path: root/backup/Mass_-_Transfer_Operations_version_backup/Chapter8_1.ipynb

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   "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"

       ]

      },

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3kWps2FevwssvQ2ioWal4SKl4Yj5v3LlB92XdeTrwaV4s8iJbPtgSTalEhA4/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\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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       "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",

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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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/T6BJ9P0VwBVZlmlLOFmyWXT7QeDoNGSP7rsbODb6fhlg1aSzNyR/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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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\nvT7hM9J+mQAAAABJRU5ErkJggg==\n",

       "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",

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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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\nPdVLlxIk4sjJI3rv/Hu14PiCOmfvHL1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W0kkUehaSkyqt5+w1E42NjdU2oaK0ZX1tWRu4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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": {}

  }

 ]

}