{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 9 : Temperature and Pressure Effects"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.1 page no : 209"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "# Variables\n",
      "CpA = 35.            # j/mol.K\n",
      "CpB = 45.             # j/mol.K\n",
      "CpR = 70.            # j/mol.K\n",
      "T1 = 25.             # C\n",
      "T2 = 1025.           # C\n",
      "Hr = -50000.\n",
      "\n",
      "# Calculations\n",
      "#Enthalpy balance for 1mol A,1 mol B,2 mol R\n",
      "nA = 1.\n",
      "nB = 1.\n",
      "nR = 2.\n",
      "dH = nA*CpA*(T1-T2)+nB*CpB*(T1-T2)+(Hr)+nR*CpR*(T2-T1);\n",
      "\n",
      "# Results\n",
      "print \" dHJ at temperature 1025C is %.f \"%(dH)\n",
      "if dH>0 :\n",
      "    print \"Reaction is Exothermic\"\n",
      "else:\n",
      "     print \"Reaction is endothermic at 1025OC\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " dHJ at temperature 1025C is 10000 \n",
        "Reaction is Exothermic\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.2 page no : 213"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%matplotlib inline\n",
      "\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "from numpy import *\n",
      "\n",
      "Ho = -75300.                       # J/mol\n",
      "Go = -14130.                       # J/mol\n",
      "R = 8.3214\n",
      "T1 = 298.\n",
      "\n",
      "# Calculations\n",
      "#With all specific heais alike,dCp = 0\n",
      "Hr = -Ho;\n",
      "K298 = math.exp(-Go/(R*T1));\n",
      "\n",
      "\n",
      "#Taking different values of T\n",
      "T1 = array([2,15,25,35,45,55,65,75,85,95])                  #degree celcius\n",
      "T = array([278,288,298,308,318,328,338,348,358,368])        #kelvin\n",
      "\n",
      "XAe = zeros(10)\n",
      "\n",
      "for i in range(10):\n",
      "    K = K298*math.exp((Hr/R)*((1./T[i])-(1./298)));\n",
      "    XAe[i] = K/(K+1);\n",
      "\n",
      "# Results\n",
      "plot(T1,XAe)\n",
      "xlabel(\"Temperature, C\")\n",
      "ylabel(\"XAe\")\n",
      "print (\" From the graph we see temp must stay below 78 C if conversion of 75% or above is expected\")\n",
      "show()\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        " From the graph we see temp must stay below 78 C if conversion of 75% or above is expected"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEMCAYAAAArnKpYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x31ac650>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.3 page no : 217"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "# Variables \n",
      "\n",
      "# from example 9.2\n",
      "XA = 0.581; \n",
      "t = 1.                    #min\n",
      "XAe = 0.89;\n",
      "XAe1 = 0.993;\n",
      "T1 = 338.\n",
      "T2 = 298.\n",
      "R = 8.314\n",
      "\n",
      "\n",
      "# Calculations\n",
      "k1_338 = -(XAe/t)*math.log(1-(XA/XAe));\n",
      "XA1 = 0.6;\n",
      "t1 = 10.                #min\n",
      "\n",
      "k1_298 = -(XAe1/t1)*math.log(1-(XA1/XAe1));\n",
      "E1 = (R*math.log(k1_338/k1_298))*(T1*T2)/(T1-T2)\n",
      "ko = k1_338/(math.exp(-E1/(R*T1)))\n",
      "\n",
      "# Results\n",
      "print \" The rate constants are k = exp[75300/RT-24.7] min-1\"\n",
      "print \" k1 = exp[17.2-48900/RT] min-1\"\n",
      "print \" k2 = exp[41.9-123800/RT] min-1 \"\n",
      "print \" E1 = %.2f\"%E1\n",
      "print \" K0 = %.2f\"%ko"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The rate constants are k = exp[75300/RT-24.7] min-1\n",
        " k1 = exp[17.2-48900/RT] min-1\n",
        " k2 = exp[41.9-123800/RT] min-1 \n",
        " E1 = 48679.81\n",
        " K0 = 31411847.30\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.4 page no : 229"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "CAo = 4.                    #mol/litre\n",
      "FAo = 1000.                 #mol/min\n",
      "A = 0.405                   #litre/mol.min\n",
      "\n",
      "# Calculations\n",
      "t = CAo*A;\n",
      "V = FAo*A;\n",
      "\n",
      "# Results\n",
      "print \" Part a\"\n",
      "print \" The space time needed is %.2f min\"%(t)\n",
      "print \" The Volume needed is %.f litres\"%(V)\n",
      "\n",
      "\n",
      "# Note : We do not have value of 'rA'. so part B can not be calculated and plotted"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Part a\n",
        " The space time needed is 1.62 min\n",
        " The Volume needed is 405 litres\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.5 pageno : 231"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "CAo = 4.                # mol/liter\n",
      "FAo = 1000.             # mol/min\n",
      "XA = 0.8;               # %\n",
      "Cp = 250.               #cal/molA.K\n",
      "Hr = 18000.             #cal/molA\n",
      "rA = 0.4;\n",
      "\n",
      "# Calculations and Results\n",
      "V = FAo*XA/rA;\n",
      "\n",
      "print \" Part a\"\n",
      "print \" The size of reactorlitres) needed is %.f litres\"%(V)\n",
      "\n",
      "slope = Cp/Hr;\n",
      "#Using graph\n",
      "Qab1 = Cp*20;               #cal/molA\n",
      "Qab = Qab1*1000;            #cal/min\n",
      "Qab = Qab*0.000070;         #KW\n",
      "\n",
      "print \" Part b\"\n",
      "print \" Heat Duty of precooler is %.2f kW\"%(Qab)\n",
      "\n",
      "Qce1 = Cp*37;               #cal/molA fed\n",
      "Qce = Qce1*1000;            #cal/min\n",
      "Qce = Qce*0.000070;         #KW\n",
      "\n",
      "print \" Heat Duty of postcooler is %.2f kW\"%(Qce)\n",
      "\n",
      "# answers may vary because of rounding error."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Part a\n",
        " The size of reactorlitres) needed is 2000 litres\n",
        " Part b\n",
        " Heat Duty of precooler is 350.00 kW\n",
        " Heat Duty of postcooler is 647.50 kW\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.6 pageno : 233"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "FAo = 1000.         #mol/min\n",
      "Area = 1.72;\n",
      "\n",
      "# Calculations\n",
      "V = FAo*Area;\n",
      "\n",
      "# Results\n",
      "print \" The volume of adiabatic plug flow reactor is %.f litres\"%(V),\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The volume of adiabatic plug flow reactor is 1720 litres\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.7 page no : 234"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "FAo = 1000.             #mol/min\n",
      "Area = (0.8-0)*1.5;\n",
      "\n",
      "# Calculations\n",
      "V = FAo*Area;\n",
      "\n",
      "# Results\n",
      "print \" The volume required is %.f litres\"%(V),\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The volume required is 1200 litres\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}