diff options
Diffstat (limited to 'Stoichiometry_And_Process_Calculations/ch6.ipynb')
-rwxr-xr-x | Stoichiometry_And_Process_Calculations/ch6.ipynb | 354 |
1 files changed, 354 insertions, 0 deletions
diff --git a/Stoichiometry_And_Process_Calculations/ch6.ipynb b/Stoichiometry_And_Process_Calculations/ch6.ipynb new file mode 100755 index 00000000..8a560517 --- /dev/null +++ b/Stoichiometry_And_Process_Calculations/ch6.ipynb @@ -0,0 +1,354 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 6 : Vapour Pressure" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Example 6.1 pageno : 133" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\n", + "# variables \n", + "P = 500. #kPa liquid\n", + "SV = 0.2813 #m**3/kg wet steam\n", + "\n", + "# Calculation \n", + "Vsaturatedl = 1.093 * 10**-3 #m**3/kg\n", + "Vsaturatedv = 0.3747 #m**3/kg\n", + "\n", + "# let the fraction of vapour be y\n", + "#(1-y)*Vsaturatedl + y*Vsaturatedv = SV\n", + "#then we get, (1-y)*(1.093*10**-3) + y*(0.3747) = 0.2813\n", + "y = (SV - Vsaturatedl)/(Vsaturatedv - Vsaturatedl) \n", + "P1 = y * 100 \n", + "P2 = 100 - P1 \n", + "\n", + "# Result\n", + "print \"Percentage of Vapour = %.f %%\"%P1\n", + "print \"Percentage of Liquid = %.f %%\"%P2\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Percentage of Vapour = 75 %\n", + "Percentage of Liquid = 25 %\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Example 6.2 pageno : 134" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import math \n", + "\n", + "# Variables \n", + "T1 = 363. #K water\n", + "T2 = 373. #K water\n", + "P2s = 101.3 #kPa pressure\n", + "\n", + "# Calculation \n", + "J = 2275. * 18 #kJ/kmol\n", + "R = 8.314 #kJ/kmolK\n", + "#ln (P2s/P1s) = J * (1/T1 - 1/T2) / R\n", + "P1s = P2s/math.exp(J * (1/T1 - 1/T2) / R) \n", + "\n", + "# Result\n", + "print \"Vapour pressure of water at 363 K = %.2f kPa\"%P1s\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Vapour pressure of water at 363 K = 70.41 kPa\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Example 6.3 pageno : 135" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import math \n", + "\n", + "# Variables \n", + "P1s = 194.9 #kPa acetone\n", + "P2s = 8.52 #kPa pressure\n", + "T1 = 353. #K acetone\n", + "T2 = 273. #K acetone\n", + "T3 = 300. #K dry air\n", + "Pair = 101.3 #kPa dry air\n", + "#math.log (P2s/P1s) = J * (1/T1 - 1/T2) / R\n", + "#let J / R = L\n", + "\n", + "# Calculation \n", + "L = math.log (P2s/P1s)/(1./T1 - 1/T2) \n", + "P3s = P1s * math.exp(L * (1./T1 - 1/T3)) \n", + "Ptotal = P3s + Pair #at saturation vapour pressure = partial pressure\n", + "\n", + "# Result \n", + "print \"(a)Final pressure of the mixture = %.2f kPa\"%Ptotal\n", + "MP = P3s * 100 / Ptotal \n", + "# mole percent = moles of acetone * 100 / total moles\n", + "#= Partial pressure of acetone * 100 / total Pressure\n", + "print \"(b)Mole percent of acetone in the final mixture = %.1f %%\"%MP\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a)Final pressure of the mixture = 130.83 kPa\n", + "(b)Mole percent of acetone in the final mixture = 22.6 %\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Example 6.4 pageno : 137" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import math \n", + "\n", + "\n", + "# Variables \n", + "A = 13.8587 # antone constants for n-heptane\n", + "B = 2911.32 \n", + "C = 56.56 \n", + "T1 = 325. #K\n", + "#Pressure at normal condition = 101.3kPa\n", + "P2 = 101.3 #kPa\n", + "\n", + "# Calculation \n", + "#Antoine equation - lnP = A - B / (T - C)\n", + "lnP = A - (B / (T1 - C)) \n", + "P1 = math.exp(lnP) \n", + "\n", + "# Result \n", + "print \"(a)Vapour pressure of n-heptane at 325K = %.2f kPa\"%P1\n", + "T2 = B/(A - math.log(P2)) + C \n", + "print \"(b)Normal boiling point of n-heptane = %.2f K\"%T2\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a)Vapour pressure of n-heptane at 325K = 20.36 kPa\n", + "(b)Normal boiling point of n-heptane = 371.62 K\n" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Example 6.5 pageno : 140" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%pylab inline\n", + "\n", + "from matplotlib.pyplot import *\n", + "\n", + "# variables \n", + "T = [273, 293, 313, 323, 333, 353, 373];\n", + "Ps = [0.61, 2.33, 7.37, 12.34, 19.90, 47.35, 101.3];\n", + "\n", + "# Calculation and Result\n", + "plot(T,Ps);\n", + "T1 = [273, 353]\n", + "Ps1 = [8.52, 194.9]\n", + "plot(T,Ps);\n", + "suptitle(\"Construction of the cox chart\")\n", + "xlabel(\"Temperature, K\")\n", + "ylabel(\"Pressure, kPa\")\n", + "show()\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" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEhCAYAAABhpec9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBGRVv8PkUOu8xTNWfQAAAAASUVORK5CYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x27db050>" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Example 6.6 page no : 143" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%pylab inline\n", + "\n", + "import math \n", + "from matplotlib.pyplot import *\n", + "\n", + "# Variables \n", + "Pswater1 = 6.08; #kPa \n", + "T1 = 313.; #K\n", + "\n", + "\n", + "# Calculation \n", + "#lnPs = 16.26205 - 3799.887/(T - 46.854)\n", + "Tb1 =3799.887/(16.26205 - math.log(Pswater1)) + 46.854;\n", + "print \"boiling point of water at 6.08kPa vapour pressure = %.2f K\"%Tb1\n", + "Pswater2 = 39.33; #kPa\n", + "T2 = 353.; #K\n", + "Tb2 =3799.887/(16.26205 - math.log(Pswater2)) + 46.854;\n", + "\n", + "# Result \n", + "print \"boiling point of water at 39.33 kPa vapour pressure = %.2f K\"%Tb2\n", + "Tb = [Tb1 ,Tb2];\n", + "T = [T1 ,T2];\n", + "plot(T,Tb);\n", + "suptitle(\"Equal pressure reference plot for sulphuric acid\")\n", + "xlabel(\"Boiling point of solution, K\")\n", + "ylabel(\"Boiling point of water, K\")\n", + "show()\n", + "T3 = 333.; #K\n", + "\n", + "#corresponding to T3 on x axis, on y we get\n", + "Tb3 = 329.; #K\n", + "Pswater3 = math.exp(16.26205 - 3799.887/(Tb3 - 46.854));\n", + "print \"Vapour pressure of solution at 333K %.2f kPa\"%Pswater3\n", + "\n", + "# deviation\n", + "deviation = .04/16.53 * 100\n", + "print \"%% deviation = %.2f %%\"%deviation" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "boiling point of water at 6.08kPa vapour pressure = 309.69 K\n", + "boiling point of water at 39.33 kPa vapour pressure = 348.67 K\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": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEfCAYAAABf1YHgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIUSx/h89BNR1L4MHOAAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x46afd50>" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Vapour pressure of solution at 333K 16.35 kPa\n", + "% deviation = 0.24 %\n" + ] + } + ], + "prompt_number": 3 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file |