path: root/Chemical_Reaction_Engineering_by_O._Levenspiel/ch5.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 5 : Ideal Reactors for a Single Reaction"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 5.1 page no : 96"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "#Concentrations in mol/litre\n",
      "CAo = 0.1                 # liquid\n",
      "CBo = 0.01                # liquid\n",
      "Cco = 0.                  # liquid\n",
      "CAf = 0.02                # outlet stream\n",
      "CBf = 0.03                # outlet stream\n",
      "Ccf = 0.04;               # outlet stream\n",
      "#Volume in litre\n",
      "V = 1.;\n",
      "#Volumetric flow rate(l/min)\n",
      "v = 1.;\n",
      "CA = CAf;CB = CBf;Cc = Ccf;\n",
      "\n",
      "# Calculations\n",
      "#Rate of reaction(mol/litre.min)\n",
      "rA = (CAo-CA)/(V/v);\n",
      "rB = (CBo-CB)/(V/v);\n",
      "rc = (Cco-Cc)/(V/v);\n",
      "\n",
      "# Results\n",
      "print \"rate of reaction of A is %.2f mol/litre.min\"%(rA)\n",
      "print \"rate of reaction of B is %.2f mol/litre.min\"%(rB)\n",
      "print \"rate of reaction of C is %.2f mol/litre.min\"%(rc)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "rate of reaction of A is 0.08 mol/litre.min\n",
        "rate of reaction of B is -0.02 mol/litre.min\n",
        "rate of reaction of C is -0.04 mol/litre.min\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 5.2 page no : 97"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%matplotlib inline\n",
      "\n",
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "from scipy import stats\n",
      "\n",
      "# Variables\n",
      "vo = array([10,3,1.2,0.5])         #Volumetric flow rates(litre/hr)\n",
      "CA = array([85.7,66.7,50,33.4])    #Concentrations (millimol/litre)\n",
      "CAo = 100.;\n",
      "V = 0.1;                           #Volume(litre)\n",
      "e = (1.-2.)/2;                     #Expansion factor is\n",
      "#Initialization\n",
      "XA = zeros(4);\n",
      "rA = zeros(4);\n",
      "m = zeros(4);\n",
      "n = zeros(4);\n",
      "\n",
      "# Calculations\n",
      "#Relation between concentration and conversion\n",
      "for i in range(4):\n",
      "    XA[i] = (1-CA[i]/CAo)/(1+e*CA[i]/CAo);\n",
      "    rA[i] = vo[i]*CAo*XA[i]/V;\n",
      "    m[i] = math.log10(CA[i]);\n",
      "    n[i] = math.log10(rA[i]);\n",
      "\n",
      "# Results\n",
      "#For nth order plot between n & m should give a straight line\n",
      "plot(m,n)\n",
      "xlabel(\"log CA\")\n",
      "ylabel(\"log (-rA)\")\n",
      "show()\n",
      "coefs = stats.linregress(m,n);\n",
      "print coefs\n",
      "print \"Intercept of the graph is %.2f\"%(coefs[1])\n",
      "print \"Slope of the graph is %.2f\"%(coefs[0])\n",
      "k = 10**coefs[1]\n",
      "n = coefs[0]\n",
      "print \" Taking n = 2, rate of equation is %.2f millimol/litre.hr\"%(k),\n",
      "print \"CA**2 \"\n",
      "print ('The sol slightly differ from that given in book because regress fn is used to calculate the slope')\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: ['rc', 'draw_if_interactive', 'e']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEMCAYAAAAxoErWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x29b4650>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1.9569709616766851, -0.39508420521305565, 0.99852626739620765, 0.001473732603792355, 0.075209753597987913)\n",
        "Intercept of the graph is -0.40\n",
        "Slope of the graph is 1.96\n",
        " Taking n = 2, rate of equation is 0.40 millimol/litre.hr CA**2 \n",
        "The sol slightly differ from that given in book because regress fn is used to calculate the slope\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 5.3 page no : 99"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "'''\n",
      "Note : The sol varies from book as the value of CB taken in book at end is wrong\n",
      "'''\n",
      "\n",
      "import math \n",
      "\n",
      "# Variables\n",
      "CAo = 1.4\n",
      "CBo = 0.8\n",
      "CRo = 0.\n",
      "#Volume(litre)\n",
      "V = 6.\n",
      "\n",
      "# Calculations\n",
      "#For 75% conversion of B\n",
      "#From stoichiometry of equation A+2B-->R\n",
      "CA = 1.4-(0.75*0.8)/2.;\n",
      "CB = 0.8-(0.75*0.8);\n",
      "CR = (0.75*0.8)/2.;\n",
      "#From the Given rate equation(mol/litre.min)\n",
      "rB = 2*(12.5*CA*CB*CB-1.5*CR);\n",
      "#Volumetric flow rate is given by\n",
      "v = V*rB/(CBo-CB);\n",
      "\n",
      "# Results\n",
      "print \" volumetric flow rate into and out of the reactor is %.1f litre/min\"%(v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " volumetric flow rate into and out of the reactor is 2.0 litre/min\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 5.4 page no : 104"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from scipy.integrate import quad \n",
      "\n",
      "# Variables\n",
      "eA = (4-2.)/2;\n",
      "CAo = 0.0625;         # mol/liter\n",
      "xAo = 0.\n",
      "xAf = 0.8              # conversion\n",
      "k = 0.01;\n",
      "\n",
      "# Calculations\n",
      "def f1(xA): \n",
      "\t return math.sqrt((1+xA)/(1-xA))\n",
      "\n",
      "X =  quad(f1,xAo,xAf)[0]\n",
      "t = math.sqrt(CAo)*X/k;\n",
      "\n",
      "# Results\n",
      "print \" Space timesec needed is %.2f sec\"%(t)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Space timesec needed is 33.18 sec\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 5.5 page no : 106"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "# Variables\n",
      "T = 922.         #Temperature(kelvin)\n",
      "P = 460000.;     # kPA\n",
      "FAo = 40.        # pure phosphine\n",
      "k = 10.\n",
      "R = 8.314;\n",
      "\n",
      "# Calculations\n",
      "CAo = P/(R*T);          # mol/m3\n",
      "e = (7-4)/4.;\n",
      "XA = 0.8;\n",
      "\n",
      "#The volume of plug flow reactor is given by\n",
      "V = FAo*((1+e)*math.log(1./(1-XA))-e*XA)/(k*CAo);\n",
      "\n",
      "# Results\n",
      "print \" volume of reactor is %.3f m**3\"%(V)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " volume of reactor is 0.148 m**3\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}