{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "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": [
      "#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": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.2,Page number:162"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.3,Page number:162"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.4,Page number:169"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.5,Page number:171"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.6,Page number:174"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.7,Page number:183"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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",
      "import matplotlib\n",
      "%matplotlib inline\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": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEZCAYAAACEkhK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5ee15fab90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5ee1400250>"
       ]
      },
      {
       "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": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.8,Page number:190"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib\n",
      "%matplotlib inline\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": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEfCAYAAACqKwpQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkoMQQggLkhyEEEJYkOQghBDCwv8BbzAzHb2Rc6YAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f51ee6d47d0>"
       ]
      },
      {
       "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": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.9,Page number:194"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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 matplotlib\n",
      "%matplotlib inline\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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEZCAYAAABxbJkKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f51ee6250d0>"
       ]
      },
      {
       "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": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 3.10,Page number:199"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#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": 20
    }
   ],
   "metadata": {}
  }
 ]
}