diff options
Diffstat (limited to 'Chemical_Reaction_Engineering_by_O._Levenspiel/ch21.ipynb')
-rwxr-xr-x | Chemical_Reaction_Engineering_by_O._Levenspiel/ch21.ipynb | 230 |
1 files changed, 230 insertions, 0 deletions
diff --git a/Chemical_Reaction_Engineering_by_O._Levenspiel/ch21.ipynb b/Chemical_Reaction_Engineering_by_O._Levenspiel/ch21.ipynb new file mode 100755 index 00000000..2ca9ab81 --- /dev/null +++ b/Chemical_Reaction_Engineering_by_O._Levenspiel/ch21.ipynb @@ -0,0 +1,230 @@ +{ + "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": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "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": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEfCAYAAAC5/EqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": {} + } + ] +}
\ No newline at end of file |