{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 7:Liquid-Liquid Extraction "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.1,Page number:429"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "\n",
      "#For water phase:\n",
      "Cw=[1.23,1.29,1.71,5.10,9.8,16.90]        #Chloroformm concentration in wt %\n",
      "Aw=[15.80,25.6,36.0,49.30,55.7,59.60]     #Acetone concentration in wt %\n",
      "\n",
      "#For Chloroform phase\n",
      "Cc=[70.0,55.7,42.9,28.4,20.4,16.9]       #Chloroformm concentration in wt% \n",
      "Ac=[28.70,42.10,52.70,61.30,61.00,59.6]   #Acetone concentration in wt %\n",
      "for i in range(0,6):\n",
      "    Cw[i]=Cw[i]/100            #Weight fraction\n",
      "    Aw[i]=Aw[i]/100            #Weight fraction\n",
      "    Cc[i]=Cc[i]/100            #Weight fraction\n",
      "    Ac[i]=Ac[i]/100            #Weight fraction\n",
      "\n",
      "a1=plot(Cw,Aw)\n",
      "a2=plot(Cc,Ac)\n",
      "X=linspace(1,0)\n",
      "Y=linspace(0,1)\n",
      "a3=plot(X,Y)\n",
      "xlabel(\"$Weight fraction of chloroform$\")\n",
      "ylabel(\"$Weight fraction of acetone$\")\n",
      "title(\"Equilibrium for water-chloroform-acetone at 298 K and 1 atm.\\n\\n\")\n",
      "a4=plot([Cw[1],Cc[1]],[Aw[1],Ac[1]],label='$Tie line$')\n",
      "a5=plot([(Cw[2]+Cw[3])/2,(Cc[2]+Cc[3])/2],[(Aw[2]+Aw[3])/2,(Ac[2]+Ac[3])/2],label='$Tie line$')\n",
      "a6=plot([Cw[5],Cc[3]],[Aw[5],Aw[1]],label='$Conjugate line$')\n",
      "legend(loc='upper right')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.legend.Legend at 0x7a98978>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ovZ2YmAhbW1uZZf755x/MmjULAODk5IS2bdvi7t278Pb2fml7FZNKs1RetZSf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\nA9dtwhN+6SZ1AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x764cd30>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.2,Page number:433"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\t#  'b'-solvent   'f'-feed   'r'-raffinate   'e'-extract   'c'-one of the   # component in \t feed\n",
      "F = 50  \t\t\t\t\t# [feed rate, kg/h]\n",
      "S = 50  \t\t\t\t\t# [solvent rate, kg/h]\n",
      "xcf = 0.6 \n",
      "xbf = 0 \n",
      "ycs = 0 \n",
      "ybs = 1.0 \n",
      "\t# The equilibrium data for this system can be obtained from Table 7.1 and    # Figure 7.6\n",
      "\t# Plot streams F (xcF = 0.6, xBF = 0.0) and S (yes = 0.0, yBs = 1.0). After  # locating \tstreams F and S, M is on the line FS  its exact location is found # by calculating xcm \tfrom\n",
      "#Calculation\n",
      "\n",
      "xcm = (F*xcf+S*ycs)/(F+S) \n",
      "\n",
      "\t# From figure 7.8\n",
      "xcr = 0.189 \n",
      "xbr = 0.013 \n",
      "yce = 0.334 \n",
      "ybe = 0.648 \n",
      "M = F+S  \t\t\t\t\t# [kg/h]\n",
      "# From equation 7.8 \n",
      "E = M*(xcm-xcr)/(yce-xcr)  \t\t\t# [kg/h]\n",
      "R = M-E  \t\t\t\t\t# [kg/h]\n",
      "\n",
      "#Result\n",
      "print\"The extract and raffinate flow rates are\",round(E,2),\"kg/h and\",round(R,2),\" kg/h respectively\\n\"\n",
      "\n",
      "print\"The compositions when one equilibrium stage is used for the separation is\",xcr,\"and\",xbr,\" in raffinate phase for component b and c respectively and\",yce,\"and\",ybe,\"in extract phase for component b and c respectively\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The extract and raffinate flow rates are 76.55 kg/h and 23.45  kg/h respectively\n",
        "\n",
        "The compositions when one equilibrium stage is used for the separation is 0.189 and 0.013  in raffinate phase for component b and c respectively and 0.334 and 0.648 in extract phase for component b and c respectively\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.4,Page number:439"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "#Variable declaration\n",
      "\n",
      "\t# C-acetic acid    A-water\n",
      "\t# f-feed   r-raffinate   s-solvent\n",
      "fd = 1000  \t\t\t\t\t\t# [kg/h]\n",
      "xCf = 0.35 \t\t\t\t\t\t# [fraction of acid]\n",
      "xAf = 1-xCf  \t\t\t\t\t\t# [fraction of water]\n",
      "\t# Solvent is pure\n",
      "xAr = 0.02 \n",
      "yCs = 0 \n",
      "\n",
      "import math\n",
      "from pylab import *\n",
      "from numpy import *\n",
      "print \"Solution 7.4(a)\\n\"\n",
      "\t# Solution(a)\n",
      "\n",
      "\t# From Figure 7.15\n",
      "xCMmin = 0.144 \n",
      "\t# From equation 7.11\n",
      "Smin = fd*(xCMmin-xCf)/(yCs-xCMmin)  \t\t\t\t# [kg/h]\n",
      "\n",
      "#result\n",
      "print\"The minimum amount of solvent which can be used is\",round(Smin),\"kg/h\"\n",
      "\n",
      "print \"Solution7.4(b)\" \n",
      "\t# Solution(b)\n",
      "\n",
      "S = 1.6*Smin  \t\t\t\t\t\t\t# [kg/h]\n",
      "\t# From equation 7.11\n",
      "xCM = (fd*xCf+S*yCs)/(fd+S) \n",
      "\n",
      "\t# Data for equilibrium line\n",
      "\t# Data_eqml = [xCeq yCeq]\n",
      "Data_eqml =matrix([[0.0069,0.0018],[0.0141,0.0037],[0.0289,0.0079],[0.0642,0.0193],[0.1330,0.0482],[0.2530,0.1140],[0.3670,0.2160],[0.4430,0.3110],[0.4640,0.3620]]) \n",
      "\n",
      "\t# Data for operating line\n",
      "\t# Data_opl = [xCop yCop]\n",
      "Data_opl =matrix([[0.02,0],[0.05,0.009],[0.1,0.023],[0.15,0.037],[0.20,0.054],[0.25,0.074],[0.30,0.096],[0.35,0.121]]) \n",
      "\n",
      "\n",
      "a1=plot(Data_eqml[:,0],Data_eqml[:,1],label='$Equilibrium line$')\n",
      "a2=plot(Data_opl[:,0],Data_opl[:,1],label='$Operating line$') \n",
      "legend(loc='upper right') \n",
      "xlabel(\"wt fraction of acetic acid in water solutions, xC\") \n",
      "ylabel(\"wt fraction of acetic acid in ether solutions, yC\") \n",
      "title('Mc-Cabe thiele Diagram')\n",
      "\n",
      "\t# Now number of theoritical stages is determined by drawing step by step  # stairs from\t \txC = 0.35 to xC = 0.02\n",
      "\t# From figure 7.16\n",
      "\t# Number of theoritical stages 'N' is\n",
      "N = 8  \n",
      "\n",
      "show(a1)\n",
      "show(a2)\n",
      "print\"\\nThe number of theoretical stages if the solvent rate used is 60 percent above the minimum is \",N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Solution 7.4(a)\n",
        "\n",
        "The minimum amount of solvent which can be used is 1431.0 kg/h\n",
        "Solution7.4(b)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+++aOxrQMJozIyEieffZZXFxcCAwMZP/+/TRs2JDKlSvnep6xIzT69etHv379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\nAgaDwWAwBPE/TB4aqVW+sZ0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7629c88>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "The number of theoretical stages if the solvent rate used is 60 percent above the minimum is  8\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.5,Page number:444"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\n",
      "\t# C-nicotine    A-water    B-kerosene\n",
      "\t# F-feed    R-raffinate    S-solvent\n",
      "F = 1000  \t\t\t\t\t# [feed rate, kg/h]\n",
      "xAF = 0.99  \t\t\t\t\t# [fraction of water in feed]\n",
      "\t# Because the solutions are dilute therefore\n",
      "xCF = 0.01 \t\t\t\t\t# [fraction of nicotene in feed, kg nicotene/kg \t\t\t\t\t\twater]\n",
      "xCR = 0.001  \t\t\t\t\t# [fraction of nicotene in raffinate, kg \t\t\t\t\t\tnicotene/kg water ]\n",
      "m = 0.926  \t\t\t\t\t# [kg water/kg kerosene]\n",
      "import math\n",
      "\n",
      "print \"Solution 7.5(a) \" \n",
      "# Solution(a)\n",
      "\n",
      "yCS = 0  \t\t\t\t\t# [kg nicotene/kg water]\n",
      "\n",
      "\t# Because, in this case, both the equilibrium and operating lines are       # straight,if \tthe minimum solvent flow rate Bmin is used, the concentration # of the exiting extract, \tyCmax, will be in equilibrium with xCF. Therefore\n",
      "yCmax = m*xCF  # [kg nicotene/kg kerosene]\n",
      "\n",
      "A = F*xAF  \t\t\t\t\t# [kg water/h]\n",
      "\t# From equation 7.17\n",
      "Bmin = A*(xCF-xCR)/(yCmax-yCS)  \t\t# [kg kerosene/h]\n",
      "\n",
      "#Result\n",
      "print\"The minimum amount of solvent which can be used is\",round(Bmin),\"kerosene/h\"\n",
      "\n",
      "print\"\\nSolution 7.5(b)\" \n",
      "\t# Solution(b)\n",
      "\n",
      "B = 1.2*Bmin  \t\t\t\t\t# [kg kerosene/h]\n",
      "EF = m*B/A \n",
      "Nt = math.log((xCF-yCS/m)/(xCR-yCS/m)*(1-1/EF)+1/EF)/math.log(EF) \n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The number of theoretical stages if the solvent rate used is 20 percent above the minimum is\",round(Nt,2)\n",
      "\n",
      "print \"Solution7.5(c)\" \n",
      "\t# Solution(c)\n",
      "\n",
      "Eme = 0.6  \t\t\t\t\t# [Murphree stage efficiency]\n",
      "\t# from equation 7.20\n",
      "Eo = math.log(1+Eme*(EF-1))/math.log(EF)  \t# [overall efficiency]\n",
      "Nr = Nt/Eo  \t\t\t\t\t# [number of real stages]\n",
      "\n",
      "#Result\n",
      "print\"The number of real stages required is\",round(Nr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Solution 7.5(a) \n",
        "The minimum amount of solvent which can be used is 962.0 kerosene/h\n",
        "\n",
        "Solution 7.5(b)\n",
        "The number of theoretical stages if the solvent rate used is 20 percent above the minimum is 6.64\n",
        "Solution7.5(c)\n",
        "The number of real stages required is 11.0\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.6,Page number:449"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# C-styrene    A-ethylbenzene    B-diethylene glycol\n",
      "F = 1000  # [kg/h]\n",
      "XF = 0.6  # [wt fraction of styrene]\n",
      "XPE = 0.9 \n",
      "XN = 0.1 \n",
      "# All above fractions are on solvent basis\n",
      "# Equilibrium Data for Ethylbenzene (A)-Diethylene Glycol (B)-Styrene (C) at 298 K\n",
      "# Data_eqm = [X Y] \n",
      "# X - kg C/kg (A+C) in raffinate solution\n",
      "# Y - kg C/kg (A+C) in extract solution\n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "from pylab import*\n",
      "from numpy import*\n",
      "\n",
      "\n",
      "Data_eqm=matrix([[0,0],[0.087,0.1429],[0.1883,0.273],[0.288,0.386],[0.384,0.48],[0.458,0.557],[0.464,0.565],[0.561,0.655],[0.573,0.674],[0.781,0.863],[0.9,0.95],[1,1]])\n",
      "\n",
      "\n",
      "\n",
      "#Illustration 7.6(a)\n",
      "# Solution(a)\n",
      "\n",
      "# Minimum theoretical stages are determined on the XY equilibrium distribution diagram, stepping them off from the diagonal line to the equilibrium curve, beginning at XPE = 0.9 and ending at XN = 0.1\n",
      "\n",
      "Data_opl=matrix([[0,0],[0.09,0.09],[0.18,0.18],[0.27,0.27],[0.36,0.36],[0.45,0.45],[0.54,0.54],[0.63,0.63],[0.72,0.72],[0.81,0.81],[0.90,0.90],[1,1]]) \n",
      "\n",
      "\n",
      "a1=plot(Data_eqm[:,0],Data_eqm[:,1],label='$Equilibrium line$')\n",
      "a2=plot(Data_opl[:,0],Data_opl[:,1],label='$Operating line$') \n",
      "\n",
      "legend(loc='upper left') \n",
      "title('Equilibrium-distribution diagram and minimum number of stages')\n",
      "xlabel(\"$X,kg C/kg (A+C) in raffinate solution$\") \n",
      "ylabel(\"$Y,kg C/kg (A+C) in extract solution$\") \n",
      "\n",
      "show(a1)\n",
      "show(a2)\n",
      "# Figure 7.20\n",
      "Nmin = 9  # [number of ideal stages]\n",
      "\n",
      "print\"Ans.(a)The minimum number of theoretical stages are \",Nmin\n",
      "\n",
      "#Illustration 7.6(b) \n",
      "# Solution(b)\n",
      "\n",
      "\t# Since the equilibrium-distribution curve is everywhere concave downward# ,the tie line \twhich when extended passes through F provides the minimum\n",
      "\t# reflux ratio\n",
      "\t# From figure 7.19\n",
      "NdeltaEm = 11.04 \n",
      "NE1 = 3.1 \n",
      "\t# From equation 7.30\t\n",
      "\t# Y = R_O/P_E, external reflux ratio\n",
      "Ymin = (NdeltaEm-NE1)/NE1  # [kg reflux/kg extract product]\n",
      "\n",
      "print\"Ans.(b)The minimum extract reflux ratio is\",round(Ymin,3),\"kg reflux/kg extract product\"\n",
      "\n",
      "#Illustration 7.6(c) \n",
      "# Solution(c)\n",
      "\n",
      "Y = 1.5*Ymin  # [kg reflux/kg extract product]\n",
      "# From equation 7.30\n",
      "NdeltaE = Y*NE1+NE1 \n",
      "# From figure 7.19\n",
      "NdeltaR = -24.90 \n",
      "# From figure 7.21\n",
      "N = 17.5  # [number of equilibrium stages]\n",
      "\n",
      "# From figure 7.19\n",
      "# For XN = 0.1 NRN = 0.0083\n",
      "NRN = 0.0083 \n",
      "# Basis: 1 hour\n",
      "\n",
      "# e = [P_E R_N] \n",
      "# Solution of simultaneous equation\n",
      "def G(e):\n",
      "    f1 = F-e[0]-e[1] \n",
      "    f2 = F*XF-e[0]*XPE-e[1]*XN \n",
      "    return(f1,f2)\n",
      "\n",
      "# Initial guess:\n",
      "e = [600,300] \n",
      "y = fsolve(G,e) \n",
      "P_E = y[0]  # [kg/h]\n",
      "R_N = y[1]  # [kg/h]\n",
      "\n",
      "R_O = Y*P_E  # [kg/h]\n",
      "E_1 = R_O+P_E  # [kg/h]\n",
      "\n",
      "B_E = E_1*NE1  # [kg/h]\n",
      "E1 = B_E+E_1  # [kg/h]\n",
      "RN = R_N*(1+NRN)  # [kg/h]\n",
      "S = B_E+R_N*NRN  # [kg/h]\n",
      "\n",
      "print\"Ans(c.)The number of theoretical stages are\",N\n",
      "print\"The important flow quantities at an extract reflux ratio of 1.5 times the minimum value are\\n\\n\"\n",
      "print\"PE =\",round(P_E),\"kg/h\\n\",\"RN =\",round(R_N),\"kg/h\\n\",\"RO =\",round(R_O),\"kg/h\\n\",\"E1 =\",round(E_1),\"kg/h\\n\",\"BE =\",round(B_E),\" kg/h\\n\",\"E1 =\",round(E1),\"kg/h\\n\",\"RN =\",round(RN),\"kg/h\\n\",\"S =\",round(S),\"kg/h\\n\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HYPRoGDIEypQxzrYNud+PqVnSZ0MqIQt07949QkJCiImJYe/evQUdjhBF3q5d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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7961b70>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Ans.(a)The minimum number of theoretical stages are  9\n",
        "Ans.(b)The minimum extract reflux ratio is 2.561 kg reflux/kg extract product\n",
        "Ans(c.)The number of theoretical stages are 17.5\n",
        "The important flow quantities at an extract reflux ratio of 1.5 times the minimum value are\n",
        "\n",
        "\n",
        "PE = 625.0 kg/h\n",
        "RN = 375.0 kg/h\n",
        "RO = 2401.0 kg/h\n",
        "E1 = 3026.0 kg/h\n",
        "BE = 9381.0  kg/h\n",
        "E1 = 12407.0 kg/h\n",
        "RN = 378.0 kg/h\n",
        "S = 9384.0 kg/h\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.7,Page number:454"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\n",
      "Ff = 1.89  \t\t\t\t\t\t# [cubic m/min]\n",
      "Fs = 2.84  \t\t\t\t\t\t# [cubic m/min]\n",
      "t = 2  \t\t\t\t\t\t\t# [min]\n",
      "\n",
      "\n",
      "print \"Solution 7.7(a)\\n\"\n",
      "\t# Solution(a)\n",
      "import math\n",
      "Q = Ff+Fs  \t\t\t\t\t# [total flow rate, cubic m/min]\n",
      "Vt = Q*t  \t\t\t\t\t# [cubic m]\n",
      "\t# For a cylindrical vessel H = Dt\n",
      "Dt = (4*Vt/math.pi)**(1.0/3.0)  \t\t# [m]\n",
      "H = Dt  \t\t\t\t\t# [m]\n",
      "\n",
      "#Result\n",
      "print\"The diameter and height of each mixing vessel is\",round(Dt,1),\" m and\",round(H,1),\" m respectively\"\n",
      "\n",
      "print\"Solution 7.7(b) \"\n",
      "\t# Solution(b)\n",
      "\t# Based on a recommendation of Flynn and Treybal (1955),\n",
      "P = 0.788*Vt  \t\t\t\t\t# [mixer power, kW]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The agitator power for each mixer is\",round(P,2),\"kW\"\n",
      "\n",
      "print\"\\nSolution 7.7(c)\"\n",
      "\t# Solution(c)\n",
      "\n",
      "\t# Based on the recommendation by Ryan et al. (1959), the disengaging area  # in the \t\tsettler is\n",
      "\t# Dt1*L1 = Q/a = Y\n",
      "a = 0.2 \t\t\t\t\t# [cubic m/min-square m]\n",
      "Y = Q/a  \t\t\t\t\t# [square m]\n",
      "\t\t\t# For L/Dt = 4\n",
      "Dt1 = (Y/4)**0.5  \t\t\t\t# [m]\n",
      "L1 = 4*Dt1  \t\t\t\t\t# [m]\n",
      "\n",
      "#Result\n",
      "print\"The diameter and length of a settling vessel is\",round(Dt1,2),\" m and\",round(L1,2),\" m respectively\"\n",
      "\n",
      "print\"Solution 7.7(d)\"\n",
      "\t# Solution(d)\n",
      "\t# Total volume of settler\n",
      "Vt1 = math.pi*Dt1**2*L1/4  \t\t\t# [cubic m]\n",
      "tres1 = Vt1/Q  \t\t\t\t\t# [min]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The residence time in the settling vessel is\",round(tres1,1),\"min\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Solution 7.7(a)\n",
        "\n",
        "The diameter and height of each mixing vessel is 2.3  m and 2.3  m respectively\n",
        "Solution 7.7(b) \n",
        "The agitator power for each mixer is 7.45 kW\n",
        "\n",
        "Solution 7.7(c)\n",
        "The diameter and length of a settling vessel is 2.43  m and 9.73  m respectively\n",
        "Solution 7.7(d)\n",
        "The residence time in the settling vessel is 9.5 min\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.8,Page number:456"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\n",
      "Ff = 1.61  \t\t\t\t\t# [flow rate of feed, kg/s]\n",
      "Fs = 2.24  \t\t\t\t\t# [flow rate of solvent, kg/s]\n",
      "t = 2*60  \t\t\t\t\t# [residence time in each mixer, s]\n",
      "df = 998  \t\t\t\t\t# [density of feed, kg/cubic m]\n",
      "uf = 0.89*10**-3  \t\t\t\t# [viscosity of feed, kg/m.s]\n",
      "ds = 868  \t\t\t\t\t# [density of solvent, kg/cubic m]\n",
      "us = 0.59*10**-3  \t\t\t\t# [viscosity of solvent, kg/m.s]\n",
      "sigma = 0.025  \t\t\t\t\t# [interfacial tension, N/m]\n",
      "g = 9.8  \t\t\t\t\t# [square m/s]\n",
      "import math\n",
      "\n",
      "Qf = Ff/df  \t\t\t\t\t# [volumetric flow rate of feed, cubic m/s]\n",
      "Qs = Fs/ds  \t\t\t\t\t# [volumetric flow rate of solvent, cubic m/s]\n",
      "\t# Volume fractions in the combined feed and solvent entering the mixer \n",
      "phiE = Qs/(Qs+Qf)  \n",
      "phiR = 1-phiE \n",
      "\n",
      "print\"\\nSolution7.8(a)\\n\" \n",
      "\t# Solution(a)\n",
      "import math\n",
      "Q = Qf+Qs  \t\t\t\t\t# [total flow rate, cubic m/s]\n",
      "Vt = Q*t  \t\t\t\t\t# [vessel volume, cubic m]\n",
      "\t# For a cylindrical vessel,  H = Dt\n",
      "\t# Therefore,Vt = math.pi*Dt**3/4\n",
      "Dt = (4*Vt/math.pi)**(1.0/3.0)  \t\t# [ diameter, m]\n",
      "H = Dt  \t\t\t\t\t# [height, m]\n",
      "Di = Dt/3  \t\t\t\t\t# [m]\n",
      "\n",
      "#Result\n",
      "print\"The height and diameter of the mixing vessel are\",round(Dt,3),\" m and\",round(H,3),\" m respectively.\\n\"\n",
      "print\"The diameter of the flat-blade impeller is\",round(Di,3),\" m\"\n",
      "\n",
      "print\"\\nSolution (b)\\n\" \n",
      "\t# Solution(b)\n",
      "\n",
      "\t# For the raffinate phase dispersed:\n",
      "phiD = phiR \n",
      "phiC = phiE \n",
      "deltad = df-ds  \t\t\t\t# [kg/cubic m]\n",
      "rowM = phiD*df+phiC*ds  \t\t\t# [kg/cubic m]\n",
      "uM = us/phiC*(1 + 1.5*uf*phiD/(us+uf))  \t# [kg/m.s]\n",
      "\t# Substituting in equation 7.34\n",
      "ohm_min=math.sqrt(1.03*phiD**0.106*g*deltad*(Dt/Di)**2.76*(uM**2*sigma/(Di**5*rowM*g**2*deltad**2))**0.084/(Di*rowM))*60  \t\t\t# [rpm]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The minimum rate of rotation of the impeller for complete and uniform dispersion.is\",round(ohm_min),\"rpm.\"\n",
      "\n",
      "print\"\\nSolution 7.8(c)\"\n",
      "\t# Solution(c)\n",
      "\n",
      "ohm = 1.2*ohm_min  \t\t\t\t# [rpm]\n",
      "\n",
      "\t# From equation 7.37\n",
      "Re = ohm/60*Di**2*rowM/uM  \t\t\t# [Renoylds number]\n",
      "\t# Then according to Laity and Treybal (1957), the power number, Po = 5.7\n",
      "Po = 5.7\n",
      "\t# From equation 7.37\n",
      "P = Po*(ohm/60)**3*Di**5*rowM/1000  \t\t# [kW]\n",
      "\t# Power density\n",
      "Pd = P/Vt  \t\t\t\t\t# [kW/cubic m]\n",
      "\n",
      "#Result\n",
      "print\"The power requirement of the agitator at 1.20 times the minimum rotation rate is\",round(P,3),\" kW.\"\n",
      "print\"Power density is\",round(Pd,3),\"kW/m^3\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Solution7.8(a)\n",
        "\n",
        "The height and diameter of the mixing vessel are 0.862  m and 0.862  m respectively.\n",
        "\n",
        "The diameter of the flat-blade impeller is 0.287  m\n",
        "\n",
        "Solution (b)\n",
        "\n",
        "The minimum rate of rotation of the impeller for complete and uniform dispersion.is 152.0 rpm.\n",
        "\n",
        "Solution 7.8(c)\n",
        "The power requirement of the agitator at 1.20 times the minimum rotation rate is 0.287  kW.\n",
        "Power density is 0.571 kW/m^3\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.9,Page number:460"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\t# From example 7.8\n",
      "Di = 0.288  \t\t\t\t\t# [m]\n",
      "sigma = 0.025  \t\t\t\t\t# [N/m]\n",
      "ohm = 152.0*1.2/60.0  \t\t\t\t# [rps]\n",
      "ds = 868.0  \t\t\t\t\t# [kg/cubic m]\n",
      "phiD = 0.385 \n",
      "\n",
      "#Calculation\n",
      "\n",
      "import math \n",
      "\t# Therefore from equation 7.49\n",
      "We = Di**3*ohm**2.0*ds/sigma  \t\t\t# [Weber number]\n",
      "\n",
      "\t# From equation 7.50\n",
      "dvs = Di*0.052*(We)**-0.6*math.exp(4*phiD)  \t# [m]\n",
      "\t# Substituting in equation 7.48\n",
      "a = 6*phiD/dvs  \t\t\t\t# [square m/cubic m]\n",
      "dvs=dvs*1000\t\t\t\t\t#[mm]\n",
      "#Result\n",
      "print\"The Sauter mean drop diameter and the interfacial area is\",round(dvs,3),\" mm and\",round(a),\" square m/cubic m respectively.\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The Sauter mean drop diameter and the interfacial area is 0.326  mm and 7081.0  square m/cubic m respectively.\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.10,Page number:461"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\n",
      "Dd = 1.15*10**-9  \t\t\t# [molecular diffusivity of furfural in water, square \t\t\t\t\tm/s]\n",
      "Dc = 2.15*10**-9  \t\t\t# [molecular diffusivity of furfural in toluene, square \t\t\t\t\tm/s]\n",
      "m = 10.15  \t\t\t\t# [equilibrium distribution coefficient, cubic m \t\t\t\t\traffinate/cubic m extract]\n",
      "\n",
      "print\"Solution7.10(a)\\n\" \n",
      "\t# Solution(a)\n",
      "\t# From example 7.8 and 7.9\n",
      "dvs = 3.26*10**-4  \t\t\t# [m]\n",
      "Shd = 6.6  \t\t\t\t# [sherwood number for dispersed phase]\n",
      "\t# From equation 7.52\n",
      "kd = Shd*Dd/dvs  \t\t\t# [dispersed phase mass transfer coefficient, m/s]\n",
      "\n",
      "#Result\n",
      "print\"The dispersed-phase mass-transfer coefficient is\",round(kd,7),\" m/s\"\n",
      "\n",
      "print\"\\n Solution 7.10(b) \"\n",
      "\t# Solution(b)\n",
      "\n",
      "dd = 998 \n",
      "dc = 868  \t\t\t\t# [density of continuous phase, kg/cubic m]\n",
      "uc = 0.59*10**-3  \t\t\t# [viscosity of continuous phase, kg/m.s]\n",
      "ohm = 182.2  \t\t\t\t# [rpm]\n",
      "g = 9.8  \t\t\t\t# [square m/s]\n",
      "Di = 0.288  \t\t\t\t# [m]\n",
      "sigma = 0.025  \t\t\t\t# [N/m]\n",
      "phiD = 0.385 \n",
      "Dt = 0.863  \t\t\t\t# [m] \n",
      "Scc = uc/(dc*Dc) \n",
      "Rec = Di**2*ohm/60*dc/uc \n",
      "Fr = Di*(ohm/60)**2/g \n",
      "Eo = dd*dvs**2*g/sigma \n",
      "\n",
      "\t# From equation 7.53\n",
      "Shc=1.237*10**-5*Rec**(2.0/3.0)*Scc**(1.0/3.0)*Fr**(5.0/12.0)*Eo**(5.0/4.0)*phiD**(-1.0/2.0)*(Di/dvs)**2*(dvs/Dt)**(1.0/2.0) \n",
      "\t# Therefore \n",
      "kc = Shc*Dc/dvs  \t\t\t# [continuous phase mass transfer coefficient, m/s]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The continuous-phase mass-transfer coefficient is\",round(kc,5),\"m/s\"\n",
      "\n",
      "print\"Solution 7.10(c)\"\n",
      "\t# Solution(c)\n",
      "\n",
      "a = 7065  \t\t\t\t\t# [square m/cubic m]\n",
      "Vt = 0.504  \t\t\t\t\t# []\n",
      "Qd = 0.097/60  \t\t\t\t\t# [cubic m/s]\n",
      "Qc = 0.155/60  \t\t\t\t\t# [cubic m/s]\n",
      "\n",
      "\t# From equation 7.40\n",
      "Kod = kd*kc*m/(m*kc+kd)  \t\t\t# [m/s]\n",
      "\t# From equation 7.45\n",
      "N_tod = Kod*a*Vt/Qd \n",
      "\t# From equation 7.46\n",
      "Emd = N_tod/(1+N_tod)  \n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The Murphree dispersed phase efficiency is \",round(Emd,3)\n",
      "\n",
      "\n",
      "print\"Solution 7.10(d)\" \n",
      "\t# Solution(d)\n",
      "\t# From equation 7.57\n",
      "fext = Emd/(1+Emd*Qd/(m*Qc)) \n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The fractional extraction of furfural is\",round(fext,3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Solution7.10(a)\n",
        "\n",
        "The dispersed-phase mass-transfer coefficient is 2.33e-05  m/s\n",
        "\n",
        " Solution 7.10(b) \n",
        "The continuous-phase mass-transfer coefficient is 0.00076 m/s\n",
        "Solution 7.10(c)\n",
        "The Murphree dispersed phase efficiency is  0.981\n",
        "Solution 7.10(d)\n",
        "The fractional extraction of furfural is 0.925\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.11,Page number:466"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\t# Preliminary Design of an RDC\n",
      "T = 293  \t\t\t\t\t# [K]\n",
      "F1 = 12250  \t\t\t\t\t# [flow rate for dispersed organic phase, kg/h]\n",
      "F2 = 11340  \t\t\t\t\t# [flow rate for continuous aqueous phase, kg/h]\n",
      "d1 = 858  \t\t\t\t\t# [kg/cubic m]\n",
      "d2 = 998  \t\t\t\t\t# [kg/cubic m]\n",
      "n = 12  \t\t\t\t\t# [Equilibrium stages]\n",
      "\n",
      "#Calculation\n",
      "\n",
      "import math\n",
      "Qd = F1/d1  \t\t\t\t\t# [cubic m/h]\n",
      "Qc = F2/d2  \t\t\t\t\t# [cubic m/h]\n",
      "\n",
      "\t# Assume that based on information in Table 7.5\n",
      "\t# Vd+Vc = V = 22 m/h\t\n",
      "V = 22  \t\t\t\t\t# [m/h]\n",
      "\t# Therefore column cross sectional area \n",
      "Ac = (Qd+Qc)/V  \t\t\t\t# [square m]\n",
      "\t# Column diameter\n",
      "Dt = math.sqrt(4*Ac/math.pi)  \t\t\t# [m]\n",
      "\n",
      "\t# Assume that based on information in Table 7.5\n",
      "\t# 1/HETS = 2.5 to 3.5   m^-1\n",
      "\t# Therefore\n",
      "HETS = 1.0/3.0  \t\t\t\t# [m/theoritical stages]\n",
      "\t# Column height\n",
      "Z = n*HETS  \t\t\t\t\t# [m]\n",
      "\n",
      "#Result\n",
      "print\"The height and diameter of an RDC to extract acetone from a dilute toluene-acetone solution is\",Z,\" m and\",round(Dt,2),\"square m respectively\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The height and diameter of an RDC to extract acetone from a dilute toluene-acetone solution is 4.0  m and 1.13 square m respectively\n"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}