path: root/Mass_-_Transfer_Operations/Chapter11_1.ipynb

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   "cells": [

    {

     "cell_type": "heading",

     "level": 1,

     "metadata": {},

     "source": [

      "Chapter 11: Absorption And Ion Exchange"

     ]

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.1: Page 575"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.1\n",

      "# Page: 575\n",

      "\n",

      "print'Illustration 11.1 - Page: 575\\n\\n'\n",

      "\n",

      "# Solution\n",

      "import numpy\n",

      "import matplotlib.pyplot as plt\n",

      "%matplotlib inline\n",

      "#*****Data*****#\n",

      "Temp = 30.0;# [OC]\n",

      "#*************#\n",

      "\n",

      "# From Fig. 11.5 (Pg 572)\n",

      "# The isosteres for various concentrations are straight and their slopes are measured with the help of milimeter rule.\n",

      "# Data = [X(kg acetone/kg carbon) lambda(slope of isostere)]\n",

      "Data = numpy.array([[0.05 ,1.170],[0.10, 1.245],[0.15 ,1.3],[0.20 ,1.310],[0.25 ,1.340],[0.30 ,1.327]]);# [kg acetone/kg carbon]\n",

      "lambdar = 551.0;# [reference at 30 OC,kJ/kg]\n",

      "Val = numpy.zeros(shape=(6,5));\n",

      "for i in range(0,6):\n",

      "    Val[i,0] = Data[i,0];# [kg acetone/kg carbon]\n",

      "    Val[i,1] = Data[i,1];# [slope of isostere]\n",

      "    Val[i,2] = -Data[i,1]*lambdar;# [kJ/kg acetone]\n",

      "\n",

      "\n",

      "plt.plot(Val[:,0],Val[:,2])\n",

      "plt.grid();\n",

      "xlabel(\"X (kg carbon / kg acetone)\");\n",

      "ylabel(\"Differential heat of adsorption (kJ / kg acetone)\");\n",

      "title(\"Graphical Integration\");\n",

      "plt.show()\n",

      "# Area: The area under the curve between X = 0 to X = X\n",

      "# Corresponding to Data(:,1):\n",

      "Area = numpy.array([-29.8 ,-63.0, -97.9 ,-134.0, -170.5, -207.5]);\n",

      "for i in range(0,6):\n",

      "    Val[i,3] = Area[i];\n",

      "    Val[i,4] = Area[i]+(lambdar*Val[i,0]);\n",

      "print \" (1) = X(kg acetone/kg carbon) \\n (2)= Slope of isostere \\n (3)= Differential heat of adsorption(kJ/kg acetone) \\n (4)=deltaH_prime(vapour(kJ/kg carbon)) \\n (5)=deltaH(liquid(kJ/kg carbon)\"\n",

      "print\"(1) \\t \\t \\t \\t (2)  \\t \\t \\t  \\t (3) \\t \\t \\t \\t \\t \\t \\t \\t (4) \\t \\t \\t \\t \\t \\t (5) \" \n",

      "for i in range(0,6):\n",

      "    print Val[i,0],\" \\t \\t \\t \",Val[i,1],\" \\t \\t \",Val[i,2],\" \\t \\t \\t \\t \\t \",Val[i,3],\" \\t \\t \\t \\t\",Val[i,4]\n",

      "#the answers are slightly different in textbook due to approximation while here answers are precise"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.1 - Page: 575\n",

        "\n",

        "\n"

       ]

      },

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JJbfmzoWddoJ994ULLyx0NM65bMnXXF4/mdk1ZrZv9Li2WJOJW14u2ofXXBOe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\n",

       "text": [

        "<matplotlib.figure.Figure at 0x76f12b0>"

       ]

      },

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        " (1) = X(kg acetone/kg carbon) \n",

        " (2)= Slope of isostere \n",

        " (3)= Differential heat of adsorption(kJ/kg acetone) \n",

        " (4)=deltaH_prime(vapour(kJ/kg carbon)) \n",

        " (5)=deltaH(liquid(kJ/kg carbon)\n",

        "(1) \t \t \t \t (2)  \t \t \t  \t (3) \t \t \t \t \t \t \t \t (4) \t \t \t \t \t \t (5) \n",

        "0.05  \t \t \t  1.17  \t \t  -644.67  \t \t \t \t \t  -29.8  \t \t \t \t-2.25\n",

        "0.1  \t \t \t  1.245  \t \t  -685.995  \t \t \t \t \t  -63.0  \t \t \t \t-7.9\n",

        "0.15  \t \t \t  1.3  \t \t  -716.3  \t \t \t \t \t  -97.9  \t \t \t \t-15.25\n",

        "0.2  \t \t \t  1.31  \t \t  -721.81  \t \t \t \t \t  -134.0  \t \t \t \t-23.8\n",

        "0.25  \t \t \t  1.34  \t \t  -738.34  \t \t \t \t \t  -170.5  \t \t \t \t-32.75\n",

        "0.3  \t \t \t  1.327  \t \t  -731.177  \t \t \t \t \t  -207.5  \t \t \t \t-42.2\n"

       ]

      }

     ],

     "prompt_number": 1

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.2: Page 596"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.2\n",

      "# Page: 596\n",

      "\n",

      "print'Illustration 11.2 - Page: 596\\n\\n'\n",

      "\n",

      "# solution\n",

      "import numpy\n",

      "import matplotlib.pyplot as plt\n",

      "%matplotlib inline\n",

      "#*****Data*****#\n",

      "# x:kg carbon/kg soln\n",

      "# y_star: Equilibrium colour, units/kg soln.\n",

      "# X:adsorbate concentration, units/kg carbon\n",

      "# Data = [x Y_star]\n",

      "Data =numpy.array([[0, 9.6],[0.001, 8.6],[0.004 ,6.3],[0.008, 4.3],[0.02 ,1.7],[0.04, 0.7]]);\n",

      "Yo = 9.6;# [units of colour/kg soln]\n",

      "Y1 = 0.1*Yo;# [units of colour/kg soln]\n",

      "Ls = 1000.0;# [kg soln]\n",

      "#****************#\n",

      "\n",

      "\n",

      "n = 1.66;# [slope of line]\n",

      "# At X = 663, Y_star = 4.3\n",

      "# From eqn. 11.5\n",

      "X = 663;\n",

      "Y_star = 4.3;\n",

      "m = Y_star/X**n;\n",

      "# Freundlich Equation:\n",

      "def f76(X):\n",

      "    return m*X**n\n",

      "X = numpy.arange(0,1000,1);\n",

      "\n",

      "plt.plot(X,f76(X));\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"units of colour/kg carbon\");\n",

      "plt.ylabel(\"units of colour/kg solution\");\n",

      "title(\"Equilibium Data(on arithmetic scale)\");\n",

      "plt.show()\n",

      "# Single Stage Operation:\n",

      "# Since fresh carbn is used:\n",

      "Xo = 0;# [units/kg carbon]\n",

      "# From scf(30):\n",

      "X1 = 270;# [units/kg carbon]\n",

      "Data2 =numpy.array([[Xo, Yo],[X1, Y1]]);\n",

      "\n",

      "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",

      "plt.plot(Data2[:,0],Data2[:,1],label=\"Operating line curve\")\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"units of colour/kg carbon\");\n",

      "plt.ylabel(\"units of colour/kg solution\");\n",

      "plt.legend(loc='upper left');\n",

      "plt.title(\"Single stage operation\");\n",

      "plt.show()\n",

      "# From Eqn. 11.4:\n",

      "Ss = Ls*((Yo-Y1)/(X1-Xo));# [kg carbon/kg soln]\n",

      "print\"Quantity of fresh carbon recquired for single stage operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",

      "\n",

      "# Two stage cross current operation:\n",

      "# For the minimumamount of carbon:\n",

      "X1 = 565;# [units/kg carbon]\n",

      "Y1 = 3.30;# [units of colour/kg soln]\n",

      "X2 = 270;# [units/kg carbon]\n",

      "Y2 = 0.96;# [units of colour/kg soln]\n",

      "Data3 = numpy.array([[Xo ,Yo],[X1 ,Y1]]);\n",

      "Data4 = numpy.array([[0 ,Y1],[X2 ,Y2]]);\n",

      "\n",

      "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",

      "plt.plot(Data3[:,0],Data3[:,1],label=\"First of two Cocurrent\")\n",

      "plt.plot(Data4[:,0],Data4[:,1],label=\"Second of two Cocurrent\")\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"units of colour/kg carbon\");\n",

      "plt.ylabel(\"units of colour/kg solution\");\n",

      "plt.legend(loc='upper left');\n",

      "plt.title(\"Two stage Cross current operation\");\n",

      "plt.show()\n",

      "# From Eqn. 11.8:\n",

      "Ss1 = Ls*(Yo-Y1)/(X1-Xo);# [kg]\n",

      "Ss2 = Ls*(Y1-Y2)/(X2-Xo);# [kg]\n",

      "Ss = Ss1+Ss2;# [kg]\n",

      "print\"Quantity of fresh carbon recquired for two stage crosscurrent operation: \",Ss,\" kg carbon/1000 kg solution\\n\"\n",

      "\n",

      "# Two Stage counter current operation:\n",

      "Yo = 9.6;\n",

      "Y2 = 0.96;\n",

      "# By trial and error:\n",

      "XNpPlus1 = 0;\n",

      "X1 = 675;\n",

      "Data5 = numpy.array([[X1 ,Yo],[XNpPlus1 ,Y2]]);\n",

      "\n",

      "plt.plot(X,f76(X),label=\"Equilbrium curve\")\n",

      "plt.plot(Data5[:,0],Data5[:,1],label=\"Two stage Counter Current\");\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"units of colour/kg carbon\");\n",

      "plt.ylabel(\"units of colour/kg solution\");\n",

      "plt.legend(loc='upper left');\n",

      "plt.title(\"Two stage Counter Current operation\");\n",

      "# By eqn 11.14:\n",

      "Ss = Ls*(Yo-Y2)/(X1-XNpPlus1);\n",

      "print\"Quantity of fresh carbon recquired for two stage Counter Current operation: \",Ss,\" kg carbon/1000 kg solution\\n\""

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.2 - Page: 596\n",

        "\n",

        "\n"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

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       "text": [

        "<matplotlib.figure.Figure at 0x76f16d8>"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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Tviud53s/H9NyjTHx5+ef3bzMxx/v+hQSZSC8qPcxiEgfcg6el4OqRu0GAD8S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\nTA6WGIwxxuRgicEYY0wOlhiMMcbk8P+dWPmzYQm/hQAAAABJRU5ErkJggg==\n",

       "text": [

        "<matplotlib.figure.Figure at 0x7930c18>"

       ]

      },

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Quantity of fresh carbon recquired for single stage operation:  32.0  kg carbon/1000 kg solution\n",

        "\n"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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Ly0t2796d4mdIq+TojBkz4suoVqpUSfbv3y8iiavsiehKe8OHD0+k53HjxknR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\nIshDeZSyCAYj7J780X29czf7hLt7L3vPVzU1Mz0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       "text": [

        "<matplotlib.figure.Figure at 0x7938c88>"

       ]

      },

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Quantity of fresh carbon recquired for two stage crosscurrent operation:  19.8171091445  kg carbon/1000 kg solution\n",

        "\n",

        "Quantity of fresh carbon recquired for two stage Counter Current operation: "

       ]

      },

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        " 12.8  kg carbon/1000 kg solution\n",

        "\n"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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AASpVquRnpYoSGiS9USc9PENtPWlboOjxxvpPP8GoUZHMmgXnzsUQE5P6/jEx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\n11JDW5QYru6N8ucZxCZjzTHUciVc0hTgfuA6233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       "text": [

        "<matplotlib.figure.Figure at 0x7d9ad68>"

       ]

      }

     ],

     "prompt_number": 2

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.3: Page 602"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.3\n",

      "# Page: 602\n",

      "\n",

      "print'Illustration 11.3 - Page: 602\\n\\n'\n",

      "\n",

      "# Solution\n",

      "import numpy\n",

      "from scipy.optimize import fsolve\n",

      "import math\n",

      "#***Data***#\n",

      "T = 1.0; #[m]\n",

      "di = 0.203;# [m]\n",

      "n = 1;# [for one impeller]\n",

      "Density_S = 2300.0;# [kg/cubic m]\n",

      "Density_p = 2300.0;# [kg/cubic m]\n",

      "C = 0.150;# [m]\n",

      "S = 50.0;# [kg]\n",

      "g = 9.807;# [m/s]\n",

      "dp = 8*10**(-4);# [m]\n",

      "N = 8.33; #[r/s]\n",

      "Temp=25;# [OC]\n",

      "#*************#\n",

      "\n",

      "# Assume:\n",

      "Po = 5;\n",

      "viscosity_L = 8.94*10**(-4);# [kg/m.s]\n",

      "Density_L = 998.0;# [kg/cubic m]\n",

      "delta_Density = Density_S-Density_L;# [kg/cubic m]\n",

      "# By Eqn. 11.23:\n",

      "Vts = g*dp**2*delta_Density/(18*viscosity_L);# [m/s]\n",

      "# By defn. of power number:\n",

      "# P = Po*Density_m*di**5*Ni**3\n",

      "# vm = math.pi*T**2*(Z+C)/4\n",

      "# Solid Volume = S/Density_p;\n",

      "# If these are substituted in Eqn. 11.22\n",

      "def f(Z):\n",

      "    return (((Z+C)**(1.3/3))*math.exp(4.35*Z/(T-0.1)))-((1.0839*Po*di**(11.0/2)*N**3*Density_p**(2.0/3))/(g*Vts*T**(7.0/6)*S**(2.0/3)))\n",

      "Z = fsolve(f,7);# [m]\n",

      "phi_Sm = 4*S/(math.pi*T**2*(Z+C)*Density_p);\n",

      "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_L);# [kg/cubic m]\n",

      "phi_Ss = 0.6;\n",

      "viscosity_m = viscosity_L/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",

      "Re = di**2*N*Density_m/viscosity_m;\n",

      "P = Po*Density_m*N**3*di**5;# [W]\n",

      "print \"Agitator Power required: \",round(P),\" W\\n\"\n",

      "#the answers are slightly different in textbook due to approximation while here answers are precise"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.3 - Page: 602\n",

        "\n",

        "\n",

        "Agitator Power required:  1113.0  W\n",

        "\n"

       ]

      }

     ],

     "prompt_number": 65

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.4: Page 604"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.4\n",

      "# Page: 604\n",

      "\n",

      "print'Illustration 11.4 - Page: 604\\n\\n'\n",

      "\n",

      "import math\n",

      "#****Data*****#\n",

      "# b: kerosene c:water\n",

      "# c:kg water/cubic m liquid\n",

      "Density_l = 783;# [kg/cubic m]\n",

      "viscosity_l = 1.7*10**(-3);# [kg/m.s]\n",

      "Mb = 200;# [kg/kmol]\n",

      "Density_p = 881;# [kg/cubic m]\n",

      "m = 0.522;# [(kg water/cubic m kerosene)/(kg water/kg gel)]\n",

      "Xo = 0;# [kg H2O/kg gel]\n",

      "#**************#\n",

      "\n",

      "# Solution (a)\n",

      "co = Density_l*4*10**(-5);# [kg water/cubic m]\n",

      "c1 = Density_l*5*10**(-6);# [kg water/cubic m]\n",

      "# For Ss minimum:\n",

      "X1 = c1/m;# [kg H2O/kg gel]\n",

      "# By Water Balance:\n",

      "SsminByVl = (co-c1)/(X1-Xo);# [kg gel/cubic m kerosene]\n",

      "print\"Minimum Solid/Liquid ratio used:\",SsminByVl,\" kg gel/cubic m kerosene\"\n",

      "print\"\\n\"\n",

      "\n",

      "# Solution (b)\n",

      "# Basis: 1 batch,1.7 cubic m kerosene\n",

      "Vl = 1.7;# [cubic m]\n",

      "Ss = 16*1.7;# [kg gel]\n",

      "V = Ss/Density_p;# [Xol. solid, cubic m]\n",

      "Vt = 1.7+V;# [Total batch volume, cubic m]\n",

      "# Take Z = T\n",

      "T = (Vt*4/math.pi)**(1.0/3);# [m]\n",

      "# To allow for the  adequate free board:\n",

      "h = 1.75;# [Vessel height,m]\n",

      "# Use a six-blade disk impeller.\n",

      "# From Fig. 11.26:\n",

      "# dp corresponding to 14 mesh:\n",

      "dp = 1.4/1000;# [m]\n",

      "TBydi1 = 2.0;\n",

      "Value1 = (Density_p-Density_l)/Density_l;\n",

      "# From Fig. 11.26:\n",

      "TBydi2 = 4.4;\n",

      "TBydiAv = (TBydi1+TBydi2)/2.0;\n",

      "di = T/TBydiAv;# [m]\n",

      "fr = 0.6;# [settled volume fraction of solids]\n",

      "Vs = V/fr;# [cubic m]\n",

      "depth = Vs/((math.pi*(T**2))/4);# [m]\n",

      "# The depth of settled solid is negligible.\n",

      "# Locate the turbine 150mm from the bottom of the tank.\n",

      "C = 0.150;# [m]\n",

      "\n",

      "# Power:\n",

      "# Use the sufficient agitator power to lift the solids to 0.6 m above the bottom of the vessel.\n",

      "Z_prime = 0.6-C;# [m]\n",

      "# The properties of the slurry in 0.6 m above the bottom of the vessel.\n",

      "Vm = 0.6*math.pi*T**2.0/4;# [square m]\n",

      "phi_Sm = V/Vm;# [vol fraction solid]\n",

      "# From Eqn. 11.24:\n",

      "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",

      "# From Eqn. 11.25:\n",

      "phi_Ss = 0.8;\n",

      "viscosity_m = viscosity_l/(1-(phi_Sm/phi_Ss))**1.8;# [kg/m.s]\n",

      "g = 9.81;# [m/s^2]\n",

      "# From Eqn. 11.23:\n",

      "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",

      "Vts = g*dp**2*delta_Density/(18*viscosity_l);# [m/s]\n",

      "# From Eqn. 11.22:\n",

      "n = 1.0;\n",

      "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*(TBydiAv**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",

      "# Assume:\n",

      "Po = 5.0;\n",

      "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",

      "# Use:\n",

      "N1 = 2.0;# [r/s]\n",

      "Re = di**2.0*N1*Density_m/viscosity_m;\n",

      "# From fig. 6.5: Po = 5\n",

      "# Hence our assumption was right.\n",

      "print\"Power delivered to the slurry: \",round((P*(N1/N)**3),2),\" W\\n\",\n",

      "print\"Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\\n\"\n",

      "\n",

      "# Mass transfer: \n",

      "# From Eqn. 11.28:\n",

      "Rep = (dp**(4.0/3))*(P/Vl)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",

      "# From Eqn. 2.44:\n",

      "Temp = 298;# [K]\n",

      "phi = 1.0;\n",

      "Va = 0.0756;# [Chapter 2 notation]\n",

      "Dl = ((117.3*10**(-18))*((phi*Mb)**0.5)*Temp)/(viscosity_l*(Va**(0.6)));\n",

      "ScL = viscosity_l/(Density_l*Dl);\n",

      "if  dp<(2.0/1000):\n",

      "    # From Eqn. 11.29:\n",

      "    ShL = 2+(0.47*Rep**0.62*(1/TBydiAv**0.17)*ScL**0.36);\n",

      "else:\n",

      "    # From Eqn. 11.30:\n",

      "    ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",

      "\n",

      "kL = ShL*Dl/dp;# [m/s]\n",

      "apS = (math.pi*dp**2)/(math.pi*dp**3*Density_p/6.0);\n",

      "apL = apS*16;# [square m/cubic m liquid]\n",

      "Ratio = Ss/(Vl*m);\n",

      "# From Eqn. 11.40:\n",

      "thetha = math.log((co/c1)/(1+(1/Ratio)-(1/Ratio)*(co/c1)))/((1+(1/Ratio))*kL*apL);\n",

      "print\"Contacting Time required: \",round(thetha/60,2),\" min\\n\"\n",

      "#the answers are slightly different in textbook due to approximation while here answers are precise"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.4 - Page: 604\n",

        "\n",

        "\n",

        "Minimum Solid/Liquid ratio used: 3.654  kg gel/cubic m kerosene\n",

        "\n",

        "\n",

        "Power delivered to the slurry:  350.05  W\n",

        "Power to the motor will be larger, depending on the efficiency of the motor and speed reducer.\n",

        "\n",

        "Contacting Time required:  8.3  min\n",

        "\n"

       ]

      }

     ],

     "prompt_number": 69

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.5: Page 606"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.5\n",

      "# Page: 606\n",

      "\n",

      "print'Illustration 11.5 - Page: 606\\n\\n'\n",

      "\n",

      "# Solution\n",

      "\n",

      "import math\n",

      "import numpy.linalg as lin\n",

      "#*****Data******#\n",

      "Vl = 1.1*10**(-4);# [cubic m/s]\n",

      "Ss = 0.0012;# [kg/s]\n",

      "Density_p = 1120;# [kg/cubic m]\n",

      "dp = 8*10**(-4);# [m]\n",

      "Ds = 2*10**(-11);# [square m/s]\n",

      "Dl = 7.3*10**(-10);# [square m/s]\n",

      "m = 0.2;# [(kg Cu2+/cubic m soln)/(kg Cu2+/kg resin)]\n",

      "T = 1;# [m]\n",

      "#********************#\n",

      "\n",

      "Z = T;# [m]\n",

      "# The particles will be lifted to the top of the vessel.\n",

      "Z_prime = 0.5;# [m]\n",

      "viscosity_l = 8.94*10**(-4);# [kg/m.s]\n",

      "Density_l = 998;# [kg/cubic m]\n",

      "delta_Density = Density_p-Density_l;# [kg/cubic m]\n",

      "g = 9.80;# [m/square s]\n",

      "# From Eqn. 11.23:\n",

      "Vts = g*dp**2*delta_Density/(18*viscosity_l);\n",

      "Vm = math.pi*T**2*Z/4.0;# [cubic m]\n",

      "Vs = Ss/Density_p;# [cubic m/s]\n",

      "phi_Sm = Vs/(Vs+Vl);# [vol fraction]\n",

      "# From eqn. 11.24:\n",

      "Density_m = (phi_Sm*Density_p)+((1-phi_Sm)*Density_l);# [kg/cubic m]\n",

      "# From Eqn. 11.22:\n",

      "n = 1.0;\n",

      "di = 0.3;# [m]\n",

      "P = (g*n*Density_m*Vm*Vts)*(phi_Sm**(2.0/3))*((T/di)**(1.0/2))*math.exp((4.35*Z_prime/T)-0.1);# [W]\n",

      "# To estimate the impeller speed:\n",

      "# Assume:\n",

      "Po = 5;\n",

      "N = (P/(Po*Density_m*di**5))**(1.0/3);# [r/s]\n",

      "Re = di**2*N*Density_m/viscosity_l;\n",

      "# From fig. 6.5: Assumption of Po was correct.\n",

      "print\"Speed of the impeller:\",round(N,2),\" r/s\\n\"\n",

      "vT = (math.pi/4.0)*T**2*Z;# [cubic m]\n",

      "vL = vT*(1-phi_Sm);\n",

      "# From Eqn. 11.28:\n",

      "Rep = (dp**(4.0/3))*(P/vL)**(1.0/3)*(Density_l**(2.0/3)/viscosity_l);\n",

      "ScL = viscosity_l/(Density_l*Dl);\n",

      "if  dp<(2.0/1000):\n",

      "    # From Eqn. 11.29:\n",

      "    ShL = 2+(0.47*Rep**0.62*((di/T)**0.17)*ScL**0.36);\n",

      "else:\n",

      "    # From Eqn. 11.30:\n",

      "    ShL = 0.222*Rep**(3.0/4)*ScL**(1.0/3);\n",

      "\n",

      "ShL = 130.3;# Value wrong in book\n",

      "kL = ShL*Dl/dp;# [m/s]\n",

      "# Since the dispersion is uniform throughout the vessel, the residence time for both liquid and solid is same.\n",

      "thetha = vL*(1-phi_Sm)/Vl;# [s]\n",

      "# From Fig. 11.27:\n",

      "abcissa = m*kL*dp/(2*Ds*Density_p);\n",

      "Parameter = 2*m*kL*thetha/(dp*Density_p);\n",

      "co = 100*Density_l/10.0**6;# [kg/cubic m]\n",

      "EMS = 0.63;\n",

      "Xo = 0;\n",

      "# From Eqn. 11.44:\n",

      "# (1): X1-(EMS/m)*c1 = 0\n",

      "# Solute balance:\n",

      "# (2): (Ss*X1)+(vL*c1) = (vL*co)+(Xo*Ss)\n",

      "a = [[1 ,-(EMS/m)],[Ss ,Vl]];\n",

      "b = [0,(Vl*co)+(Xo*Ss)];\n",

      "soln =lin.solve(a,b);\n",

      "X1 = soln[0];\n",

      "c1 = soln[1];\n",

      "print\"Effluent Cu2+ conc. \",round(c1*10**(6)/Density_l,2),\" ppm\\n\","

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.5 - Page: 606\n",

        "\n",

        "\n",

        "Speed of the impeller: 2.71  r/s\n",

        "\n",

        "Effluent Cu2+ conc.  2.83  ppm\n"

       ]

      }

     ],

     "prompt_number": 78

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.6: Page 616"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.6\n",

      "# Page: 616\n",

      "\n",

      "print'Illustration 11.6 - Page: 616\\n\\n'\n",

      "from scipy.optimize import fsolve\n",

      "# Solution\n",

      "\n",

      "#*****Data*****#\n",

      "# a: air b:silica\n",

      "Density_a = 1.181;# [kg/cubic m]\n",

      "Density_b = 671.2;# [kg/cubic m]\n",

      "kSap = 0.965;# [kg H2O/square m s]\n",

      "Y1 = 0.005;# [kg H2O/kg dry air]\n",

      "Y2 = 0.0001;# [kg H2O/kg dry air]\n",

      "Ss = 0.680;# [square m/s]\n",

      "Gs = 1.36;# [kg/square m.s]\n",

      "X2 = 0;# [kg H2O/kg dry air]\n",

      "# Equilibrium function:\n",

      "m = 0.0185;\n",

      "#************#\n",

      "X1 = (Gs*(Y1-Y2)/Ss)+X2;# [kg H2O/kg dry air]\n",

      "def  f77(X):\n",

      "    return  m*X \n",

      "Y2_star = f77(X2);# [kg H2O/kg dry gel]\n",

      "Y1_star = f77(X1);# [kg H2O/kg dry gel]\n",

      "deltaY = ((Y1-Y1_star)-(Y2-Y2_star))/math.log((Y1-Y1_star)/(Y2-Y2_star));\n",

      "NtoG = (Y1-Y2)/deltaY;\n",

      "# If the fixed bed data are to be used for estimating the mass transfer coeffecient for a moving bed of solids\n",

      "va = Ss/Density_b;# [m/s]\n",

      "vb = Gs/Density_a;# [m/s]\n",

      "rel_v = va+vb;# [relative velocity,m/s]\n",

      "G_prime = rel_v*Density_a;# [relative mass velocity of air,kg/square m s]\n",

      "HtG = Gs/(31.6*G_prime**0.55);# [m]\n",

      "HtS = Ss/kSap;# [m]\n",

      "# By Eqn. 11.52:\n",

      "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",

      "Z = NtoG*HtoG;# [m]\n",

      "print\"Height of continuous countercurrent isothermal absorber for drying: \",round(Z,4),\" m\\n\""

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.6 - Page: 616\n",

        "\n",

        "\n",

        "Height of continuous countercurrent isothermal absorber for drying:  0.2511  m\n",

        "\n"

       ]

      }

     ],

     "prompt_number": 81

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.7: Page 619"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.7\n",

      "# Page: 619\n",

      "\n",

      "print'Illustration 11.7 - Page: 619\\n\\n'\n",

      "\n",

      "# Solution\n",

      "\n",

      "import numpy.linalg as lin\n",

      "import matplotlib.pyplot as plt\n",

      "%matplotlib inline\n",

      "\n",

      "#*****Data*****#\n",

      "# a: C2H4 b:C3H8\n",

      "# The equlibrium curve is plotted in Fig.11.33 (Pg 620)\n",

      "# C3H8 is more strongly adsorbed component and composition in the gas and adsorbate are expressed as weight fraction C3H8.\n",

      "Ma = 28;# [kg/kmol]\n",

      "Mb = 44.1;# [kg/kmol]\n",

      "xaF = 0.6;# [mole fraction]\n",

      "xbF = 0.4;# [mole fraction]\n",

      "xa1 = 0.05;# [mole fraction]\n",

      "xa2 = 0.95;# [mole fraction]\n",

      "#***************#\n",

      "\n",

      "xF = xbF*Mb/((xbF*Mb)+(xaF*Ma));# [wt. fraction C3H8]\n",

      "xb1 = 1-xa1;# [mole fraction]\n",

      "x1 = xb1*Mb/((xb1*Mb)+xa1*Ma);# [wt. fraction C3H8]\n",

      "xb2 = 1-xa2;# [mole fraction]\n",

      "x2 = xb2*Mb/((xb2*Mb)+(xa2*Ma));# [wt. fraction C3H8]\n",

      "# Basis: 100 kg feed gas\n",

      "F = 100.0;# [kg]\n",

      "# (1): R2+PE = F [From Eqn. 11.63]\n",

      "# (2): (R2*x2)+(PE*x1) = (F*xF) [From Eqn. 11.64]\n",

      "# Solving simultaneously:\n",

      "a = [[1, 1],[x2 ,x1]];\n",

      "b = [F,(F*xF)];\n",

      "soln = lin.solve(a,b);\n",

      "R2 = soln[0];# [kg]\n",

      "PE = soln[1];# [kg]\n",

      "# Point F at xF and point E1 at x1 are located on the diagram.\n",

      "# From the diagram:\n",

      "N1 = 4.57;# [kg carbon/kg adsorbate]\n",

      "# The minimum reflux ratio is found as it is for the extraction.\n",

      "delta_Em = 5.80;\n",

      "Ratio = (delta_Em/N1)-1;# [kg reflux gas/kg product]\n",

      "R1_m = Ratio*PE;# [kg]\n",

      "E1_m = R1_m+PE;# [kg]\n",

      "B_m = N1*E1_m;# [kg carbon/100 kg feed]\n",

      "Ratio1 = 2*Ratio;\n",

      "# From Eqn. 11.58:\n",

      "N_deltaE = (Ratio1+1.0)*N1;# [kg carbon/kg adsorbate]\n",

      "# Point deltaE is located on the diagram:\n",

      "R1 = Ratio1*PE;# [kg]\n",

      "E1 = R1+PE;# [kg]\n",

      "B = N1*E1;# [kg]\n",

      "N_deltaR = -(B/R2);# [kg carbon/kg adsorbate]\n",

      "# Random lines such as the delta_RK are drawn from detaR, and the intersection of equilibrium curves are projected downward in the manner shown to provide the adsorption section operating curve.\n",

      "# Similarly random lines such as delta_EJ are drawn from deltaE, and the intersections are projected downwards to provide the enriching section operating curve.\n",

      "# Data = [x x_star]\n",

      "Data = numpy.array([[0.967 ,0.825],[0.90, 0.710],[0.80 ,0.60],[0.70, 0.50],[0.60 ,0.43],[0.512 ,0.39],[0.40 ,0.193],[0.30, 0.090],[0.20, 0.041],[0.0763, 0.003]]);\n",

      "Val = zeros(10);\n",

      "for i in range(0,10):\n",

      "    Val[i] = 1/((Data[i,0])-Data[i,1]);\n",

      "plt.plot(Data[:,0],Val);\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"x\");\n",

      "plt.ylabel(\"1 / (x-x*)\");\n",

      "plt.title(\"Graphical Integraion\");\n",

      "# The area under the curve between x1 & xF, for the enriching section:\n",

      "Area1 = 2.65;\n",

      "# The area under the curve between xF & x2, for the adsorption section:\n",

      "Area2 = 2.67;\n",

      "r = Ma/Mb;\n",

      "# From Eqn.11.66:\n",

      "# For the enriching section:\n",

      "NtoG1 = Area1-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",

      "# For the adsortion section:\n",

      "NtoG2 = Area2-math.log((1+(r-1)*x1)/(1+(r-1)*xF));\n",

      "NtoG = NtoG1+NtoG2;\n",

      "print\"Number of transfer units: \",NtoG"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.7 - Page: 619\n",

        "\n",

        "\n",

        "Number of transfer units:  5.77763695068\n"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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       "text": [

        "<matplotlib.figure.Figure at 0x7dc14a8>"

       ]

      }

     ],

     "prompt_number": 5

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.8: Page 627"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.8\n",

      "# Page: 627\n",

      "\n",

      "print'Illustration 11.8 - Page: 627\\n\\n'\n",

      "\n",

      "# Solution\n",

      "\n",

      "from scipy.optimize import fsolve\n",

      "\n",

      "#******Data******#\n",

      "rate = 0.1;# [kg/s]\n",

      "conc = 3.0;# [kg vapour/100cubic m]\n",

      "Density_p = 720.0;# [kg/cubic m]\n",

      "Density_bed = 480.0;# [kg/cubic m]\n",

      "capablity = 0.45;# [kg vapour/kg carbon]\n",

      "dp = 0.0028;# [m]\n",

      "time = 3.0;# [h]\n",

      "#********************#\n",

      "\n",

      "Vap_adsorbed = time*3600.0*rate;# [kg]\n",

      "C_required = Vap_adsorbed*1.0/capablity;\n",

      "# Two beds will be needed: one adsorbing and another regenerated.\n",

      "totC_required = 2*C_required;# [kg]\n",

      "print\"Amount of carbon required: \",totC_required,\" kg\\n\",\n",

      "Vol = (C_required/Density_bed);\n",

      "# Assume:\n",

      "Z = 0.5;# [m]\n",

      "Area = Vol/Z;# [square m]\n",

      "# From Eqn. 6.66:\n",

      "T = 35.0;# [OC]\n",

      "viscosity_air = 1.82*10**(-5);# [kg/m.s]\n",

      "Density_air = (29/22.41)*(273.0/(T+273));\n",

      "e = 1-(Density_bed/Density_p);\n",

      "G = rate*(100.0/conc)*(Density_air/(Area));# [kg/square m.s]\n",

      "Re = dp*G/viscosity_air;\n",

      "Z = 0.5;# [m]\n",

      "def f78(delta_p):\n",

      "    return ((delta_p/Z)*(e**3*dp*Density_air)/((1-e)*G**2))-(150*(1-e)/Re)-1.75\n",

      "delta_p = fsolve(f78,7);\n",

      "print\"The pressure drop is:\",round(delta_p,2),\" N/square m\\n\"\n",

      "#the answers are slightly different in textbook due to approximation while here answers are precise"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.8 - Page: 627\n",

        "\n",

        "\n",

        "Amount of caron required:  4800.0  kg\n",

        "The pressure drop is: 1413.31  N/square m\n",

        "\n"

       ]

      }

     ],

     "prompt_number": 88

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.9: Page 636"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.9\n",

      "# Page: 636\n",

      "\n",

      "print'Illustration 11.9 - Page: 636\\n\\n'\n",

      "import matplotlib.pyplot as plt\n",

      "%matplotlib inline\n",

      "# Solution\n",

      "\n",

      "#*****Data******#\n",

      "Yo = 0.00267;# [kg H2O/kg dry air]\n",

      "Yb = 0.0001;# [kg H2O/kg dry air]\n",

      "Ye = 0.024;# [kg H2O/kg dry air]\n",

      "Z = 0.61;# [m]\n",

      "G_prime = 0.1295;# [kg/square m.s]\n",

      "#******************#\n",

      "\n",

      "# The equilicrium data is plotted in Fig. 11.45 (Pg 637)\n",

      "# The gel is initially \"dry\" and the effluent air initially of so low a humidity asto be substantially dry, so that the operating line passes through the origin of the figure\n",

      "# The operating line is then drawn to intersect the equilibrium curve.\n",

      "# Data = [Y[kg H2O/kg dry air] Y_star[kg H2O/kg dry air]]\n",

      "Data =numpy.array([[0.0001, 0.00003],[0.0002, 0.00007],[0.0004 ,0.00016],[0.0006, 0.00027],[0.0008, 0.00041],[0.0010, 0.00057],[0.0012 ,0.000765],[0.0014, 0.000995],[0.0016, 0.00123],[0.0018 ,0.00148],[0.0020 ,0.00175],[0.0022 ,0.00203],[0.0024 ,0.00230]])\n",

      "Val1 = zeros(13);\n",

      "# Val1 = [1/(Y-Y_star)]\n",

      "for i in range(0,13):\n",

      "    Val1[i] = 1/(Data[i,0]-Data[i,1]);\n",

      "\n",

      "# Graphical Integration:\n",

      "plt.plot(Data[:,0],Val1);\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"Y(kg H20 / kg dry air)\");\n",

      "plt.ylabel(\"1 / (Y-Y_star)\");\n",

      "plt.title(\"Graphical Integration\");\n",

      "plt.show()\n",

      "# Area under The curve between Y = Yb and Y = Y:\n",

      "Area = [0 ,0.100 ,2.219 ,2.930 ,3.487 ,3.976 ,4.438 ,4.915, 5.432, 6.015, 6.728 ,7.716 ,9.304];\n",

      "# The total number of transfer unit corresponding to adsorption zone:\n",

      "NtoG = 9.304;\n",

      "Val2 = zeros(13);\n",

      "Val3 = zeros(13);\n",

      "# Val2 = [(w-wb)/wo]\n",

      "# Val3 = [Y/Yo]\n",

      "for i in range(0,13):\n",

      "    Val2[i] = Area[i]/NtoG;\n",

      "    Val3[i] = Data[i,0]/Yo;\n",

      "\n",

      "# Eqn. 11.74 can be arranged as follows:\n",

      "# f = integrate((1-(Y/Yo)),(w-wb)/wa,0,1)\n",

      "\n",

      "plt.plot(Val2,Val3);\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"(w-wb) / wo\");\n",

      "plt.ylabel(\"Y / Yo\");\n",

      "plt.title(\"Break through curve\");\n",

      "plt.show()\n",

      "# From area above the curve of scf(2):\n",

      "f = 0.530;\n",

      "\n",

      "Gs = G_prime;# [kg/square m.s]\n",

      "# From Illustration: 11.6\n",

      "kYap = 31.6*G_prime**0.55;# [kg H2O/cubic m s delta_Y]\n",

      "kSap = 0.965;# [kg H2O/cubic m s delta_X]\n",

      "# From Fig. 11.48:\n",

      "Xt = 0.0858;# [kg H2O/kg gel]\n",

      "# From Eqn. 11.76:\n",

      "Ss = Yo*Gs/Xt;# [kg/square m.s]\n",

      "m = 0.0185;# [average slope of equilibrium curve]\n",

      "# From Eqn. 11.51 & Eqn. 11.52:\n",

      "HtG = Gs/kYap;# [m]\n",

      "HtS = Ss/kSap;# [m]\n",

      "HtoG = HtG+(m*Gs/Ss)*HtS;# [m]\n",

      "# From Eqn. 11.79:\n",

      "Za = NtoG*HtoG;# [m]\n",

      "# From Eqn. 11.74:\n",

      "Degree = (Z-(f*Za))/Z;\n",

      "Density_bed = 671.2;# [Illustration 11.6, kg/cubic m]\n",

      "mass_gel = Z*Density_bed;# [kg/square m]\n",

      "# At saturation point the gel contins:\n",

      "Y1 = mass_gel*Degree*Xt;# [kg H2O/square m cross section]\n",

      "# The air introduces:\n",

      "Y2 = Gs*Yo;# [kg/square m s]\n",

      "print\"Time to reach breakpoint is: \",round((Y1/(Y2*3600)),4),\" h\\n\""

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.9 - Page: 636\n",

        "\n",

        "\n"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

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R81jEii0Wn34K48bBkCFJ1yS3khrtVrZ+8Qv4f/8PZs9OuibOuXIwcmS4VfaG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\nDTe3c64xy5aFlTYmTQrJ5vn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       "text": [

        "<matplotlib.figure.Figure at 0x7d96080>"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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F8ZIsDJsCs/O234qea8XMDjezaYR7Q5+WYDxSRu66C84/Hx59FL75zbSjEaku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\n",

       "text": [

        "<matplotlib.figure.Figure at 0x781dda0>"

       ]

      },

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Time to reach breakpoint is:  24.7778  h\n",

        "\n"

       ]

      }

     ],

     "prompt_number": 6

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.10: Page 640"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.10\n",

      "# Page: 640\n",

      "\n",

      "print'Illustration 11.10 - Page: 640\\n\\n'\n",

      "\n",

      "\n",

      "# Solution\n",

      "\n",

      "#*****Data******#\n",

      "# a:N2 b:H2O\n",

      "Mb = 18;# [kg/kmol]\n",

      "Ma = 29;# [kg/kmol]\n",

      "Z = 0.268;# [m]\n",

      "Xo_solid = 0.01;# [kg H20/kg solid]\n",

      "Density_bed = 712.8;# [kg/cubic m]\n",

      "T = 28.3;# [OC]\n",

      "P = 593;# [kN/square m]\n",

      "Gs = 4052;# [kg/square m.h]\n",

      "Xo_gas = 1440*10**(-6);# [mole fraction]\n",

      "#********************#\n",

      "\n",

      "# Yo_star is in equilibrium with Xo:\n",

      "Xo = 0;# [kg H20/kg solid]\n",

      "Yo_star = 0;# [kg H20/kg N2]\n",

      "thetha_t = 12.8;# [h]\n",

      "thetha_b = 9;# [h]\n",

      "# The breakthrough data are plotted in the manner of Fig. 11.47 (Pg 639) and thetha_s is dtermined:\n",

      "thetha_s = 10.9;# [h]\n",

      "Xt = 0.21;# [kg H20/kg solid]\n",

      "# From Eqn. 11.81:\n",

      "LUB = (Z/thetha_s)*(thetha_s-thetha_b);\n",

      "# For thetha_b = 15 h\n",

      "thetha_b = 15;# [h]\n",

      "Yo = (Xo_gas/(1-Xo_gas))*(Mb/Ma);# [kg H20/kg N2]\n",

      "# From Eq. 11.82:\n",

      "Zs = Gs*(Yo-Yo_star)*thetha_b/(Density_bed*(Xt-Xo_solid));# [m]\n",

      "# From Eqn. 11.85:\n",

      "Z = LUB+Zs;\n",

      "print\"Height of adsorbent column:\",round(Z,4),\" m\\n\""

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.10 - Page: 640\n",

        "\n",

        "\n",

        "Height of adsorbent column: 0.0467  m\n",

        "\n"

       ]

      }

     ],

     "prompt_number": 93

    },

    {

     "cell_type": "heading",

     "level": 2,

     "metadata": {},

     "source": [

      "Ex11.11: Page 654"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "\n",

      "\n",

      "# Illustration 11.11\n",

      "# Page: 645\n",

      "\n",

      "print'Illustration 11.11 - Page: 645\\n\\n'\n",

      "\n",

      "# Solution\n",

      "\n",

      "import math\n",

      "from scipy.optimize import fsolve\n",

      "import matplotlib.pyplot as plt\n",

      "%matplotlib inline\n",

      "\n",

      "#****Data****#\n",

      "# For collection of Cu2+:\n",

      "V = 37850.0;# [l/h]\n",

      "c1 = 20.0;# [meq Cu2+/l]\n",

      "c2 = 0.01*c1;# [meq Cu2+/l]\n",

      "Mass_rate = 2.0;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",

      "exchanged = V*(c1-c2);# [meq/h]\n",

      "X2 = 0.30;# [meq Cu2+/g]\n",

      "#************#/\n",

      "\n",

      "# The point(c2,X2) is plotted in Fig. 11.48(a), Pg 645:\n",

      "# For the minimum resin/solution ratio and an infinitely tall tower, the operating line pass though point P.\n",

      "X = 4.9;# [meq Cu2+/g]\n",

      "MinRate = exchanged/(X-X2);# [g/h]\n",

      "Rate = 1.2*MinRate;# [g/h]\n",

      "# Copper balance:\n",

      "X1 = (exchanged/Rate)+X2;# [meq Cu2+/g resin]\n",

      "# The point (c1,x1) is ploted in Fig. 11.48(a) and operating line drawn can be straight line at this low conc.\n",

      "# Adapting Eqn. 11.48 and rearranging:\n",

      "# S*Z*Density_s = (V/Mass_rate)*integrate(1/(c-c_star),c,c1,c2)\n",

      "# Mass_rate = KL_prime*ap/Density_s\n",

      "# From the equilibrium curve:\n",

      "# Data = [c c_star]\n",

      "Data = numpy.array([[20 ,2.4],[16 ,1.9],[12, 0.5],[8 ,0.25],[4 ,0.10],[2 ,0.05],[1 ,0.02],[0.2, 0]]);\n",

      "Val = zeros(8);\n",

      "for i in range(0,8):\n",

      "    Val[i] = 1/(Data[i,0]-Data[i,1]);\n",

      "\n",

      "plt.plot(Data[:,0],Val);\n",

      "plt.grid('on');\n",

      "plt.xlabel(\"c\");\n",

      "plt.ylabel(\"1 / (c-c*)\");\n",

      "plt.title(\"Graphical Integration\");\n",

      "# From Graphical Integration:\n",

      "Area = 5.72;\n",

      "# holdup = S*Z*Density_s\n",

      "holdup = V*Area/(Mass_rate);\n",

      "print\"Resin Holdup: \",holdup,\"g\\n\"\n",

      "\n",

      "# Regeneration of resin:\n",

      "# For 70% utilisation of 2N acid, feed must contain:\n",

      "V = exchanged;\n",

      "F = V/(0.70*2000);# [l/h]\n",

      "c1 = 0;# [meq Cu2+/l]\n",

      "c2 = V*1.0/F;# [meq Cu2+/l]\n",

      "X1 = 0.30;# [meq Cu2+/g resin]\n",

      "X2 = 4.12;# [meq cu2+/g resin]\n",

      "# The points (c1,X1) and (c2,X2) are plotted on Fig 11.48(b), Pg 645\n",

      "c1_star = 120.0;# [meq Cu2+/l]\n",

      "c2_star = 1700.0;# [meq Cu2+/l]\n",

      "logmean = ((c1_star-c1)-(c2_star-c2))/math.log((c1_star-c1)/(c2_star-c2));\n",

      "Mass_rate = 0.018;# [meq Cu2+/g resin h (meq Cu2+/l)]\n",

      "# Substituting in equation:\n",

      "def  f79(holdup):\n",

      "    return (V*(c2-c1))-(Mass_rate*holdup*logmean)\n",

      "holdup = fsolve(f79,7);\n",

      "print\"Resin Holdup in the regeneration Tower is \",round(holdup,3),\" g\\n\"\n",

      "#the answers are in textbook is wrong"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "Illustration 11.11 - Page: 645\n",

        "\n",

        "\n",

        "Resin Holdup: "

       ]

      },

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        " 108251.0 g\n",

        "\n",

        "Resin Holdup in the regeneration Tower is  296720391.501  g\n",

        "\n"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

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       "text": [

        "<matplotlib.figure.Figure at 0x783b160>"

       ]

      }

     ],

     "prompt_number": 7

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [],

     "language": "python",

     "metadata": {},

     "outputs": []

    }

   ],

   "metadata": {}

  }

 ]

}