{
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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 3 :  Interpretation of Batch Reactor Data"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 3.1 page no : 60"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%pylab inline\n",
      "\n",
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "from scipy import stats\n",
      "\n",
      "# Variables\n",
      "#Given\n",
      "t = [0, 20, 40 ,60, 120 ,180, 300];        # time\n",
      "C_A = [10 ,8, 6, 5, 3, 2, 1];              # concentration\n",
      "CAo = 10.;\n",
      "k = zeros(7)\n",
      "CA_inv = zeros(7)\n",
      "\n",
      "# Calculations\n",
      "#Guesmath.sing 1st order kinetics\n",
      "for i in range(7):\n",
      "    k[i] = math.log(CAo/C_A[i]);\n",
      "    CA_inv[i] = 1/C_A[i];\n",
      "\n",
      "T = array([18.5,23,35]);\n",
      "CAo = array([10,5,2]);\n",
      "CA = zeros(3)\n",
      "log_Tf = zeros(3)\n",
      "log_CAo = zeros(3)\n",
      "\n",
      "for i in range(3):\n",
      "    CA[i] = 0.8*CAo[i];\n",
      "    log_Tf[i] = math.log10(T[i]);\n",
      "    log_CAo[i] = math.log10(CAo[i]);\n",
      "\n",
      "# Results\n",
      "plot(log_CAo,log_Tf)\n",
      "plot(log_CAo,log_Tf,\"go\")\n",
      "xlabel(\"Ln CAO\")\n",
      "ylabel(\"log r\")\n",
      "#plot(log_Tf,log_CAo)\n",
      "#coeff1 = linalg.lstsq(log_CAo,log_Tf);\n",
      "slope, intercept, r_value, p_value, std_err = stats.linregress(log_CAo,log_Tf)\n",
      "coeff1 = stats.linregress(log_CAo,log_Tf)\n",
      "n = 1-coeff1[0];\n",
      "print \"From graph we get slope and intercept for calculating rate eqn\"\n",
      "k1 = ((0.8**(1-n))-1)*(10.**(1-n))/(18.5*(n-1));\n",
      "print \" The rate equation is given by %.3f\"%(k1),\n",
      "print \"CA**1.4 mol/litre.sec\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "From graph we get slope and intercept for calculating rate eqn"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " The rate equation is given by 0.005 CA**1.4 mol/litre.sec\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x33a3310>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 3.2 page no : 65"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "import math\n",
      "# Variables\n",
      "CA = array([10,8,6,5,3,2,1]);              # concentration\n",
      "T = array([0,20,40,60,120,180,300]);       # time\n",
      "y = array([-0.1333,-0.1031,-0.0658,-0.0410,-0.0238,-0.0108,-0.0065]);     # slope\n",
      "\n",
      "log_y = zeros(7)\n",
      "log_CA = zeros(7)\n",
      "\n",
      "# Calculations\n",
      "for i in range(7):\n",
      "    log_y[i] = log10(complex(y[i]));\n",
      "    log_CA[i] = log10(CA[i]);\n",
      "\n",
      "\n",
      "# Results\n",
      "plot(log_CA,log_y)\n",
      "plot(log_CA,log_y,\"go\")\n",
      "xlabel(\"log10 CA\")\n",
      "ylabel(\"log10 (-dCA/dt)\")\n",
      "show()\n",
      "coeff1 = stats.linregress(log_CA,log_y);\n",
      "n = coeff1[0];\n",
      "k = -10**(coeff1[1]);\n",
      "print \" After doing linear regression, the slope and intercept of the graph is %.0f, %.0f\"%(-coeff1[1],coeff1[0])\n",
      "print \" The rate equation is therefore given by %.3f\"%(-k),\n",
      "print \"CA**1.375 mol/litre.sec\"\n",
      "print ('The answer slightly differs from those given in book as regress fn is used for \\\n",
      " calculating slope and intercept')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\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",
        "-c:14: ComplexWarning: Casting complex values to real discards the imaginary part\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jR1tRrriYzz6DO+6A996Du+6yuhqR+kWr3Ipb+fhjSEw0Fx688UarqxFxX1rl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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x365aa90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " After doing linear regression, the slope and intercept of the graph is 2, 1\n",
        " The rate equation is therefore given by 0.005 CA**1.375 mol/litre.sec\n",
        "The answer slightly differs from those given in book as regress fn is used for  calculating slope and intercept\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 3.4 page no : 73"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "\n",
      "# Variables\n",
      "k1 = 2.3           # temperatures\n",
      "k2 = 2.3\n",
      "T1 = 400.          # K\n",
      "T2 = 500.          # K\n",
      "\n",
      "# Calculations\n",
      "R = 82.06*10**-6;\n",
      "R1 = 8.314\n",
      "E = (math.log(k2/k1)*R)/(1./T1-1./T2)\n",
      "\n",
      "# Results\n",
      "print \"using pressure units is %.0f EJ/mol\"%(E)\n",
      "\n",
      "#pA = CA*RT\n",
      "#-rA = 2.3(RT)**2*CA**2\n",
      "k1 = 2.3*(R*T1)**2\n",
      "k2 = 2.3*(R*T2)**2\n",
      "E = (math.log(k2/k1)*R1)/(1./T1-1./T2)\n",
      "print \"using concentration units is %.f J/mol\"%(E)\n",
      "\n",
      "# Answers might be different because of Rounding error"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "using pressure units is 0 EJ/mol\n",
        "using concentration units is 7421 J/mol\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}