{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 7 : Chemical Kinetics"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 7.3 page number 305"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "r = 3**2*3;   #according to the rate reaction\n",
      "print \"reaction reate will be increased by with 3 times increase in pressure = %f times\"%(r)\n",
      "\n",
      "r = 3**2*3;   #according to the rate reaction\n",
      "print \"reaction reate will be increased by with 3 times decrease in volume = %f times\"%(r)\n",
      "\n",
      "r = 3**2;   #according to the rate reaction\n",
      "print \"reaction reate will be increased by with 3 times increase in conc of NO = %f times\"%(r)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "reaction reate will be increased by with 3 times increase in pressure = 27.000000 times\n",
        "reaction reate will be increased by with 3 times decrease in volume = 27.000000 times\n",
        "reaction reate will be increased by with 3 times increase in conc of NO = 9.000000 times\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 7.10 page number 316\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "from scipy.optimize import fsolve \n",
      "import math \n",
      "moles_A = 3.;\n",
      "moles_B = 5.;\n",
      "K = 1.;\n",
      "\n",
      "def F(x):\n",
      "    return 15.-8*x;\n",
      "\n",
      "\n",
      "x = 10.;\n",
      "y = fsolve(F,x)\n",
      "\n",
      "print \"amount of A transformed = %f percent\"%(y*100/3)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "amount of A transformed = 62.500000 percent\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 7.11 page number 316\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "from scipy.optimize import fsolve \n",
      "import math \n",
      "Cp = 0.02;\n",
      "Cq = 0.02;\n",
      "K = 4*10**-2;\n",
      "Cb = 0.05;\n",
      "Cb_i = Cb+Cp;\n",
      "a = (Cp*Cq)/(K*Cb);\n",
      "\n",
      "def F(x):\n",
      "    return x-0.02-a;\n",
      "\n",
      "x = 10.;\n",
      "y = fsolve(F,x)\n",
      "\n",
      "print \"conc of A= %f mol/l\"%(y)\n",
      "print \"conc of B= %f mol/l\"%(Cb_i)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "conc of A= 0.220000 mol/l\n",
        "conc of B= 0.070000 mol/l\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 7.12 page number 316\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "Ce_N2 = 3.;   #equilibrium conc of N2\n",
      "Ce_H2 = 9.;   #equilibrium conc of H2\n",
      "Ce_NH3 = 4.;  #equilibrium conc oh NH3\n",
      "\n",
      "C_N2 = Ce_N2 + 0.5*Ce_NH3;\n",
      "C_H2 = Ce_H2 + 1.5*Ce_NH3;\n",
      "\n",
      "print \"concentration of N2 = %f mol/l \\nconcentration of H2 = %f mol/l\"%(C_N2,C_H2)\n",
      "\n",
      "n_H2 = 3.;   #stotiometric coefficient\n",
      "n_N2 = 1.;   #stotiometric coefficient\n",
      "n_NH3= 2.;   #stotiometric coefficient\n",
      "delta_n = n_H2+n_N2-n_NH3;\n",
      "if delta_n > 0:\n",
      "    print  \"delta_n =%f  since delta_n is greater than 0,equilibrium will shift to right with increase in volume\"%(delta_n)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "concentration of N2 = 5.000000 mol/l \n",
        "concentration of H2 = 15.000000 mol/l\n",
        "delta_n =2.000000  since delta_n is greater than 0,equilibrium will shift to right with increase in volume\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 7.13 page number 317\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "from scipy.optimize import fsolve \n",
      "import math \n",
      "moles_A = 0.02;\n",
      "K = 1.;\n",
      "\n",
      "moles_B = 0.02;\n",
      "def F(x):\n",
      "    return  moles_A*moles_B-(moles_A+moles_B)*x;\n",
      "\n",
      "x = 10.;\n",
      "y = fsolve(F,x)\n",
      "print \"amount of A transformed = %f percent\"%(y*100/0.02)\n",
      "\n",
      "moles_B = 0.1;\n",
      "y = fsolve(F,x)\n",
      "print \"amount of A transformed = %f percent\"%(y*100/0.02)\n",
      "\n",
      "moles_B = 0.2;\n",
      "y = fsolve(F,x)\n",
      "print \"amount of A transformed = %.0f percent\"%(y*100/0.02)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "amount of A transformed = 50.000000 percent\n",
        "amount of A transformed = 83.333333 percent\n",
        "amount of A transformed = 91 percent\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 7.15 page no : 319\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "%matplotlib inline\n",
      "t = array([0,5,10,15,20,25])\n",
      "C_A = array([25,18.2,13.2,9.6,7,5.1])\n",
      "\n",
      "s = 0;\n",
      "k = zeros(6)\n",
      "\n",
      "for i in range(1,6):\n",
      "    k[i] = (1./t[i])*math.log(25./C_A[i])\n",
      "    #print  (k[i],\"k values for various conc.\")\n",
      "    s = s+k[i]\n",
      "\n",
      "print \"average value of k = %f\"%(s/5)\n",
      "print  (\"ra =- 0.06367*CA\",\"since its a first order reaction,\")\n",
      "\n",
      "subplot(221) \n",
      "plot(t,C_A)\n",
      "\n",
      "xlabel(\"time\")\n",
      "ylabel(\"concentration\")\n",
      "suptitle(\"integral method\")\n",
      "\n",
      "ra = array([1.16,0.83,0.60,0.43])\n",
      "C_A = array([18.2,13.2,9.6,7])\n",
      "\n",
      "subplot(222) \n",
      "plot(C_A,ra)\n",
      "plot(C_A,ra,'ro')\n",
      "xlabel(\"Concentration\")\n",
      "ylabel(\"-ra\")\n",
      "suptitle(\"differential method\")\n",
      "xlim(1,20)\n",
      "ylim(.1,2)\n",
      "print \"rate from differential method = -0.064*CA\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "average value of k = 0.063680\n",
        "('ra =- 0.06367*CA', 'since its a first order reaction,')\n",
        "rate from differential method = -0.064*CA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['draw_if_interactive', 'new_figure_manager']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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rVjQvUgNTvSDNggVAerrU0TDGmP6onEBuz5499bZ98803OglGSt7e4hKVs2YB773Hi9Yz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\n6kMaAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2b3a490>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 7.16 page no : 322\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "E = 75200.    #in J/mol\n",
      "E1 = 50100.   #in J/mol\n",
      "R = 8.314    #in J/mol K\n",
      "T = 298.   #in K\n",
      "\n",
      "ratio = math.exp((E1-E)/(R*T));\n",
      "rate_increase = ratio**-1\n",
      "\n",
      "print  \"increase in rate of reaction =\",rate_increase,\"times\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "increase in rate of reaction = 25106.6042072 times\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}