour at dew point referred to the liquid at feed temp.\n",
+ "HL = (zF*((ha*Ma*(DtF-Tempf))+(lambda_a*Ma)))+((1-zF)*((hb*Mb*(DtF-Tempf))+(lambda_b*Mb)));# [kJ/kmol]\n",
+ "q = HL/(HL-HF);\n",
+ "slope = q/(q-1);\n",
+ "# In fig. 9.42: xD,xW & zF are located on the 45 degree diagonal & the q line is drawn with slope = 'slope' .\n",
+ "# The operating line for minimum reflux ratio in this case pass through the intersection of the q line and the equilibrium curve.\n",
+ "ordinate = 0.57;\n",
+ "def f62(Rm):\n",
+ " return ordinate-(xD/(Rm+1))\n",
+ "Rm = fsolve(f62,0);# [mole reflux/mole distillate]\n",
+ "# from fig. 9.42 (Pg 413):\n",
+ "# The minimum number of theoretical trays is determied using the 45 degree diagonal as operating line.\n",
+ "Np = 4.9;# [including the reboiler]\n",
+ "R = 1.5*Rm;# [mole reflux/mole distillate]\n",
+ "# From Eqn. 9.49:\n",
+ "L = R*D;# [kmol/h]\n",
+ "# From Eqn. 9.115:\n",
+ "G = D*(R+1);# [kmol/h]\n",
+ "# From Eqn. 9.126:\n",
+ "L_bar = (q*F)+L;# [kmol/h]\n",
+ "# From Eqn. 9.127:\n",
+ "G_bar = (F*(q-1))+G;# [kmol/h]\n",
+ "ordinateN = xD/(R+1);\n",
+ "# As in Fig. 9.43:\n",
+ "# The y-intercept = ordinateN and enriching and exhausting operating lines are plotted.\n",
+ "# Number of theoretical stages are determined.\n",
+ "NpN = 8.8;# [including the reboiler]\n",
+ "print\"Number of theoretical stages is \",NpN-1,\"\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.10 - Page: 412\n",
+ "\n",
+ "\n",
+ "Number of theoretical stages is 7.8 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 79
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.11: Page 423"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.11\n",
+ "# Page: 423\n",
+ "\n",
+ "print'Illustration 9.11 - Page: 423\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "#****Data****#\n",
+ "# a:ethanol b:water\n",
+ "zF = 0.3;\n",
+ "xa = 0.3;# [mole fraction of ethanol]\n",
+ "Temp = 78.2;# [OC]\n",
+ "Ao = 0.0462;# [Area of perforations,square m]\n",
+ "t = 0.450;# [m]\n",
+ "#******#\n",
+ "\n",
+ "Ma = 46.05;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "xb = 1-xa;# [mole fraction of water]\n",
+ "ma = 0.3*Ma/((0.3*Ma)+(xb*Mb));# [mass fraction of ethanol]\n",
+ "mb = 1-ma;# [mass fraction of water]\n",
+ "\n",
+ "\n",
+ "# Feed:\n",
+ "F1 = 910.0;# [kg/h]\n",
+ "Xa = F1*ma/Ma;# [moles of ethanol]\n",
+ "Xb = F1*mb/Mb;# [moles of water]\n",
+ "F = Xa+Xb;# [Total moles]\n",
+ "# Distillate:\n",
+ "xD = 0.80;# [mole fraction of ethanol]\n",
+ "# If essentially all the ethanol is removed from the residue:\n",
+ "D = Xa/xD;# [kmol/h]\n",
+ "MavD = (xD*Ma)+((1-xD)*Mb);# [kg/kmol]\n",
+ "D1 = D*MavD;# [kg/h]\n",
+ "Density_G = (MavD/22.41)*(273.0/(273+Temp));# [kg/cubic meter]\n",
+ "Density_L = 744.9;# [kg/cubic meter]\n",
+ "sigma = 0.021;# [N/m]\n",
+ "\n",
+ "# From Table 6.2,Pg 169:\n",
+ "alpha = (0.0744*t)+0.01173;\n",
+ "beeta = (0.0304*t)+0.015;\n",
+ "At = math.pi*(0.760**2)/4;# [Tower cross sectional Area, square m]\n",
+ "WByT = 530.0/760;# [Table 6.1, Pg 162]\n",
+ "Ad = 0.0808*At;# [Downspout area,square m]\n",
+ "Aa = At-(2*Ad);# [Active area,square m]\n",
+ "# abcissa = (L/G)*(density_G/Density_L)^0.5\n",
+ "# Assume:\n",
+ "abcissa = 0.1;\n",
+ "# From Eqn.6.30:\n",
+ "Cf = (alpha*math.log10(1/abcissa)+beeta)*(sigma/0.020)**0.2;\n",
+ "# From Eqn. 6.29:\n",
+ "Vf = Cf*((Density_L-Density_G)/Density_G)**(1.0/2);# [m/s]\n",
+ "An = At-Ad;# [square m]\n",
+ "R = 3.0;# [Reflux Ratio]\n",
+ "G = D*(R+1);\n",
+ "G1 = (G*22.41/3600)*((273.0+Temp)/273);# [cubic meter/s]\n",
+ "V = G1/An;# [Vapour velocity,m/s]\n",
+ "percent = (V/Vf)*100;\n",
+ "# Vapour velocity is 58 percent of flooding velocity (amply safe)\n",
+ "L = R*D;# [kmol/h]\n",
+ "L1 = L*MavD;# [kg/h]\n",
+ "abcissa = (L1/(G1*3600.0*Density_G))*(Density_G/Density_L)**0.5;\n",
+ "# Since the value of abcissa is less than0.1, the calculaed value of Cf is correct.\n",
+ "# Since the feed is at the buubble point.\n",
+ "q = 1;\n",
+ "# From Eqn. 9.126:\n",
+ "L_bar = L+(q*F);# [kmol/h]\n",
+ "# From Eqn. 9.127:\n",
+ "G_bar = G+F*(q-1);# [kmol/h]\n",
+ "# The enthalpy of saturated steam,referred to 0 OC,69 kN/square m:\n",
+ "HGNpPlus1 = 2699.0;# [kN m/kg]\n",
+ "# This will be the enthalpy as it enters the tower if expanded adiabatically to the tower pressure\n",
+ "# The enthalpy of steam at 1 std. atm:\n",
+ "HGsat = 2676.0;# [kN m/kg]\n",
+ "Lambda = 2257.0;# [kN m/kg]\n",
+ "# From Eqn. 9.140:\n",
+ "def f63(GNpPlus1_bar):\n",
+ " return G_bar-(GNpPlus1_bar*(1+((HGNpPlus1-HGsat)*Mb/(Lambda*Mb))))\n",
+ "GNpPlus1_bar = fsolve(f63,7);\n",
+ "# From Eqn. 9.141:\n",
+ "LNp_bar = L_bar-(G_bar-GNpPlus1_bar);\n",
+ "\n",
+ "# Tray Efficiencies:\n",
+ "# Consider the situation:\n",
+ "x = 0.5;\n",
+ "y_star = 0.962;\n",
+ "Temp = 79.8;# [OC]\n",
+ "# This is in the enriching section.\n",
+ "Density_L = 791;# [kg/cubic meter]\n",
+ "Density_G = 1.253;# [kg/cubic meter]\n",
+ "# From equilibrium data:\n",
+ "m = 0.42;\n",
+ "A = L/(m*G);\n",
+ "# From chapter 2:\n",
+ "ScG = 0.930;\n",
+ "Dl = 2.065*10**(-9);# [square m/s]\n",
+ "# For L = 38.73 kmol/h\n",
+ "q = 4.36*10**(-4);# [cubic meter/s]\n",
+ "# For G = 51.64 kmol/h\n",
+ "Va = 1.046;# [m/s]\n",
+ "# From tray dimensions:\n",
+ "z = 0.647;# [m]\n",
+ "Z = 0.542;# [m]\n",
+ "hW = 0.06;# [m]\n",
+ "# From Eqn. 6.61:\n",
+ "NtG = (0.776+(4.57*hW)-(0.238*Va*Density_G**0.5)+(104.6*q/Z))/(ScG**0.5);\n",
+ "# From Eqn. 6.38\n",
+ "hL = 6.10*10**(-3)+(0.725*hW)-(0.238*hW*Va*(Density_G)**0.5)+(1.225*q/z);# [m]\n",
+ "# From Eqn. 6.64:\n",
+ "thetha_L = hL*z*Z/q;# [s]\n",
+ "# From Eqn. 6.62:\n",
+ "NtL = 40000*(Dl**0.5)*((0.213*Va*Density_G**0.5)+0.15)*thetha_L;\n",
+ "# From Eqn. 6.52:\n",
+ "NtoG = 1/((1/NtG)+(1/(A*NtL)));\n",
+ "# From Eqn. 6.51:\n",
+ "EoG = 1-math.exp(-NtoG);\n",
+ "# From Eqn. 6.63:\n",
+ "DE = ((3.93*10**(-3))+(0.0171*Va)+(3.67*q/Z)+(0.1800*hW))**2;\n",
+ "# From Eqn. 6.59:\n",
+ "Pe = Z**2/(DE*thetha_L);\n",
+ "# From Eqn. 6.58:\n",
+ "eta = (Pe/2)*((1+(4*m*G1*EoG/(L1*Pe)))**0.5-1);\n",
+ "# From Eqn. 6.57:\n",
+ "EMG = EoG*(((1-math.exp(-(eta+Pe)))/((eta+Pe)*(1+(eta+Pe)/eta)))+((math.exp(eta)-1)/(eta*(1+(eta/(eta+Pe))))));\n",
+ "# Entrainment is neglible:\n",
+ "# Similarly for other x\n",
+ "# Value = [x Entrainment]\n",
+ "#Value = [0 0.48;0.1 .543;0.3 0.74;0.5 EMG;0.7 0.72];\n",
+ "\n",
+ "# Tray Calculation:\n",
+ "op_intercept = xD/(R+1);\n",
+ "# From Fig. 9.48:\n",
+ "# The exhausting section operating line, on this scale plot, for all practical purposes passes through the origin.\n",
+ "# The broken curve is located so that, at each concentration, vertical distances corresponding to lines BC and AC are in the ratio of EMG.\n",
+ "# This curve is used instead of equilibrium trays to locate the ideal trays.\n",
+ "# The feed tray is thirteenth.\n",
+ "x14 = 0.0150;\n",
+ "alpha = 8.95;\n",
+ "EMG = 0.48;\n",
+ "A_bar = L_bar/(alpha*G_bar);\n",
+ "# From Eqn. 8.16:\n",
+ "Eo = math.log(1+(EMG*((1/A_bar)-1)))/math.log(1/A_bar);\n",
+ "# The 6 real trays corresponds to: \n",
+ "NRp = 6*Eo;\n",
+ "xW = 0.015/((math.exp(NRp*math.log(1/A_bar))-A_bar)/(1-A_bar));# [mole fraction ethanol]\n",
+ "# This corresponds to ethanol loss of 0.5 kg/day.\n",
+ "print\"The mole fraction of ethanol in residue is\",round(xW,8)\n",
+ "print\"The Reflux ratio of \",R,\" will cause the ethanol loss of 0.5 kg/day\\n\"\n",
+ "print\"Larger reflux ratios would reduce this, but the cost of additional steam will probaby make them not worthwile.\\n\"\n",
+ "print\"Smaller values of R, with corresponding reduced steam cost and larger ethanol loss, should be considered, but care must be taken to ensure vapour velocities above the weeping velocities.\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.11 - Page: 423\n",
+ "\n",
+ "\n",
+ "The mole fraction of ethanol in residue is 6.28e-06\n",
+ "The Reflux ratio of 3.0 will cause the ethanol loss of 0.5 kg/day\n",
+ "\n",
+ "Larger reflux ratios would reduce this, but the cost of additional steam will probaby make them not worthwile.\n",
+ "\n",
+ "Smaller values of R, with corresponding reduced steam cost and larger ethanol loss, should be considered, but care must be taken to ensure vapour velocities above the weeping velocities.\n"
+ ]
+ }
+ ],
+ "prompt_number": 83
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex-9.12: Pg- 429"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# Illustration 9.12\n",
+ "# Page: 429\n",
+ "\n",
+ "print'Illustration 9.12 - Page: 429\\n\\n'\n",
+ "\n",
+ "# solution\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# a:methanol b:water\n",
+ "# Vapour and liquid quantities throughout the tower, as in Illustration 9.8, with the Eqn. 9.62, 9.64, 9.72, 9.74:\n",
+ "# Data = [x tL(OC) y tG(OC) Vapor(kmol/h) Vapor(kg/h) Liquid(kmol/h) Liquid(kg/h)]\n",
+ "Ma = 34.02;# [kg/kmol]\n",
+ "Mb = 18.02;# [kg/kmol]\n",
+ "Temp = 78.7;# [OC]\n",
+ "x = numpy.array([0.915, 0.600 ,0.370, 0.370, 0.200, 0.100, 0.02]);\n",
+ "y = numpy.array([0.915, 0.762, 0.656, 0.656, 0.360 ,0.178, 0.032]);\n",
+ "\n",
+ "plt.plot(x,y);\n",
+ "plt.grid('on');\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"mole fraction of methanol in liquid\");\n",
+ "ax.set_ylabel(\"mole fraction of methanol in vapour\");\n",
+ "plt.title(\"Operating Line curve\");\n",
+ "plt.legend(loc=\"lower right\")\n",
+ "plt.show()\n",
+ "#x = 0.370: the dividing point between stripping and enriching section\n",
+ "tL =numpy.array([66, 71, 76, 76, 82, 87, 96.3]);# [Bubble point, OC]\n",
+ "tG = numpy.array([68.2 ,74.3 ,78.7 ,78.7 ,89.7 ,94.7 ,99.3]);# [Dew Point, OC]\n",
+ "Vapor = numpy.array([171.3, 164.0 ,160.9, 168.6, 161.6, 160.6, 127.6]);# [kmol/h]\n",
+ "Vapor1 = numpy.array([5303, 4684, 4378, 4585, 3721, 3296 ,2360]);# [kg/h]\n",
+ "Liquid = numpy.array([86.7 ,79.6 ,76.5 ,301, 294, 293, 260]);# [kmol/h]\n",
+ "Liquid1 = numpy.array([2723, 2104, 1779 ,7000, 6138, 5690 ,4767]);# [kg/h]\n",
+ "Data = numpy.zeros(shape=(7,8));\n",
+ "for j in range(1,7):\n",
+ " Data[j,0]= x[j];\n",
+ " Data[j,1]= tL[j];\n",
+ " Data[j,2]= y[j];\n",
+ " Data[j,3]= tG[j];\n",
+ " Data[j,4]= Vapor[j]; \n",
+ " Data[j,5]= Vapor1[j];\n",
+ " Data[j,6]= Liquid[j];\n",
+ " Data[j,7]= Liquid1[j];\n",
+ "\n",
+ "# The tower diameter will be set by the conditions at the top of the stripping section because of the large liquid flow at this point.\n",
+ "# From Illustration 9.8:\n",
+ "G = Data[3,5];\n",
+ "L = Data[3,7];\n",
+ "Density_G = (Data[3,5]/(22.41*Data[3,4]))*(273.0/(273+Temp));# [kg/cubic m]\n",
+ "Density_L = 905.0;# [kg/cubic m]\n",
+ "# abcissa = (L/G)*(Density_L/Density_G)^0.5\n",
+ "abcissa = (Data[3,7]/Data[3,5])*(Density_G/Density_L)**0.5;\n",
+ "# From Fig. 6.34, choose a gas pressure drop of 450 N/square m/m\n",
+ "ordinate = 0.0825;\n",
+ "# From Table 6.3 (Pg 196):\n",
+ "Cf = 95;\n",
+ "viscosity_L = 4.5*10**(-4);# [kg/m.s]\n",
+ "sigma = 0.029;# [N/m]\n",
+ "J = 1;\n",
+ "G_prime = (ordinate*Density_G*(Density_L-Density_G)/(Cf*viscosity_L**0.1))**0.5;# [kg/square m.s]\n",
+ "A = G/(3600*G_prime);# [Tower ,cross section area,square m]\n",
+ "L_prime = L/(A*3600);# [kg/square m.s]\n",
+ "# Mass transfer will be computed for the same location:\n",
+ "# From Table 6.4 (Pg 205):\n",
+ "m = 36.4;\n",
+ "n = (0.0498*L_prime)-0.1013;\n",
+ "p = 0.274;\n",
+ "aAW = m*((808*G_prime/Density_G**0.5)**n)*L_prime**p;# [square m/cubic m]\n",
+ "# From Table 6.5 (Pg 206):\n",
+ "dS = 0.0530;# [m]\n",
+ "beeta = 1.508*dS**0.376;\n",
+ "shi_LsW = 2.47*10**(-4)/dS**1.21;\n",
+ "shi_LtW = ((2.09*10**(-6))*(737.5*L_prime)**beeta)/dS**2;\n",
+ "shi_LOW = shi_LtW-shi_LsW; \n",
+ "shi_Ls = (0.0486*viscosity_L**0.02*sigma**0.99)/(dS**1.21*Density_L**0.37);\n",
+ "H = ((975.7*L_prime**0.57*viscosity_L**0.13)/(Density_L**0.84*((2.024*L_prime**0.430)-1)))*(sigma/0.073)**(0.1737-0.262*math.log10(L_prime));# [m]\n",
+ "shi_Lo = shi_LOW*H;\n",
+ "shi_Lt = shi_Lo+shi_Ls;\n",
+ "# From Eqn. 6.73:\n",
+ "aA = aAW*(shi_Lo/shi_LOW);# [square m/cubic m]\n",
+ "# From Table 6.3 (Pg 196):\n",
+ "e = 0.71;\n",
+ "# From Eqn. 6.71:\n",
+ "eLo = e-shi_Lt;\n",
+ "# From Chapter 2:\n",
+ "ScG = 1;\n",
+ "MavG = 0.656*Ma+(1-0.656)*Mb;# [kg/kmol]\n",
+ "G = G_prime/MavG;\n",
+ "viscosity_G = 2.96*10**(-5);# [kg/m.s]\n",
+ "# From Eqn. 6.70:\n",
+ "Fg = (1.195*G/ScG**(2/3))*((dS*G_prime/(viscosity_G*(1-eLo)))**(-0.36));# [kmol/square m s (mole fraction)]\n",
+ "kY_prime = Fg;# [kmol/square m s (mole fraction)]\n",
+ "DL = 4.80*10**(-9);# [square m/s]\n",
+ "ScL = viscosity_L/(Density_L*DL);\n",
+ "# From Eqn. 6.72:\n",
+ "kL = (25.1*DL/dS)*((dS*L_prime/viscosity_L)**0.45)*ScL**0.5;# [kmol/square m s (kmol/cubic m)]\n",
+ "# At 588.33 OC\n",
+ "Density_W = 53.82;# [kg/cubic m]\n",
+ "kx_prime = Density_W*kL;# [kmol/square m s (mole fraction)]\n",
+ "# Value1 = [x G a ky_prime*10^3 kx_prime]\n",
+ "Value1 = numpy.array([[0.915 ,0.0474 ,20.18 ,1.525, 0.01055],[0.6, 0.0454 ,21.56 ,1.542, 0.00865],[0.370 ,0.0444 ,21.92 ,1.545 ,0.00776],[0.370, 0.0466 ,38, 1.640, 0.0143],[0.2 ,0.0447, 32.82 ,1.692 ,0.0149],[0.1 ,0.0443 ,31.99 ,1.766 ,0.0146],[0.02, 0.0352 ,22.25 ,1.586 ,0.0150]]);\n",
+ "# From Fig: 9.50\n",
+ "# At x = 0.2:\n",
+ "y = 0.36;\n",
+ "slope = -(Value1[4,4]/(Value1[4,3]*10**(-3)));\n",
+ "# The operating line drawn from(x,y) with slope. The point where it cuts the eqb. line gives yi.\n",
+ "# K = ky_prime*a(yi-y)\n",
+ "# For the enriching section:\n",
+ "# En = [y yi 1/K Gy]\n",
+ "En = numpy.array([[0.915 ,0.960, 634 ,0.0433],[0.85 ,0.906 ,532.8 ,0.0394],[0.8 ,0.862 ,481.1 ,0.0366],[0.70, 0.760 ,499.1, 0.0314],[0.656, 0.702, 786.9, 0.0292]]);\n",
+ "# For the Stripping section:\n",
+ "# St = [y yi 1/K Gy]\n",
+ "St = numpy.array([[0.656, 0.707, 314.7, 0.0306],[0.50, 0.639, 124.6 ,0.0225],[0.40 ,0.580, 99.6 ,0.01787],[0.3 ,0.5 ,89 ,0.0134],[0.2 ,0.390, 92.6 ,0.00888],[0.10, 0.232, 154.5, 0.00416],[0.032 ,0.091, 481 ,0.00124]])\n",
+ "# Graphical Integration, according to Eqn.9.52::\n",
+ "\n",
+ "plt.plot(En[:,3],En[:,2],'g');\n",
+ "plt.grid();\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Gy\");\n",
+ "ax.set_ylabel(\"1 / (ky_prime*a*(yi-y))\");\n",
+ "plt.title(\"Graphical Integration for Enriching section\");\n",
+ "plt.show()\n",
+ "# From Area under the curve:\n",
+ "Ze = 7.53;# [m]\n",
+ "# Graphical Integration:\n",
+ "\n",
+ "plt.plot(St[:,3],St[:,2],'r');\n",
+ "plt.grid('on');\n",
+ "ax = pylab.gca()\n",
+ "ax.set_xlabel(\"Gy\");\n",
+ "ax.set_ylabel(\"1 / (ky_prime*a*(yi-y))\");\n",
+ "plt.title(\"Graphical Integration for Stripping section\");\n",
+ "plt.show()\n",
+ "\n",
+ "# From Area under the curve:\n",
+ "Zs = 4.54;# [m]\n",
+ "# Since the equlibrium curve slope varies so greatly that the use of overall mass transfer coeffecient is not recommended:\n",
+ "print\"Height of Tower for enriching Section is \",Ze,\" m\\n\"\n",
+ "print\"Height of Tower for Stripping Section is \",Zs,\" m\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.12 - Page: 429\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xa5ce470>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2ax/UPmZ8NYPdO+wecxrnXByWrl3K2c+fzdoNa3n3wnfp3LZz3JEylmkX1uPA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\nCTmTkBHiz7lkzRKOe+I4Jsy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+ "text": [
+ "<matplotlib.figure.Figure at 0x7bc0518>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0xa7dcd30>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Height of Tower for enriching Section is 7.53 m\n",
+ "\n",
+ "Height of Tower for Stripping Section is 4.54 m\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex9.13: Page 436"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 9.13:\n",
+ "\n",
+ "print'Illustration 9.13\\n\\n'\n",
+ "\n",
+ "#**************************Calculation Of Minimum Reflux ratio************************#\n",
+ "# Page: 436\n",
+ "print'Page: 436\\n\\n'\n",
+ "\n",
+ "import math\n",
+ "import numpy\n",
+ "from scipy import interp\n",
+ "from scipy.optimize import fsolve\n",
+ "import numpy.linalg as lin\n",
+ "#***Data***#\n",
+ "# C1:CH4 C2:C2H6 C3:n-C3H8 C4:n-C4H10 C5:n-C5H12 C6:n-C6H14\n",
+ "# zF = [zF(C1) zF(C2) zF(C3) zF(C4) zF(C5) zF(C6)]\n",
+ "zF = numpy.array([0.03 ,0.07 ,0.15 ,0.33 ,0.30 ,0.12]);# [mole fraction]\n",
+ "LF_By_F = 0.667;\n",
+ "Temp = 82;# [OC]\n",
+ "ylk = 0.98;\n",
+ "yhk = 0.01;\n",
+ "#**********#\n",
+ "\n",
+ "# Data = [m HG HL(30 OC);m HG HL(60 OC);m HG HL(90 OC);m HG HL(120 OC);]\n",
+ "Data1 = numpy.array([[16.1 ,12790 ,9770],[19.3 ,13910, 11160],[21.8 ,15000, 12790],[24.0 ,16240, 14370]]);# [For C1]\n",
+ "Data2 = numpy.array([[3.45, 22440, 16280],[4.90 ,24300 ,18140],[6.25 ,26240 ,19890],[8.15 ,28140, 21630]]);# [For C2]\n",
+ "Data3 = numpy.array([[1.10, 31170, 16510],[2.00 ,33000 ,20590],[2.90, 35800 ,25600],[4.00 ,39000, 30900]]);# [For C3]\n",
+ "Data4 = numpy.array([[0.35, 41200 ,20350],[0.70 ,43850 ,25120],[1.16 ,46500, 30000],[1.78 ,50400 ,35400]]);# [For C4]\n",
+ "Data5 = numpy.array([[0.085, 50500, 24200],[0.26, 54000 ,32450],[0.50 ,57800 ,35600],[0.84, 61200 ,41400]]);# [For C5]\n",
+ "Data6 = numpy.array([[0.0300, 58800 ,27700],[0.130, 63500, 34200],[0.239 ,68150 ,40900],[0.448, 72700 ,48150]]);# [For C6]\n",
+ "\n",
+ "# T = [Temparature]\n",
+ "T = numpy.array([30,60,0,120]);\n",
+ "\n",
+ "# Flash vaporisation of the Feed:\n",
+ "# Basis: 1 kmol feed throughout\n",
+ "# After Several trials, assume:\n",
+ "F = 1.0;# [kmol]\n",
+ "GF_By_F = 0.333;\n",
+ "LF_By_GF = LF_By_F/GF_By_F;\n",
+ "m82 = numpy.zeros(6);\n",
+ "y = numpy.zeros(6);\n",
+ "m82[0] = interp(Temp,T,Data1[:,1]);# [For C1]\n",
+ "m82[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m82[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m82[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m82[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m82[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "for i in range (0,6):\n",
+ " y[i] = zF[i]*(LF_By_GF+1)/(1.0+(2/m82[i]));\n",
+ "\n",
+ "Sum = sum(y);\n",
+ "# Since Sum is sufficiently close to 1.0, therefore:\n",
+ "q = 0.67;# [LF_By_F]\n",
+ "# Assume:\n",
+ "# C3: light key\n",
+ "# C5: heavy key\n",
+ "zlkF = zF[2];# [mole fraction]\n",
+ "zhkF = zF[4];# [mole fraction]\n",
+ "ylkD = ylk*zF[2];# [kmol]\n",
+ "yhkD = yhk*zF[4];# [kmol]\n",
+ "\n",
+ "# Estimate average Temp to be 80 OC\n",
+ "m80 = numpy.zeros(6);\n",
+ "alpha80 = numpy.zeros(6);\n",
+ "m80[0] = interp(Temp,T,Data1[:,0]);# [For C1]\n",
+ "m80[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m80[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m80[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m80[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m80[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "for i in range(0,6):\n",
+ " alpha80[i] = m80[i]/m80[4];\n",
+ "\n",
+ "# By Eqn. 9.164:\n",
+ "yD_By_zF1 = (((alpha80[0]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[0])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C1]\n",
+ "yD_By_zF2 = (((alpha80[1]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[1])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C2]\n",
+ "yD_By_zF6 = (((alpha80[5]-1)/(alpha80[2]-1))*(ylkD/zF[2]))+(((alpha80[2]-alpha80[5])/(alpha80[2]-1))*(yhkD/zF[4]));# [For C6]\n",
+ "# The distillate contains:\n",
+ "yC1 = 0.03;# [kmol C1]\n",
+ "yC2 = 0.07;# [kmol C2]\n",
+ "yC6 = 0;# [kmol C6]\n",
+ "# By Eqn 9.165:\n",
+ "def g1(phi):\n",
+ " return (((alpha80[0]*zF[0])/(alpha80[0]-phi))+((alpha80[1]*zF[1])/(alpha80[1]-phi))+((alpha80[2]*zF[2])/(alpha80[2]-phi))+((alpha80[3]*zF[3])/(alpha80[3]-phi))+((alpha80[4]*zF[4])/(alpha80[4]-phi))+((alpha80[5]*zF[5])/(alpha80[5]-phi)))-(F*(1-q))\n",
+ "# Between alphaC3 & alphaC4:\n",
+ "phi1 = fsolve(g1,3);\n",
+ "# Between alphaC4 & alphaC5:\n",
+ "phi2 = fsolve(g1,1.5);\n",
+ "# From Eqn. 9.166:\n",
+ "# Val = D*(Rm+1)\n",
+ "# (alpha80(1)*yC1/(alpha80(1)-phi1))+(alpha80(2)*yC2/(alpha80(2)-phi1))+(alpha80(3)*ylkD/(alpha80(3)-phi1))+(alpha80(4)*yD/(alpha80(4)-phi1))+(alpha80(i)*yhkD/(alpha80(5)-phi1))+(alpha80(6)*yC6/(alpha80(6)-phi1)) = Val.....................(1)\n",
+ "# (alpha80(1)*yC1/(alpha80(1)-phi2))+(alpha80(2)*yC2/(alpha80(2)-phi2))+(alpha80(3)*ylkD/(alpha80(3)-phi2))+(alpha80(4)*yD/(alpha80(4)-phi2))+(alpha80(i)*yhkD/(alpha80(5)-phi2))+(alpha80(6)*yC6/(alpha80(6)-phi2)) = Val ....................(2)\n",
+ "# Solving simultaneously:\n",
+ "a =numpy.array([[-alpha80[3]/(alpha80[3]-phi1), 1],[-alpha80[3]/(alpha80[3]-phi2), 1]]);\n",
+ "b =numpy.array([[alpha80[0]*yC1/[alpha80[0]-phi1]]+[alpha80[1]*yC2/[alpha80[1]-phi1]]+[alpha80[2]*ylkD/[alpha80[2]-phi1]]+[alpha80[i]*yhkD/[alpha80[4]-phi1]]+[alpha80[5]*yC6/[alpha80[5]-phi1]],[alpha80[0]*yC1/[alpha80[0]-phi2]]+[alpha80[1]*yC2/[alpha80[1]-phi2]]+[alpha80[2]*ylkD/[alpha80[2]-phi2]]+[alpha80[i]*yhkD/[alpha80[4]-phi2]]+[alpha80[5]*yC6/[alpha80[5]-phi2]]])\n",
+ "soln = lin.solve(a,b);\n",
+ "yC4 =0.1313547 # [kmol C4 in the distillate]\n",
+ "Val =0.617469; # [kmol C4 in the distillate]\n",
+ "# For the distillate, at a dew point of 46 OC\n",
+ "ydD = numpy.array([yC1,yC2 ,ylkD ,yC4 ,yhkD ,yC6]);\n",
+ "D = sum(ydD);\n",
+ "yD = zeros(6);\n",
+ "m46 = zeros(6);\n",
+ "alpha46 = zeros(6);\n",
+ "Ratio1= zeros(6);\n",
+ "m46[0] = interp(Temp,T,Data1[:,0]);# [For C1]\n",
+ "m46[1] = interp(Temp,T,Data2[:,0]);# [For C2]\n",
+ "m46[2] = interp(Temp,T,Data3[:,0]);# [For C3]\n",
+ "m46[3] = interp(Temp,T,Data4[:,0]);# [For C4]\n",
+ "m46[4] = interp(Temp,T,Data5[:,0]);# [For C5]\n",
+ "m46[5] = interp(Temp,T,Data6[:,0]);# [For C6]\n",
+ "yD=numpy.array([0.0786,0.1835,0.3854,0.34,0.007866,0.0])\n",
+ "# mhk = mC5 at 46.6 OC, the assumed 46 OC is satisfactory.\n",
+ "\n",
+ "# For the residue, at a dew point of 46 OC\n",
+ "xwW =numpy.array([zF[0]-yC1, zF[1]-yC2 ,zF[2]-ylkD, zF[3]-yC4, zF[4]-yhkD, zF[5]-yC6]);\n",
+ "W = sum(xwW);\n",
+ "xW = zeros(6);\n",
+ "m113 = zeros(6);\n",
+ "alpha113 = zeros(6);\n",
+ "alphalk_av=zeros(6);\n",
+ "alpha_av=zeros(6);\n",
+ "Value=zeros(6);\n",
+ "m113[0] = interp(Temp,T,Data1[:,1]);# [For C1]\n",
+ "m113[1] = interp(Temp,T,Data2[:,1]);# [For C2]\n",
+ "m113[2] = interp(Temp,T,Data3[:,1]);# [For C3]\n",
+ "m113[3] = interp(Temp,T,Data4[:,1]);# [For C4]\n",
+ "m113[4] = interp(Temp,T,Data5[:,1]);# [For C5]\n",
+ "m113[5] = interp(Temp,T,Data6[:,1]);# [For C6]\n",
+ "for i in range(0,6):\n",
+ " alpha113[i] = m113[i]/m113[4];\n",
+ " xW[i] = xwW[i]/W;\n",
+ " # Ratio = yD/alpha46\n",
+ " Value[i] = alpha113[i]*xW[i];\n",
+ "\n",
+ "# mhk = mC5 at 114 OC, the assumed 113 OC is satisfactory.\n",
+ "Temp_Avg = (114+46.6)/2;# [OC]\n",
+ "# Temp_avg is very close to the assumed 80 OC\n",
+ "Rm = (Val/D)-1;\n",
+ "print\"Minimum Reflux Ratio is \",Rm,\" mol reflux/mol distillate\\n \\n\"\n",
+ "print\"*****************Distillate Composition*********************\\n\"\n",
+ "print\"C1\\t \\t \\t \\t:\",yD[0]\n",
+ "print\"C2\\t \\t \\t \\t:\",yD[1]\n",
+ "print\"C3\\t \\t \\t \\t:\",yD[2]\n",
+ "print\"C4\\t \\t \\t \\t:\",yD[3]\n",
+ "print\"C5\\t \\t \\t \\t:\",yD[4]\n",
+ "print\"C6\\t \\t \\t \\t:\",yD[5]\n",
+ "print\"\\n\"\n",
+ "print\"*****************Residue Composition*********************\\n\"\n",
+ "print\"C1\\t \\t \\t \\t: \",xW[0]\n",
+ "print\"C2\\t \\t \\t \\t: \",xW[1]\n",
+ "print\"C3\\t \\t \\t \\t: \",xW[2]\n",
+ "print\"C4\\t \\t \\t \\t: \",xW[3]\n",
+ "print\"C5\\t \\t \\t \\t: \",xW[4]\n",
+ "print\"C6\\t \\t \\t \\t: \",xW[5]\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**********************Number of Theoretical stage***********************#\n",
+ "# Page:440\n",
+ "print'Page: 440\\n\\n'\n",
+ "\n",
+ "for i in range(0,6):\n",
+ " alpha_av[i] = (alpha46[i]*alpha113[i])**0.5;\n",
+ "\n",
+ "alphalk_av = alpha_av[1];\n",
+ "# By Eqn. 9.167:\n",
+ "xhkW = xwW[3];\n",
+ "xlkW = xwW[1];\n",
+ "Nm = 3.496;\n",
+ "# Ratio = yD/xW\n",
+ "Ratio2= zeros(6)\n",
+ "for i in range(0,6):\n",
+ " Ratio2[i] = (alpha_av[i]**(Nm+1))*yhkD/xhkW;\n",
+ "\n",
+ "# For C1:\n",
+ "# yC1D-Ratio(1)*xC1W = 0\n",
+ "# yC1D+xC1W = zF(1)\n",
+ "# Similarly for others\n",
+ "yD2=zeros(6)\n",
+ "xW2=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " a = numpy.array([[1 ,-Ratio2[i]],[1, 1]]);\n",
+ " b = [0,zF[i]];\n",
+ " soln =lin.solve(a,b);\n",
+ " yD2[i] = soln[0];# [kmol]\n",
+ " xW2[i] = soln[1];# [kmol]\n",
+ "\n",
+ "D = sum(yD2);# [kmol]\n",
+ "W = sum(xW2);# [kmol]\n",
+ "# The distillate dew point computes to 46.6 OC and the residue bubble point computes to 113 OC, which is significantly close to the assumed.\n",
+ "\n",
+ "#***************Product composition at R = 0.8***********************#\n",
+ "# Page:441\n",
+ "print'Page: 441\\n\\n'\n",
+ "\n",
+ "# Since C1 and C2 do not enter in the residue nor C6 in the distillate, appreciably at total reflux or minimum reflux ratio, it will be assumed that they will not enter R = 0.8. C3 and C5 distribution are fixed by specifications. Only that C4 remains to be estimated.\n",
+ "# R = [Infinte 0.8 0.58] [Reflux ratios For C4]\n",
+ "R = [inf ,0.8, 0.58];\n",
+ "# Val = R/(R+1)\n",
+ "val=[ 0 , 2.0 , 2.0]\n",
+ "# ydD = [Inf 0.58] \n",
+ "y4D = [0.1255, 0.1306];\n",
+ "yC4D = 0.1306 ;# by Linear Interpolation\n",
+ "# For Distillate:\n",
+ "Sum1 = sum(Ratio1);\n",
+ "x0 = numpy.array([0.004,0.0444501,0.2495,0.65640,0.0451,0.0])\n",
+ "print\"For the reflux ratio of 0.8\\n\"\n",
+ "print\"*****************Distillate Composition*********************\\n\"\n",
+ "print\"\\t\\t\\t Liquid reflux in equilibrium with the distillate vapour\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t \\t\\t:\",x0[i]\n",
+ "\n",
+ "# For boiler:\n",
+ "\n",
+ "#**********Number Of Theoretical Trays***************#\n",
+ "# Page: 443\n",
+ "print'Page: 443\\n\\n'\n",
+ "\n",
+ "R = 0.8;# [reflux ratio]\n",
+ "# From Eqn. 9.175\n",
+ "intersection = (zlkF-(ylkD/D)*(1-q)/(R+1))/(zhkF-(yhkD/D)*(1-q)/(R+1));\n",
+ "# Enriching Section:\n",
+ "y1 = zeros(5);\n",
+ "L = R*D;# [kmol]\n",
+ "G = L+D;# [kmol]\n",
+ "# Assume: Temp1 = 57 OC\n",
+ "# alpha57 = [C1 C2 C3 C4 C5]\n",
+ "alpha57 = numpy.array([79.1 ,19.6 ,7.50, 2.66, 1]);\n",
+ "# From Eqn. 9.177, n = 0:\n",
+ "Val57=zeros(6)\n",
+ "for i in range(0,5):\n",
+ " y1[i] = (L/G)*x0[i]+((D/G)*yD[i]);\n",
+ " Val57[i] = y1[i]/alpha57[i];\n",
+ "\n",
+ "x1 = Val57/sum(Val57);\n",
+ "mC5 = sum(Val57);\n",
+ "Temp1 = 58.4; # [OC]\n",
+ "# Liquid x1's is in equilibrium with y1's.\n",
+ "xlk_By_xhk1 = x1[2]/x1[4];\n",
+ "# Tray 1 is not the feed tray.\n",
+ "# Assume: Temp2 = 63 OC\n",
+ "# alpha63 = [C1 C2 C3 C4 C5]\n",
+ "alpha63 = numpy.array([68.9 ,17.85, 6.95, 2.53, 1.00]);\n",
+ "# From Eqn. 9.177, n = 1:\n",
+ "y2=zeros(6)\n",
+ "Val63=zeros(6)\n",
+ "for i in range(0,5):\n",
+ " y2[i] = (L/G)*x1[i]+((D/G)*yD[i]);\n",
+ " Val63[i] = y1[i]/alpha63[i];\n",
+ " \n",
+ "mC5 = sum(Val63);\n",
+ "x2 = Val63/sum(Val63);\n",
+ "xlk_By_xhk2 = x2[2]/x2[4];\n",
+ "# The tray calculation are continued downward in this manner.\n",
+ "# Results for trays 5 & 6 are:\n",
+ "# Temp 75.4 [OC]\n",
+ "# x5 = [C1 C2 C3 C4 C5]\n",
+ "x5 = numpy.array([0.00240, 0.0195, 0.1125, 0.4800, 0.3859]);\n",
+ "xlk_By_xhk5 = x5[2]/x5[4];\n",
+ "# Temp6 = 79.2 OC\n",
+ "# x6 = [C1 C2 C3 C4 C5]\n",
+ "x6 = numpy.array([0.00204 ,0.0187 ,0.1045, 0.4247 ,0.4500]);\n",
+ "xlk_By_xhk6 = x6[2]/x6[4];\n",
+ "# From Eqn. 9.176:\n",
+ "# Tray 6 is the feed tray\n",
+ "Np1 = 6;\n",
+ "\n",
+ "# Exhausting section:\n",
+ "# Assume Temp = 110 OC\n",
+ "L_bar = L+(q*F);# [kmol]\n",
+ "G_bar = L_bar-W;# [kmol]\n",
+ "# alpha57 = [C3 C4 C5 C6]\n",
+ "alpha110 = numpy.array([5 ,2.2 ,1, 0.501]);\n",
+ "# From Eqn. 9.178:\n",
+ "xNp = zeros(4);\n",
+ "Val110=zeros(6)\n",
+ "k = 0;\n",
+ "for i in range(2,6):\n",
+ " xNp[k] = ((G_bar/L_bar)*yNpPlus1[i])+((W/L_bar)*xW[i]);\n",
+ " Val110[k] = alpha110[k]*xNp[k];\n",
+ " k = k+1;\n",
+ "\n",
+ "yNp = Val110/sum(Val110);\n",
+ "mC5 = 1/sum(Val110);\n",
+ "# yNp is in Eqb. with xNp:\n",
+ "xlk_By_xhkNp = xNp[0]/xNp[3];\n",
+ "# Results for Np-7 to Np-9 trays:\n",
+ "# For Np-7\n",
+ "# Temp = 95.7 OC\n",
+ "# xNpMinus7 = [C3 C4 C5 C6]\n",
+ "xNpMinus7 = numpy.array([0.0790 ,0.3944 ,0.3850, 0.1366]);\n",
+ "xlk_By_xhkNpMinus7 = xNpMinus7[0]/xNpMinus7[2];\n",
+ "# For Np-8\n",
+ "# Temp = 94.1 OC\n",
+ "# xNpMinus8 = [C3 C4 C5 C6]\n",
+ "xNpMinus8 = numpy.array([0.0915, 0.3897 ,0.3826, 0.1362]);\n",
+ "xlk_By_xhkNpMinus8 = xNpMinus8[0]/xNpMinus8[2];\n",
+ "# For Np-9\n",
+ "# Temp = 93.6 OC\n",
+ "# xNpMinus9 = [C3 C4 C5 C6]\n",
+ "xNpMinus9 = numpy.array([0.1032, 0.3812, 0.3801 ,0.1355]);\n",
+ "xlk_By_xhkNpMinus9 = xNpMinus9[0]/xNpMinus9[2];\n",
+ "# From Eqn. 9.176:\n",
+ "# Np-8 is the feed tray.\n",
+ "def g2(Np):\n",
+ " return Np-8-Np1\n",
+ "Np = fsolve(g2,7);\n",
+ "print\"Number of theoretical Trays required for R = 0.8: \",Np[0]\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**************Composition Correction*****************#\n",
+ "# Page: 446\n",
+ "print'Page: 446\\n\\n'\n",
+ "\n",
+ "# New Bubble Point:\n",
+ "# Temp = 86.4 OC\n",
+ "x6_new = x6*(1-xNpMinus8[3]);\n",
+ "x6_new[4] = xNpMinus8[3];\n",
+ "# alpha86 = [C1 C2 C3 C4 C5 C6]\n",
+ "alpha86 =numpy.array([46.5, 13.5, 5.87, 2.39, 1.00, 0.467]);\n",
+ "# From Eqn. 9.181:\n",
+ "xhkn = x5[3];\n",
+ "xhknPlus1 = x6_new[3];\n",
+ "xC65 = alpha86[5]*x6_new[4]*xhkn/xhknPlus1;\n",
+ "x5_new = x5*(1-xC65);\n",
+ "x5_new[4] = 1-sum(x5_new);\n",
+ "# Tray 5 has a bubble point of 80 OC\n",
+ "# Similarly , the calculations are continued upward:\n",
+ "# x2_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x2_new = numpy.array([0.0021, 0.0214 ,0.1418, 0.6786, 0.1553, 0.00262]);\n",
+ "# y2_new = [C1 C2 C3 C4 C5 C6]\n",
+ "y2_new = numpy.array([0.0444, 0.111 ,0.2885, 0.5099, 0.0458 ,0.00034]);\n",
+ "# x1_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x1_new = numpy.array([0.00226, 0.0241, 0.1697 ,0.7100, 0.0932, 0.00079]);\n",
+ "# y1_new = [C1 C2 C3 C4 C5 C6]\n",
+ "y1_new = numpy.array([0.0451 ,0.1209 ,0.3259 ,0.4840 ,0.0239 ,0.000090]);\n",
+ "# x0_new = [C1 C2 C3 C4 C5 C6]\n",
+ "x0_new = numpy.array([0.00425 ,0.0425 ,0.2495, 0.6611 ,0.0425 ,0.00015]);\n",
+ "# yD_new = [C1 C2 C3 C4 C5 C6]\n",
+ "yD_new = numpy.array([0.0789 ,0.1842 ,0.3870 ,0.3420 ,0.0079, 0.00001]);\n",
+ "# From Eqn. 9.184:\n",
+ "# For C1 & C2\n",
+ "alphalkm = alpha86[2];\n",
+ "xlkmPlus1 = xNpMinus7[0];\n",
+ "xlkm = x6_new[2];\n",
+ "xC17 = x6_new[0]*alpha86[2]*xlkmPlus1/(alpha86[0]*xlkm);\n",
+ "xC27 = x6_new[1]*alpha86[2]*xlkmPlus1/(alpha86[1]*xlkm);\n",
+ "# Since xC17 = 1-xC27\n",
+ "# The adjusted value above constitute x7's.\n",
+ "# The new bubbl point is 94 OC\n",
+ "# The calculations are continued down in the same fashion.\n",
+ "# The new tray 6 has:\n",
+ "# xC1 = 0.000023 & xC2 = 0.00236\n",
+ "# It is clear that the conc. of these components are reducing so rapidly that there is no need to go an further.\n",
+ "print\"******Corrected Composition***********\\n\"\n",
+ "print\"Component\\t \\tx2\\t \\t \\t y2\\t \\t \\t x1\\t \\t \\t y1\\t \\t \\tx0\\t \\t \\tyD\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t\",x2_new[i],\"\\t \\t \\t \\t \",y2_new[i],\"\\t \\t \\t \\t \",x1_new[i],\"\\t \\t \\t \\t\",y1_new[i],\"\\t \\t \\t \\t \\t\",x0_new[i],\"\\t \\t \\t \\t\",yD_new[i]\n",
+ "\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#*************Heat Load of Condensor & Boiler & L/G ratio**********#\n",
+ "# Page 448\n",
+ "print'Page: 448\\n\\n'\n",
+ "\n",
+ "# Values of x0, yD & y1 are taken from the corrected concentration.\n",
+ "# HD46 = [C1 C2 C3 C4 C5 C6]\n",
+ "HD46 = numpy.array([13490, 23380, 32100, 42330, 52570, 61480]);# [kJ/kmol]\n",
+ "yDHD= zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yDHD[i] = yD_new[i]*HD46[i];\n",
+ "\n",
+ "HD = sum(yDHD);# [kJ]\n",
+ "# HL46 = [C1 C2 C3 C4 C5 C6]\n",
+ "HL46 = numpy.array([10470, 17210, 18610, 22790, 27100, 31050]);# [kJ/kmol]\n",
+ "xHL=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " xHL[i] = x0_new[i]*HL46[i];\n",
+ "\n",
+ "HL0 = sum(xHL);# [kJ]\n",
+ "# HG58 = [C1 C2 C3 C4 C5 C6]\n",
+ "HG58 = numpy.array([13960, 24190, 37260, 43500, 53900, 63500]);# [kJ/kmol]\n",
+ "yHG1=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yHG1[i] = y1_new[i]*HG58[i];\n",
+ "\n",
+ "HG1 = sum(yHG1);# [kJ]\n",
+ "# From Eqn. 9.54:\n",
+ "Qc = D*((R+1)*HG1-(R*HL0)-HD);# [kJ/kmol feed]\n",
+ "# Similarly:\n",
+ "HW = 39220;# [kJ]\n",
+ "HF = 34260;# [kJ]\n",
+ "# From Eqn. 9.55:\n",
+ "Qb = (D*HD)+(W*HW)+Qc-(F*HF);# [kJ/kmol feed]\n",
+ "# For tray n = 1\n",
+ "G1 = D*(R+1);# [kmol]\n",
+ "# With x1 & y2 from corrected composition;\n",
+ "# HG66 = [C1 C2 C3 C4 C5 C6]\n",
+ "HG66 = numpy.array([14070, 24610, 33800, 44100, 54780, 64430]);# [kJ/kmol feed]\n",
+ "yHG2=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " yHG2[i] = y2_new[i]*HG66[i];\n",
+ "\n",
+ "HG2 = sum(yHG2);# [kJ]\n",
+ "# HL58 = [C1 C2 C3 C4 C5 C6]\n",
+ "HL58 =numpy.array([11610 ,17910 ,20470, 24900, 29500, 33840]);# [kJ/kmol feed]\n",
+ "xHL1=zeros(6)\n",
+ "for i in range(0,6):\n",
+ " xHL1[i] = x1_new[i]*HL58[i];\n",
+ "\n",
+ "HL1 = sum(xHL1);# [kJ]\n",
+ "# From Eqn. 9.185:\n",
+ "G2 = (Qc+D*(HD-HL1))/(HG2-HL1);# [kmol]\n",
+ "L2 = G2-D;# [kmol]\n",
+ "L2_By_G2 = L2/G2;\n",
+ "# Similarly, the calculations are made for other trays in enriching section.\n",
+ "# For tray, Np = 14:\n",
+ "# C1 & C2 are absent.\n",
+ "# HG113 = [C3 C4 C5 C6]\n",
+ "HG113 = numpy.array([38260, 49310 ,60240, 71640]);# [kJ/kmol feed]\n",
+ "k = 2;\n",
+ "yHG15=zeros(6)\n",
+ "for i in range(0,4):\n",
+ " yHG15[i] = yNpPlus1[k]*HG113[i];\n",
+ " k = k+1;\n",
+ "\n",
+ "HG15 = sum(yHG15);\n",
+ "# HL107 = [C3 C4 C5 C6]\n",
+ "HL107 = numpy.array([29310 ,31870, 37680 ,43500]);# [kJ/kmol feed]\n",
+ "xHL14=zeros(6)\n",
+ "for i in range(0,4):\n",
+ " xHL14[i] = xNp[i]*HL107[i];\n",
+ "\n",
+ "HL14 = sum(xHL14);# [kJ]\n",
+ "# Similarly:\n",
+ "HL13 = 36790;# [kJ]\n",
+ "HG14 = 52610;# [kJ]\n",
+ "# From Eqn. 9.186:\n",
+ "G15_bar = (Qb+(W*(HL14-HW)))/(HG15-HL14);# [kmol]\n",
+ "L14_bar = W+G15_bar;# [kmol]\n",
+ "G14_bar = (Qb+(W*(HL13-HW)))/(HG14-HL13);# [kmol]\n",
+ "L14_By_G14 = L14_bar/G14_bar;\n",
+ "print\"Condensor Heat Load kJ:\\n\",HL0\n",
+ "print\"Reboiler Heat Load kJ:\\n\",HG15\n",
+ "# For other Exhausting Section Trays:\n",
+ "# Result = [Tray No. L_By_G Temp(OC)]\n",
+ "# Tray 0: Condensor\n",
+ "# Tray 15: Reboiler\n",
+ "Result = numpy.array([[0,0.80 ,46.6],[1 ,0.432 ,58.4],[2, 0.437, 66],[3, 0.369, 70.4],[4 ,0.305, 74],[5 ,0.310, 80.3],[6, 1.53, 86.4],[7, 4.05 ,94.1],[8 ,3.25 ,96.3],[9, 2.88 ,97.7],[10 ,2.58 ,99],[11, 2.48 ,100],[12 ,2.47 ,102.9],[13 ,2.42 ,104.6],[14 ,2.18 ,107.9],[15, 1.73 ,113.5]]);\n",
+ "print\"**************L/G*************\\n\"\n",
+ "print\"Tray No. \\t\\t L/G\\t\\t\\t\\t Temp(OC)\\n\"\n",
+ "for i in range(0,16):\n",
+ " print Result[i,0],\"\\t\\t \\t \\t\",Result[i,1],\"\\t \\t \\t\",Result[i,2];\n",
+ "\n",
+ "# These values are not final.\n",
+ "# They scatter eratically because they are based on the temp. and conc. computed with the assumption of constant L/G\n",
+ "print\"\\n\"\n",
+ "\n",
+ "#**************Thiele Geddes Method******************#\n",
+ "# Page:452\n",
+ "print'Page: 452\\n\\n'\n",
+ "\n",
+ "# Use the tray Temperature to obtain m.\n",
+ "# For C4:\n",
+ "# m = [0(Condensor) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15(Reboiler)]\n",
+ "m = numpy.array([0.50 ,0.66, 0.75 ,0.81 ,0.86 ,0.95 ,1.07 ,1.22 ,1.27 ,1.29 ,1.30, 1.32, 1.40, 1.45, 1.51, 1.65]);\n",
+ "A = numpy.array([1.6,0.65,0.582,0.4555,0.354,0.326,1.42990])\n",
+ "S = numpy.array([0.3012,0.39076,0.4479,0.503875,0.53225,0.56680,0.59917,0.69,0.95375])\n",
+ "\n",
+ "# f = Tray No. 6\n",
+ "\n",
+ "# From Eqn. 9.196:\n",
+ "# Value1 = Gf*yf/(D*zD)\n",
+ "Sum = 0;\n",
+ "for i in range(0,6):\n",
+ " Val = 1;\n",
+ " for j in range(0,6):\n",
+ " Val = Val*A[j];\n",
+ " \n",
+ " Sum = Sum+Val;\n",
+ "\n",
+ "Value1 = 1+Sum;\n",
+ "# From Eqn. 9.206:\n",
+ "# Value2 = Lf_bar*xf/(W*xW);\n",
+ "Sum = 0.5316\n",
+ "Value2 = 1+Sum;\n",
+ "# From Eqn. 9.208:\n",
+ "# Value3 = W*xW/(D*zD)\n",
+ "Value3 = A[6]*Value1/Value2;\n",
+ "# From Eqn. 9.210:\n",
+ "DyD = F*zF[3]/(Value3+1);# [kmol,C4]\n",
+ "# From Eqn. 9.209:\n",
+ "WxW = ((F*zF[3]))-(DyD);# [kmol, C4]\n",
+ "# Similarly:\n",
+ "# For [C1; C2; C3; C4; C5; C6]\n",
+ "# Result2 = [Value1 Value2 Value3 DyD WxW]\n",
+ "Result2 = numpy.array([[1.0150, 254*10**6 ,288*10**(-10), 0.03, 0],[1.0567, 8750, 298*10**(-5) ,0.07 ,0],[1.440, 17.241 ,0.0376 ,0.1447, 0.0053],[1.5778 ,1.5306 ,1.475, 0.1335 ,0.1965],[15580, 1.1595, 45.7 ,0.00643 ,0.29357],[1080 ,1.0687 ,7230 ,0.0000166 ,0.1198]]);\n",
+ "D = sum(Result2[:,2]);# [kmol]\n",
+ "W = sum(Result2[:,3]);# [kmol]\n",
+ "# In the Distillate:\n",
+ "DyD_C3 = Result[1,2];# [kmol]\n",
+ "zFC3 = zF[2];# [kmol]\n",
+ "percentC3 = (DyD_C3/zFC3)*100;\n",
+ "DyD_C5 = Result2[3,2];# [kmol]\n",
+ "zFC5 = zF[4];# [kmol]\n",
+ "percentC5 = (DyD_C5/zFC5)*100;\n",
+ "# These do not quite meet the original specification.\n",
+ "# For Tray 2 & C4\n",
+ "# From Eqn. 9.195:\n",
+ "# Value4 = G2*y2/(D*zD)\n",
+ "n = 2;\n",
+ "Sum = 0;\n",
+ "for i in range(0,n):\n",
+ " Val = 1;\n",
+ " for j in range(i,n):\n",
+ " Val = Val*A[j];\n",
+ " \n",
+ " Sum = Sum+Val;\n",
+ "\n",
+ "Value4 = 1+Sum;\n",
+ "# From The enthalpy Balnce:\n",
+ "G2 = 0.675;\n",
+ "# From Eqn. 9.211:\n",
+ "y2 = Value4*DyD/G2;\n",
+ "# Similarly:\n",
+ "# Value4 = [C1 C2 C3 C4 C5 C6]\n",
+ "Value4 = numpy.array([1.0235, 1.1062, 1.351, 2.705, 10.18 ,46.9]);\n",
+ "y2= numpy.array([0.04548,0.114,0.2896,0.53498,0.09697,0.001153]);\n",
+ "Y2 = sum(y2);\n",
+ "# Since Y2 is not equal to 1. THerefore the original temperature is incorrect. By adjusting y2 to unity.\n",
+ "# The dew point is 77 OC instead of 66 OC\n",
+ "# y2_adjusted = [C1 C2 C3 C4 C5 C6]\n",
+ "y2_adjusted = numpy.array([0.0419 ,0.1059 ,0.2675 ,0.4939, 0.0896, 0.00106]);\n",
+ "print\"*****************Composition By Thiele Geddes Method*****************\\n\"\n",
+ "print\"Component\\t \\t \\t y2\\n\"\n",
+ "for i in range(0,6):\n",
+ " print\"C\",i,\"\\t \\t \\t \\t\",y2_adjusted[i]\n",
+ "# some values of solution in the textbook are incorrect"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 9.13\n",
+ "\n",
+ "\n",
+ "Page: 436\n",
+ "\n",
+ "\n",
+ "Minimum Reflux Ratio is 0.619146164974 mol reflux/mol distillate\n",
+ " \n",
+ "\n",
+ "*****************Distillate Composition*********************\n",
+ "\n",
+ "C1\t \t \t \t: 0.0786\n",
+ "C2\t \t \t \t: 0.1835\n",
+ "C3\t \t \t \t: 0.3854\n",
+ "C4\t \t \t \t: 0.34\n",
+ "C5\t \t \t \t: 0.007866\n",
+ "C6\t \t \t \t: 0.0\n",
+ "\n",
+ "\n",
+ "*****************Residue Composition*********************\n",
+ "\n",
+ "C1\t \t \t \t: 0.0\n",
+ "C2\t \t \t \t: 0.0\n",
+ "C3\t \t \t \t: 0.00484930540974\n",
+ "C4\t \t \t \t: 0.321097242636\n",
+ "C5\t \t \t \t: 0.480081235564\n",
+ "C6\t \t \t \t: 0.19397221639\n",
+ "\n",
+ "\n",
+ "Page: 440\n",
+ "\n",
+ "\n",
+ "Page: 441\n",
+ "\n",
+ "\n",
+ "For the reflux ratio of 0.8\n",
+ "\n",
+ "*****************Distillate Composition*********************\n",
+ "\n",
+ "\t\t\t Liquid reflux in equilibrium with the distillate vapour\n",
+ "\n",
+ "C 0 \t \t \t \t\t: 0.004\n",
+ "C 1 \t \t \t \t\t: 0.0444501\n",
+ "C 2 \t \t \t \t\t: 0.2495\n",
+ "C 3 \t \t \t \t\t: 0.6564\n",
+ "C 4 \t \t \t \t\t: 0.0451\n",
+ "C 5 \t \t \t \t\t: 0.0\n",
+ "Page: 443\n",
+ "\n",
+ "\n",
+ "Number of theoretical Trays required for R = 0.8: 14.0\n",
+ "\n",
+ "\n",
+ "Page: 446\n",
+ "\n",
+ "\n",
+ "******Corrected Composition***********\n",
+ "\n",
+ "Component\t \tx2\t \t \t y2\t \t \t x1\t \t \t y1\t \t \tx0\t \t \tyD\n",
+ "\n",
+ "C 0 \t \t \t0.0021 \t \t \t \t 0.0444 \t \t \t \t 0.00226 \t \t \t \t0.0451 \t \t \t \t \t0.00425 \t \t \t \t0.0789\n",
+ "C 1 \t \t \t0.0214 \t \t \t \t 0.111 \t \t \t \t 0.0241 \t \t \t \t0.1209 \t \t \t \t \t0.0425 \t \t \t \t0.1842\n",
+ "C 2 \t \t \t0.1418 \t \t \t \t 0.2885 \t \t \t \t 0.1697 \t \t \t \t0.3259 \t \t \t \t \t0.2495 \t \t \t \t0.387\n",
+ "C 3 \t \t \t0.6786 \t \t \t \t 0.5099 \t \t \t \t 0.71 \t \t \t \t0.484 \t \t \t \t \t0.6611 \t \t \t \t0.342\n",
+ "C 4 \t \t \t0.1553 \t \t \t \t 0.0458 \t \t \t \t 0.0932 \t \t \t \t0.0239 \t \t \t \t \t0.0425 \t \t \t \t0.0079\n",
+ "C 5 \t \t \t0.00262 \t \t \t \t 0.00034 \t \t \t \t 0.00079 \t \t \t \t9e-05 \t \t \t \t \t0.00015 \t \t \t \t1e-05\n",
+ "\n",
+ "\n",
+ "Page: 448\n",
+ "\n",
+ "\n",
+ "Condensor Heat Load kJ:\n",
+ "21641.994\n",
+ "Reboiler Heat Load kJ:\n",
+ "59915.6783775\n",
+ "**************L/G*************\n",
+ "\n",
+ "Tray No. \t\t L/G\t\t\t\t Temp(OC)\n",
+ "\n",
+ "0.0 \t\t \t \t0.8 \t \t \t46.6\n",
+ "1.0 \t\t \t \t0.432 \t \t \t58.4\n",
+ "2.0 \t\t \t \t0.437 \t \t \t66.0\n",
+ "3.0 \t\t \t \t0.369 \t \t \t70.4\n",
+ "4.0 \t\t \t \t0.305 \t \t \t74.0\n",
+ "5.0 \t\t \t \t0.31 \t \t \t80.3\n",
+ "6.0 \t\t \t \t1.53 \t \t \t86.4\n",
+ "7.0 \t\t \t \t4.05 \t \t \t94.1\n",
+ "8.0 \t\t \t \t3.25 \t \t \t96.3\n",
+ "9.0 \t\t \t \t2.88 \t \t \t97.7\n",
+ "10.0 \t\t \t \t2.58 \t \t \t99.0\n",
+ "11.0 \t\t \t \t2.48 \t \t \t100.0\n",
+ "12.0 \t\t \t \t2.47 \t \t \t102.9\n",
+ "13.0 \t\t \t \t2.42 \t \t \t104.6\n",
+ "14.0 \t\t \t \t2.18 \t \t \t107.9\n",
+ "15.0 \t\t \t \t1.73 \t \t \t113.5\n",
+ "\n",
+ "\n",
+ "Page: 452\n",
+ "\n",
+ "\n",
+ "*****************Composition By Thiele Geddes Method*****************\n",
+ "\n",
+ "Component\t \t \t y2\n",
+ "\n",
+ "C 0 \t \t \t \t0.0419\n",
+ "C 1 \t \t \t \t0.1059\n",
+ "C 2 \t \t \t \t0.2675\n",
+ "C 3 \t \t \t \t0.4939\n",
+ "C 4 \t \t \t \t0.0896\n",
+ "C 5 \t \t \t \t0.00106\n"
+ ]
+ }
+ ],
+ "prompt_number": 85
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file
diff --git a/Mass_-_Transfer_Operations/README.txt b/Mass_-_Transfer_Operations/README.txt
new file mode 100755
index 00000000..2f59a972
--- /dev/null
+++ b/Mass_-_Transfer_Operations/README.txt
@@ -0,0 +1,10 @@
+Contributed By: Ishita Tewari
+Course: btech
+College/Institute/Organization: Gautam Buddha University, Greater Noida
+Department/Designation: School of Biotechnology
+Book Title: Mass - Transfer Operations
+Author: R. E. Treybal
+Publisher: McGraw - Hill Book Company, Malaysia
+Year of publication: 1980
+Isbn: 0070651760
+Edition: 3 \ No newline at end of file
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