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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 22  :\n",
      "\n",
      "G/L Reactions on Solid \n",
      "\n",
      "Catalysts: Trickle Beds, Slurry \n",
      "\n",
      "Reactors, and Three-Phase \n",
      "\n",
      "Fluidized Beds"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 22.1 pageno : 511"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "\n",
      "# Variables\n",
      "PA = 101325.            #Pa\n",
      "HA = 36845.             #PA.m3.l/mol\n",
      "CBo = 1000.             #mol/m3\n",
      "v = 10**-4              #m3*l/s\n",
      "h = 5.                  #m\n",
      "A = 0.1                 #m2\n",
      "\n",
      "# Calculations\n",
      "CA = PA/HA;\n",
      "FBo = v*CBo;\n",
      "Vr = A*h;\n",
      "dp = 5*10**-3;          #mcat\n",
      "d_solid = 4500.         #kg/m3cat\n",
      "De = 8*10**-10;         #m3l/mcat.s\n",
      "n = 0.5;\n",
      "b = 1.;\n",
      "k = 2.35*10**-3;\n",
      "L = dp/6.;\n",
      "kai_overall = 0.02;\n",
      "kac_ac = 0.05;\n",
      "f = 0.6;\n",
      "#For a half-order reaction\n",
      "Mt = L*math.sqrt((n+1)*(k*d_solid*(CA)**(n-1))/(2*De));\n",
      "E = 1/Mt;\n",
      "rA = (1/((1/(kai_overall))+(1/(kac_ac))+(1/(k*b*(CA**(n-1))*E*f*d_solid))))*(PA/HA);\n",
      "#From Material Balance\n",
      "XB = b*rA*Vr/FBo;\n",
      "\n",
      "# Results\n",
      "print \" The conversion of acetone is %.3f\"%(XB)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The conversion of acetone is 0.158\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 22.2 pageno : 513"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "from numpy import *\n",
      "\n",
      "# Variables\n",
      "PA = 14.6*101325;           #Pa\n",
      "HA = 148000.;               # liquid\n",
      "Vr = 2.;\n",
      "Vl = Vr;\n",
      "b = 1.;\n",
      "fs = 0.0055;\n",
      "\n",
      "# Calculations\n",
      "k = 5.*10**-5;              #m6l/kg.molcat.s\n",
      "dp = 5*10**-5;              #mcat\n",
      "kac = 4.4*10**-4;kai = 0.277;   #m3l/m3.r.s\n",
      "density = 1450.;                #kg/m3\n",
      "De = 5*10**-10;                 #m3l/mcat.s\n",
      "L = dp/6;                       #for spherical particle\n",
      "CA = PA/HA;\n",
      "X = 0.9;\n",
      "CBo = 2500.\n",
      "CB = CBo*(1-X);\n",
      "ac = 6*fs/dp;\n",
      "K = kac*ac;\n",
      "\n",
      "CB = [2500,1000,250];\n",
      "e = [0.19,0.29,0.5];\n",
      "Mt = zeros(3)\n",
      "rA = zeros(3)\n",
      "inv_rA = zeros(3)\n",
      "for i in range(3):\n",
      "    Mt[i] = L*math.sqrt(k*CB[i]*density/De);\n",
      "    rA[i] = CA/((1./kai)+(1./K)+(1./(k*density*e[i]*fs*CB[i])))\n",
      "    inv_rA[i] = 1/rA[i];\n",
      "\n",
      "# Results\n",
      "plot(CB,inv_rA)\n",
      "ylabel(\"1/-rA\")\n",
      "Area = 3460.\n",
      "t = Vl*Area/(b*Vr);\n",
      "t = t/60.                         #min\n",
      "print \" The time required for 90 percentage conversion of reactant is %.f\"%t,\n",
      "print \"min\"\n",
      "show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        " The time required for 90 percentage conversion of reactant is 58"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " min\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['draw_if_interactive', 'e']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JkyksLKRt27YsWLCAzMxMz3Cak/C56mrz2ok334Rbb7U7jYi0hFe/O42zOHHihPH88897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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x32eab90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}