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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 10: Stage and Contineous Gas-Liquid Seaparation Procesess"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.2-1 Page Number 587 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Dissolved Oxygen Concentration in Water\n",
      "\n",
      "#Variable Declaration\n",
      "H = 4.38e4                   #Henry's law constant atm/mol fraction\n",
      "yO2 = 0.21                   #mol fraction\n",
      "\n",
      "#Calculations\n",
      "    #y = Hx\n",
      "xO2 = yO2/H\n",
      "\n",
      "#Results\n",
      "print 'Mol fraction of oxygen in water, xA: %5.3e'%xO2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mol fraction of oxygen in water, xA: 4.795e-06\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.3-1 Page Number 589 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Equilibrium Stage Contact for CO2-Air-Water\n",
      "from scipy.optimize import root\n",
      "\n",
      "#Variable Declaration\n",
      "P = 1.            #Absolute pressure of gas mixture (atm)\n",
      "yA2 = 0.2         #Mole fraction of CO2\n",
      "Vm = 100.         #Feed inlet  \n",
      "Lm = 300.         #Flowarte of liquid entering (kg mol water/hr)\n",
      "xC0 = 1.\n",
      "xB0 = 0.\n",
      "xA0 = 0\n",
      "H = 0.142e4       #Henry's law constant from appendix A.3 in atm/mol frac\n",
      "\n",
      "#Calculations\n",
      "Lmd = Lm\n",
      "Vmd = (1-yA2)*Vm\n",
      "Hd = H/P\n",
      "#Lmd*xA0/(1-xA0) + Vmd*yA2/(1-yA2) = Lmd*xA1/(1-xA1) + Vmd*yA1/(1-yA1)\n",
      "#yA1 = Hd* xA1 \n",
      "RHS = (Vmd*yA2)/(1-yA2)\n",
      "f = lambda xA1: Lmd*xA1/(1-xA1) + Vmd*Hd*xA1/(1-Hd*xA1)-Vmd*yA2/(1-yA2)\n",
      "sol = root(f, 0.0001)\n",
      "xA1 = sol.x[0]\n",
      "#Results\n",
      "yA1 = Hd*xA1\n",
      "L1 = Lmd/(1-xA1)\n",
      "V1 = Vmd/(1-yA1)\n",
      "\n",
      "print 'Outlet liquid and vapor phase compositions are xA1: %5.2e and yA1: %4.2f'%(xA1,yA1)\n",
      "print 'Liquid and vapor rates from colums are L1:%4.1f kgmol/h V1: %4.1f kgmol/h respectively'%(L1,V1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Outlet liquid and vapor phase compositions are  xA1: 1.41e-04 and yA1: 0.20\n",
        "Liquid and vapor rates from colums are L1:300.0 V1: 100.0 respectively\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.3-2 Page Number 591"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Absorption of Acetone in a Countercurrent Stage Tower\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "#Variable Declaration\n",
      "VN1 = 30.       #Inlet Gas Flow Rate, kg mol/hr\n",
      "L0 = 90.        #Inlet Liquid Flow Rate, kg mol/hr\n",
      "xA0 = 0.0       #Mole fraction of Acetone in entering Water\n",
      "yAN1 = 0.01     #Mole fraction of Acetone in entering Air\n",
      "AAAds = 90.     #Percent of acetone absorbed\n",
      "H = 2.53\n",
      "\n",
      "\n",
      "#Calculations\n",
      "        # moles of acetone entering\n",
      "\n",
      "AVi = yAN1*VN1\n",
      "Ai = (1-yAN1)*VN1\n",
      "AVo = (1-AAAds/100)*AVi\n",
      "ALo = AAAds*AVi/100\n",
      "V1 = AVo + Ai \n",
      "yA1 = AVo/Ai\n",
      "LN = L0 + ALo\n",
      "xAN = ALo/LN\n",
      "\n",
      "\n",
      "y = np.arange(0,0.012,0.0005)\n",
      "x = y/H\n",
      "\n",
      "plt.plot(x,y)\n",
      "plt.text(.0035, .007, r'Equilibrium Curve')\n",
      "plt.text(.001, .009, r'Operating Line')\n",
      "plt.plot(xAN,yAN1,'ro')\n",
      "plt.annotate('$(x_{AN},y_{AN+1})$', xy=(xAN,yAN1), xytext=(xAN,yAN1))\n",
      "plt.plot(xA0,yA1,'ro')\n",
      "plt.annotate('$(x_{A0},y_{A1})$', xy=(xA0,yA1), xytext=(xA0,yA1+0.001))\n",
      "\n",
      "m = (yAN1-yA1)/(xAN-xA0)\n",
      "\n",
      "def ypos(xop):\n",
      "    return m*xop + yA1\n",
      "\n",
      "def xpos(yop):\n",
      "    return yop/H\n",
      "\n",
      "yy = m*x + yA1\n",
      "plt.plot(x,yy,'r-')\n",
      "plt.xlabel('Mole fraction of acetone in water, $x_A$')\n",
      "plt.ylabel('Mole fraction of acetone in air, $y_A$')\n",
      "plt.plot([xAN,xAN],[0.0,yAN1],'b--')\n",
      "plt.plot([0.0,xAN],[yAN1,yAN1],'b--')\n",
      "x1 = xA0\n",
      "y1 = yA1\n",
      "n = 0\n",
      "while x1 <= xAN:\n",
      "    x2 = xpos(y1)\n",
      "    y2 = y1\n",
      "    if x2 > xAN:\n",
      "        plot([x1,x2], [y1,y2], 'k-', lw=1)  #Draw Horizontal line to equilibrium curve\n",
      "        dxt = x2-x1\n",
      "        dx = xAN-x1\n",
      "        dxbydxt = dx/dxt\n",
      "        n = n + dxbydxt\n",
      "        break\n",
      "    plt.text(x2, y2-0.0007, str(n+1))\n",
      "    plot([x1,x2], [y1,y2], 'k-', lw=1)      #Draw Horizontal line to equilibrium curve\n",
      "    x1 = x2\n",
      "    y1 = y2\n",
      "    y2 = ypos(x1) \n",
      "    plot([x1, x2], [y1, y2], 'k-', lw=1)    #Draw a vertical line to operating line\n",
      "    n = n+1\n",
      "    x1 = x2\n",
      "    y1 = y2 \n",
      "  \n",
      "print \"Acetone laden air rate from the absorber:\",V1, \"kgmol air/hr\"\n",
      "print \"Acetone concentration in leaving stream\", round(yA1,6)\n",
      "print \"Acetone + Water rate from the absorber:\", LN, \"kmol/hr\"\n",
      "print \"Acetone concentrqtion in liquid leaving absorber:\",round(xAN,5)\n",
      "print \"Amount of acetone etering with air\", round(AVi,4),\"kmol acetone/hr\"\n",
      "print \"Amount of air entering \", round(Ai,4), \" kgmol air/hr\"\n",
      "print \"Amount of acetone leaving with treated air\", round(AVo,4), \"kgmol/hr\"\n",
      "print \"Amount of acetone leaving with Water\", round(ALo,4), \"kgmol/hr\"\n",
      "print \"Number of Stages:\", round(n,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Acetone laden air rate from the absorber: 29.73 kgmol air/hr\n",
        "Acetone concentration in leaving stream 0.00101\n",
        "Acetone + Water rate from the absorber: 90.27 kmol/hr\n",
        "Acetone concentrqtion in liquid leaving absorber: 0.00299\n",
        "Amount of acetone etering with air 0.3 kmol acetone/hr\n",
        "Amount of air entering  29.7  kgmol air/hr\n",
        "Amount of acetone leaving with treated air 0.03 kgmol/hr\n",
        "Amount of acetone leaving with Water 0.27 kgmol/hr\n",
        "Number of Stages:  5.1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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v4L33gBs3KvyRNzVVVKGtWgUMHAhcvQq80UGLlRMlXl77+OOPcfjwYfTt2xdH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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x661c3b0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.3-3 Page Number 593"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Number of Stages by Analytical Equation\n",
      "\n",
      "import numpy as np\n",
      "\n",
      "#Variable Declaration\n",
      "VN1 = 30.       #Inlet Gas Flow Rate, kg mol/hr\n",
      "L0 = 90.        #Inlet Liquid Flow Rate, kg mol/hr\n",
      "xA0 = 0.0       #Mole fraction of Acetone in entering Water\n",
      "yAN1 = 0.01     #Mole fraction of Acetone in entering Air\n",
      "AAAds = 90.     #Percent of acetone absorbed\n",
      "m = 2.53\n",
      "\n",
      "\n",
      "#Calculations\n",
      "        # moles of acetone entering\n",
      "AVi = yAN1*VN1\n",
      "Ai = (1-yAN1)*VN1\n",
      "AVo = (1-AAAds/100)*AVi\n",
      "ALo = AAAds*AVi/100\n",
      "V1 = AVo + Ai \n",
      "yA1 = AVo/Ai\n",
      "LN = L0 + ALo\n",
      "xAN = ALo/LN\n",
      "\n",
      "A1 = L0/(m*V1)\n",
      "AN = LN/(m*VN1)\n",
      "Agm = sqrt(A1*AN)\n",
      "\n",
      "N = log((yAN1-m*xA0)/(yA1-m*xA0)*(1-1/Agm)+1/Agm)/log(Agm)\n",
      "\n",
      "#Results\n",
      "print \"Geometric mean Absorption Factor:\", round(Agm,3)\n",
      "print \"Number of stages required for separation:\", round(N,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Geometric mean Absorption Factor: 1.193\n",
        "Number of stages required for separation: 5.05\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.4-1 Page Number 597"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Number of Stages by Analytical Equation\n",
      "\n",
      "import numpy as np\n",
      "from scipy.interpolate import interp1d\n",
      "from scipy.optimize import root\n",
      "\n",
      "#Variable Declaration\n",
      "yAG = 0.380       #Inlet Gas Flow Rate, kg mol/hr\n",
      "xAL = 0.100       #Inlet Liquid Flow Rate, kg mol/hr\n",
      "ky = 1.465e-3     #Gas phase mass transfer coefficient, kmol/(m2.s)\n",
      "kx = 1.967e-3     #Gas phase mass transfer coefficient, kmol/(m2.s)\n",
      "xA = np.array([0.00,0.05,0.10,0.15,0.20,0.25,0.30,0.35])\n",
      "yA = np.array([0.00,0.022,0.052,0.087,0.131,0.187,0.265,0.385])\n",
      "\n",
      "#Calculations\n",
      "f = interp1d(xA, yA, kind='cubic')\n",
      "y = f(xA)\n",
      "plot(xA,yA,'ro-')\n",
      "#(1-xA)iM = 1., (1-yA)iM = 1.\n",
      "#Slope of operating line -(kxd/(1-xA)iM )/(kyd/(1-yA)iM)\n",
      "m = -kx/ky\n",
      "c = yAG - m*xAL\n",
      "plot(xAL, yAG,'bo')\n",
      "xlabel('$mol\\ fraction\\ in\\ liquid\\ phase,\\ x_A$')\n",
      "ylabel('$mol\\ fraction\\ in\\ gas\\ phase,\\ y_A$')\n",
      "ff = lambda x: f(x) - (m*x+c)\n",
      "def lm(a,b):\n",
      "    return ((1-a)-(1-b))/log((1-a)/(1-b))\n",
      "\n",
      "def flux(k,a,b):\n",
      "    return k*(a-b)/lm(a,b)\n",
      "\n",
      "n = 4\n",
      "er = 1.0\n",
      "i=0\n",
      "while er>= 0.065:\n",
      "    i+=1\n",
      "    sol = root(ff, 0.002)\n",
      "    xAi = sol.x[0]\n",
      "    yAi = f(xAi)\n",
      "    plot(xAi, yAi,'o-',label=str(i))\n",
      "    plot([xAL,xAi], [yAG, yAi], lw=0.3)\n",
      "    ylm = lm(yAi,yAG)\n",
      "    xlm = lm(xAi,xAL)\n",
      "    mc = -kx/xlm/(ky/ylm)\n",
      "    NAy = flux(ky,yAG,yAi)\n",
      "    NAx = flux(kx,xAi,xAL)\n",
      "    #er = abs((NAx-NAy)/NAx)\n",
      "    er = abs((m-mc)/m)\n",
      "    m = (n*m + mc)/(n+1)\n",
      "legend(loc='lower right')  \n",
      "#Results\n",
      "print \"Liquid phase Interphase concentration:\", round(xAi,3)\n",
      "print \"Gas  phase  Interphase  concentration:\", round(yAi,3)\n",
      "print 'Flux  in  Gas  phase: %4.2e'%(NAy)\n",
      "print 'Flux in Liquid phase: %4.2e'%(NAx)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Liquid phase Interphase concentration: 0.257\n",
        "Gas  phase  Interphase  concentration: 0.196\n",
        "Flux  in  Gas  phase: 3.80e-04\n",
        "Flux in Liquid phase: 3.78e-04\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T6mRSQtm3bx/5+fk8++yzzJ8/n1dffZVhw4axcOFCo67XGJl9hw0bxsmTJ9mwYQMTJ06U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\nhBBCTOL/AMYcuoSlk302AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5fcfd50>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.4-2 Page Number 601"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Overall Mass Transfer Coefficients from Film Coefficients\n",
      "import numpy as np\n",
      "from scipy.interpolate import interp1d\n",
      "from scipy.optimize import root\n",
      "\n",
      "#Variable Declarion\n",
      "yAG = 0.380       #Inlet Gas Flow Rate, kg mol/hr\n",
      "xAL = 0.100       #Inlet Liquid Flow Rate, kg mol/hr\n",
      "ky = 1.465e-3     #Gas phase mass transfer coefficient, kmol/(m2.s)\n",
      "kx = 1.967e-3     #Gas phase mass transfer coefficient, kmol/(m2.s)\n",
      "xA = np.array([0.00,0.05,0.10,0.15,0.20,0.25,0.30,0.35])\n",
      "yA = np.array([0.00,0.022,0.052,0.087,0.131,0.187,0.265,0.385])\n",
      "xAi = 0.2572\n",
      "yAi = 0.1964\n",
      "\n",
      "f = interp1d(xA, yA, bounds_error=False, fill_value=-10)\n",
      "\n",
      "def flux(k,a,b):\n",
      "    return k*(a-b)/lm(a,b)\n",
      "\n",
      "def lm(a,b):\n",
      "    return ((1-a)-(1-b))/log((1-a)/(1-b))\n",
      "\n",
      "#f2 = lambda x: f(x)- yAG #sol = root(f2, 0.3, method='hybr') xAs = sol.x[0] print xAs\n",
      "yAs = f(xAL)\n",
      "md = (yAi-yAs)/(xAi-xAL)\n",
      "yLMs = lm(yAs,yAG)\n",
      "yLM = lm(yAi,yAG)\n",
      "xLM = lm(xAi,xAL)\n",
      "InvKydby_yLMsGas = 1/(ky/yLM) \n",
      "InvKydby_yLMsLiquid =  md/(kx/xLM)\n",
      "InvKydby_yLMs = InvKydby_yLMsGas + InvKydby_yLMsLiquid\n",
      "Kyd = (1/InvKydby_yLMs)*yLMs\n",
      "Rgasper = InvKydby_yLMsGas*100/InvKydby_yLMs \n",
      "Rliquidper = InvKydby_yLMsLiquid*100/InvKydby_yLMs\n",
      "NA = Kyd*(yAG-yAs)/yLMs\n",
      "#Results\n",
      "print \"Individual Film resistances for Gas and Liquid film are:\", round(InvKydby_yLMsGas,2),round(InvKydby_yLMsLiquid,2), \"respectively\"\n",
      "print 'Overall Mass Transfer Coefficient: %5.3e'%(Kyd), \"kgmol/(m2.s)\"\n",
      "print \"Percentage Resistance in Gas and Liquid film is:\", round(Rgasper,1),\"&\", round(Rliquidper,1),\"respectively\"\n",
      "print 'The Flux of A is: %5.3e'%(NA), \"kgmol/(m2.s)\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Individual Film resistances for Gas and Liquid film are: 483.16 382.41 respectively\n",
        "Overall Mass Transfer Coefficient: 8.924e-04 kgmol/(m2.s)\n",
        "Percentage Resistance in Gas and Liquid film is: 55.8 & 44.2 respectively\n",
        "The Flux of A is: 3.789e-04 kgmol/(m2.s)\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.5-1 Page Number 608"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Design of Water-Cooling Tower Using Film Coefficients\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from scipy.interpolate import interp1d\n",
      "from scipy.optimize import root\n",
      "import scipy.integrate as integrate\n",
      "\n",
      "#Variable Declarion\n",
      "P = 101325      #Atmospheric pressure\n",
      "G = 1.356       #Inlet Gas Flow Rate, kg mol/hr\n",
      "L = 1.356       #Inlet Liquid Flow Rate, kg mol/hr\n",
      "TL1 = 29.4      #Initial temperature of water in \u00b0C\n",
      "TL2 = 43.3      #Final temperature of water in \u00b0C\n",
      "TG1 = 29.4      #Inlet air temperature of air in \u00b0C\n",
      "Tw  = 23.9      #Wet bulb temperature of air in \u00b0C\n",
      "kGa = 1.207e-7  #Mass Transfer Coefficient, kmol/(m2.s.Pa)\n",
      "K = 4.187e4     #J/(kg.K)\n",
      "cL = 4187.       #J/(kg.K)\n",
      "T0 = 0.0\n",
      "H1 = 0.0165     #Humidity of entering air, kgWater/kgDryAir\n",
      "Mb = 29.\n",
      " \n",
      "\n",
      "#Calculation\n",
      "t = np.array([15.6,26.7,29.6,32.2,35.0,37.8,40.6,43.3,46.1])\n",
      "H  = np.array([43.68e3,84.0e3,97.2e3,112.1e3,128.9e3,148.2e3,172.1e3,197.2e3,224.5e3])\n",
      "\n",
      "f = interp1d(t,H,kind='cubic',bounds_error=False)\n",
      "T = np.arange(28.,46.,1)\n",
      "Hy = f(T)\n",
      "\n",
      "Hy1 = (1.005+1.88*H1)*1e3*(TG1-0.0)+2.501e6*H1\n",
      "Hy2 = L*cL*(TL2-TL1)/G + Hy1\n",
      "plt.figure(1)\n",
      "plt.plot(t,H,'k-',T,Hy,'k-')\n",
      "plt.plot([TL1,TL2],[Hy1,Hy2],'ro-')\n",
      "plt.xlabel('$Liquid Temperature, C$')\n",
      "plt.ylabel('$Enthalpy Hy, J/kg$')\n",
      "\n",
      "m1=(Hy2-Hy1)/(TL2-TL1)\n",
      "c1=Hy1-m1*TL1\n",
      "m2 = K\n",
      "TT = linspace(TL1,TL2,11)\n",
      "plt.xlim(25,45)\n",
      "plt.ylim(40000,200000)\n",
      "Hyi = ()\n",
      "Hy = ()\n",
      "cnt = 0\n",
      "for i in TT:\n",
      "    y1 = m1*i+c1\n",
      "    c2 = y1-m2*i\n",
      "    ff = lambda xx: f(xx)-y1+K*(xx-i)\n",
      "    sol = root(ff,40)\n",
      "    x2 = sol.x[0]\n",
      "    y2 = f(x2)\n",
      "    Hy = np.append(Hy,y1)\n",
      "    Hyi = np.append(Hyi,y2) \n",
      "    cnt = cnt + 1\n",
      "    plt.plot([i,x2],[y1,y2],'o-')\n",
      "InvHyimHy = 1/(Hyi-Hy)\n",
      "\n",
      "z = (G/(Mb*kGa*P))*integrate.simps(InvHyimHy,Hy)\n",
      "plt.figure(2)\n",
      "plt.plot(Hy,InvHyimHy)\n",
      "plt.fill_between(Hy,InvHyimHy,0,color='0.6')\n",
      "plt.text(90000, 0.00002, 'Area = '+str(round(z,2))+' m')\n",
      "plt.xlabel('$Hy$')\n",
      "plt.ylabel('$1/(Hyi-Hy)$')\n",
      "plt.show()\n",
      "#Results\n",
      "print \"Height of packed cooling tower:\", round(z,2),\"m\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vk6WlpbIIwd7enuLi4kgul79WhDBz5kwiIgoKCipXhGBhYUEymYwePXqk/PlV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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x624b5d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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a2mXFERFJiUwG+Ptfhb//VcTHH8H58wocOaLCH/7ggaFDr2HWLAMWL+4PhYLX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\ndbdNR90bYERE1FZgYCACAwM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       "text": [
        "<matplotlib.figure.Figure at 0x63dd2b0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Height of packed cooling tower: 7.01 m\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.6-1 Page Number 614"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Absorption of SO2 in Plate Tray\n",
      "from scipy.optimize import root\n",
      "from scipy.interpolate import interp1d\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "#Variable Declaration\n",
      "ysiP = 0.20   #Mole fraction of SO2 entering\n",
      "ysoP = 0.02   #Mole fraction of SO2 leaving  \n",
      "P = 101325.   #Total pressure at which SO2 is leaving in Pa\n",
      "Fair = 150    #Flowrate of air in kg/hrm2\n",
      "Fwat = 6000   #Flowrate of water in kg/hrm2\n",
      "effP = 0.25   #Fractional efficiency\n",
      "MWair = 29    #Molecular weight of air \n",
      "MWW = 18      #Molecular weight of water \n",
      "x = np.array([0.0000,0.0005,0.001,0.0015,0.002,0.0025,0.003,0.0035,0.004,0.0045,0.005,0.0055,0.006]) \n",
      "y = np.array([0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.]) \n",
      "xe = np.array([0.0,0.0000562,0.0001403,0.000280,0.000422,0.000564,0.000842,0.001403,0.001965,0.00279,0.00420,0.00698]) \n",
      "ye = np.array([0.0,0.000658,0.00158,0.00421,0.00763,0.0112,0.01855,0.0342,0.0513,0.0775,0.121,0.212]) \n",
      "\n",
      "#Calculations\n",
      "Vd = Fair/MWair\n",
      "Ld = Fwat/MWW\n",
      "k1 = Vd*ysiP/(1-ysiP)\n",
      "k2 = Vd*ysoP/(1-ysoP)\n",
      "f = lambda x: k1 -( Ld*(x/(1-x)) + k2) \n",
      "sol = root(f,0.002)\n",
      "xn = sol.x[0]\n",
      "\n",
      "for i in range(len(y)):\n",
      "    k1 = Vd*ysoP/(1-ysoP) + Ld*(x[i]/(1-x[i]))\n",
      "    ff = lambda yy: Vd*(yy/(1-yy))-k1\n",
      "    sol  = root(ff,.1)\n",
      "    y[i] = sol.x[0]\n",
      "\n",
      "f1 = interp1d(x,y, bounds_error=False)\n",
      "f11 = interp1d(y,x, bounds_error=False)\n",
      "f2 = interp1d(ye,xe, bounds_error=False)\n",
      "plt.plot(x,y,'r-',xe,ye,'b-')\n",
      "\n",
      "plt.text(.0037, .1, 'Equilibrium Curve')\n",
      "plt.text(.0025, .22, 'Operating Line')\n",
      "plt.annotate('$(x_N,y_{N+1})$', xy=(xn,ysiP), xytext=(0.004,0.2))\n",
      "plt.annotate('$(x_0,y_1)$', xy=(0,ysoP), xytext=(0.0003,0.12))\n",
      "def ypos(xop):\n",
      "    return f1(xop)\n",
      "\n",
      "def xpos(yop):\n",
      "    return f2(yop)\n",
      "\n",
      "xmax = f11(ysiP)\n",
      "x1 = x[0]\n",
      "y1 = y[0]\n",
      "plot(x[0],y[0],'ro-')\n",
      "plot([0.,xn], [ysiP,ysiP], 'k--', lw=1)\n",
      "plot([xn,xn], [ysiP,0.], 'k--', lw=1)\n",
      "plot(xn,ysiP,'bo-')\n",
      "xlabel(\"Liquid phase mole fraction\")\n",
      "ylabel(\"Vapor phase mole fraction\")\n",
      "n = 0\n",
      "while x1 <= xmax:\n",
      "    x2 = xpos(y1)\n",
      "    y2 = y1\n",
      "    plt.text(x2, y2-0.013, str(n+1))\n",
      "    plot([x1,x2], [y1,y2], 'k-', lw=1)      #Draw Horizontal line to equilibrium curve\n",
      "    if x2 > xmax:\n",
      "        dxt = x2-x1\n",
      "        dx = xmax-x1\n",
      "        dxbydxt = dx/dxt\n",
      "        n = n + dxbydxt\n",
      "        break\n",
      "    x1 = x2\n",
      "    y1 = y2\n",
      "    y2 = ypos(x1)\n",
      "    plot([x1, x2], [y1, y2], 'r-', lw=1)    #Draw a vertical line to operating line\n",
      "    n = n+1\n",
      "    x1 = x2\n",
      "    y1 = y2 \n",
      "\n",
      "print 'Number of Stages for separation', round(n,2)\n",
      "print 'Actual Number of stages', round(n/effP,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of Stages for separation 2.26\n",
        "Actual Number of stages 9.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0bNkS8+bNU13AWogWJBL1JZUCv/wCrFzJ/x02LO/nRSJekFGN0O+k8qSk8L3I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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8875710>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.6-2 Page Number 621 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Absorption of Acetone in a PAcked Tower\n",
      "from scipy.optimize import root\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "#Variable Declaration\n",
      "P, T= 101325., 293.   #column pressure and temperature\n",
      "y1, y2 = 0.026, 0.005 #Compositions in gas phase\n",
      "x2 = 0.0              #Composition of fresh solvent feed\n",
      "V, L = 13.65, 45.36   #Vapor and liquid rates to the coloumn \n",
      "kyd, kxd = 3.78e-2, 6.16e-2  #Film coefficient for gas and liquid phase, (kmol/(s.m3.mol frac))\n",
      "xA, pA, PmmHg = 0.0333, 30., 760.   #liquid equilibrium mole frac at partial presure \n",
      "A = 0.186              #Tower cross sectional area\n",
      "#Calculations\n",
      "def func1(s1,c1,s2,c2,s3,x0,y0):\n",
      "    c3 = c1 - (s3-s1)*x0\n",
      "    x = (c3 - c2)/(s2 - s3)\n",
      "    y = s2*x + c2\n",
      "    return [x,y]\n",
      "\n",
      "def lm(a,b,c,d):\n",
      "    return ((a-b)-(c-d))/log((a-b)/(c-d))\n",
      "\n",
      "S = (pA/PmmHg)/xA\n",
      "k1 = V*y1/(1-y1)-V*y2/(1-y2)\n",
      "k1 = k1/L\n",
      "x1 = k1/(1+k1)\n",
      "yA = pA/PmmHg\n",
      "me = yA/xA\n",
      "ce = 0.0\n",
      "mop = (y2-y1)/(x2-x1)\n",
      "cop = y2 - mop*x2\n",
      "xe = np.arange(0.,0.015,0.001)\n",
      "ye = me*xe\n",
      "plt.plot(xe,ye,'b-')\n",
      "plt.plot([x1,x2], [y1,y2], 'ko-', lw=0.5)\n",
      "m1  = -(kxd/(1-x1))/(kyd/(1-y1))\n",
      "m2  = -(kxd/(1-x2))/(kyd/(1-y2))\n",
      "[xi1,yi1]=func1(mop,cop,me,ce,m1,x1,y1)\n",
      "plt.plot([x1,xi1], [y1,yi1], 'ko-', lw=0.5)\n",
      "[xi2,yi2]=func1(mop,cop,me,ce,m2,x2,y2)\n",
      "plt.plot([x2,xi2], [y2,yi2], 'ko-', lw=0.5)\n",
      "\n",
      "plt.text(.006, .006, 'Equilibrium Curve')\n",
      "plt.text(.001, .02, 'Operating Line')\n",
      "\n",
      "plt.annotate('$(x_1,y_1)$', xy=(x1,y1), xytext=(x1,y1+0.002),\n",
      "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
      "            )\n",
      "\n",
      "plt.annotate('$(x_2,y_2)$', xy=(0,y2), xytext=(0.0005,y2),\n",
      "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
      "            )\n",
      "plt.xlabel(\"Liquid phase mole fraction, $x$\")\n",
      "plt.ylabel(\"Vapor phase mole fraction, $y$\")\n",
      "plt.plot([0,x1,x1], [y1,y1,0], 'ko-', lw=0.5)\n",
      "plt.plot([0,xi1,xi1], [yi1,yi1,0], 'ko-', lw=0.5)\n",
      "plt.plot([0,xi2,xi2], [yi2,yi2,0], 'ko-', lw=0.5)\n",
      "V1 = V/(1-y1)\n",
      "V2 = V/(1-y2)\n",
      "V = (V1+V2)*.5/3600.\n",
      "L = L/3600.\n",
      "dyyilm = lm(y1,yi1,y2,yi2)\n",
      "zy = V*(y1-y2)/(kyd*dyyilm*A)\n",
      "dxxilm = lm(xi1,x1,xi2,x2)\n",
      "zx = L*(x1-x2)/(kxd*dxxilm*A)\n",
      "y1s = me*x1\n",
      "y2s = me*x2\n",
      "plt.plot([0,x1,x1], [y1s,y1s,0], 'ko-', lw=0.5)\n",
      "d1yilm1 = lm(1.,yi1,1.,y1)\n",
      "d1yilm2 = lm(1.,yi2,1.,y2)\n",
      "d1yilm = (d1yilm1 + d1yilm2)/2.\n",
      "d1xilm = lm(1.,x1,1.,xi1)\n",
      "d1ys1lm = lm(1.,y1s,1.,y1)\n",
      "d1ys2lm = lm(1.,y2s,1.,y2)\n",
      "d1yslm = (d1ys1lm + d1ys2lm)/2.\n",
      "Kya = 1./(d1yslm*(1/(kyd/d1yilm) + me/(kxd/d1xilm)))\n",
      "dyyslm = lm(y1,y1s,y2,y2s)\n",
      "z = V*(y1-y2)/(A*Kya*dyyslm)\n",
      "#Results\n",
      "print \"The values of kya, kxa, Kya are:\", round(kyd,4),round(kxd,4),round(Kya,4),\" kgmol/(s.m2. mol frac) respectively\"\n",
      "print \"Tower Height calculated using kya:\", round(zy,4),'m'\n",
      "print \"Tower Height calculated using kxa:\", round(zx,4),'m'\n",
      "print \"Tower Height calculated using Kya:\", round(z,4),'m'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The values of kya, kxa, Kya are: 0.0378 0.0616 0.0223  kgmol/(s.m2. mol frac) respectively\n",
        "Tower Height calculated using kya: 1.9489 m\n",
        "Tower Height calculated using kxa: 1.9394 m\n",
        "Tower Height calculated using Kya: 1.8973 m\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mzJjB+++/T6NGjUxdjhCAFj7t2rVj7ty5xMXFMWTIEHbu3CnhYyQGn+k8bJGu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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x61d7b30>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.6-3 Page Number 625"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Use of Transfer Unit for Packed Tower\n",
      "\n",
      "#Variable Declaration\n",
      "kyd = 0.0378        #Film coefficient in kmol/s.m3\n",
      "kxd = 0.0616        #Film coefficient in kmol/s.m3  \n",
      "Kya = 0.022523      #Overall mass transfer coefficient\n",
      "V = 3.852e-3        #Molar flowrate kmol/s\n",
      "A = 0.186           #Cross sectional area of the tower m2\n",
      "y1 = 0.026\n",
      "y2 = 0.005\n",
      "    #From answers to previous problem\n",
      "yi1 = 0.0154\n",
      "yi2 = 0.0020\n",
      "d1yilm = 0.97925\n",
      "d1yilm1 = 0.979249088369\n",
      "d1yilm2 = 0.996443506323\n",
      "d1xilm = 0.99023 \n",
      "d1yslm = 0.98313\n",
      "dyyilm = 0.00590\n",
      "d1yslm = 0.983131759622 \n",
      "d1ys2m = 0.997497911442\n",
      "dyyslm = 0.0102576937583\n",
      "\n",
      "#Calculations\n",
      "    #PART A\n",
      "HG = V/(kyd*A)\n",
      "NG1 = (d1yilm1/(1-y1) + d1yilm2/(1-y2) )/2.\n",
      "NG2 = (y1-y2)/dyyilm\n",
      "NG = NG1*NG2\n",
      "z = HG*NG \n",
      "#Results\n",
      "print \"Answers to Part A\"\n",
      "print \"Height of gas transfer unit\",round(HG,3)\n",
      "print \"Number of gas transfer unit\",round(NG,3)\n",
      "print \"Total Height of tower\", round(z,4),'m'\n",
      "\n",
      "    #PART B\n",
      "HoG = V/(Kya*A)\n",
      "NoG1 = (d1ys1lm/(1-y1) + d1ys2lm/(1-y2))/2.\n",
      "NoG2 = (y1-y2)/dyyslm\n",
      "NoG = NoG1*NoG2\n",
      "z = HoG*NoG \n",
      "\n",
      "#Results\n",
      "print \"Answers to Part B\"\n",
      "print \"Height of Overall gas transfer unit\",round(HoG,3)\n",
      "print \"Number of Overall gas transfer unit\",round(NoG,3)\n",
      "print \"Total Height of tower\", round(z,4),'m'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Answers to Part A\n",
        "Height of gas transfer unit 0.548\n",
        "Number of gas transfer unit 3.571\n",
        "Total Height of tower 1.9567\n",
        "Answers to Part B\n",
        "Height of Overall gas transfer unit 0.919\n",
        "Number of Overall gas transfer unit 2.059\n",
        "Total Height of tower 1.8936\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.7-1 Page Number 628"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Desing of an Absorption Tower with Concentrated Gas Mixture\n",
      "from scipy.interpolate import interp1d\n",
      "import numpy as np\n",
      "import scipy.integrate as integrate\n",
      "\n",
      "#Variable Declaration\n",
      "dp = 0.0254       #Diameter of the rings ,m\n",
      "x2 = 0.0          \n",
      "P = 101325        #Total pressure ,Pa\n",
      "y1 = 0.20         #Inlet Mole fraction of SO2 \n",
      "y2 = 0.02         #Outlet Mole fraction of SO2 \n",
      "Vd = 6.53e-4      #Molar flowrate of air ,kmol/s\n",
      "Ld = 4.20e-2      #Molar flowrate of water ,kmol/s\n",
      "AT = 0.0929       #Tower cross sectional area,m2\n",
      "xe = np.array([0.0,0.0000562,0.0001403,0.000280,0.000422,0.000564,0.000842,0.001403,0.001965,0.00279,0.00420,0.00698])\n",
      "ye = np.array([0.0,0.000658,0.00158,0.00421,0.00763,0.0112,0.01855,0.0342,0.0513,0.0775,0.121,0.212])\n",
      "yop = np.array([0.02,0.04,0.07,0.13,0.20])\n",
      "xop = np.zeros(len(yop))\n",
      "Gy = np.zeros(len(yop))\n",
      "Gx = np.zeros(len(yop))\n",
      "V = np.zeros(len(yop))\n",
      "L = np.zeros(len(yop))\n",
      "kyda = np.zeros(len(yop))\n",
      "kxda = np.zeros(len(yop))\n",
      "xi = np.array([0.00046,0.00103,0.00185,0.00355,0.00565])\n",
      "yi = np.array([0.0090,0.0235,0.0476,0.1015,0.1685])\n",
      "d1y = 1.-yop\n",
      "dyyi = yop-yi\n",
      "d1yyilm = ((1.-yop)-(1.-yi))/log((1.-yop)/(1.-yi))\n",
      "\n",
      "#Calcualations\n",
      "def funkxda(gx):\n",
      "    return 0.152*gx**0.82\n",
      "\n",
      "def funkyda(gx,gy):\n",
      "    return 0.0594*gx**0.25*gy**0.7\n",
      "\n",
      "k1 = (Ld*x2/(1.-x2)+Vd*(y1/(1.-y1)-y2/(1.-y2)))/Ld\n",
      "x1 = k1/(1.+k1)\n",
      "\n",
      "for j in range(len(yop)):\n",
      "    k1 = (Ld*x1/(1.-x1)+Vd*(-y1/(1.-y1)+yop[j]/(1.-yop[j])))/Ld\n",
      "    xop[j] = k1/(1+k1)\n",
      "    Gy[j] = (Vd*29.+Vd*yop[j]*64.1/(1.-yop[j]))/AT\n",
      "    Gx[j] = (Ld*18.+Ld*xop[j]*64.1/(1.-xop[j]))/AT\n",
      "    V[j] = Vd/(1.-yop[j])\n",
      "    L[j] = Ld/(1.-xop[j])\n",
      "    kxda[j] = funkxda(Gx[j])\n",
      "    kyda[j] = funkyda(Gx[j],Gy[j])\n",
      "\n",
      "fyop = V/((kyda*AT/d1yyilm)*d1y*dyyi)\n",
      "Height = integrate.simps(fyop,yop)\n",
      "\n",
      "#Results\n",
      "print \"Height of the tower:\",round(Height,3), \"m\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Height of the tower: 1.574 m\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 10.8-1 Page Number 633"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Prediction of Film Coefficients for Ammonia Absorption\n",
      "\n",
      "#Variable Declaration\n",
      "T = 303            #Temperature of the tower in K\n",
      "dp = .0254         #Diameter of the ring m\n",
      "P = 101325         #Total pressure in Pa\n",
      "Gx = 2.543         #Flowrate in kg/s.m2\n",
      "Gy = 0.339         #Flowrate in kg/s.m2        \n",
      "m = 1.2\n",
      "mua = 1.86e-5      #Viscosity of air in Pa.s\n",
      "rhoa = 1.168       #Density of air in kg/m3\n",
      "DAB = 2.379e-5     #Diffusivity of NH3 in water , m2/s\n",
      "alpha = 0.557      #Constant in correlation for gas phase mass transfer coefficient\n",
      "beta = 0.32        #Constant in correlation for gas phase mass transfer coefficient\n",
      "gama = -0.51       #Constant in correlation for gas phase mass transfer coefficient\n",
      "muw15 = 1.1404e-3  #Viscosity of water at 15\u00b0C\n",
      "muw30 = 0.8007e-3  #Viscosity of water at 30\u00b0C\n",
      "DABw15 = 1.77e-9   #Diffusivity of NH3 in water at 15\u00b0C\n",
      "rhow = 996.        #Desity of water in kg/m3\n",
      "theta = 2.35e-3    #Constant in correlation for liquid phase mass transfer coefficient\n",
      "eta = 0.22         #Constant in correlation for liquid phase mass transfer coefficient\n",
      "#Calcualations\n",
      "V = Gy/(29.0)\n",
      "L = Gx/18.0\n",
      "Sca = mua/(rhoa*DAB)\n",
      "HG = alpha*Gy**beta*Gx**gama*Sca**0.5\n",
      "DABw = muw15*303/(muw30*288)*DABw15\n",
      "Scw = muw30/(rhow*DABw)\n",
      "HL = theta*(Gx/muw30)**eta*Scw**0.5\n",
      "kyda = V/HG\n",
      "kxda = L/HL\n",
      "Kyda = 1./(1./kyda + m/kxda)\n",
      "ResGf = (1./kyda)/(1./Kyda)*100\n",
      "\n",
      "#Results\n",
      "print \"Predicted film coefficients are as follows \"\n",
      "print \"Gas phase film cofficient \",round(kyda,4),\"kmol/(s.m3.mol frac)\"\n",
      "print \"Liquid phase film cofficient \",round(kxda,3),\"kmol/(s.m3.mol frac)\"\n",
      "print \"Overall Gas phase film cofficient \",round(Kyda,4),\"kmol/(s.m3.mol frac)\"\n",
      "print \"Percentage resistance in gas film is \",round(ResGf,1), \"%\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Predicted film coefficients are as follows \n",
        "Gas phase film cofficient  0.0584 kmol/(s.m3.mol frac)\n",
        "Liquid phase film cofficient  0.586 kmol/(s.m3.mol frac)\n",
        "Overall Gas phase film cofficient  0.0521 kmol/(s.m3.mol frac)\n",
        "Percentage resistance in gas film is  89.3 %\n"
       ]
      }
     ],
     "prompt_number": 25
    }
   ],
   "metadata": {}
  }
 ]
}