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author: hardythe1 2015-04-07 15:58:05 +0530
committer: hardythe1 2015-04-07 15:58:05 +0530
commit: 92cca121f959c6616e3da431c1e2d23c4fa5e886 (patch)
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+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:9fbb8ba54aec06ad3c69036b535306d5fcf2bbf41aaf2bb73bb23d87c87b8396"
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+ "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",
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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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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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\n",
+       "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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\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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3DD8sBxJlRJPXeGGIK0Hgb7/leI03a+boN8xr3DA8wfrx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\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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hIB00kPlAcy8IrAd6Ar2iykwB+gMTvYCzLT94pBNloXP8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\n",
+       "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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+       "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
author	hardythe1	2015-04-07 15:58:05 +0530
committer	hardythe1	2015-04-07 15:58:05 +0530
commit	92cca121f959c6616e3da431c1e2d23c4fa5e886 (patch)
tree	205e68d0ce598ac5caca7de839a2934d746cce86 /Mass_-_Transfer_Operations/Chapter10_1.ipynb
parent	b14c13fcc6bb6d01c468805d612acb353ec168ac (diff)
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