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 },
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 3:lnterphase Mass Transfer"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.1,Page number:161"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Illustration 3.1\n",
      "#Application of Raoult\u2019s Law to a Binary System\n",
      "\n",
      "#Variable declaration\n",
      "\t# a-benzene   b-toluene\n",
      "T = 300  \t\t\t\t\t# [K]\n",
      "x_a = 0.4  \t\t\t\t\t# [mole fraction in liquid phase]\n",
      "\t# Antoine constants for benzene and toluene are given\n",
      "\t# For benzene\n",
      "A_a = 15.9008 \n",
      "B_a = 2788.51 \n",
      "C_a = -52.36 \n",
      "# For toluene\n",
      "A_b = 16.0137 \n",
      "B_b = 3096.52 \n",
      "C_b = -53.67 \n",
      "\n",
      "#Calculation\n",
      "\n",
      "import math\n",
      "\n",
      "\t# Using equation 3.5 vapor pressure of component 'a' and 'b'\n",
      "P_a = math.exp(A_a-(B_a/(T+C_a)))  \t\t# [mm of Hg]\n",
      "P_b = math.exp(A_b-(B_b/(T+C_b)))  \t\t# [mm of Hg]\n",
      "\n",
      "P_a = P_a*101.3/760  \t\t\t\t# [kPa]\n",
      "P_b = P_b*101.3/760  \t\t\t\t# [kPa]\n",
      "\t# Partial pressure of component 'a' and 'b'\n",
      "p_a = x_a*P_a  \t\t\t\t\t# [kPa]\n",
      "p_b = (1-x_a)*P_b  \t\t\t\t# [kPa]\n",
      "P_total = p_a+p_b  \t\t\t\t# [kPa]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The total equilibrium pressure of the binary system of benzene and toluene is\",round(P_total,2),\" kPa\" \n",
      "\n",
      "y_a = p_a/P_total  \t\t\t\t# [mole fraction in vapor phase]\n",
      "print\"The composition of the vapor in equilibrium is\",round(y_a,3)  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total equilibrium pressure of the binary system of benzene and toluene is 8.02  kPa\n",
        "The composition of the vapor in equilibrium is 0.688\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.2,Page number:162"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Illustration 3.2\n",
      "#Henry's Law: Saturation of Water with Oxygen\n",
      "\n",
      "\n",
      "#Variable declaration\n",
      "\t# A-oxygen   B-water\n",
      "T = 298  \t\t\t\t\t# [K]\n",
      "H = 4.5*10**4  \t\t\t\t\t# [atm/mole fraction]\n",
      "P = 1  \t\t\t\t\t\t# [atm]\n",
      "row_B = 1000.0 \t\t\t\t\t# [density of water, kg/cubic m]\n",
      "M_B = 18.0  \t\t\t\t\t# [Molecular mass of water, gram/mole]\n",
      "M_A = 32.0  \t\t\t\t\t# [,Molecular mass of oxygen, gram/mole]\n",
      "\n",
      "\n",
      "\t# Dry air contains 21% oxygen  then p_A = y*P = 0.21 atm\n",
      "\t# Therefore using Henry's Law\n",
      "p_A = 0.21  \t\t\t\t\t# [atm]\n",
      "x_A = p_A/H  \t\t\t\t\t# [mole fraction in liquid phase]\n",
      "\n",
      "\t# Basis: 1L of saturated solution\n",
      "\t# For 1 L of very dilute solution of oxygen in water, the total moles of solution, n_t, \twill be approximately equal to the moles of water\n",
      "n_t = row_B/M_B\n",
      "\t# Moles of oxygen in 1L saturated solution is\n",
      "n_o = n_t*x_A  \t\t\t\t\t# [mole]\n",
      "\t# Saturation concentration\n",
      "c_A = n_o*M_A*1000  \t\t\t\t# [mg/L]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The saturation concentration of oxygen in water exposed to dry air at 298 K and 1 atm is\",round(c_A,2),\"mg/L\"  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The saturation concentration of oxygen in water exposed to dry air at 298 K and 1 atm is 8.3 mg/L\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.3,Page number:162"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Illustration 3.3\n",
      "#Material Balances Combined with Equilibrium Relations: Algebraic Solution \n",
      "\n",
      "#Variable declaration\n",
      "\t# a-ammonia   b-air   c-water\n",
      "T = 300  \t\t\t\t\t# [K]\n",
      "P = 101.3  \t\t\t\t\t# [kPa]\n",
      "R = 8.314  \t\t\t\t\t# [cubic m.Pa/mole.K]\n",
      "V_b = 15.0  \t\t\t\t\t# [cubic m]\n",
      "m_a = 10.0  \t\t\t\t\t# [kg]\n",
      "m_c = 45.0  \t\t\t\t\t# [kg]\n",
      "M_a = 17.0  \t\t\t\t\t# [molecular mass of ammonia, gram/mole\n",
      "M_c = 18.0  \t\t\t\t\t# [molecular mass of water, gram/mole]\n",
      "\n",
      "\n",
      "#Calculations\n",
      "\n",
      "n_b = V_b*P/(R*T)  \t\t\t\t# [kmole]\n",
      "n_a = m_a/M_a  \t\t\t\t\t# [kmole]\n",
      "n_c = m_c/M_c  \t\t\t\t\t# [kmole]\n",
      "\n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "\n",
      "def f12(L_a):\n",
      "    return(((n_a-L_a)/(n_b+n_a-L_a))-(10.51*(0.156+(0.622*(L_a/(n_c+L_a))*(5.765*(L_a/(n_c+L_a))-1)))*(L_a/(n_c+L_a)))) \n",
      "L_a = fsolve(f12,0.3)  # [kmole]\n",
      "\n",
      "x_a = L_a/(n_c+L_a) \n",
      "y_a = (n_a-L_a)/(n_b+n_a-L_a) \n",
      "gammma_a = 0.156+0.622*x_a*(5.765*x_a-1) \n",
      "\n",
      "#Result\n",
      "\n",
      "print\"At equilibrium the ammonia content of the liquid phase will be\",x_a[0] \n",
      "print\"At equilibrium the ammonia content of the gas phase will be\",y_a[0] \n",
      "print\"The amount of ammonia absorbed by the water will be\",round(L_a[0],3),\"kmole\"   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "At equilibrium the ammonia content of the liquid phase will be 0.144495485596\n",
        "At equilibrium the ammonia content of the gas phase will be 0.214117393686\n",
        "The amount of ammonia absorbed by the water will be 0.422 kmole\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.4,Page number:169"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Illustration 3.4\n",
      "#Mass-Transfer Resistances During Absorption of Ammonia by Water\n",
      "\n",
      "#Variable declaration\n",
      "\t#  a-ammonia\n",
      "T  = 300  \t\t\t\t\t# [K]\n",
      "P = 101.3  \t\t\t\t\t# [kPa]\n",
      "Kg = 2.75*10**-6  \t\t\t\t# [kmole/square m.s.kPa]\n",
      "m = 1.64 \n",
      "res = 0.85  \t\t\t\t\t# [gas phase resistance]\n",
      "xa_g = 0.115/100.0  \t\t\t\t# [mole fraction of NH3 in liquid phase at a \t\t\t\t\t\t\tpoint]]\n",
      "\n",
      "#Calculations\n",
      "\n",
      "ya_g = 8.0/100.0  \t\t\t\t\t# [mole fraction of NH3 in gas phase at a \t\t\t\t\t\t\tpoint]\n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "\n",
      "Ky = Kg*P  \t\t\t\t\t# [kmole/square m.s]\n",
      "\t# Using equation 3.24\n",
      "ky = Ky/res  \t\t\t\t\t# [kmole/square m.s]\n",
      "\t\t\t\t\t\t# Using equation 3.21\n",
      "def f12(kx):\n",
      "    return((m/kx)-(1/Ky)+(1/ky)) \n",
      "kx = fsolve(f12,0.0029)  \t\t\t# [kmole/square m.s]\n",
      "\n",
      "\t# Interfacial concentrations at this particular point in the column, using equation \t\t\t(3.15)\n",
      "ystar_a = m*xa_g \n",
      "\t# Using equation 3.12\n",
      "N_a = Ky*(ya_g-ystar_a)  \t\t\t# [kmole/square m.s]\n",
      "\t# Gas-phase interfacial concentration from equation (3.9)\n",
      "ya_i = ya_g-(N_a/ky) \n",
      "\t# Since the interfacial concentrations lie on the equilibrium line, therefore\n",
      "xa_i = ya_i/m \n",
      "\t# Cross checking the value of N_a\n",
      "N_a = kx*(xa_i-xa_g)  \t\t\t\t# [kmole/square m.s]\n",
      "\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The individual liquid film coefficient and gas film coefficient are\",round(kx[0],5),\"kmole/square m.s and \",round(ky,6),\"kmole/square m.s respectively\" \n",
      "\n",
      "print\"The gas phase and liquid phase interfacial concentrations are\",round(ya_i,5),\"and,\",round(xa_i,5),\"respectively\" "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The individual liquid film coefficient and gas film coefficient are 0.00305 kmole/square m.s and  0.000328 kmole/square m.s respectively\n",
        "The gas phase and liquid phase interfacial concentrations are 0.0136 and, 0.00829 respectively\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.5,Page number:171"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Illustration 3.5\n",
      "#Absorption of Ammonia by Water: Use of F-Type Mass-Transfer Coefficients \n",
      "\n",
      "#Variable declaration\n",
      "\t#  a-ammonia\n",
      "T = 300  \t\t\t\t\t# [K]\n",
      "P = 101.3  \t\t\t\t\t# [kPa]\n",
      "ya_g = 0.6  \t\t\t\t\t# [ammonia concentration in bulk gas]\n",
      "xa_l = 0.12  \t\t\t\t\t# [ammonia concentration in bulk liquid]\n",
      "Fl = 3.5*10**-3 \t \t\t\t# [kmole/square m.s]\n",
      "Fg = 2*10**-3  \t\t\t\t\t# [kmole/square m.s]\n",
      "\n",
      "#Calculations\n",
      "\n",
      "import math\n",
      "from scipy.optimize import fsolve \n",
      "# Algebraic solution (a)\n",
      "\n",
      "\n",
      "def f12(xa_i):\n",
      "    return(1-(1-ya_g)*((1-xa_l)/(1-xa_i))**(Fl/Fg) - 10.51*(0.156+0.622*xa_i*(5.765*xa_i-1))*xa_i) \n",
      "xa_i = fsolve(f12,0.2) \n",
      "\n",
      "ya_i = 1-(1-ya_g)*((1-xa_l)/(1-xa_i))**(Fl/Fg) \n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The local gas and liquid interfacial concentrations are\",round(ya_i[0],3),\"and\",round(xa_i[0],3),\"respectively\" \n",
      "\t# Using equation 3.28 \n",
      "\n",
      "N_a = Fg*math.log((1-ya_i)/(1-ya_g)) \n",
      "\n",
      "print\"The local ammonia mass-transfer flux is\",round(N_a,5),\"kmole/square m.s\"\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The local gas and liquid interfacial concentrations are 0.494 and 0.231 respectively\n",
        "The local ammonia mass-transfer flux is 0.00047 kmole/square m.s\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.6,Page number:174"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Illustration 3.6\n",
      "#Distillation of a Mixture of Methanol and Water in a Packed Tower: Use of F-Type #Mass-Transfer oefficients\n",
      "\n",
      "#Variable declaration\n",
      "#  a-methanol   b-water\n",
      "T = 360.0  # [K]\n",
      "P = 101.3  # [kPa]\n",
      "lambda_a = 33.3  # [MJ/kmole]\n",
      "lambda_b = 41.3  # [MJ/kmole]\n",
      "Fg = 0.0017  # [kmole/square m.s]\n",
      "Fl = 0.0149  # [kmole/square m.s]\n",
      "yag = 0.36  # [bulk gas phase concentration]\n",
      "xag = 0.20  # [bulk liquid phase concentration]\n",
      "R = 1.987  \n",
      "\n",
      "\n",
      "#Calculations\n",
      "\n",
      "import math\n",
      "from numpy import *\n",
      "from scipy.optimize import fsolve\n",
      "\n",
      "\n",
      "sia_ag= 1.0/(1-(lambda_a/lambda_b))\n",
      "print\"sia_g is\",sia_ag\n",
      "#sia_ag =5.155 \n",
      "sia_al = sia_ag \n",
      "# Therefore equation 3.29 becomes\n",
      "# yai = 5.155-4.795(4.955/(5.155-xai))**8.765\n",
      "\n",
      "# Using equation 3.33, 3.34, 3.35\n",
      "V2 = 18.07  # [cubic cm/mole]\n",
      "V1 = 40.73  # [cubic cm/mole]\n",
      "a12 = 107.38  # [cal/mole]\n",
      "a21 = 469.5  # [cal/mole]\n",
      "\n",
      "# Solution of simultaneous equation \n",
      "def F(e):\n",
      "    x1,x2,y1,y2,e4=e\n",
      "     \n",
      "    #####SIDE Quantities#####\n",
      "    a=(V1/V2)*math.exp(-a21/(R*e4))           #d2lT\n",
      "    a1=(V2/V1)*math.exp(-a12/(R*e4))             #dl2(T) \n",
      "    b=math.exp(16.2620-3800.0/(e4-47))           #P2(T)\n",
      "    b1=math.exp(16.5938-3644.3/(e4-33))           #P1(T)\n",
      "    c=math.exp(-log(x2+x1*a)-x1*(a1/(x1+x2*a1)-a/(x2+x1*a)))     #gamma_2\n",
      "    d=math.exp(-log(x1+x2*a1)-x1*(a1/(x1+x2*a1)-a/(x2+x1*a)))    #gamma_1\n",
      "    #########################\n",
      "    x1=1-x2\n",
      "    y2=1-y1\n",
      "    y1=5.155-4.795*(4.955/(5.155-x2))**(Fl/Fg) \n",
      "    y1=x1*d*b1/P\n",
      "    y2=x2*c*b/P\n",
      "    return(x1,x2,y1,y2,e4)    \n",
      "# Initial guess\n",
      "y = fsolve(F,(0.1,0.9,0.2,0.8,300)) \n",
      "xai = y[0] \n",
      "xbi = y[1] \n",
      "yai = y[2] \n",
      "ybi = y[3] \n",
      "T = y[4]  # [K]\n",
      "\n",
      "print\"yai is\",yai\n",
      "print\"ybi is\",ybi\n",
      "print\"xai is\",xai\n",
      "print\"xbi is\",xbi\n",
      "print\"Temperature is\",T,\"K\" \n",
      "# Local Methanol flux, using equation 3.28\n",
      "Na = sia_ag*Fg*math.log((sia_ag-yai)/(sia_ag-yag))  # [kmole/square m.s]\n",
      "# From energy balance\n",
      "Nb = -(lambda_a/lambda_b)*Na\n",
      "#Result\n",
      "\n",
      "print\"Local Methanol flux is\",round(Na,4),\" (APPROX) kmole/square m.s\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sia_g is 5.1625\n",
        "yai is 0.2\n",
        "ybi is 0.8\n",
        "xai is 0.1\n",
        "xbi is 0.9\n",
        "Temperature is 300.0 K\n",
        "Local Methanol flux is 0.0003  (APPROX) kmole/square m.s\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:46: RuntimeWarning: invalid value encountered in log\n",
        "-c:51: RuntimeWarning: invalid value encountered in double_scalars\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.7,Page number:183"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Illustration 3.7\n",
      "#Recovery of Benzene Vapors from a Mixture with Air\n",
      "\n",
      "\n",
      "#Variable declaration\n",
      "\t# 1-benzene a-absorber s-steams\n",
      "T = 300  \t\t\t\t\t\t# [K]\n",
      "P = 101.3  \t\t\t\t\t\t# [kPa]\n",
      "R = 8.314  \t\t\t\t\t\t# [gas constant]\n",
      "v = 1  \t\t\t\t\t\t\t# [cubic m/s]\n",
      "\t# Gas in\n",
      "y1a = 0.074 \n",
      "\t# Liquid in\n",
      "x2a = 0.0476\n",
      "\t# Recovery is 85 %\n",
      "\t# Calculations for absorber section\n",
      "\n",
      "\n",
      "#Calculations\n",
      "\n",
      "import math\n",
      "from pylab import *\n",
      "from numpy import *\n",
      "\n",
      "\n",
      "V1a = P*v/(R*T)  \t\t\t\t\t# [kmole/s]\n",
      "\t\t\t\t\t\t\t# Inert gas molar velocity\n",
      "Vsa = V1a*(1-y1a)  \t\t\t\t\t# [kmole/s]\n",
      "Y1a = y1a/(1-y1a)  \t\t\t\t\t# [kmole of benzene/kmole of dry gas]\n",
      "\n",
      "X2a = x2a/(1-x2a)  \t\t\t\t\t# [kmole of benzene/kmole of oil]\n",
      "\t# Since the absorber will recover 85% of the benzene in the entering gas, the \t\tconcentration of the gas leaving it will be\n",
      "r = 0.85 \n",
      "Y2a = (1-r)*Y1a  \t\t\t\t\t# [kmole of benzene/kmole of dry gas]\n",
      "\t# The benzene-wash oil solutions are ideal, and the pressure is low  therefore, Raoult\u2019s \t\tlaw applies. From equations 3.1, 3.44, and 3.45\n",
      "\t#      yia = 0.136*xia\n",
      "\t# or   Yia/(1+Yia) = 0.136*Xia/(1+Xia)\n",
      "\n",
      "\t# Data_eqm = [Xia,Yia]\n",
      "Data_eqm =matrix([[0,0],[0.1,0.013],[0.2,0.023],[0.3,0.032],[0.4,0.04],[0.6,0.054],[0.8,0.064],[1,0.073],[1.2,0.080],[1.4,0.086]])\n",
      "\n",
      "\t# Here because of the shape of equilibrium curve, the operating line for minimum oil rate \tmust be tangent to curve\n",
      "\t# Therefore\n",
      "\t# From the curve X1a_max = 0.91\n",
      "X1a_max = 0.91  \t\t\t\t\t# [kmol benzene/kmol oil]\n",
      "\n",
      "\t# For minimum operating line slope is \n",
      "S = (Y1a-Y2a)/(X1a_max-X2a)  \t\t\t\t# [kmol oil/kmol air]\n",
      "\t# Therfore\n",
      "Lsa_min = S*Vsa  \t\t\t\t\t# [kmole oil/s]\n",
      "Data_minSlope1 = matrix([[X2a,Y2a],[X1a_max,Y1a]]) \n",
      " \n",
      "\t# For Actual operating line, oil flow rate is twice the minimum\n",
      "Lsa = 2*Lsa_min  \t\t\t\t\t# [kmole oil/s]\n",
      "M_oil = 198  \t\t\t\t\t\t# [molecular weight of oil, gram/mole]\n",
      "\n",
      "Wsa = Lsa*M_oil  \t\t\t\t\t# [mass flow rate of oil, kg/s]\n",
      "\t# Using equation 3.47 to calculate the actual concentration of the liquid phase leaving \tthe absorber\n",
      "X1a = X2a + Vsa*(Y1a-Y2a)/Lsa  \t\t\t\t# [kmol benzene/kmol oil]\n",
      "Data_opline1 = matrix([[X2a,Y2a],[X1a,Y1a]]) \n",
      "figure(1)\n",
      "a1=plot(Data_eqm[:,0],Data_eqm[:,1],label='$Equilibrium line for absorber$')\n",
      "a2=plot(Data_minSlope1[:,0],Data_minSlope1[:,1],label='$Minimum Flow Rate Line for absorber$')\n",
      "a3=plot(Data_opline1[:,0],Data_opline1[:,1],label='$Operating Line for absorber$') \n",
      "legend(loc='lower right')\n",
      "plt.grid()\n",
      "xlabel(\"Xa,mole benzene/mole oil\") \n",
      "ylabel(\"Ya, mole benzene/mole air\") \n",
      "title(\"XY diagram for the absorber of Example 3.7\")\n",
      "show(a1)\n",
      "show(a2)\n",
      "show(a3)\n",
      "\t# Calculations for stripping section\n",
      "Lss = Lsa \n",
      "X2s = X1a \n",
      "X1s = X2a \n",
      "Y1s = 0 \n",
      "T = 373  # [K]\n",
      "\t# Applying Raoult\u2019s law at this temperature gives us\n",
      "\t# yis = 1.77*xis\n",
      "\t# Yis/(1+Yis) = 1.77*Xis/(1+Xis)\n",
      "\n",
      "\t# Equilibrium data \n",
      "\t# Data_equm = [Xis,Yis]\n",
      "Data_equm = matrix([[0,0],[0.05,0.092],[0.1,0.192],[0.15,0.3],[0.2,0.418],[0.25,0.548],[0.3,0.691],[0.35,0.848],[0.4,1.023],[0.45,1.219],[0.5,1.439]]) \n",
      "\n",
      "\t# Similar procedure as above is followed\n",
      "\t# The operating line for minimum oil rate must be tangent to curve \t\n",
      "\t# Therefore from the curve\n",
      "Y2s_max = 1.175  \t\t\t\t\t# [kmol benzene/kmol steam]\n",
      "S = (Y2s_max-Y1s)/(X2s-X1s)  \t\t\t\t# [kmole oil/kmole steam]\n",
      "Vss_min = Lss/S  \t\t\t\t\t# [kmole/s]\n",
      "Vss = 1.5*Vss_min  \t\t\t\t\t# [kmole/s]\n",
      "Mss = 18 \t\t\t\t\t\t# [molecular weight of steam, gram/mole]\n",
      "Wss = Vss*Mss  \t\t\t\t\t\t# [kg steam/s]\n",
      "\n",
      "Data_minSlope2 = matrix([[X1s,Y1s],[X2s,Y2s_max]]) \n",
      "\n",
      "Y2s_act = Y1s + Lss*(X2s-X1s)/Vss  \t\t\t# [kmol benzene/kmol steam]\n",
      "\n",
      "Data_opline2 = matrix([[X1s,Y1s],[X2s,Y2s_act]]) \n",
      "\n",
      "figure(2)\n",
      "a1=plot(Data_equm[:,0],Data_equm[:,1],label='$Equilibrium line for stripping$')\n",
      "a2=plot(Data_minSlope2[:,0],Data_minSlope2[:,1],label='$Minimum Flow Rate for stripping Line$')\n",
      "a3=plot(Data_opline2[:,0],Data_opline2[:,1],label='$Operating Line for stripping$') \n",
      "#legend(\"Equilibrium line for stripping\",\"Minimum Flow Rate for stripping Line\",\"Operating Line #for stripping\") \n",
      "legend(loc='upper left')\n",
      "xlabel(\"Xa, mole benzene/mole oil\") \n",
      "ylabel(\"Ya, mole benzene/mole air\") \n",
      "title(\"XY diagram for stripper of Example 3.7\")\n",
      "plt.grid()\n",
      "show(a1)\n",
      "show(a2)\n",
      "show(a3)\n",
      "#Result\n",
      "\n",
      "print\"The oil circulation rate and steam rate required for the operation is\",round(Wsa,2),\"kg/s\",round(Wss,3),\" kg steam/s respectively\" "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9+RceHo4mTZrAwsICS5YsyTPPuHHjYGFhARsbG8TGxgIAHjx4ADs7O9k/fX19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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d08d30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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H4ebmhqtXr/I6BwDE6jGrqakhKioK/v7+2L17N+7cuQMvLy9s3boVffr0wfbt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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x79ad6a0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The oil circulation rate and steam rate required for the operation is 1.18 kg/s 0.059  kg steam/s respectively\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.8,Page number:190"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Illustration 3.8\n",
      "#Adsorption of Nitrogen Dioxide on Silica Gel\n",
      "\n",
      "\n",
      "from numpy import *\n",
      "from pylab import *\n",
      "#Variable declaration\n",
      "\t# 1-Nitrogen dioxide  2-air\n",
      "T = 298  \t\t\t\t\t# [K]\n",
      "P = 101.3  \t\t\t\t\t# [kPa]\n",
      "y1 = 0.015 \n",
      "V1 = 0.5  \t\t\t\t\t# [mass flow rate of the gas entering the \t\t\t\t\t\tadsorber, kg/s]\n",
      "M1 = 46  \t\t\t\t\t# [gram/mole]\n",
      "M2 = 29  \t\t\t\t\t# [gram/mole]\n",
      "\n",
      "\n",
      "#Calculations\n",
      "\n",
      "\t# Data_eqm1 = [P1 m] (where 'P1' is Partial pressure of NO2 in mm of Hg, 'm' is solid \tconcentration in kg NO2/kg gel)\n",
      "Data_eqm1 = matrix([[0,0],[2,0.4],[4,0.9],[6,1.65],[8,2.60],[10,3.65],[12,4.85]]) \n",
      "\n",
      "\n",
      "Y1 = y1*M1/((1-y1)*M2)  \t\t\t# [kg NO2/kg air]\n",
      "\t# For 85% removal of the NO2,\n",
      "Y2 = 0.15*Y1  \t\t\t\t\t# [kg NO2/kg air]\n",
      "\t# Since the entering gel is free of NO2,\n",
      "X2 = 0 \n",
      "\n",
      "Data_eqm =matrix([[0,0],[0.4,0.42],[0.9,0.83],[1.65,1.26],[2.6,1.69],[3.65,2.11],[4.85,2.54]]) \n",
      "\n",
      "\t# The operating line for minimum slope is tangent to curve, from which we get\n",
      "X1_max = 0.0375  \t\t\t\t# [kg NO2/kg gel]\n",
      "\n",
      "wb1 = 1/(1+Y1) \n",
      "Vs = V1*wb1  \t\t\t\t\t# [mass velocity of the air, kg/s] \n",
      "Ls_min = Vs*(Y1-Y2)/(X1_max-X2)  \t\t# [kg gel/s]\n",
      "Data_minSlope = matrix([[X2,Y2],[X1_max,Y1]])*100 \n",
      "\t\t\t\t\t\t# Operating line\n",
      "Ls = 2*Ls_min  \t\t\t\t\t# [kg gel/s]\n",
      "\n",
      "X1 = X2 + Vs*(Y1-Y2)/Ls  \t\t\t# [kg NO2/kg gel]\n",
      "\n",
      "\n",
      "#Result\n",
      "\n",
      "\n",
      "a1=plot(Data_eqm[:,0],Data_eqm[:,1],label='$Equilibrium line$')\n",
      "a2=plot(Data_minSlope[:,0],Data_minSlope[:,1],label='$Minimum Flow Rate Line$')\n",
      "a3=plot((3.75,3.75),(0,2.45),'--')\n",
      "a4=plot((0,3.75),(2.45,2.45),'r--')\n",
      "legend(loc='upper left')\n",
      "xlabel(\"$Xa*100, kg NO2/kg gel$\") \n",
      "ylabel(\"$Ya*100, kh NO2/kg air$\") \n",
      "title('X-Y diagram for the adsorber')\n",
      "annotate(\"X1(max)\", xy=(3.75, 2.45), xytext=(4,2.5),\n",
      "            arrowprops=dict(facecolor='black', shrink=0.01),\n",
      "            )\n",
      "show(a1)\n",
      "show(a2)\n",
      "show(a3)\n",
      "show(a4)\n",
      "print\"Mass flow rate of the and the composition of the gel leaving the absorber are\",round(Ls,3),\"kg gel/s and\",round(X1,4),\"kg No2/kg gel\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LSyMiIltbW/L09CR3d/cCyxIR9ejRg+Li4ohI9Mt769atRCT68ho7dixFRkZS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\nJaAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x79c2c50>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mass flow rate of the and the composition of the gel leaving the absorber are 0.535 kg gel/s and 0.0187 kg No2/kg gel\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.9,Page number:194"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Illustration 3.9\n",
      "#Cocurrent Adsorption of NO, on Silica Gel\n",
      "\n",
      "\n",
      "#Variable declaration\n",
      "\t# 1-Nitrogen dioxide  2-air\n",
      "\t# From Example 3.8\n",
      "Y1 = 0.0242  \t\t\t\t\t# [kg NO2/kg air]\n",
      "Y2 = 0.0036  \t\t\t\t\t# [kg NO2/kg air]\n",
      "Vs = 0.488  \t\t\t\t\t# [kg air/s]\n",
      "M1 = 46  \t\t\t\t\t# [gram/mole]\n",
      "M2 = 29  \t\t\t\t\t# [gram/mole]\n",
      "\t# However here\n",
      "import math \n",
      "from pylab import *\n",
      "from numpy import *\n",
      "X1 = 0 \n",
      "\n",
      "\n",
      "#Calculations\n",
      "\n",
      "\t# Data_eqm1 = [P1 m] (where 'P1' is Partial pressure of NO2 in mm of Hg, 'm' is solid \tconcentration in kg NO2/kg gel)\n",
      "Data_eqm1 =matrix([[0,0],[2,0.4],[4,0.9],[6,1.65],[8,2.60],[10,3.65],[12,4.85]]) \n",
      "\n",
      "\t# The equilibrium data are converted to mass ratios as follows:\n",
      "\t# Yi = P1/(760-P1)*46/29 (kg NO2/kg air)  Xi = m/100 (kg NO2/kg gel)\n",
      "\t# Equilibrium data\n",
      "\t# Data_eqm = [Xi*100 Yi*100]\n",
      "\n",
      "Data_eqm=matrix([[0,0],[0.4,0.4185242],[0.9,0.8392629],[1.65,1.2622336],[2.6,1.6874541],[3.65,2.1149425],[4.85,2.5447169]]) \n",
      "\t# From the intersection of the minimum operating line and equilibrium curve\n",
      "X2_max = 0.0034  \t\t\t\t# [kg NO2/kg gel]\n",
      "S = (Y1-Y2)/(X1-X2_max)  \t\t\t# [kg gel/kg air]\n",
      "Ls_min = -S*Vs  \t\t\t\t# [kg/s]\n",
      "\n",
      "Ls = 2*Ls_min  \t\t\t\t\t# [kg/s]\n",
      "Data_minSlope =matrix([[X1,Y1],[X2_max,Y2]])*100 \n",
      "\n",
      "\n",
      "#Result\n",
      "\n",
      "\n",
      "a1=plot(Data_eqm[:,0],Data_eqm[:,1],label='$Equilibrium line $')\n",
      "a2=plot(Data_minSlope[:,0],Data_minSlope[:,1],label='$Minimum Flow Rate Line$') \n",
      "legend(loc='upper right') \n",
      "xlabel(\"Xa*100, kg NO2/kg gel\") \n",
      "ylabel(\"Ya*100, kh NO2/kg air\") \n",
      "title('X-Y diagram for the cocurrent adsorber')\n",
      "annotate(\"M\", xy=(0.34,0.36), xytext=(0.34,0.36)\n",
      "            )\n",
      "annotate(\"X2(max)\", xy=(0.34,0.0), xytext=(0.34,0.01)\n",
      "            )\n",
      "show(a1)\n",
      "show(a2)\n",
      "print\"The mass velocity of the silica gel required for cocurrent operation is\",round(Ls,2),\"kg/s which is 11 times that required for countercurrent operation\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sXOY+ZIFnFmMM/BljdRvPLMYYY6xKOAkwxpgS4yTAGGNKjJMAY4wpMU4CjDGm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\nAUEQBCMmgoAgCIIR+y8GWmE/HhvA+QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x79d1da0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The mass velocity of the silica gel required for cocurrent operation is 5.91 kg/s which is 11 times that required for countercurrent operation\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.10,Page number:199"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Illustration 3.10\n",
      "#Benzene Recovery System: Number of Ideal Stages\n",
      "\n",
      "#Variable declaration\n",
      "# From Example 3.7\n",
      "X2a = 0.05  \n",
      "X0 = X2a  \t\t\t\t# [kmole benzene/kmole oil]\n",
      "Y2a = 0.012 \n",
      "Y1 = Y2a  \t\t\t\t# [kmole benzene/kmole dry gas]\n",
      "X1a = 0.480  \n",
      "Xn = X1a  \t\t\t\t# [kmole benzene/kmole oil]\n",
      "Y1a = 0.080  \n",
      "Yn1 = Y1a  \t\t\t\t# [kmole benzene/kmole dry gas]\n",
      "\t# Ideal stages for absorber section\n",
      "\n",
      "m = 0.097  \t\t\t\t# [mole of oil/mole of dry gas]\n",
      "Lsa = 0.006  \t\t\t\t# [kmole/s]\n",
      "Vsa = 0.038  \t\t\t\t# [kmole/s]\n",
      "\n",
      "#Calculations\n",
      "\n",
      "A = Lsa/(m*Vsa)  \t\t\t# [Absorption factor]\n",
      "import math\n",
      "\t# From equation 3.54 by Kremser equation\n",
      "Nk = math.log((((Yn1-m*X0)*(1-1/A))/(Y1-m*X0))+1/A)/(math.log(A)) \n",
      "\n",
      "\n",
      "\t# Ideal stages from graph \n",
      "\t# Stair case construction is being made between equilibrium curve and operating line from \tpiont X2a,Y2a to X1a,Y1a\n",
      "\t# A more precise estimate of stages\n",
      "\t# From figure 3.25 or from graph made for absorber in Example 3.7\n",
      "Xa = 0.283 \n",
      "Xb = 0.480 \n",
      "Xc = 0.530 \n",
      "Na = 3+(Xb-Xa)/(Xc-Xa)\n",
      "print\"ABSORBER\"\n",
      "print\"The number of ideal stages from graph in the absorber is\",round(Na,1)\n",
      "print\"Number of ideal stages from Kremser equation in the absorber is\",round(Nk,2)\n",
      "\t# Ideal satges for stripping section\n",
      "X2s = 0.480  \n",
      "X0 = X2s  \t\t\t\t# [kmol benzene/kmol oil]\n",
      "Y2s = 0.784  \n",
      "Y1 = Y2s  \t\t\t\t# [kmol benzene/kmol steam]\n",
      "X1s = 0.05  \n",
      "Xn = X1s  \t\t\t\t# [kmol benzene/kmol oil]\n",
      "Y1s = 0  \n",
      "Yn1 = Y1s  \t\t\t\t# [kmol benzene/kmol steam]\n",
      "\n",
      "\t# Similarly here also stair case construction is being made between equilibrium curve and \toperating line from piont X0,Y1 to Xn,Yn1\n",
      "\t# A more precise estimate of stages\n",
      "\t# From figure 3.26 or from graph made for stripping section in Example 3.7\n",
      "Ns = 5+(0.070-0.050)/(0.070-0.028) \n",
      "\n",
      "#Result\n",
      "print\"STRIPPER\"\n",
      "print\"The number of ideal stages from graph in the stripping section is\",round(Ns,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ABSORBER\n",
        "The number of ideal stages from graph in the absorber is 3.8\n",
        "Number of ideal stages from Kremser equation in the absorber is 3.16\n",
        "STRIPPER\n",
        "The number of ideal stages from graph in the stripping section is 5.5\n"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}