path: root/Engineering_&_Chemical_Thermodynamics_by_M._D._Koretsky/ch6.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 6 : Multi component Phase Equillibrium"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.3  Page No : 330"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "\n",
      "# Variables\n",
      "slop = -4222.1 ;\n",
      "R = 8.314 ;\n",
      "\n",
      "# Calculations\n",
      "del_h_vap = -R * slop * 10**-3 ;\n",
      "%matplotlib inline\n",
      "\n",
      "def f(x):\n",
      "    return -4222.1 * x + 17.556\n",
      "\n",
      "xdata = linspace(0.0032,0.004,8) ;\n",
      "ydata = f(xdata) ;\n",
      "plot(xdata,ydata) ;\n",
      "xlabel(\"1/T[k**-1]\")\n",
      "ylabel(\"Ln(Psat[kPa])\")\n",
      "suptitle(\"least-squares linear fit\")\n",
      "show()\n",
      "\n",
      "# Results\n",
      "print \"Enthalpy of vapourisation of GaCH3)3 = %.1f kJ/mol\"%(del_h_vap) ;\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', 'new_figure_manager']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEhCAYAAACHjCx5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3453bd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Enthalpy of vapourisation of GaCH3)3 = 35.1 kJ/mol\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.5 pageno : 332\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# variables\n",
      "MWni = 58.69           #g/mol\n",
      "Psni = 8900            # kg/m**3 \n",
      "Sni = 1.75             # J/m**2  surface tension of solid Ni\n",
      "deltaHfus = -17.48      # kJ/mol\n",
      "Tm = 1728               #K\n",
      "R = 2*10**-9\n",
      "\n",
      "\n",
      "# calculations\n",
      "Uni = MWni/(Psni*1000)\n",
      "T = Tm*(1+((2*Uni*Sni)/(deltaHfus*R)))\n",
      "\n",
      "# results\n",
      "print \"molar density of the solid  %.1e m**3/mol\" %Uni\n",
      "print \"T = %.f K\"%T\n",
      "# incorrect answer in textbook"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "molar density of the solid  6.6e-06 m**3/mol\n",
        "T = -1139085 K\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.8  Page No : 347"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "from numpy import zeros\n",
      "from matplotlib.pylab import *\n",
      "\n",
      "# Variables\n",
      "MW1 = 119.5 ;\n",
      "MW2 = 58 ;\n",
      "A = [0,4.77,9.83,14.31,19.38,23.27,25.53,25.07,21.55,13.56,0] ;\n",
      "B = [0,.1 , .2 , .3 ,.4 ,.5 ,.6 ,.7 ,.8 ,.9,1] ;\n",
      "\n",
      "C = zeros(11)\n",
      "D = zeros(11)\n",
      "# Calculations\n",
      "print \"    For weight percent  %.3f    del_h_mix = %.1f J/mol\"%(C[0],D[0])\n",
      "for i in range(1,11):\n",
      "    x1 = (B[i] / MW1) / (B[i]/MW1 + (1 - B[i]) / MW2) ;\n",
      "    x2 = 1 - x1 ;\n",
      "    MW = x1 * MW1 + x2 * MW2 ;\n",
      "    del_h_mix = - 1*(A[i]) * MW ;\n",
      "    C[i] = del_h_mix ;\n",
      "    D[i] = x1 ;\n",
      "    print \"    For weight percent  %.3f    del_h_mix = %.1f J/mol\"%(x1,del_h_mix)\n",
      "\n",
      "xdata = D ;\n",
      "ydata = C ;\n",
      "plot(xdata ,ydata) ;\n",
      "xlabel(\"xCHCI3\")\n",
      "ylabel(\"delta Hmix[J/mol]\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "    For weight percent  0.000    del_h_mix = 0.0 J/mol\n",
        "    For weight percent  0.051    del_h_mix = -291.7 J/mol\n",
        "    For weight percent  0.108    del_h_mix = -635.6 J/mol\n",
        "    For weight percent  0.172    del_h_mix = -981.5 J/mol\n",
        "    For weight percent  0.244    del_h_mix = -1415.4 J/mol\n",
        "    For weight percent  0.327    del_h_mix = -1817.3 J/mol\n",
        "    For weight percent  0.421    del_h_mix = -2142.2 J/mol\n",
        "    For weight percent  0.531    del_h_mix = -2272.9 J/mol\n",
        "    For weight percent  0.660    del_h_mix = -2124.7 J/mol\n",
        "    For weight percent  0.814    del_h_mix = -1465.1 J/mol\n",
        "    For weight percent  1.000    del_h_mix = 0.0 J/mol\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['f']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.text.Text at 0x361ce50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEPCAYAAACOU4kjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEIIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3453b90>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.9, Page 349"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sympy.solvers import solve\n",
      "from sympy import Symbol\n",
      "\n",
      "#Variables\n",
      "cp1 = 27.5    #J/mol-K\n",
      "cp2 = 25      #J/mol-K\n",
      "cp3 = 20      #J/mol-K\n",
      "T1 = 4+273    #K\n",
      "\n",
      "#Calculations\n",
      "#Since only species 2 and 3 are involved,\n",
      "deltaH1_unmix = -160  #J\n",
      "deltaH11_mix = 1100    #J\n",
      "\n",
      "deltaH_mix = deltaH1_unmix+deltaH11_mix    #J\n",
      "T2 = Symbol('T2')\n",
      "Tf = solve(deltaH_mix+235*(T2-T1))\n",
      "\n",
      "#Result\n",
      "print \"The final temperature is\",Tf,\"K\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The final temperature is [273] K\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.10  Page No : 353"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Variables\n",
      "A = [-32669,-31840,-28727,-26978,-24301,-20083,-13113] ;\n",
      "B = [20 ,10 ,5 ,4 ,3 ,2 ,1] ;\n",
      "\n",
      "# Calculations and Results\n",
      "C = zeros(7)\n",
      "D = zeros(7)\n",
      "\n",
      "for i in range(7):\n",
      "    del_h_mix = A[i] / (1. + B[i]) ;\n",
      "    C[i] = del_h_mix ;\n",
      "    D[i] = 1. / (1 + B[i]) ;\n",
      "    print \"For mole fraction %.3f   the entropy of mixing is %.0f J/mol\"%(D[i],C[i])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "For mole fraction 0.048   the entropy of mixing is -1556 J/mol\n",
        "For mole fraction 0.091   the entropy of mixing is -2895 J/mol\n",
        "For mole fraction 0.167   the entropy of mixing is -4788 J/mol\n",
        "For mole fraction 0.200   the entropy of mixing is -5396 J/mol\n",
        "For mole fraction 0.250   the entropy of mixing is -6075 J/mol\n",
        "For mole fraction 0.333   the entropy of mixing is -6694 J/mol\n",
        "For mole fraction 0.500   the entropy of mixing is -6556 J/mol\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.12  Page No : 359"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Variables\n",
      "x1 = 0.3 ;\n",
      "x2 = 1- x1 ;\n",
      "B11 = -910. ;\n",
      "B22 = -1330. ;\n",
      "B12 = -2005. ;\n",
      "T = 333. ; \t\t\t#[K]\n",
      "P = 10. * 10**5 ;\n",
      "R = 8.314 ;\n",
      "\n",
      "# Calculations and Results\n",
      "v1 = round(R * T /P * 10**6 + B11,-1) ;\t\t\t#....E6.9A\n",
      "print \"     v1 = %g cm**3/mol\"%(v1)\n",
      "\n",
      "V_bar_1 = (R * T / P) * 10**6+ (x1**2 + 2 * x1 * x2) *3.12 * B11 +  x2**2 * B12  - x2**2 * B22 ;\t\t\t#.....E6.9B\n",
      "print \"     V_bar_1 = %.f cm**3/mol\"%(V_bar_1) ;\n",
      "\n",
      "del_v_mix = x1 * x2 * (2 * B12 - B11 - B22) ;\t\t\t#.....E6.9C\n",
      "print \"     del_v = %.f cm**3/mol\"%(del_v_mix);\n",
      "\n",
      "# Note : answer are slightly different because of rounding error."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     v1 = 1860 cm**3/mol\n",
        "     V_bar_1 = 990 cm**3/mol\n",
        "     del_v = -372 cm**3/mol\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.13  Page No : 360"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import zeros\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Variables\n",
      "h_H2SO4 = 1.596 ; \t\t\t#[kJ/mol]\n",
      "h_H2O = 1.591 ; \t\t\t#[kJ/mol]\n",
      "C1 = -74.40 ;\n",
      "C2 = 0.561 ;\n",
      "A = [0 ,0.1 , 0.2 ,0.3 ,0.4 ,0.5 ,0.6 ,0.7 ,0.8 ,0.9 ,1] ;\n",
      "B = [1 ,0.9 ,0.8 ,0.7 ,0.6 ,0.5 ,0.4 ,0.3 ,0.2 ,0.1 ,0] ;\n",
      "\n",
      "# Calculations\n",
      "y_data_1 = zeros(11)\n",
      "y_data_2 = zeros(11)\n",
      "x_data = zeros(11)\n",
      "for i in range(11):\n",
      "    H_bar_H2SO4 = h_H2SO4 + C1 * B[i]**2 - 2 * C2 * C1 * A[i] * B[i]**2 ;\n",
      "    H_bar_H2O = h_H2O + C1 * A[i]**2 -C2 * C1 * A[i]**2 * (1 - 2 * B[i]) ;\n",
      "    y_data_1[i] =  H_bar_H2SO4 ;\n",
      "    y_data_2[i] =  H_bar_H2O ;\n",
      "    x_data[i] = A[i] ;\n",
      "\n",
      "plot(x_data,y_data_1) ;\n",
      "plot(x_data,y_data_2) ;\n",
      "xlabel(\"xH2SO4\")\n",
      "ylabel(\"Partial molar enthalpy (J/mol)\")\n",
      "#suptitle(\"Partial molar enthalpies of water and sulfuric acid at 21\u00b0C.\")\n",
      "m = y_data_1[5]  ;\n",
      "s = y_data_2[5]  ;\n",
      "print \"For equimolar mixture del_H_H2SO4 = %.1f kJ/mol   del_H_H2O = %.1f kJ/mol\"%(m,s);\n",
      "show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "For equimolar mixture del_H_H2SO4 = -6.6 kJ/mol   del_H_H2O = -17.0 kJ/mol\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEPCAYAAABcA4N7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3613750>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.16  Page No : 365"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Variables\n",
      "C1 = 1.596 ;\n",
      "C2 = 1.591 ;\n",
      "C3 = -74.40 ;\n",
      "C4 = -0.561 ;\n",
      "A = [ 0 ,0.1 ,0.2 ,0.3 ,0.4 ,0.5 ,0.6 ,0.7 ,0.8 ,0.9 ,1] ;\n",
      "m = (-C1 + C2 + C3 * ( C4 * 0.25)) * 1000 ;\n",
      "\n",
      "# Calculations\n",
      "C = zeros(11)\n",
      "for i in range(11):\n",
      "    x_H2O = A[i] ;\n",
      "    x_H2SO4 = 1- x_H2O ;\n",
      "    h = C1 * x_H2SO4 + C2 * x_H2O + C3 * x_H2SO4 * x_H2O *(1 + C4 * x_H2SO4) ;\n",
      "    C[i] = h * 10**3;\n",
      "\n",
      "y1 = C[5]\n",
      "\n",
      "def f613(x):\n",
      "    return -m * (x - 0.5 ) + y1 ;\n",
      "\n",
      "F = zeros(11)\n",
      "for i in range(11):\n",
      "    F[i] = f613(A[i]) ;\n",
      "\n",
      "# Results\n",
      "plot(A,C);\n",
      "plot(A,F)\n",
      "plot(0.5,-12000,\"go\")\n",
      "xlabel(\"XH2O\")\n",
      "ylabel(\"h(J/mol)\")\n",
      "suptitle(\"Graphical determination of values for the partial molar enthalpies of sulfuric acid and water\")\n",
      "\n",
      "print \"The partial molar property can be obtained by drawing tangent at mole fraction 0.5 .\"\n",
      "\n",
      "show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "The partial molar property can be obtained by drawing tangent at mole fraction 0.5 .\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": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAEhCAYAAADYsWF1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQUtERKQgBi0REZGC/gEyGiloVAhGGQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f36783f0250>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}