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{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 21 : The Rate and Performance Equations"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 21.1 page no : 486"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "from scipy import stats\n",
      "\n",
      "# Variables\n",
      "t = [0,2,4,6];                 # time\n",
      "XA = [0.75,0.64,0.52,0.39];    # XA\n",
      "t1 = 4000.                      #kg.s/m3\n",
      "density_s = 1500.               #kg/m3\n",
      "De = 5.*10**-10;\n",
      "d = 2.4*10**-3;\n",
      "\n",
      "# Calculations\n",
      "y = zeros(4)\n",
      "for i in range(4):\n",
      "    y[i] = math.log((1./(1-XA[i]))-1);\n",
      "\n",
      "plot(y,t)\n",
      "ylabel(\"ln(CAO/CA -1)\")\n",
      "\n",
      "coeff  = stats.linregress(t,y);\n",
      "kd = coeff[0];\n",
      "k = math.exp(coeff[1])/t1;\n",
      "L = d/6;\n",
      "Mt = L*math.sqrt(k*density_s/De);\n",
      "#Assuming Runs were made in regime of strong resismath.tance to  pore diffusion\n",
      "\n",
      "k1 = ((math.exp(coeff[1]))**2)*(L**2)*density_s/(t1*t1*De);\n",
      "kd1 = -2*coeff[0];\n",
      "Mt = L*math.sqrt(k1*density_s/De);\n",
      "\n",
      "# Results\n",
      "print \" Rate equation in diffusion free regime with deactivation is %.2f  m**3/kg.s\"%(k1)\n",
      "print \" CA*a with  -da/dthr-1  is %.2f a,hr**-1\"%(kd1)\n",
      "\n",
      "#In strong pore diffusion\n",
      "k2 = k1*math.sqrt(De/(k1*density_s));\n",
      "print \" CA*a**0.5/L with  -da/dthr-1 is %.2f a,hr**-1\"%(kd1),\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        " Rate equation in diffusion free regime with deactivation is 0.27  m**3/kg.s\n",
        " CA*a with  -da/dthr-1  is 0.51 a,hr**-1\n",
        " CA*a**0.5/L with  -da/dthr-1 is 0.51 a,hr**-1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['draw_if_interactive']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f7102416750>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 21.2 pageno : 492"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "from scipy.integrate import quad \n",
      "from matplotlib.pyplot import *\n",
      "from numpy import *\n",
      "\n",
      "# Variables\n",
      "PAo = 3.                #atm\n",
      "R = 82.06*10**-6        #m3.atm/mol.k\n",
      "T = 730.                #k\n",
      "W = 1000.               #kg\n",
      "FAo = 5000.             #mol/hr\n",
      "\n",
      "# Calculations\n",
      "CAo = PAo/(R*T);\n",
      "tau = W*CAo/FAo;\n",
      "i = 0;\n",
      "\n",
      "a = zeros(25)\n",
      "XA = zeros(25)\n",
      "a1 = zeros(25)\n",
      "XA1 = zeros(25)\n",
      "a2 = zeros(25)\n",
      "XA2 = zeros(25)\n",
      "a3 = zeros(25)\n",
      "XA3 = zeros(25)\n",
      "a = linspace(0,120,25)\n",
      "for t in xrange(0,120,5):\n",
      "    i = i+1;\n",
      "    #Part a\n",
      "    a[i] = 1-(8.3125*10**-3)*t;\n",
      "    XA[i] = (tau**2)*a[i]/(1+(tau**2)*a[i]);\n",
      "    #Part b\n",
      "    a1[i] = math.exp(-0.05*t);\n",
      "    XA1[i] = (tau**2)*a1[i]/(1+(tau**2)*a1[i]);\n",
      "    #Part c\n",
      "    a2[i] = 1/(1+3.325*t);\n",
      "    XA2[i] = (tau**2)*a2[i]/(1+(tau**2)*a2[i]);\n",
      "    #Part d\n",
      "    a3[i] = 1/(math.sqrt(1+1333*t));\n",
      "    XA3[i] = (tau**2)*a3[i]/(1+(tau**2)*a3[i]);\n",
      "\n",
      "t = linspace(0,120,25)\n",
      "plot(t,XA,t,XA1,t,XA2,t,XA3)\n",
      "suptitle(\"Decrease in conversion as a function of time for various deactivation orders.\")\n",
      "xlabel(\"Time, days\")\n",
      "ylabel(\"Xa\")\n",
      "\n",
      "def f13(t): \n",
      "\t return ((100*(1-(8.3125*10**-3)*t))/(1+100*(1-(8.3125*10**-3)*t)))\n",
      "\n",
      "XA_avg = (1./120) * quad(f13,0,120)[0]\n",
      "\n",
      "\n",
      "def f14(t): \n",
      "\t return (100.*math.exp(-0.05*t))/(1+100*math.exp(-0.05*t))\n",
      "\n",
      "XA1_avg = (1./120)* quad(f14,0,120)[0]\n",
      "\n",
      "\n",
      "def f15(t): \n",
      "\t return ((100*(1./(1+3.325*t)))/(1+100*(1/(1+3.325*t))))\n",
      "\n",
      "XA2_avg = (1./120)* quad(f15,0,120)[0]\n",
      "\n",
      "\n",
      "def f16(t): \n",
      "\t return ((100*1./(math.sqrt(1+1333*t)))/(1+100*(1/math.sqrt(1+1333*t))))\n",
      "\n",
      "XA3_avg = (1./120)* quad(f16,0,120)[0]\n",
      "\n",
      "# Results\n",
      "print \" for d = 0,the mean conversion is % f\"%(XA_avg)\n",
      "print \" for d = 1,the mean conversion is % f\"%(XA1_avg)\n",
      "print \" for d = 2,the mean conversion is % f\"%(XA2_avg)\n",
      "print \" for d = 3,the mean conversion is % f\"%(XA3_avg)\n",
      "\n",
      "show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " for d = 0,the mean conversion is  0.955970\n",
        " for d = 1,the mean conversion is  0.732280\n",
        " for d = 2,the mean conversion is  0.400874\n",
        " for d = 3,the mean conversion is  0.298840\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UUzdnrZaL+cs9QKmInZur1K2TahxnB2fmdJ3D38/9zeYRmwHot6Yf7kvceXfv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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4215310>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}