path: root/Physical_And_Chemical_Equilibrium_For_Chemical_Engineers/ch11.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 11 : LIquid Liquid Liquid Solid And Gas Solid Equilibrium"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.1   Page: 272"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "V_water = 1.         #[L] volume of the water\n",
      "Temp = 25.           #[C]\n",
      "d_benzene = 0.88     #[g/ml] density of the benzene \n",
      "M_benzene = 78.      #[g/mol] molecular weight of the benzene\n",
      "M_water = 18.        #[g/mol]\n",
      "\n",
      "n_1drop = 1./20*(d_benzene/M_benzene)       #[mol/drop] \n",
      "\n",
      "n_water = 1000./M_water     #[mol]\n",
      "s_benzene = 0.000405\n",
      "n_benzene_saturate = s_benzene*n_water          #[mol]\n",
      "N_benzene = n_benzene_saturate/n_1drop          #[drops]\n",
      "\n",
      "print \"The total number of the drops of the benznene required to saturate the water is %0.0f drops\"%(N_benzene)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total number of the drops of the benznene required to saturate the water is 40 drops\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.2  Page: 273\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "m_benzene = 1000.           #[lbm]\n",
      "M_benzene = 78.             #[lbm/lbmol]\n",
      "n_benzene = m_benzene/M_benzene        #[lbmol]\n",
      "x_benzene = 0.000405\n",
      "\n",
      "n_water = n_benzene/x_benzene       #[lbmol]\n",
      "m_water = n_water*18                #[lbm] \n",
      "\n",
      "print \"The amount of the ground water that will make a saturated solution will be %e lbm\"%(m_water)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The amount of the ground water that will make a saturated solution will be 5.698006e+05 lbm\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.3  Page: 277\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "Temp = 25.          #[C]\n",
      "n_water = 3.75      #[mol]\n",
      "n_ethanol = 2.5     #[mol]\n",
      "n_benzene = 3.75    #[mol]\n",
      "\n",
      "x_water = 0.375\n",
      "x_ethanol = 0.250\n",
      "x_benzene = 0.375\n",
      "\n",
      "x_water_w = 64.9        #[%]\n",
      "x_ethanol_w = 31.75     #[%]\n",
      "x_benzene_w = 3.37      #[%]\n",
      "\n",
      "x_water_b = 6.43        #[%]\n",
      "x_ethanol_b = 18.94     #[%]\n",
      "x_benzene_b = 74.62     #[%]\n",
      "\n",
      "print \"The composition of the two equilibrium phases i.e. water-reach phase and benzene reach phase is as \"\n",
      "print \"\\t\\t\\t\\tWater-reach phase\\t\\tbenzene-reach phase\"\n",
      "print \"Mol water\\t\\t\\t%f\\t\\t\\t\\t%f\"%(x_water_w,x_water_b)\n",
      "print \"Mol ethanol\\t\\t\\t%f\\t\\t\\t\\t%f\"%(x_ethanol_w,x_ethanol_b)\n",
      "print \"Mol benzene\\t\\t\\t%f\\t\\t\\t\\t%f\"%(x_benzene_w,x_benzene_b)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The composition of the two equilibrium phases i.e. water-reach phase and benzene reach phase is as \n",
        "\t\t\t\tWater-reach phase\t\tbenzene-reach phase\n",
        "Mol water\t\t\t64.900000\t\t\t\t6.430000\n",
        "Mol ethanol\t\t\t31.750000\t\t\t\t18.940000\n",
        "Mol benzene\t\t\t3.370000\t\t\t\t74.620000\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.4  Page: 282\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "Temp = 25.              #[C]\n",
      "x_1in2 = 405.           #[ppm]\n",
      "x_2in1 = 3000.          #[ppm]\n",
      "\n",
      "x_water_w = (10**(6)-405.)/(10**(6))\n",
      "x_benzene_w = 1-x_water_w\n",
      "\n",
      "x_benzene_b =(10.**(6.)-3000.)/(10**(6))\n",
      "x_water_b = 1-x_benzene_b\n",
      "\n",
      "\n",
      "y_benzene = 1/(x_benzene_w)\n",
      "y_water = 1/(x_water_b)\n",
      "\n",
      "print \" Activity coefficient of benzene in water is %f\"%(y_benzene)\n",
      "print \" Activity coefficient of water in benzene is %f\"%(y_water)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Activity coefficient of benzene in water is 2469.135802\n",
        " Activity coefficient of water in benzene is 333.333333\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.5  Page: 283\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from numpy import * \n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "R = 8.314           #[J/(mol*K)] Universal gas consmath.tant\n",
      "T = 298.15          #[K] Temperature\n",
      "g_a_0 = 2.          #[kj/mol] Gibb's free energy of the pure species 'a'\n",
      "g_b_0 = 1.          #[kj/mol] Gibb's free energy of the pure species 'b'\n",
      "\n",
      "for a in range(4):\n",
      "    def f(x): \n",
      "        return  x*g_a_0 + (1-x)*g_b_0 + (R*T)/1000*(x*log(x) + (1-x)*log(1-x) + x*a*(1-x)**(2) + (1-x)*a*(x)**(2))\n",
      " \n",
      "    x=linspace(0.000001,0.990001,100)\n",
      "    a = f(x)        \n",
      "    plot(x,a)\n",
      "    \n",
      "xlabel(\" mole fraction of species a,x_a\");\n",
      "ylabel(\" gibb''s free energy per mole of mixture,g_mixture kJ/mol\");\n",
      "annotate(\"A = 0\",xy=(.7,0))\n",
      "annotate(\"A = 1\",xy=(.6,0.5))\n",
      "annotate(\"A = 2\",xy=(.6,0.95))\n",
      "annotate(\"A = 3\",xy=(.6,1.50))\n",
      "suptitle(\"Calculated values of the molar Gibbs energy of a binary mixture at 25 \u0014 C, assuming \\\n",
      "the symmetrical activity coefficient equation, with various values of A.\")\n",
      "\n",
      "show()\n",
      "print \" The plot is shown in the graphic window.\"\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: ['f', 'draw_if_interactive']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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qy4rhw4fT3r17iYhozZo1wo2K0qxrREQE6enp0b///ktSqZRcXV1pxIgRRCQr9+QX1ZrO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\nguIkgzHGGGOMMbag/gEWLAEI2B1+ZAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2d7f510>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The plot is shown in the graphic window.\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.6   Page: 283\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "from numpy import *\n",
      "\n",
      "\n",
      "T = 92 + 273.15         #[K] Temperature of the system\n",
      "R = 8.314               #[m**(3)*Pa/(mol*K)] universal gas consmath.tant\n",
      "\n",
      "A = 1.2739\n",
      "B = 3.9771\n",
      "\n",
      "\n",
      "\n",
      "def f(x_a): \n",
      "\t return  (R*T/1000)*( x_a*log(x_a) + (1-x_a)*log(1-x_a) + \\\n",
      "     x_a*(B**(2)*A*(1-x_a)**(2)/(A*x_a + B*(1-x_a))**(2)) + (1-x_a)* \\\n",
      "     (A**(2)*B*x_a**(2)/(A*x_a + B*(1-x_a))**(2)) )\n",
      "\n",
      "x_a = linspace(0.000001,0.99999,100)\n",
      "a = f(x_a)\n",
      "plot(x_a,a)\n",
      "\n",
      "x = linspace(0.0,0.99999,100)\n",
      "a = 1.2090312*x-1.251495764\n",
      "plot(x,1.2090312*x-1.251495764)\n",
      "xlabel(\" Mol fraction of species a, x_a\");\n",
      "ylabel(\" g_mix - sum(x_i*g_i0)\");\n",
      "\n",
      "show()\n",
      "\n",
      "print \"Thus based on the several assumptions that the Van Laar equation is an accurate representation of LLE\"\n",
      "print \"we would conclude that at 92 deg C water-n-butanol does form two liquid phases.\"\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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q1qypfH19VVxcnFJKqcjISNWuXbuMdmfOnFHe3t7Kzc1NvfHGGyo+Pj7L/nT8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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x353ea90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Thus based on the several assumptions that the Van Laar equation is an accurate representation of LLE\n",
        "we would conclude that at 92 deg C water-n-butanol does form two liquid phases.\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.7   Page: 286\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "R = 8.314           #[J/(ml*K)]\n",
      "\n",
      "\n",
      "\n",
      "h_mix = -2967.7*R/1000          #[kJ/mol] Heat of mixing of water-in-benzene\n",
      "\n",
      "h_mix_1 = 522.9*R/1000          #[kJ/mol] Heat of mixing of benzene-in-water\n",
      "\n",
      "print \" Heat of mixing of water-in-benzene is given as %f kJ/mol\"%(h_mix)\n",
      "print \" Heat of mixing of benzene-in-water is given as    %f kJ/mol\"%(h_mix_1)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Heat of mixing of water-in-benzene is given as -24.673458 kJ/mol\n",
        " Heat of mixing of benzene-in-water is given as    4.347391 kJ/mol\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.8  Page: 287\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "\n",
      "T_i = 50.           #[F] Initial temperature of the system \n",
      "T_f = 20.           #[F] Final temperature of the system\n",
      "M_gas = 115.        #[g/mol] Molecular weight of gasoline at room temperature\n",
      "M_water = 18.       #[g/mol] Molecular weight of water at the room temperaature\n",
      "d = 720.            #[g/L] density of gasoline at the room temperature\n",
      "\n",
      "s_50 = 0.00026      #[mol fraction]\n",
      "\n",
      "s_20 = 0.0001       #[mol fraction]\n",
      "\n",
      "s_rej = s_50 - s_20     # mol of water per mole of gasoline \n",
      "\n",
      "w = (s_rej*d*M_water)/M_gas     #[g water/L gasoline]\n",
      "\n",
      "print \" The amount of water that will come out of the solution in the gasoline will be %f g water/L gasoline\"%(w)\n",
      "print \" At 20 deg F we would expect this water to become solid ice, forming a piece large enough to plug the fuel line of a parked auto.\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The amount of water that will come out of the solution in the gasoline will be 0.018031 g water/L gasoline\n",
        " At 20 deg F we would expect this water to become solid ice, forming a piece large enough to plug the fuel line of a parked auto.\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.9   Page: 290\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "import math \n",
      "\n",
      "Temp = 25.      #[C]\n",
      "x_water = 5.    #[mo]\n",
      "x_benzene = 0.1 #[mol]\n",
      "\n",
      "\n",
      "\n",
      "x_ethanol_water_rich = 3.817        #[%]\n",
      "x_ethanol_benzene_rich = 1.010      #[%]\n",
      "\n",
      "K = x_ethanol_water_rich/x_ethanol_benzene_rich\n",
      "\n",
      "X = array([3.817,7.968,12.977,18.134,23.540,24.069,27.892,31.725,35.510,39.382,41.062,41.771])\n",
      "Y = array([1.010,3.323,5.860,9.121,12.939,13.340,16.090,18.943,22.444,26.216,29.341,33.093])\n",
      "Z = X/Y\n",
      "\n",
      "plot(Y,Z)\n",
      "xlabel(\"Mol% ethanol in benzene-rich phase \");\n",
      "ylabel(\"Distribution coefficient of ethanol, K_ethanol\");\n",
      "show()\n",
      "K_1 = 4\n",
      "\n",
      "m_water_rich = 100*K_1/(K_1+1)\n",
      "m_benzene_rich = 100/(K_1+1)\n",
      "\n",
      "print \" Ethanol''s 0.1 mol distributed in the water rich phase will be   %f mol%% of the total mol\"%(m_water_rich)\n",
      "print \" Ethanol''s 0.1 mol distributed in the benzene rich phase will be %f mol%% of the total mol\"%(m_benzene_rich)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hFYPKmYLN5edre0gvXw7ffQfR0doZxKBBYIfN94QQNWCVyWvBwcHs3buXevXq\nAVBSUkJoaCj79++veVIzSVGwrwsXtAX4li+H5GTo00crEH37QqNG9k4nhKiKVZqPdDodOWW2/crJ\nySm3Paeo+5o0gdGjtTOHP/6AXr1g3jy46SZ46CFtCe+iInunFELUlklnCp988gmTJ08mNjYWpRRb\ntmzhtddeM3YO24KcKTimkydh5UrtDOL337XVWkeOhLvugr9OLIUQdmSV5iOAEydOsHv3bnQ6HZGR\nkbS28RKcUhQc35Ej8NlnWoE4cUJbrXXkSOjSRZsjIYSwPasUhc8//5zY2Fi8vLwArfkoKSmJIUOG\n1DypmaQoOJfff4dPP9XWW8rPh/vu0wpEaKgUCCFsySpFQa/X8+OPP5a7LTQ0lL1795qfsIakKDgn\npbSNgJYv1y7162vFYeRICAiwdzoh6j6rdDRX9oIlsmu8MIFOByEh2h4Phw7BkiWQm6vNi9DrYeZM\nreNaCOEYTDpTePjhh2nWrBlPPvkkSinmzZvHuXPnWLhwoQ0iauRMoW4pLdWGti5frnVU33qrdvYw\nYgT4+Ng7nRB1h1Waj/Ly8pg+fTobN24EoFevXkyZMsXk1VItQYpC3VVcDJs2aQXiiy8gOFhbtK9/\nf21WtfRBCFFzVht9ZG9SFK4Ply5pGwN9+aW2D4SbmzZBrksX6NRJKxIuJi/OIoSQoiDqDKVg3z5t\nS9HduyE1FU6fhrAwrUAYLlIohKiaFAVRp505o20OlJp65XJ1oYiIgA4dpFAIAVIUxHVICoUQVbNK\nUTh16hQffPABGRkZFBcXGw/00Ucf1TypmaQoCHNcXSj27NFuMxSKiAjtXykUoq6zSlG44447uOuu\nu+jUqRMuf/0fpNPpuOeee2qe1ExSFERtlS0Ue/Zo/xoKhaFIBAdrfRRm7jYrhMOySlGw9ezlykhR\nENZwdaH4+Wc4fBjattVmXF99adLE3omFMI9VisKUKVO444476N+/f63C1YYUBWErly9rs69/+aX8\n5bffoGnTyotFq1Yyn0I4JqsUBQ8PD/Lz82nQoAH169c3HujChQs1T2omKQrC3kpLITOzYrH45Rco\nKam8WPj6Sp+FsC8ZfSSEHWRnV14sTp/W+iiuLhYdOki/hbANqxWFNWvWsHXrVnQ6HdHR0QwcOLDG\nIWtCioJwRnl58OuvFYtFRoa23lNEBHTurP0bGipbmwrLs0pRmDx5Mrt37+aBBx5AKcXy5cuJiIhg\n5syZtQpWngeIAAAYh0lEQVRrDikKoi65fBkOHNA6t3fv1i6//gq33XalUHTuDEFB2nLjQtSUVYpC\ncHAwe/fupd5f+yuWlJQQGhrK/v37a57UTFIURF1XWAg//qgVCEOxyMjQhskaikREhFY4ZKtTYSpz\nvztdTX3RnJwcWrRoAWg7r+lkqIUQFuXmBlFR2sUgNxfS0rQCkZgIr7wCp05BeHj5Mwo/Pxn9JCzD\npDOFTz75hMmTJxMTEwPAli1beO211xg5cqS18xnJmYIQmrNntTOJsk1PBQXl+yc6d5Z9KYTGah3N\nJ06cYPfu3eh0OiIjI2ndunWNQ9aEFAUhqnbyZPkisXs3NGhQ/mwiIgJatrR3UmFrFi0Kv/zyCwEB\nAaSmppZ7YUPTUXh4eC3jmk6KghCmUwqOHCnfP5GaCs2blz+bCA/XJuSJusuiReHRRx/lgw8+ICYm\nptI+hM2bN9csZQ1IURCidkpL4eDB8oVi71644QYIDNQuHTtq/8qSHnWHVZqPCgsLcXNzu+Zt1iRF\nQQjLKynR1no6cEC7/Pyz9u+vv2pnFWULheHi5WXv1MIcVikK4eHh/PDDD9e8zZqkKAhhO6WlWvNT\n2UJx4IA28c7Ts2KhCAyEvwYnCgdj0SGpJ0+e5MSJE+Tn5/PDDz+glDKueZSfn2/SAcaOHctXX32F\nt7d3pfMakpKSGDx4MLfeeisA99xzD1OmTDH5DQghLM/FRRvm6ucHZdfBLC2FY8euFIqUFFi4UPu5\nUaOKhaJjR615SjiPas8UFi1axMKFC9mzZw8RERHG2z09PRkzZgzDhg275gG2bduGh4cHDz30UJVF\n4Y033mDt2rXVB5UzBSEcllJw/PiVM4qyzVGurhULRWCgrCxrKxY9U4iPjyc+Pp5Vq1bVeEOd7t27\nk5GRUe1j5MteCOem00GbNtqld+8rtysFf/5ZvlCsXKkVi9LSioUiMBBuukmKhT2ZNKP5p59+4uef\nfzY2HxlMnTq11gF0Oh07duxAr9fj4+PDrFmzCAwMrPXrCiHsT6eDG2/ULnFxV25XSltZtmyx+OIL\n+OkncHeHHj0gNlb7t00b++W/HplUFBo3bmwsBgUFBaxbt85iX9zh4eFkZmbi7u7O+vXrGTJkCL//\n/nulj01ISDD+HBMTY5xhLYRwLjodeHtrl7L/GyulbWa0aRN8+SU88ww0a3alSMTGas1OompJSUkk\nJSXV+Pk12k/h0qVL9O7dmy1btpj0+IyMDAYOHGjSAnp+fn6kpqbSvHnz8kGlT0GI605pqXb2sGkT\nbN4MW7dqzUuGIhEdLaOersXc784a7Ql18eJFjh8/XpOnVpCVlWUMnJKSglKqQkEQQlyfXFwgJAQm\nTYI1a7RNixYt0vbQ/uADbXRUWBj84x+wbh3YcDPIOsuk5qPg4GDjz6WlpZw6dcrk/oRRo0axZcsW\nTp8+Tdu2bZk2bRpFRUUAjB8/npUrV/Luu+/i6uqKu7s7y5cvr8HbEEJcD+rV05boiIiAf/4Tioq0\nmdmbN8Obb8KoUVpntaE/ols3aNzY3qmdi0nNR4bRQzqdDldXV7y9vY17NduKNB8JIa6lsBB27dKK\nxKZN2rLjYWFXmpu6dNGWKL+eWG2V1NTUVJKTk3FxcaFbt242XQwPpCgIIcx38SJs336lSBw4AJGR\nV84kOneu+zvbWaUovPLKK6xYsYJhw4ahlGLNmjUMHz6cl156qVZhzSFFQQhRWxcuwLZtVzqu09O1\nJqYePbT5FSEhdW+OhFWKgr+/P/v27TMugFdQUIBer69y6Kg1SFEQQlja2bOwZQts3Ahff61tVnT3\n3dC3L/TsWTcW/7PK6CMfHx8KCgqM1wsLC2kjM0qEEE6ueXMYOhTeeUc7a0hKAr0e5s/XRjh17w4z\nZmh9E9fL36TVnin87W9/AyAzM5OUlBR6/zV/fcOGDURGRvL555/bJiVypiCEsK2CAu0sYv167ZKb\nq51F3H231tTUrJm9E5rGos1HCxcurPIFdTod8fHxNUtZA1IUhBD2dOiQ1sS0fr02iS44WGtm6ttX\nG+HkUqNZX9ZntdFH9iZFQQjhKAoLtcJgOIs4dw769NEKRO/ejjXL2qJFYcSIEaxYsYKgoKAK23Hq\ndDr27dtX86RmkqIghHBUhw9fOYvYskWbQNe3r9bUFBFh37MIixaFEydOcNNNN3HkyJFKX9TX17dG\nIWtCioIQwhlcuqQNezUUiexs7eyhb1/tbKJlS9vmsXjzUXFxMb169WLz5s21DlcbUhSEEM7oyJEr\nBWLzZrj99ivDXjt31pbusCaLD0l1dXXFxcWFnJycWgUTQojr0S23wPjx2n4R2dnw2mvayKZHH9WW\nAb//fliyBE6dsndSjUkdzYMGDSItLY3evXvj7u6uPVGnY+7cuVYPaCBnCkKIuiYzUzuL+PprbQJd\n+/ZXRjR16WKZvgirjD5atGhRuV3XDD/LkFQhhLCMoiLYsUNrZtq5U1uKwxJNSxbdo9ng3LlzTJo0\nqdxtb731lnnJhBBCVKl+fW3ToOho++Yw6eRk0aJFFW5buHChpbMIIYSws2rPFD755BM+/vhjDh8+\nzMCBA4235+bm0sKRZmcIIYSwiGqLQteuXbnxxhvJzs7m2WefNbZLNWnShJCQEJsEFEIIYTsmdTTn\n5eXRqFEj6tWrx2+//cZvv/1G3759bbr7mnQ0CyGE+awy+qhTp05s27aNc+fO0a1bNzp37kyDBg1Y\ntmxZrcKaQ4qCEEKYzyr7KZSWluLu7s7q1at54oknWLFiBT/99FONQwohhHBMJk+N2LlzJ8uWLaN/\n//6AViiEEELULSYVhbfeeouZM2cydOhQOnbsyKFDh4iNjbV2NiGEEDYm+ykIIUQdZtEZzU8//TRz\n5swpN0eh7IHWrl1rfkIhhBAOq9qi8NBDDwHwj3/8o8J9V2+6I4QQwvmZ3HyUnZ0NwA033GDVQFWR\n5iMhhDCfRYekKqVISEigZcuW+Pv74+/vT8uWLZk2bVqtgwohhHA81RaFN998k+3bt7N7927OnTvH\nuXPnSElJYfv27bzxxhu2yiiEEMJGqi0Kixcv5uOPP8bPz89426233sqyZctYvHixSQcYO3YsrVq1\nIjg4uMrHTJw4kQ4dOqDX60lLSzMxunNJSkqyd4Raceb8zpwdJL+9OXt+c1VbFIqLiyvtQ7jhhhso\nLi426QAPP/wwX3/9dZX3JyYmkp6ezsGDB3n//feZMGGCSa/rbJz9Pyxnzu/M2UHy25uz5zdXtUWh\nugXvTF0Mr3v37jRr1qzK+9euXWvcwS0qKoqcnByysrJMem0hhBCWVe2Q1H379uHp6VnpfQUFBRYJ\ncPz4cdq2bWu83qZNG44dO0arVq0s8vpCCCHMoGzg8OHDKigoqNL7BgwYoJKTk43X4+LiVGpqaoXH\nAXKRi1zkIpcaXMxh0h7N1uTj40NmZqbx+rFjx/Dx8anwOCVzFIQQwupMXiXVWgYNGmQcybRr1y68\nvLyk6UgIIezE6mcKo0aNYsuWLZw+fZq2bdsybdo0ioqKABg/fjz9+vUjMTGR9u3b07hxYxYsWGDt\nSEIIIapiVmOTHaxfv17ddtttqn379uq1116zdxyz3XLLLSo4OFiFhoaqzp072zvONT388MPK29u7\nXB/QmTNnVM+ePVWHDh1Ur1691Llz5+yYsHqV5X/55ZeVj4+PCg0NVaGhoWr9+vV2TFi9o0ePqpiY\nGBUYGKg6duyo5syZo5Ryjt9BVdmd5fMvKChQkZGRSq/Xq4CAADV58mSllHN89kpVnd/cz9+hi0Jx\ncbFq166dOnz4sLp8+bLS6/XqwIED9o5lFl9fX3XmzBl7xzDZ1q1b1Q8//FDuS/W5555T//73v5VS\nSr322mvq+eeft1e8a6osf0JCgpo9e7YdU5nu5MmTKi0tTSmlVG5urvL391cHDhxwit9BVdmd6fO/\nePGiUkqpoqIiFRUVpbZt2+YUn71BZfnN/fzt3qdQnZSUFNq3b4+vry/169dn5MiRrFmzxt6xzKac\nqJO8snklZeeSxMfH88UXX9gjmkmqmhfjLL+D1q1bExoaCoCHhwcBAQEcP37cKX4HVWUH5/n83d3d\nAbh8+TIlJSU0a9bMKT57g8ryg3mfv0MXhcrmMBj+I3MWOp2Onj17EhERwQcffGDvODWSlZVl7Pxv\n1aqVU04ufPvtt9Hr9TzyyCPk5OTYO45JMjIySEtLIyoqyul+B4bsXbp0AZzn8y8tLSU0NJRWrVoR\nGxtLx44dneqzryw/mPf5O3RRqAt7Nmzfvp20tDTWr1/PvHnz2LZtm70j1YpOp3O638uECRM4fPgw\ne/fu5cYbb6x0fxBHk5eXxz333MOcOXMqTCB19N9BXl4ew4cPZ86cOXh4eDjV5+/i4sLevXs5duwY\nW7duZfPmzeXud/TP/ur8SUlJZn/+Dl0Urp7DkJmZSZs2beyYyHw33ngjoK0XNXToUFJSUuycyHyt\nWrXizz//BODkyZN4e3vbOZF5vL29jf8zjxs3zuF/B0VFRdxzzz2MHj2aIUOGAM7zOzBkf/DBB43Z\nne3zB2jatCn9+/cnNTXVaT77sgz59+zZY/bn79BFISIigoMHD5KRkcHly5f59NNPGTRokL1jmSw/\nP5/c3FwALl68yLffflvtarGOatCgQSxatAiARYsWGf9ndxYnT540/vz555879O9AKcUjjzxCYGAg\nkyZNMt7uDL+DqrI7y+d/+vRpY9NKQUEBGzZsICwszCk+e6g6v6GggYmfv+X7vy0rMTFR+fv7q3bt\n2qkZM2bYO45Z/vjjD6XX65Ver1cdO3Z0ivwjR45UN954o6pfv75q06aN+uijj9SZM2dUXFycww/J\nU6pi/vnz56vRo0er4OBgFRISogYPHqz+/PNPe8es0rZt25ROp1N6vb7cEEJn+B1Ulj0xMdFpPv99\n+/apsLAwpdfrVXBwsPrPf/6jlFJO8dkrVXV+cz9/k7fjFEIIUfc5dPOREEII25KiIIQQwkiKghBC\nCCMpCkIIIYykKNRxLi4ujB492njdsO/2wIEDq33ewoUL+dvf/gZosyGDg4Pp37+/cYXb5ORknnnm\nGbPzzJgxw/hzRkaG1YcnVnWMEydOMGLECLNey9fXl7Nnz1oqmt289957LFmypMr7k5KSrvnfR3US\nEhKYPXt2jZ8v7EuKQh3XuHFjfv75ZwoLCwHYsGEDbdq0ueaszLIzNz/++GP2799P165d+eabb1BK\n8eqrrzJ16lSz88ycOdP8N2EFN910EytWrDDrOTqdzmnW8KlKSUkJ48ePL/eHgqU58oxfcW1SFK4D\n/fr146uvvgLgk08+YdSoUcYvt7NnzzJkyBD0ej133HEH+/fvB7SJSIbHKKW4dOkS+fn51K9fn6VL\nl9KvXz+8vLyqPObSpUuJiooiLCyMxx9/nNLSUiZPnkxBQQFhYWGMHj0anU5HSUkJjz32GEFBQfTp\n08dYvD744AMiIyMJDQ1l+PDhxj3Bx4wZw9NPP023bt1o164dq1atMmZ87rnnCA4OJiQkhM8++6za\nz6TsGcTChQsZNmwYffv2xd/fn+eff77K5/3nP/8hJCSEqKgoDh06BEB2djbDhw8nMjKSyMhIduzY\nAWh/MY8dO5bY2FjatWvH22+/DcD//vc/wsLCCAsLw8/Pjx49egDw7bff0rVrVzp16sS9997LxYsX\nAe0MJSEhgU6dOhESEsJvv/0GaBMix44dS1RUFOHh4axdu7bSzDExMfz973+nc+fOzJkzh2nTphn/\nkk9PT6dnz56EhobSqVMn/vjjD3Q6HXl5eYwYMYKAgAAefPDBKl930qRJhIWFERwczO7du433HThw\noML7Bhg6dCgREREEBQUZ1wIrKSlhzJgxxt/dW2+9BcChQ4fo27cvERER3HXXXcb3LazMinMphAPw\n8PBQ+/btU8OHD1eFhYUqNDRUJSUlqQEDBiillHrqqafUK6+8opRSatOmTSo0NFQppdSCBQvUU089\npZRSasmSJSosLEyNHj1a5ebmqh49eqji4uIqj3ngwAE1cOBA42MmTJigFi9ebMxjcPjwYeXq6qp+\n/PFHpZRS9957r1q6dKlSSpVbbnzKlCnq7bffVkopFR8fr+69917jcdq3b6+UUmrlypWqV69eqrS0\nVGVlZambb75Z/fnnn1XuD1729gULFqhbb71VXbhwQRUWFqpbbrlFHTt2rMJzfH19jRMQFy9ebPwM\nR40aZdxn/MiRIyogIEAppa1j361bN3X58mV1+vRp1aJFi3KfW1FRkerevbtat26dys7OVnfddZfK\nz89XSmlLNBt+L76+vuqdd95RSin13//+V40bN04ppdQLL7xg/LzOnTun/P39jUsnlxUTE6OefPJJ\n4/WySylHRkaqL774Qiml1KVLl1R+fr7avHmzatq0qTp+/LgqLS1Vd9xxR7l91Mu+7mOPPaaU0pYs\nN3yeL7/8suratWul7/vs2bNKKaXy8/NVUFCQOnPmjNqzZ4/q1auX8XXPnz+vlFKqR48e6uDBg0op\npXbt2qV69OhRIYOwPLvv0SysLzg4mIyMDD755BP69+9f7r7t27ezevVqAGJjYzlz5oxxaQ6DBx98\n0PjX4iuvvMLTTz/NV199xZIlS2jbti2zZ88u12SwceNGUlNTiYiIALQp961bt640m5+fHyEhIQB0\n6tSJjIwMAPbv38+UKVM4f/48eXl53H333YDWNGFYZiAgIMC4YmVycjL3338/Op0Ob29voqOjSUlJ\nMbnPIi4uzrjwXGBgIBkZGZXuFT5q1CgARo4cyd///ncAvvvuO3755RfjY3Jzc7l48SI6nY7+/ftT\nv359WrRogbe3N1lZWdx0000ATJw4kbi4OPr378+6des4cOAAXbt2BbSljw0/AwwbNgyA8PBw4+/r\n22+/5csvv2TWrFkAXLp0iczMTG677bYKue+7774Kt+Xl5XHixAkGDx4MQIMGDYz3RUZGGnOGhoaS\nkZFBt27dqvw8unfvzoULFzh//jw6nY4BAwZU+r7nzJljXHo6MzOT9PR0/P39+eOPP5g4cSL9+/en\nd+/e5OXlsXPnznL9PpcvX65wfGF5UhSuE4MGDeLZZ59ly5YtZGdnl7tPXdVOXlWb8IkTJ9i9ezdT\np04lJiaGzZs3M336dDZu3EjPnj3LPTY+Pr5cp3JVGjZsaPy5Xr16xuajMWPGsHbtWoKDg1m0aBFJ\nSUnGx5X98jJkr6y935y27atzlJSUXPM5htdXSvH999+Xy1VZ1nr16lFcXAxoTVaZmZn897//Nd7f\nq1cvPv7442rzlX0NgNWrV9OhQ4dyjx07dixpaWn4+Piwbt06QOtbMsfVn0fZY1bH8JlU9r6TkpLY\nuHEju3btws3NjdjYWAoLC/Hy8uLHH3/km2++4X//+x+fffYZb731Fl5eXqSlpZmVW9Se9ClcJ8aO\nHUtCQoJxfXWD7t27s2zZMkAbdXLDDTfg4eFR6Wu89NJLTJ8+HdD++ldKodPpjO39BnFxcaxcudJY\nfM6ePcvRo0cBqF+/fpVfMKpMP0ZeXh6tW7emqKiIpUuXXvMLvnv37nz66aeUlpaSnZ3N1q1biYyM\nrPY51bm6wBhu+/TTTwH49NNPjX/J9+7dm7lz5xof9+OPP1b72qmpqcyePbvcCKAuXbqwfft2Yz/F\nxYsXOXjwYLWv06dPn3LHNXyBfvTRR6SlpRkLQlXvz8PDgzZt2hg3rrp06VKF3+W1GD6P5ORkvLy8\naNKkSZWf3YULF2jWrBlubm78+uuv7Nq1C4AzZ85QUlLCsGHDmD59OmlpaXh6euLn58fKlSuNz9+3\nb59Z2UTNSFGo4wxfpj4+Pjz11FPG2wy3JyQkkJqail6v58UXXzSuBnn1uvF79+7FxcXFuLPW/fff\nT0hICDt37jQ27RgEBATw6quv0rt3b/R6Pb179zau1PjYY48REhJi7Ggue4yy16dPn05UVBR33nkn\nAQEBlb6nsj8PHTqUkJAQ9Ho9cXFxvP7668YljqsqKIbbK1sjv7Ln6HQ6zp07h16v5+233+bNN98E\nYO7cuezZswe9Xk/Hjh157733qsyqlGLevHmcO3eO2NhYwsLCeOyxx2jZsiULFy5k1KhR6PV6unbt\nWmnHatmsL730EkVFRYSEhBAUFMTLL79c6fus7r0vWbKEuXPnotfrufPOO/nzzz9N/jwA3NzcCA8P\n54knnmD+/PkVMpZ9/t13301xcTGBgYG88MIL3HHHHYC2mZbhsxg9erRxhNqyZcuYP38+oaGhBAUF\nVdmRLixLFsQTQtRIbGwss2fPJjw83N5RhAXJmYIQQggjOVMQQghhJGcKQgghjKQoCCGEMJKiIIQQ\nwkiKghBCCCMpCkIIIYykKAghhDD6f+NRTP1s7PsOAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x37ae490>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Ethanol''s 0.1 mol distributed in the water rich phase will be   80.000000 mol% of the total mol\n",
        " Ethanol''s 0.1 mol distributed in the benzene rich phase will be 20.000000 mol% of the total mol\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.10   Page: 293\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "\n",
      "Temp = 20.          #[C]\n",
      "s = 36.0            #[g per 100 g of water]\n",
      "M_NaCl = 58.5       #[g/mol] molecular weight of NaCl \n",
      "M_water = 18.       #[g/mol] molecular weight of water\n",
      "\n",
      "w = s/(s+100)\n",
      "w_percent = w*100       #[wt %]\n",
      "\n",
      "x = (s/M_NaCl)/((s/M_NaCl)+(100/M_water))\n",
      "x_percent = x*100       #[mol %]\n",
      "\n",
      "print \" Weight fraction of the NaCl in the saturated solution is %0.1f wt %%\"%(w_percent)\n",
      "print \" Mol fraction of the NaCl in the saturated solution is    %0.0f mol %%\"%(x_percent)\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Weight fraction of the NaCl in the saturated solution is 26.5 wt %\n",
        " Mol fraction of the NaCl in the saturated solution is    10 mol %\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.11  Page: 293\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "\n",
      "T_inlet = 68.           #[F]\n",
      "T_outlet = 110.         #[F]\n",
      "\n",
      "s_inlet_carbonate = 60.         #[ppm]\n",
      "s_inlet_sulphate = 2020.        #[ppm]\n",
      "\n",
      "s_outlet_carbonate = 30.        #[ppm]\n",
      "s_outlet_sulphate = 2000.       #[ppm]\n",
      " # This is close enough to the solubility of the gypsum at 68F \n",
      " # so we conclude that we would not expect either form of CaSO4 to prdcipitate\n",
      " \n",
      "w = s_inlet_carbonate - s_outlet_carbonate         #[ppm]\n",
      "\n",
      "print  \" Total amount of the solid left behind in the heater will be        %0.1f ppm\"%(w)\n",
      "\n",
      " # Now if a typical houshold water heater heats 100 gallons/per day , we would expect to deposite \n",
      "w_per_day = w*10**(-6)*100*8.33                    #[lb/day]\n",
      "print  \" Total amount of the solid left behind in the heater per day will be %.3f lb/day\"%(w_per_day)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Total amount of the solid left behind in the heater will be        30.0 ppm\n",
        " Total amount of the solid left behind in the heater per day will be 0.025 lb/day\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.12   Page: 298\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "\n",
      "x_2 = 0.1\n",
      "x_ideal_benzene = 0.114\n",
      "x_ideal_CCl4 = 0.152\n",
      "\n",
      "y_i_1 = x_ideal_benzene/x_2# corresponding to practically ideal solution\n",
      "\n",
      "y_i_2 = x_ideal_CCl4/x_2# corresponding to mild type II behavior \n",
      "\n",
      "print \" Activity coefficient in benzene corresponding to practically ideal solution is %0.2f\"%(y_i_1)\n",
      "print \" Activity coefficient in CCl4 corresponding to mild type II behavior         is %0.2f\"%(y_i_2)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Activity coefficient in benzene corresponding to practically ideal solution is 1.14\n",
        " Activity coefficient in CCl4 corresponding to mild type II behavior         is 1.52\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.13   Page: 299\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "\n",
      "T = 273.15+20           #[K]\n",
      "\n",
      "T_m = 273.15+800        #[K]\n",
      "delta_h_fusion = 30219.         #[J/g]\n",
      "R = 8.314               #[J/(mol*K)]\n",
      "\n",
      "a = delta_h_fusion/(R*T)*(T_m/T-1)\n",
      "\n",
      "x_NaCl_into_y_i_1 = 1/math.exp(a)\n",
      "\n",
      "x_NaCl = 1/math.exp(a)*1\n",
      "\n",
      "print \" The solubility of the NaCl in water at 20 deg C is %e \"%( x_NaCl)\n",
      "print \" But the experimental value is 0.1 so Similar to the results in book our results are very far wrong\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The solubility of the NaCl in water at 20 deg C is 4.704794e-15 \n",
        " But the experimental value is 0.1 so Similar to the results in book our results are very far wrong\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.14  Page: 301\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "\n",
      "P = 1*14.7          #[psia]\n",
      "T = 30.             #[F]\n",
      "p_ice = 0.0808     #[psia]\n",
      "x_water_in_ice = 1.00\n",
      "\n",
      "y_water_vapour = x_water_in_ice*p_ice/P\n",
      "\n",
      "print \" Equilibrium concentration of water vapour in the air is %0.4f\"%(y_water_vapour)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Equilibrium concentration of water vapour in the air is 0.0055\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      " Example 11.15  Page: 302\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "T = 273.15+35           #[K]\n",
      "p_d = 100.              #[atm]\n",
      "R = 82.06               #[(cm**(3)*atm)/(mol*K)]\n",
      "\n",
      "p_naphthalene = 0.00065         #[atm]\n",
      "\n",
      "x_naphthalene = 1.00\n",
      "P = p_d\n",
      "\n",
      "y_naphthalene = x_naphthalene*p_naphthalene/P\n",
      "v = 132.                #[cm**(3)/mol]\n",
      "p_c = 1.                #[atm]\n",
      "f_d_by_f_c = math.exp(v/(R*T)*(p_d-p_c))\n",
      "y_naphthalene = f_d_by_f_c*y_naphthalene\n",
      "\n",
      "print \"Estimated solubility of naphthalene in CO2 is %e\"%(y_naphthalene)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Estimated solubility of naphthalene in CO2 is 1.089816e-05\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}