path: root/Chemical_Reaction_Engineering/ch18.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 18 Solid Catalyzed Reactions"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 18.1 pageno : 407"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "# Variables\n",
      "dp = 2.4*(10**-3);L = dp/6;\n",
      "De = 5*10**-5;\n",
      "Keff = 1.6;\n",
      "h = 160.                #heat transfer coefficient(KJ/hr.m2cat.K)\n",
      "kg = 300.               #mass transfer coefficient(m3/hr.m2cat)\n",
      "Hr = -160.              #KJ/molA\n",
      "CAg = 20.               #mol/m3\n",
      "rA_obs = 10.**5         #mol/hr.m3cat\n",
      "\n",
      "# Calculations and Results\n",
      "kobs = rA_obs/CAg;\n",
      "Vp = 3.14*(dp**3)/6;\n",
      "S = 3.14*(dp**2);\n",
      "ratio = kobs*Vp/(kg*S);\n",
      "\n",
      "print \" Part a\"\n",
      "if ratio<0.01 :\n",
      "    print \" Resistance to mass transport to film should not influence rate of reaction\"\n",
      "else:\n",
      "    print \" Resistance to mass transport to film should influence rate of reaction\"\n",
      "\n",
      "\n",
      "print \" Part b\"\n",
      "\n",
      "Mw = rA_obs*(L**2)/(De*CAg);\n",
      "print \" Mw =  %.f\"%(Mw)\n",
      "if Mw>4:\n",
      "    print \" Pore diffusion is influencing and hence strong pore diffusion\"\n",
      "else:\n",
      "    print \" Pore diffusion is  not influencing and hence weak pore diffuusion\"\n",
      "\n",
      "dt_max_pellet = De*(CAg-0)*(-Hr)/Keff;\n",
      "#Temp variation Across the gas film\n",
      "dt_max_film = L*rA_obs*(-Hr)/h;\n",
      "\n",
      "print \" Part c\"\n",
      "print \" dTmax,pellet is %.1f C\"%(dt_max_pellet)\n",
      "print \" degree C  dTmax,film is %.2f C\"%(dt_max_film)\n",
      "print \" degree C\"\n",
      "if dt_max_pellet<1:\n",
      "    print \" Pellet is close to uniform in temperature\"\n",
      "else:\n",
      "    print \" There is a variation in temp within pellet\"\n",
      "if dt_max_film<1:\n",
      "    print \" Film is close to uniform in temperature\"\n",
      "else:\n",
      "    print \" There is a variation in temp within Film\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Part a\n",
        " Resistance to mass transport to film should not influence rate of reaction\n",
        " Part b\n",
        " Mw =  16\n",
        " Pore diffusion is influencing and hence strong pore diffusion\n",
        " Part c\n",
        " dTmax,pellet is 0.1 C\n",
        " degree C  dTmax,film is 40.00 C\n",
        " degree C\n",
        " Pellet is close to uniform in temperature\n",
        " There is a variation in temp within Film\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 18.2 page no : 408"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%matplotlib inline\n",
      "\n",
      "from matplotlib.pyplot import *\n",
      "from numpy import *\n",
      "import math \n",
      "from scipy import stats\n",
      "\n",
      "# Variables\n",
      "PAo = 3.2;\n",
      "R = 0.082           #litre.atm/mol.k\n",
      "T = 390.            #k\n",
      "v = 20.             #litre/hr\n",
      "W = 0.01            #/kg\n",
      "CA_in = [0.1,0.08,0.06,0.04];\n",
      "CA_out = [0.084,0.07,0.055,0.038];\n",
      "\n",
      "# Calculations\n",
      "CAo = PAo/(R*T);\n",
      "FAo = CAo*v;\n",
      "eA = 3;\n",
      "\n",
      "XA_in = zeros(4)\n",
      "XA_out = zeros(4)\n",
      "dXA = zeros(4)\n",
      "rA = zeros(4)\n",
      "CA_avg = zeros(4)\n",
      "\n",
      "for i in range(4):\n",
      "    XA_in[i] = (1-CA_in[i]/CAo)/(1+eA*CA_in[i]/CAo);\n",
      "    XA_out[i] = (1-CA_out[i]/CAo)/(1+eA*CA_out[i]/CAo);\n",
      "    dXA[i] = XA_out[i]-XA_in[i];\n",
      "    rA[i] = dXA[i]/(W/FAo);\n",
      "    CA_avg[i] = (CA_in[i]+CA_out[i])/2;\n",
      "\n",
      "# Results\n",
      "plot(CA_avg,rA)\n",
      "xlabel(\"CA, mol/liter\")\n",
      "ylabel(\"-rA, mol/hr. kg cat\")\n",
      "coeff1 = stats.linregress(CA_avg,rA)\n",
      "k = coeff1[0]\n",
      "print \" The rate of reaction is %.3f mol/hr.kg\"%(k),\n",
      "print \"CA\"\n",
      "print ('The answer slightly differs from those given in book as regress fn is\\\n",
      " used for calculating slope and intercept')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        " The rate of reaction is 109.395 mol/hr.kg"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " CA\n",
        "The answer slightly differs from those given in book as regress fn is used for calculating slope and intercept\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAELCAYAAAA2mZrgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBERj/8P/TvRn68kedQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2f0a1d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 18.3 pageno : 410"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from numpy import zeros\n",
      "from scipy import stats\n",
      "\n",
      "\n",
      "# Variables\n",
      "CAo = 0.1           #mol/litre\n",
      "FAo = 2.            #mol/hr\n",
      "eA = 3.\n",
      "CA = [0.074,0.06,0.044,0.029]           #mol/litre\n",
      "W = [0.02,0.04,0.08,0.16]               #kg\n",
      "\n",
      "XA = zeros(4)\n",
      "y = zeros(4)\n",
      "x = zeros(4)\n",
      "W_by_FAo = zeros(4)\n",
      "CAout_by_CAo = zeros(4)\n",
      "XA1 = zeros(4)\n",
      "\n",
      "# Calculations\n",
      "for i in range(4):\n",
      "    XA[i] = (CAo-CA[i])/(CAo+eA*CA[i]);\n",
      "    y[i]  = (1+eA)*math.log(1/(1-XA[i]))-eA*XA[i];\n",
      "    x[i] = CAo*W[i]/FAo;\n",
      "    W_by_FAo[i] = W[i]/FAo;\n",
      "    CAout_by_CAo[i] = CA[i]/CAo;\n",
      "    XA1[i] = (1-CAout_by_CAo[i])/(1+eA*CAout_by_CAo[i]);\n",
      "\n",
      "# Results\n",
      "plot(x,y)\n",
      "xlabel(\"CA0W/FAO = W/20, hr.kg cat/liter\")\n",
      "ylabel(\"4 ln 1/(1-Xa) -3Xa\")\n",
      "coeff3 = stats.linregress(x,y);\n",
      "k = coeff3[0];\n",
      "print \" Part a, using integral method of analysis\"\n",
      "print \" The rate of reactionmol/litre is %.3f\"%(k),\n",
      "print \"CA\"\n",
      "\n",
      "#Part b\n",
      "#plotting W_by_FAo vs XA1,the calculating rA = dXA/d(W/FAo) for last 3 points,we get\n",
      "rA = [5.62,4.13,2.715];\n",
      "coeff2 = stats.linregress(CA[1:],rA);\n",
      "\n",
      "print \" Part b, using differential method of analysis\"\n",
      "print \" The rate of reactionmol/litre) is %.3f\"%(coeff2[0]),\n",
      "print \"CA\"\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        " Part a, using integral method of analysis"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " The rate of reactionmol/litre is 96.804 CA\n",
        " Part b, using differential method of analysis\n",
        " The rate of reactionmol/litre) is 93.703 CA\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ffe3c06fe90>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 18.4 pageno : 413"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "# Variables\n",
      "# from example 18.2\n",
      "XA = 0.35;\n",
      "FAo = 2000.         #mol/hr\n",
      "eA = 3.\n",
      "k = 96.\n",
      "CAo = 0.1;\n",
      "\n",
      "# Calculations\n",
      "W = ((1+eA)*math.log(1./(1-XA))-eA*XA)*(FAo/(k*CAo));\n",
      "\n",
      "# Results\n",
      "print \" The amount of catalyst kg needed in a packed bed reactor is %.f kg\"%(W)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The amount of catalyst kg needed in a packed bed reactor is 140 kg\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 18.5 pageno : 414"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "from numpy import *\n",
      "from scipy import *\n",
      "\n",
      "# Variables\n",
      "CAo = 0.1;\n",
      "eA = 3;\n",
      "rA = [3.4,5.4,7.6,9.1];            # mol A/hr. kg cat\n",
      "CA = [0.039,0.0575,0.075,0.092];   # mol/liter\n",
      "\n",
      "# Calculations\n",
      "XA = zeros(5);\n",
      "inv_rA = zeros(5);\n",
      "for i in range(4):\n",
      "    XA[i] = (1-CA[i]/CAo)/(1+eA*CA[i]/CAo);\n",
      "    inv_rA[i] = 1/rA[i];\n",
      "\n",
      "for i in range(4):\n",
      "    small = XA[i];\n",
      "    for j in range(i,4):\n",
      "        next_ = XA[j];\n",
      "        if small>next_:\n",
      "            XA[i],XA[j] = XA[j],XA[i];\n",
      "            inv_rA[i],inv_rA[j] = inv_rA[j], inv_rA[i]\n",
      "\n",
      "\n",
      "# Results\n",
      "plot(XA,inv_rA)\n",
      "xlabel(\"Xa, dimensionless\")\n",
      "ylabel(\"-1/rA, hr.kg cat/mol\")\n",
      "show()\n",
      "XA[4] = 0.35\n",
      "inv_rA[4] = 0.34;\n",
      "Area = trapz(XA,inv_rA);\n",
      "W = Area*2000.;\n",
      "print \"Amount of catalyst needed is %.f kg cat\"%(W)\n",
      "print ('The answer differs from those given in book as trapezoidal rule is used for calculating area')\n",
      "\n",
      "# Note : Answer differs because python trepz function works differnt than we do manually trepezoidal method."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEPCAYAAAC6Kkg/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa93d80a710>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Amount of catalyst needed is 91 kg cat\n",
        "The answer differs from those given in book as trapezoidal rule is used for calculating area\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 18.6 pageno : 416"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "XA = 0.35;\n",
      "FAo = 2000.             #mol/hr\n",
      "CAo = 0.1               #mol/litre\n",
      "eA = 3.\n",
      "k = 96.                 # mol/kg cat\n",
      "\n",
      "# Calculations\n",
      "CA = CAo*((1-XA)/(1+eA*XA))\n",
      "rA = k*CA;\n",
      "W = FAo*XA/rA;\n",
      "\n",
      "# Results\n",
      "print \"The amount of catalyst neededkg, is %.f kg\"%(W)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The amount of catalyst neededkg, is 230 kg\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}