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diff --git a/TRANSPORT_PROCESSES_AND_UNIT_OPERATIONS/GeankoplisChapter12.ipynb b/TRANSPORT_PROCESSES_AND_UNIT_OPERATIONS/GeankoplisChapter12.ipynb
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+{

+ "metadata": {

+  "name": "",

+  "signature": "sha256:e411d60f25996f3b9e2550f135c48ac7fbdc47ae72fe918b9fb1f21a04765972"

+ },

+ "nbformat": 3,

+ "nbformat_minor": 0,

+ "worksheets": [

+  {

+   "cells": [

+    {

+     "cell_type": "heading",

+     "level": 1,

+     "metadata": {},

+     "source": [

+      "Chapter 12: Liquid-Liquid and Fluid Solid Separation Processes"

+     ]

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.1.1 Page Number 699"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Adsorption  Isotherm for Phenol in Wastewater\n",

+      "import numpy as np\n",

+      "from scipy.optimize import curve_fit\n",

+      "import matplotlib.pyplot as plt\n",

+      "\n",

+      "#Variable Declaration\n",

+      "c = np.array([0.322,0.117,0.039,0.0061,0.0011])         #kg Phenol per m3 solution\n",

+      "q = np.array([0.150,0.122,0.094,0.059,0.045])           #kg Phenol per kg Carbon\n",

+      "\n",

+      "#Langmuir Isotherm Fitting function\n",

+      "def fit_func(x, q0, K):\n",

+      "    return q0*c/(K+c)\n",

+      "\n",

+      "params = curve_fit(fit_func, c, q)\n",

+      "[q0, K] = params[0]\n",

+      "\n",

+      "#Results\n",

+      "plt.grid(True, which='both')\n",

+      "print 'Langmuir Isotherm Fitting parameters are '\n",

+      "print \"q0  = \", round(q0,3) ,\" K = \", round(K,3)\n",

+      "\n",

+      "qi = q0*c/(K+c)\n",

+      "plt.figure(1)\n",

+      "plt.title('Langmuir Isotherm Fit giving a poor fit')\n",

+      "plt.ylabel('q,  kg phenol adsorbed/kg carbon')\n",

+      "plt.xlabel('c, kg phenol/m3 waste water')\n",

+      "plt.plot(c,q,'ro-',label='Expt. Data')\n",

+      "plt.plot(c,qi,'bo-',label='Fitted Data')\n",

+      "plt.legend(loc = 'lower right')\n",

+      "\n",

+      "#Freundlich Isotherm Fitting function\n",

+      "def fit_func(c, K, n):\n",

+      "    return K*c**n\n",

+      "\n",

+      "params = curve_fit(fit_func, c, q)\n",

+      "[K, n] = params[0]\n",

+      "\n",

+      "qi = K*c**n \n",

+      "\n",

+      "plt.figure(2)\n",

+      "plt.grid(True, which='both')\n",

+      "plt.title('Freundlich Isotherm Fitting')\n",

+      "plt.ylabel('q,  kg phenol adsorbed/kg carbon')\n",

+      "plt.xlabel('c, kg phenol/m3 waste water')\n",

+      "\n",

+      "plt.loglog(c,q,'ro-',basex=10,basey=10,label='Expt. Data')\n",

+      "plt.loglog(c,qi,'bo-',basex=10,basey=10,label='Fitted Data')\n",

+      "plt.legend(loc = 'best')\n",

+      "plt.plot()\n",

+      "\n",

+      "#plt.LogFormatterExponent(base=10)\n",

+      "#Results\n",

+      "print 'Freundlich Isotherm Fitting parameters are '\n",

+      "print \"K  = \", round(K,3) ,\" n = \", round(n,3)\n"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Langmuir Isotherm Fitting parameters are \n",

+        "q0  =  0.134  K =  0.007\n",

+        "Freundlich Isotherm Fitting parameters are "

+       ]

+      },

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "\n",

+        "K  =  0.194  n =  0.223\n"

+       ]

+      },

+      {

+       "metadata": {},

+       "output_type": "display_data",

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TMTExYt68edr4CxcuiIMHDwohhMjNzRVNmzYtk9cA+RIDOX9eiLVrhXjjDSFa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\nB+ATG4xA5ktqAAAAAElFTkSuQmCC\n",

+       "text": [

+        "<matplotlib.figure.Figure at 0x6192b10>"

+       ]

+      },

+      {

+       "metadata": {},

+       "output_type": "display_data",

+       "png": 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jV69eWL9+PZycnAAAd+/eRWRkJDZt2oSQkBCDPpeDcxyMMaY6nec4bt68KRs0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\nABg2bJjc7V1dXWXTQyNGjKgy3TNw4EAAgJ+fn+y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+       "text": [

+        "<matplotlib.figure.Figure at 0x6230810>"

+       ]

+      }

+     ],

+     "prompt_number": 1

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.2.1 Page Number 700 "

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Adsorption  Isotherm for Phenol in Wastewater\n",

+      "import numpy as np\n",

+      "from scipy.optimize import root\n",

+      "import matplotlib.pyplot as plt\n",

+      "\n",

+      "#Variable Declaration\n",

+      "M = 1.4                  #Mass of activated carbon in kg\n",

+      "S = 1.0                  #Surface area of carbon in m2\n",

+      "cF = 0.21                #Concentratio of phenol in feed in kg/m3\n",

+      "K = 0.194020642969       #Freundlich proportionality parameter \n",

+      "n = 0.222977691192       #Freundlich exponential parameter \n",

+      "\n",

+      "#Calculations\n",

+      "#Amount adsorbed + Amount left in equilibrium solution = Amount fed with solution\n",

+      "#M*K*c**n + cF*S = c*S\n",

+      "c = arange(0,0.2,0.00001)\n",

+      "q = K*c**n\n",

+      "q1 = (cF-c)*S/M\n",

+      "plt.grid(True)\n",

+      "plt.plot(c,q1,'r-',c,q)\n",

+      "plt.ylabel('q, kg phenol adsorbed/kg carbon')\n",

+      "plt.xlabel('c, kg phenol/m3 waste water')\n",

+      "f = lambda x: M*K*x**n + x - cF*S\n",

+      "sol = root(f, 0.1)\n",

+      "ce = sol.x[0]\n",

+      "qe = K*ce**n\n",

+      "plt.text(ce+0.01,qe,'Solution')\n",

+      "plt.text(ce+0.05,qe+0.02,'Isotherm')\n",

+      "plt.text(ce+0.05,qe-0.03,'Mass Balance')\n",

+      "plt.plot([0.0,ce,ce],[qe,qe,0.0])\n",

+      "plt.plot(ce, qe,'bo')\n",

+      "x =str(round(ce,4))\n",

+      "plt.text(ce,0.02,'c =' + x)\n",

+      "x =str(round(qe,4))\n",

+      "plt.text(0.001,qe + 0.005,'q =' + x)\n",

+      "ext = (cF-ce)*100/cF\n",

+      "\n",

+      "#Results\n",

+      "print \"Phenol equilibrium amount adsorbrd = \", round(qe,4) , \"kg Phenol/kg carbon\"\n",

+      "print \"Phenol equilibrium amount in solution = \", round(ce,4) , \"kg Phenol/m3 solution\"\n",

+      "print 'Precent Phenol extracted = %3.1f'%(ext), \"%\""

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Phenol equilibrium amount adsorbrd =  0.1048 kg Phenol/kg carbon\n",

+        "Phenol equilibrium amount in solution =  0.0632 kg Phenol/m3 solution\n",

+        "Precent Phenol extracted = 69.9 %\n"

+       ]

+      },

+      {

+       "metadata": {},

+       "output_type": "display_data",

+       "png": 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j2DE2w4iLA/LygP792SRm2zZmi6pCQ0MDPj4+8PHxgaOjI9auXVvu928agjJe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\nlYc3p+MikQgSiQQpKSmYMmUKUlJSoKOjU6469NK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+       "text": [

+        "<matplotlib.figure.Figure at 0x64e97f0>"

+       ]

+      }

+     ],

+     "prompt_number": 2

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.3.1 Page Number 704 "

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Scaleup of Laboratory Adsorption Column\n",

+      "import numpy as np\n",

+      "from scipy.optimize import curve_fit, root\n",

+      "import matplotlib.pyplot as plt\n",

+      "from scipy.interpolate import interp1d\n",

+      "from scipy.integrate import quad\n",

+      "\n",

+      "#Variable Declaration\n",

+      "d = 4.                #Diameter of particle in cm\n",

+      "h = 14.               #Length of the bed in cm\n",

+      "m = 79.2              #Mass of carbon in gm\n",

+      "c0 = 600              #Inlet concentration of alcohol in ppm\n",

+      "rho = 0.00115         #Density of air in g/cc\n",

+      "Q = 754.              #Flowrate of air (cc/s)\n",

+      "cbyc0brk = 0.01       #Ratio of concentration at break point \n",

+      "tbb = 6.0             #Break point for new coloumn in hr\n",

+      "\n",

+      "#Calculations \n",

+      "t = np.array([0.0,3.0,3.5,4.0,4.5,5.0,5.5,6.0,6.2,6.5,6.8])\n",

+      "cbyc0 = np.array([0.0,0.0,0.002,0.030,0.155,0.396,0.658,0.903,0.933,0.975,0.993])\n",

+      "\n",

+      "def bisection(a,b,tol):\n",

+      "    c = (a+b)/2.0\n",

+      "    while (b-a)/2.0 > tol:\n",

+      "        if f2(c) == 0:\n",

+      "            return c\n",

+      "        elif f2(a)*f2(c) < 0:\n",

+      "            b = c\n",

+      "        else :\n",

+      "            a = c\n",

+      "        c = (a+b)/2.0\n",

+      "    return c\n",

+      "\n",

+      "f = interp1d(t, cbyc0, kind='cubic',bounds_error=False)\n",

+      "plt.grid(True)\n",

+      "plt.plot(t,cbyc0,'ro')\n",

+      "tt = np.arange(0.0,7.,0.01)\n",

+      "qi =f(tt)\n",

+      "plt.plot(tt,qi,'b-')\n",

+      "plt.fill_between(t,cbyc0,1.,color='0.9')\n",

+      "plt.xlabel('$time, h$')\n",

+      "plt.ylabel('$c/c_0$')\n",

+      "plt.ylim(0.,1.)\n",

+      "plt.xlim(0.,7.)\n",

+      "#PART A\n",

+      "f2 = lambda t: f(t)-cbyc0brk\n",

+      "tb = bisection(0,4.,0.0001)\n",

+      "\n",

+      "f3 = lambda t: 1-f(t)\n",

+      "\n",

+      "tu, err = quad(f3,0.0,tb)\n",

+      "tt, err = quad(f3,0.0,t[10])\n",

+      "LUB = h*tu/tt\n",

+      "LUNB =h*(1-tu/tt)\n",

+      "plt.plot([tb,tb],[0.0,1.],'--')\n",

+      "plt.plot([0.,tb],[0.01,.01],'--')\n",

+      "\n",

+      "#Results PART A\n",

+      "print \"Results for PART A\"\n",

+      "print \"Break point time for c/c0=0.01:\",round(tb,2),\"hr\"\n",

+      "print \"Time for usable capacity of bed:\", round(tu,2), \"hr\"\n",

+      "print \"Time for complete bed length utilization\", round(tt,2),\"hr\"\n",

+      "print \"Length of used bed:\",round(LUB,2),\"cm\"\n",

+      "print \"Length of unused bed:\",round(LUNB,2),\"cm\"\n",

+      "\n",

+      "#PART B\n",

+      "print \n",

+      "print \"Results for PART B\"\n",

+      "LUBB = tbb*LUB/tb\n",

+      "print \"Length of used bed:\",round(LUBB,2),\"cm\"\n",

+      "LBB = LUBB+LUNB\n",

+      "print \"Length of unused bed:\",round(LUNB,2),\"cm\"\n",

+      "\n",

+      "mdot = Q*rho*3600.\n",

+      "OHads = mdot*c0*tt/1e6\n",

+      "Csat = OHads/m\n",

+      "fracBU = LUBB/LBB\n",

+      "\n",

+      "print \"Air flow rate:\", round(mdot,2),\"g air/hr\"\n",

+      "print \"Alcohol adsorbed:\", round(OHads,2), \"g alcohol\"\n",

+      "print \"Saturation Capacity:\", round(Csat,3), \"g alcohol/g carbon\"\n",

+      "print \"Fraction of new bed used:\", round(fracBU,3) \n",

+      "print 'Length of bed %4.1f cm'%LBB"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        " Results for PART A\n",

+        "Break point time for c/c0=0.01: 3.78 hr\n",

+        "Time for usable capacity of bed: 3.84 hr\n",

+        "Time for complete bed length utilization 5.26 hr\n",

+        "Length of used bed: 10.23 cm\n",

+        "Length of unused bed: 3.77 cm\n",

+        "\n",

+        "Results for PART B\n",

+        "Length of used bed: 16.25 cm\n",

+        "Length of unused bed: 3.77 cm\n",

+        "Air flow rate: 3121.56 g air/hr\n",

+        "Alcohol adsorbed: 9.84 g alcohol\n",

+        "Saturation Capacity: 0.124 g alcohol/g carbon\n",

+        "Fraction of new bed used: 0.812\n",

+        "Length of bed 20.0 cm\n"

+       ]

+      },

+      {

+       "metadata": {},

+       "output_type": "display_data",

+       "png": 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TU/4mZkVWz+9KW8mQ4lBSUkLr1q1ZvXo1ERERdOnShYyMjMsG0gcOHKBXr14s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\nkQpUHEREpIL/D2Ft2YMOjRWEAAAAAElFTkSuQmCC\n",

+       "text": [

+        "<matplotlib.figure.Figure at 0x6538ed0>"

+       ]

+      }

+     ],

+     "prompt_number": 5

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.5-1, Page Number 711"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Material Balance for Equilibrium Layers\n",

+      "\n",

+      "#Variable Declaration\n",

+      "mc = 30.              #Mass of isopropy ether in orignal mixture ,kg\n",

+      "ma = 10.              #Mass of acetic acid in orignal mixture ,kg\n",

+      "mb = 60.              #Mass of water in orignal mixture ,kg\n",

+      "\n",

+      "#Calculations\n",

+      "m = ma+mb+mc\n",

+      "xma = ma/m\n",

+      "xmb = mb/m\n",

+      "xmc = mc/m\n",

+      "\n",

+      "#Extract layer composition from Figure 12.5-3\n",

+      "ya = 0.04\n",

+      "yc = 0.94\n",

+      "yb = 1.- ya - yc\n",

+      "#Raffinate layer composition from Figure 12.5-3\n",

+      "xa = 0.12\n",

+      "xc = 0.02\n",

+      "xb = 1.0 - xa - xc\n",

+      "\n",

+      "#Results\n",

+      "print \"Composition of the orignal mixture for A,B,C reaspectively is \",xma,xmb,xmc\n",

+      "print \"Raffinate layer compositions of A, B, C reaspectively are\", xa,xb,xc\n",

+      "print \"Extract layer compositions of A, B, C reaspectively are\", ya,yb,yc"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Composition of the orignal mixture for A,B,C reaspectively is  0.1 0.6 0.3\n",

+        "Raffinate layer compositions of A, B, C reaspectively are 0.12 0.86 0.02\n",

+        "Extract layer compositions of A, B, C reaspectively are 0.04 0.02 0.94\n"

+       ]

+      }

+     ],

+     "prompt_number": 6

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.5-2, Page Number 714"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Amounts of phases in Solvent Extraction\n",

+      "\n",

+      "from numpy import linalg\n",

+      "#Variable Declaration\n",

+      "#From Example 12.5-\n",

+      "M = 100                  #Mass of activated carbon in kg\n",

+      "yA = 0.04\n",

+      "xA = 0.12\n",

+      "xAM = 0.1\n",

+      "#Calculation \n",

+      "#Balance on A in Feed \n",

+      "    #    yA*V + xAL = 0.01M\n",

+      "    #       V +   L =     M\n",

+      "a = np.array([[yA,xA], [1,1]])\n",

+      "b = np.array([xAM*M,M])\n",

+      "[V, L] = np.linalg.solve(a, b)\n",

+      "\n",

+      "#Results\n",

+      "print 'Kg of extract phase: %3.1f \\nKg of Raffinate phase: %3.1f'%(V,L)\n",

+      "\n",

+      "#from figure 12.5-3 \n",

+      "hg = 4.2\n",

+      "gi = 5.8\n",

+      "L = hg*M/gi\n",

+      "V = M-L\n",

+      "\n",

+      "#Results from Graph \n",

+      "\n",

+      "print 'from Graph'\n",

+      "print 'Kg of extract phase: %3.1f \\nKg of Raffinate phase: %3.1f'%(V,L)"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Kg of extract phase: 25.0 \n",

+        "Kg of Raffinate phase: 75.0\n",

+        "from Graph\n",

+        "Kg of extract phase: 27.6 \n",

+        "Kg of Raffinate phase: 72.4\n"

+       ]

+      }

+     ],

+     "prompt_number": 17

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.7-1 Page Number 718"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Material Balance for Counterurrent Stage Process\n",

+      "from numpy import linalg\n",

+      "\n",

+      "#Variable Declaration\n",

+      "Vn1 = 600        #Ispropyl ether (C pure) rate, kg/hr\n",

+      "Lo = 200         #Feed rate, kg/hr\n",

+      "xAL = 0.30       #Wt fraction of Acetic acid A in Feed\n",

+      "yCn1 = 1.0       #Wt fraction of Ether C in solvent feed\n",

+      "xCo = 0.0        #Wt fraction of Ether C in Feed\n",

+      "yAn1 = 0.0       #Wt fraction of Acetic acid solvent feed\n",

+      "#Calculation\n",

+      "M = Vn1 + Lo\n",

+      "xCM = (Vn1*yCn1 + Lo*xCo)/M\n",

+      "xAM = (Vn1*yAn1 + Lo*xAL)/M\n",

+      "\n",

+      "yA1 = 0.08\n",

+      "yC1 = 0.9 \n",

+      "xCn = 0.017\n",

+      "a = np.array([[1,1], [0.017,0.9]])\n",

+      "b = np.array([M,M*xCM])\n",

+      "[Ln,V1] = np.linalg.solve(a, b)\n",

+      "#Results\n",

+      "print \"Co-ordinates for the mixed feed are\", xCM, xAM\n",

+      "print \"Line passing through this intersect phase boundary at yA1 = 0.08 and yC1 = 0.9\" \n",

+      "print \"and xCn = 0.017 this is obtained from fig 12.7-3\"\n",

+      "print \"The amount of Raffinate\", round(Ln,0), \"kg/hr\"\n",

+      "print \"The amount of Extract\", round(V1,0), \"kg/hr\""

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Co-ordinates for the mixed feed are 0.75 0.075\n",

+        "Line passing through this intersect phase boundary at yA1 = 0.08 and yC1 = 0.9\n",

+        "and xCn = 0.017 this is obtained from fig 12.7-3\n",

+        "The amount of Raffinate 136.0 kg/hr\n",

+        "The amount of Extract 664.0 kg/hr\n"

+       ]

+      }

+     ],

+     "prompt_number": 19

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.7-3 Page Number 722 "

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Extraction of Nicotine with Immiscible Liquids\n",

+      "import numpy as np\n",

+      "from scipy.interpolate import interp1d\n",

+      "from scipy.optimize import root\n",

+      "import matplotlib.pyplot as plt\n",

+      "\n",

+      "#Variable Declaration\n",

+      "x = np.array([0,0.001010, 0.00246,0.005,0.00746,0.00988,0.0202])        #Equilibrium Data\n",

+      "y = np.array([0,0.000806, 0.001959,0.00454,0.00682,0.00904,0.0185])     \n",

+      "Lo = 100     #Feed rate, kg/hr\n",

+      "xo = 0.01    #Nicotine concentration in liquid feed, wt fraction\n",

+      "yn1 = 0.0005 #Nicotine concentration in solvent, wt fraction\n",

+      "xn = 0.001   #Nicotine concentration in raffinate\n",

+      "Vn1 = 200    #Kerosene feed rate, kg/hr\n",

+      "\n",

+      "#Calculation\n",

+      "Ld = Lo*(1.-xo)\n",

+      "Vd = Vn1*(1.-yn1)\n",

+      "\n",

+      "m = Ld/Vd\n",

+      "c = yn1 - m*xn\n",

+      "y1 = m*xo + c\n",

+      "f = interp1d(x,y, kind ='linear')\n",

+      "\n",

+      "xx = np.arange(0.0,0.00746,0.0005)\n",

+      "yo = m*xx+c\n",

+      "yy = f(xx)\n",

+      "plt.grid(True)\n",

+      "plt.plot(xx/(1.-xx),yy/(1.-yy))                 #Plot equilibrium line \n",

+      "plt.plot([xo,xn],[y1,yn1])\n",

+      "plt.xlabel('x')\n",

+      "plt.ylabel('y')\n",

+      "plt.text(.004, .006, r'$Equilibrium Curve$')\n",

+      "plt.text(.0065, .003, r'$Operating Line$')\n",

+      "\n",

+      "x1 = xo\n",

+      "y1 = y1\n",

+      "plt.plot(x1,y1,'ko')\n",

+      "plt.plot(xn,yn1,'ko')\n",

+      "plt.annotate('$(x_1,y_1)$', xy=(x1,y1), xytext=(0.009,0.005)#,\n",

+      "            #arrowprops=dict(facecolor='black', shrink=0.05),\n",

+      "            )\n",

+      "plt.annotate('$(x_n,y_n1)$', xy=(xn,yn1), xytext=(0.0012,0.0005)#,\n",

+      "            #arrowprops=dict(facecolor='black', shrink=0.05),\n",

+      "            )\n",

+      "n = 0\n",

+      "while x1 >= xn:\n",

+      "    ff = lambda z: y1 - f(z)\n",

+      "    sol = root(ff,0.0001)\n",

+      "    x2 = sol.x[0]\n",

+      "    y2 = y1\n",

+      "    plt.text(x2, y2+0.0002, str(n+1))\n",

+      "    plot([x1,x2],[y1,y2])                  #Draw Horizontal line to equilibrium curve\n",

+      "    x1 = x2\n",

+      "    y2 = m*x2+c\n",

+      "    plot([x1,x2],[y1,y2])                  #Draw Vertical line to equilibrium curve  \n",

+      "    x1 = x2\n",

+      "    y1 = y2\n",

+      "    n = n+1\n",

+      "\n",

+      "#Results\n",

+      "print 'Liquid flow rate %4.1f kg water/h' %Ld\n",

+      "print 'Kerosene flow rate %4.1f kg kerosene/h' %Vd\n",

+      "print \"Number of theoretical stages required for given separation are\", n "

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Liquid flow rate 99.0 kg water/h\n",

+        "Kerosene flow rate 199.9 kg kerosene/h\n",

+        "Number of theoretical stages required for given separation are 5\n"

+       ]

+      },

+      {

+       "metadata": {},

+       "output_type": "display_data",

+       "png": 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/Q5kypo5IPKpz188xK2IWK/5YQd+GfQkfFo5LZRdTh2XR5NllFkDGmw0elovb\nt3Vvs6xQAdats4wCU5K+F8cvH2dY0DDcFrhRyroUh0ccZtFriwpdYEpSLsyNnMkIi3fjBnTvDg4O\n8P33YCPPOiw29iftx3+PP7v+2sWo1qM4Ofokz5V7ztRhiXvInIywaNevg7c3NG4MCxaAtZmf28uc\njO4elx1ndjBtzzSOXz7Of9v8l+EthvNUablCwxhkTkYII7lyBby8wMND9zwyueDIvOWoHIKOBeG/\n15/U9FQmtp3IoCaDKG1T2tShiXyY+e9twhhkvNngbi4uXYKXXoJOnSy3wBSX70VmdibLY5fTeH5j\n/Pb4MbHtRP78958MbTbUaAWmuOSiOJIzGWFxkpJ0xaVfP5g61TILTHFwO/M2iw8uZlbELFyec2GO\n9xw61e0k97gUMzInIyzK2bO6AjNsGEyebOpoHp0lzMlcu3ONb6K/YW7UXNrWbsukdpNoXau1qcOy\nGMY+dhY4XDZnzhyuXbtmtA0KYSqnT0PHjjByZPEsMCXdhRsXmLB1As5znTl17RRhPmFs6L9BCkwx\nV2CRuXjxIq1ataJfv36EhobK2UExJOPNcPw4eHpCr15hjBlj6mjMg7l8L05ePYnvL740mt+IjOwM\nYnxjWNpzKa5VH3xeoVbMJRclUYFF5osvvuDEiRMMGzaMZcuW4eLiwocffsipU6eKIj4hntiRI/Dy\ny/Dpp9Czp6mjEXfFJscy4KcBtFnSBrsKdhwfdZzZ3rOpU7GOqUMTRlSoiX9ra2uqV6+OnZ0dNjY2\nXLt2jb59+9K5c2dmzJihdYziCXmW8DH8/MTE6O6D+fJLGDQIwNPEEZkPU3wvlFLsPrsb/z3+/HHx\nD8a+OJbvXvuOp8s8XeSx3MuS/41orcCJ/9mzZ7NixQoqV67M8OHDef3117G1tSUnJwcXFxezPaOR\niX8RFQWvvQbz50OfPqaOxjiK68S/Uorg+GCm7ZnGpVuX+MDjA4a4DaFMKQt4fk8xU+Q3Y169epUN\nGzbw/H0vNbe2tuaXX34xWiBCO2FhYRb3m9qePdC7t+4xMd27Gz63xFw8TFHkIisni7V/rsV/jz/W\nVtZMbjeZvg37YmNtXs/uke+FdgosMp9++ulDf9awYUOjBiOEMWzfDgMGwA8/QJcupo7GMqVlpbE0\nZikzwmdQu2JtpneZjpeTl9zjYoHkPhlRooSGwuDB8NNPusuVSxpzHy67nnadb/d/y+x9s2lZsyWT\n203Go7ZJ5kBWAAAgAElEQVSHqcMSj0CeXSbEQwQFwbvv6v7rIce1InXp1iW+jvyaRQcW0dW5K1vf\n2koTuyamDkuYAXl2mQWwhHsA1q0DX18ICcm/wFhCLgrLGLk4k3KGUSGjaDCvASlpKUS/G82q3quK\nXYGR74V25ExGFHsrV8LEibB1KzRtaupoLMORS0cI2BtASHwI77V4j6Mjj2JXwc7UYQkzJGcyFqAk\nXzXz3Xe6R8T8/nvhCkxJzsWjepxcRJyLoEdgDzqv6EzDKg05Pfo00zpPI/1qOgEBAfz888/Mnz+f\n4OBg4wd8n8zMTAYOHPhYfRcsWEDlypWZP38+ly9f1ufiSdYpHkKVUCV418Q/5s5Vqk4dpU6cMHUk\nRYcdO4p8mzk5OWpL/BbVcWlH5fC1g/om6ht1O+O2/uenTp1Sr7/+urpz547+sz59+qgrV64YPZa4\nuDj1xRdfPPF6oqOjVZ8+fYwQUclj7GOnnMlYgJI43jxzJnz1FezcCS6Fe407UDJz8bgKykV2TjZr\n/1zLC4teYPzW8QxvMZz4/8Tz71b/ppxtOX27YcOG8fHHH1O2bFn9Z05OTkRERBg95h07dtC8efMn\nXs++ffto3drw4E35XmhH0zmZ0NBQxowZQ3Z2NsOHD2fixIkPtBk9ejRbtmyhfPnyLFu2TP8Feljf\nKVOmsGnTJqysrKhcuTLLli2jdu3aWu6G0Ni5c+cYMmQIly5dwsrKivfee4/Ro0c/tP3nn+vmYXbu\nBHv7IgzUQqRnpbPy0Eqm751OlfJV+NTzU7rV64a11YO/kx44cICUlBSaNWuW6/OkpCSOHz9OVFQU\ntWvXpnr16hw/fpz//ve/bNmyhWPHjlG6dGn69OnDyZMn2bx5MykpKaSkpDBy5EjOnz9PZmYm58+f\np1q1agwfPpwtW7awZMkS/vWvfxEeHs7+/fupWbMmffv2Zffu3axfv56O/1y3/ueff/K///1PH8v3\n339P7dq1CQ8PZ+HChURHRzN06NBcMZ8+fZrNmzcXap3370P16tWN/ddQchj1vOgeWVlZysnJSSUk\nJKiMjAzl5uam4uLicrUJDg5W3t7eSimlIiMjlbu7e4F9U1NT9f3nzJmj3nnnnTy3r+GuCSO7cOGC\niomJUUopdePGDVWvXr0HvitKKZWTo9SHHyrVqJFSFy4UdZTmQcvhstS0VDVz70xVa1Yt1XVVVxWW\nEKZycnLy7TN79mw1YsSIXJ/l5OQoFxcXtWfPHjVjxgwVHByslFLq5ZdfVn/99Zdq166dUkqp33//\nXcXHx6tjx46pzz//XP36668qLS1NHTt2TPn4+CillPL391cRERH6dXfv3l0ppdTevXvVqlWr1A8/\n/KCUUmr37t3q/fffV1FRUUoppYYMGaLv06NHD3Xjxg2VmJioxo4dq5RSqnHjxurGjRu5Yi7sOs+c\nOfPAPpQkxj52ajZcFhUVhbOzMw4ODtja2jJgwACCgoJytdm0aRM+Pj4AuLu7k5KSQnJycr59n37a\n8CC9mzdvUqVKFa12QRSR6tWr638TrlChAq6uriQlJeVqoxSMH6+7RDksDOQXR+O5fPsyH+/4GMc5\njkQlRfHLwF/Y8uYWOjp0LPAO/czMTCpVqpTrs61bt+Lu7k7btm3Zt28fHTp0QClFcnIyGzduxNnZ\nmc2bN2NlZYWzszP169dn//79vPTSS5QpU4ZVq1bRo0cPAP744w/96EZycrL+jMHDw4OgoCB9u3bt\n2nHq1ClatWrF9evXKVVKN0hz5swZlFJUqFCBffv24eHhQWpqKqD7rgFkZGTw66+/FnqdGzduxMXF\nJdc+iIfTrMgkJibmGsayt7cnMTGxUG2SkpLy7fvRRx9Rp04dli9fzqRJk7TahRKjOI03nzlzhpiY\nGNzd3fWf5eToXjS2e7fukTFP8ntFccqF1tZuXsuY0DHUm1uP5JvJhA8LZ03fNTSvUfg5jy5dunDo\n0CH9cmpqKosWLWL27NkAXLlyhQoVKrB9+3Z69OhB2bJl6dmzJ927d6d9+/ZcunQJpRTp6enY2toC\nkJKSQv369cnIyODGjRvs378fgOjoaFq3bk10dDSpqalYWVnpt33nzh39nFBISAhdunQhIiJCvy6A\nnTt30qZNG6Kjo2nZsqU+5pUrV5KVlVXodZYrV44ePXrk2gfxcJrNyRT2GUXqMR5f8MUXX/DFF1/g\n7+/P2LFjWbp0aZ7thg4dioODAwCVKlWiWbNm+ksV7x5sZNl8lu/cucOUKVOYPXu2/sDSvr0n770H\nUVFh+PvDs88+2fbuMof9NVX8xy4fY+yCsYRFhjFy9EgOjzhM/MF4Eg8n4uLp8sjr6969O++99x52\ndnY888wzLFq0iEOHDpGYmEhWVha//PIL69atY8iQIbRq1YrZs2dz9OhRbt68yccff8zZs2epUqWK\n/iGVQ4YMYf78+VStWhUnJycuXLhAWFgYSUlJXLhwAWdnZ3bu3ElmZibp6ekArFixgpo1awK60Y7Q\n0FBsbGzo3bs3NjY2fPrpp2zbto0333yT2bNnc/36dSZMmEBWVhYnTpygS5cuKKWoVq0a6enphIWF\ncfz4cf18zJkzZ0hMTKRt27Y0bNiQMWPGcPToUerUqUPfvn3N5vvxOMthYWEsW7YMQH+8NCqjDr7d\nIyIiQnl5eemX/fz8lL+/f642vr6+KjAwUL9cv359lZycXKi+Sin1119/qUaNGuW5fQ13TWggIyND\nvfLKK+qrr77Sf5aZqdSgQUq99JJS9wyfW7QnmZOJToxWvdf0VlWnV1X/F/Z/6spt419ifK8VK1ao\nlStXarqNgiQnJyullEpJSVHvvvuuSWMpLox97NRsuKxly5bEx8dz5swZMjIyWLNmjX6s864ePXqw\nYsUKACIjI6lUqRJ2dnb59o2Pj9f3DwoKMsrljMK0lFK88847+t8QATIydE9SvnoVgoPhn+Fz8YiU\nUvx++nc6r+hM7zW96VCnAwnvJzCl4xSeK/ecZtu9cOECixcvfmCIvKhNmjSJjRs3smjRIqZOnWrS\nWCyWUUvWfUJCQlS9evWUk5OT8vPzU0optWDBArVgwQJ9m5EjRyonJyfVtGlTdeDAgXz7KqW7yatx\n48bKzc1N9e7dW128eDHPbWu8a8XKDhPcwPcodu/eraysrJSbm5tq1qyZcnNrplq33qJ69FAqLc24\n2zL3XBSksGcy2TnZakPcBtVqUSvVYF4DtTRmqUrPSs/VprjnwpgkFwbGPnbKo/4twN2x7uLg9m3o\n1QsqVdK9D+afuWCjKU65yEtBj/rPzM7kh8M/ELA3gAqlKzC53WR6NeiV5z0uxT0XxiS5MDD2sVOK\njDAbN2/q3mJZp47ujZal5PGtD3hYkbmVcYslMUuYGT6TepXrMbndZF6u+7K8JEw8MnmfjCiRrl8H\nb29o1AgWLgRreeBRoVy7c415UfOYFz2PdnXasb7felrVamXqsITQk3/KFuD+y1/NzdWr0LkzvPCC\n9gXG3HNRWEk3kpiwdQLOc505nXKaMJ+wRy4wJSUXxiC50I6cyQiTunQJunSBV16B6dNBRncK9t4v\n7/FT3E8MbjqYGN8Y6lSsY+qQhHgomZMRJpOUpDuD6dsXPv1UCkx+YpNj8d/jz5oq/2KK2s5o99FU\nKS+PVBLGJxP/hSRFxrydPQudOsHbb4OHhxW8tMPUIRUbnsrT1CGIEkwm/sUjM7fLM0+f1hWY//wH\nxo3TPfCyqA6c5paLvCil2HxiM/57/bl06xIfeHzAELchlClVxqjbKQ65KCqSC+1IkRFF6sQJ3RDZ\npEnw73+bOhrzkpWTxZoja/Df608p61JMajuJvg37YmNtY+rQhHhsMlwmisyff+om+D/7DIYNM3we\nFmaFp6fl/l3dybzD0tilzAifQZ2KdZjcbjJeTl5yj4swCRkuE8VSbKzuPpiZM+HNN00djXm4nnad\nb/d/y+x9s2lVsxU/9P4Bj9oepg5LCKOS+2QsgKnvAYiKAi8vmDvX9AXG1LkAuHjzIpN/m4zjHEeO\nXDrC1re2smngpiIvMOaQC3MhudCOnMkITe3ZA717w5Il8Nprpo7GtM6knGHG3hkEHglkYOOB7H93\nP3WfrWvqsITQlMzJCM1s3w79+8OqVbozmYcp6XMyRy4dIWBvACHxIbzX4j3GvDgGuwp2pg5LiDzJ\nnIwoFkJDYfBgWLcOLPXK0IhzEUzbM43opGjed3+fed7zqFi2oqnDEqJIyZyMBSjq8eagIBgyRPdf\ncyswWudCKUXoyVA6LuvIoA2D6OrcldOjTzOp3SSzKzAyD2EgudCOnMkIo1q3DkaNgpAQaNnS1NEU\nneycbNYfXY//Hn8yczKZ1HYS/Rv3p5S1/BMTlk3mZITRrFoFEybohsrc3ArfrzjPyaRnpbPijxVM\nD59O1fJVmdxuMt3qdcvzJWFCFAcyJyPM0uLF8Mkn8Pvv0LChqaPR3o30Gyw6sIgvI7+kqV1TlvRY\nQvs67eUGSiHuI79uWQCtx5u/+Qb+7/90zyAz9wLzpLm4fPsyH+/4GMc5jkQlRbF54Ga2vLmFDs93\nKHYFRuYhDCQX2pEzGfFEZs3SFZmdO6FuCb7l49z1c8wMn8nKQyvp27Av4cPCcansYuqwhDB7Micj\nHtvnn8OKFbohstq1H3895jwnc+zyMQL2BrDp+CaGNRvG2DZjqfl0TVOHJYRmZE5GmJxSMGUK/Pyz\n7gymRg1TR2R80YnR+O/1Z8/ZPYxqNYr4/8TzXLnnTB2WEMWOzMlYAGOONysF48fD5s26OZjiVmDy\ny4VSit9P/07nFZ3ps7YPHZ/vyOnRp5nScUqJLDAyD2EgudCO5kUmNDSUBg0a4OLiQkBAQJ5tRo8e\njYuLC25ubsTExBTYd8KECbi6uuLm5kbv3r25fv261rshgJwc3T0wu3frHhlTtaqpIzKOHJXDhqMb\ncF/szqgto3ir6VucHH2S0e6jear0U6YOT4jiTWkoKytLOTk5qYSEBJWRkaHc3NxUXFxcrjbBwcHK\n29tbKaVUZGSkcnd3L7Dv1q1bVXZ2tlJKqYkTJ6qJEyc+sG2Nd83iZGUp9c47Snl4KJWSYtx179hh\nmr+r9Kx0tTRmqWowr4FqtaiV2hC3QWXnZJskFiHMhbGPnZrOyURFReHs7IyDgwMAAwYMICgoCFdX\nV32bTZs24ePjA4C7uzspKSkkJyeTkJDw0L5dunTR93d3d2f9+vVa7obFy8qCoUMhMRF+/RUqVDB1\nRE/mVsYtFh9czKyIWdSvUp953vN4ue7Lxe4SZCGKA02HyxITE6l9z2VH9vb2JCYmFqpNUlJSgX0B\nvv/+e1599VUNoi8Z0tLScHV1pVmzZjRs2JDJkyc/Uv9bt3RPUv77bwgOLt4F5uqdqwz7ehiOcxzZ\ndXYX6/utZ9vgbXRy7GSRBUbmIQwkF9rR9EymsP9w1WNeLvfFF19QunRpBg0alOfPhw4dqj8TqlSp\nEs2aNcPznyc23v1SWcLyV199RdmyZcnOzuajjz5iz549ZGVlFdj/3DmYPt2TFi3A1zeMqCjz2J9H\nXU66kcTYBWMJiQ+hbZ22hPmGcfHPi9yKvwW1MHl8plqOjY01q3hMuRwbG2tW8RTlclhYGMuWLQPQ\nHy+NyqiDb/eJiIhQXl5e+mU/Pz/l7++fq42vr68KDAzUL9evX18lJycX2Hfp0qXKw8ND3blzJ89t\na7xrxdKtW7dUy5Yt1Z9//llg2/XrlapaVamFC5XKydE2Lq3mZOKvxKt3N72rnvV/Vr2/5X11NuWs\nJtsRoiQx9rFT0yNxZmamcnR0VAkJCSo9Pb3Aif+IiAj9xH9+fbds2aIaNmyo/v7774duW4qMQXZ2\ntnJzc1MVKlRQEyZMyLdtZqZS48cr9fzzSkVHF018xi4yB5MOqn7r+qkq06uoj7d/rP6+9fDviRAi\nt2JVZJRSKiQkRNWrV085OTkpPz8/pZRSCxYsUAsWLNC3GTlypHJyclJNmzZVBw4cyLevUko5Ozur\nOnXqqGbNmqlmzZqpESNGPLBdKTIGO3bsUEoplZKSotzd3fXL90tKUqpDB6W6dlXq8uWijO/J/65y\ncnJUWEKY6rqqq6o5q6aauXemSk1LzWNbO554WyWF5MJAcmFg7GOn5nf8e3t74+3tneszX1/fXMvz\n5s0rdF+A+Ph44wVoQSpWrEi3bt3Yv3+/fmz2rl27YOBA8PWF//0PrIvJbbo5KofgE8FM2zONv2//\nzQceH7Cx/0bKlCpj6tCEEMizy0q8y5cvU6pUKSpVqsSdO3fw8vLik08+oVOnToDuDv4vv4QZM2D5\ncvDyKvoYH+fZZVk5Waw+spqAvQGUsi7F5HaT6ePaBxtrG42iFMIyyLPLxCO5cOECPj4+5OTkkJOT\nw+DBg/UFJjUV3n4bzp2Dffvg+edNHGwh3Mm8w9LYpcwIn8HzFZ9nRpcZeDl5WeQlyEIUB8VkUEQ8\nriZNmvDll18SGxvLoUOHmDBhAgBHjkCrVmBnp3tMjLkXmOtp15m2exp1Z9cl9GQoP/b+kbChYXR1\n7vpIBebupZtCcnEvyYV25EzGAv3wA4wZoxsmGzzY1NHk7+LNi3wd+TWLDi7iVZdX2TZ4G03smpg6\nLCFEIcmcjAVJT4exY+G332D9emhiJsfqvOZkEq4lMCN8BoFHAhnUeBDjPcZT99kS/FY0IcyEzMmI\nx3L2LPTtC/b2EB0NFSuaOqK8Hbl0BP89/oSeDOW9F97j2Mhj2FWwM3VYQojHJHMyFmD69DBat4Z+\n/XRnMOZYYMLPhfNa4Gt0WdmFxtUac2r0Kfw6+Rm9wMjYu4HkwkByoR05kynBsrPhiy9gzhxdcenY\n0dQR5aaUIvRkKOWANze8yQceH7C271rK2ZYzdWhCCCOROZkS6tIleOst2LYNwPwv783MzqSUtfzO\nI4SpGfvYKcNlJdDu3fDCC9CypW5Z6R4fZPI/aZlpLNq/COc5zngs8eCX47+QnZONUkoKjBAllBSZ\nEiQnBwICdBP8CxeCn5/uc1OPN99Iv8HM8Jk4znFkw7ENLOmxhD1v76F7ve5YWxXtV9DUuTAnkgsD\nyYV25NfHEuLKFfDxgatXdVeP1alj6ojg71t/MzdqLt/u/5ZOdTsRPCiYZtWbmTosIUQRkjmZEmDf\nPt3bK/v2hWnTwNbW8DMrK93zyYrS2etnmRU+i5WHVtK3YV8+aPsBzs85F20QQojHInMyJVB2djbN\nmzfntddee6R+SsHXX8Nrr+n+O3Nm7gJT1I7+fZS3g96m+cLmlLYpzZF/H2HRa4ukwAhhwaTImIHZ\ns2fTsGHDR3oGV0oK9OkDq1ZBZCT06vXwtlqPN0cnRtN7TW86LuuIYyVHTv7nJDNemUHNp2tqut3H\nIWPvBpILA8mFdqTImNj58+cJCQlh+PDhhT5FPXhQd/VYzZqwdy84OmocZB6UUvx2+jc6r+hMn7V9\n8HTwJOH9BKZ0nMKz5Z4t+oCEEGZJ5mRM7I033uDDDz8kNTWVmTNn8ssvvzy0rVKwYAF8/DF8843u\nDv6CGHtOJkflsPHYRqbtmcbNjJtMbDuRQU0GUdqmtPE2IoQwGXl2WQmyefNmqlWrRvPmzQs8Xb9x\nA957D+LidGcv9eoVTYx3ZWRn8MOhHwjYG8AzZZ7hw3Yf0rNBzyK/BFkIUbzIEcKEwsPD2bRpE3Xr\n1mXgwIFs376dIUOGPNDu8GHdjZUVKujmXx61wDzJePOtjFvMjpyN8xxnfjzyI9+8+g37hu/jddfX\ni2WBkbF3A8mFgeRCO8XvKFGC+Pn5ce7cORISEli9ejUvv/wyK1as0P9cKfj+e3j5Zfjf/+C776Bc\nET3W6+qdq/zfzv+j7uy67Dq7i/X91rNt8DY6OXaSt1AKIQpNhsvMyL0H71u3YORIiIqCnTuhYcPH\nX6+np2eh2yamJvJV5Fd8H/M9vRr0Ytfbu2hQpcHjb9zMPEouSjrJhYHkQjsy8W+Gjh6FN96AFi3g\n22/hqacef12FnfiPvxLP9L3TWX90PUPchvDfNv+ldsXaj79hIUSxJDdjlnA//AAdOuhej7x8+ZMV\nmLvyG2+OuRBD/5/64/G9BzWfrsmJ/5zg665fl9gCI2PvBpILA8mFdjQvMqGhoTRo0AAXFxcCAgLy\nbDN69GhcXFxwc3MjJiamwL7r1q2jUaNG2NjYcPDgQa13oUikpYGvL3z6qe71yMOH685CtKCUYueZ\nnXRd1ZXugd1pXbM1p0ef5tOXPqVK+SrabFQIYZmUhrKyspSTk5NKSEhQGRkZys3NTcXFxeVqExwc\nrLy9vZVSSkVGRip3d/cC+x49elQdP35ceXp6qgMHDuS5bY13zaji45Vq1kypfv2Uun7duOu+Nw3Z\nOdkq6FiQarO4jXKe46y+O/CdSstMM+4GhRDFmrGPnZpO/EdFReHs7IyDgwMAAwYMICgoCFdXV32b\nTZs24ePjA4C7uzspKSkkJyeTkJDw0L4NGpjXRLSDgwPPPPMMNjY22NraEhUVVei+P/0E//43TJ0K\nI0Zoc/aSlZPF6iOr8d/jT2mb0kxqN4k+rn2wsbYx/saEEOIemhaZxMREatc2jO3b29uzb9++Atsk\nJiaSlJRUYF9zYWVlRVhYGM8991yh+6Snw4QJsHkzhIQYXjBmTHcy7wDlsB9tT4OWDZj1yixecXrF\noi9BDgsLkyuJ/iG5MJBcaEfTIlPYg5nS6CqwoUOH6s+EKlWqRLNmzfRfpLsTfcZa3r17NxUrVixU\n+zNnoGvXMBYcf4m5U6FVMDDvn6Dr/vPfBCMtT4UprvNoVK0RnAcrZytN9r+4LN9lLvGYcjk2Ntas\n4jHlcmxsrFnFU5TLYWFhLFu2DEB/vDQqow6+3SciIkJ5eXnpl/38/JS/v3+uNr6+viowMFC/XL9+\nfZWcnFyovp5mMidTt25d1axZM/XCCy+oRYsW5ds2KEipatWUmjVL5Z4wMYILNy6oidsmqucCnlNv\nbXhLHb542KjrF0KUfMY+dmp6JM7MzFSOjo4qISFBpaenFzjxHxERoZ/4L0xfT09PtX///jy3XZRF\nJikpSSml1KVLl5Sbm5vatWvXA20yMpSaMEGpOnWUCg/XB2mU7Z++elqN2DxCPev/rBoZPFKdvnra\nKOsVQlgeYx87Nb2EuVSpUsybNw8vLy8aNmxI//79cXV1ZeHChSxcuBCAV199FUdHR5ydnfH19WX+\n/Pn59gX4+eefqV27NpGRkXTr1g1vb28td6NANWrUAKBq1aq8/vrrD0z8nz8Pnp5w5AgcOABt2hhn\nu4cvHuatDW/R8ruWVCpbiaMjjzLv1XnUfbZurnb3DxVZMsmFgeTCQHKhIaOWLDNSVLt269YtlZqa\nqpRS6ubNm8rDw0P9+uuv+p+HhiplZ6eUn59S2dkPBPlY29x7dq/q/mN3VX1mdTVt9zSVcicl3/Y7\ndux4rO2URJILA8mFgeTCwNjHTnmszBNKSEjg9ddfByArK4s333yTyZMnk52tuyz5++/hxx+hY8c8\ngyz0y16UUoSeDGXanmmcTz3PBI8JDG02lHK2RfTETCGERTD2sVOKjAaSk2HgQLC21hUYO7uHNCxE\nkcnOyeanuJ/w3+tPdk42k9pNol+jfpSylmebCiGMT55dZubCwnSvRu7QAbZuzafA3Cc9PT33clY6\niw4sov68+syJmsNnL33GH//6g0FNBj1ygSlovDktLe2R1lecydi7geTCQHKhHSkyRpKTA35+ujOY\npUt1zyCzKeQN9Zs3b+bGjRsA3Ei/wczwmTjOcWTjsY183/N79ry9h+71uhvtJsqcnBzGjRunXz5/\n/jy//fabUdYthBD3kjEXI7hyBQYPhtRUiI4Ge/vC971w4QKpqamocoop26fw7f5v6ezYmeBBwTSr\n3swo8d29AQvg2rVrLF26lJ07d+o/c3Z2JiQkhLZt21KuqN6KZiL35sLSSS4MJBfakTOZJxQZqXvv\nS6NGsGPHoxUYgK++/Yq95fdSf159Lt26ROTwSFb3XW20AnO/Z599lnHjxvHMM8/k+rxbt24EBgZq\nsk0hhOWSIvOYlIKvv4YePWDOHJgxA2xtC9//6N9HAZizfQ7ly5fnD98/6HijI6vnr2b58uWMHDmS\n06dPF2pdR44c4fPPPycyMhLQPU7nXoUZb3ZycuLw4cOF34FiSsbeDSQXBpIL7chw2WO4fh2GDYO/\n/oK1f4fxUkUg7DFWtGMHQ1ctZ8YrMzh48CB9+vRh/fr1pKen88Ybb+hv8izI7du3sbW1RSnF0aNH\nqVq16mMEo7sEWwghjEnOZB5RTIzu6rHq1WHvXt1nytMz15/Nt27xyrRpdJw6lVemTeOXmzfZVieL\nl89+Ru2YIcwud4ibHq1Qnp5Y//PQhRYtWlCmTBkiIiLw9PTE09Oz0PMjrVu35uDBg7Rp04bIyEja\ntm2b6+eFHW++fft2ofNQXMnYu4HkwkByoR05kykkpeC77+Cjj2DuXBgwIO92wcHBvP/++5w6dUr/\n2c6YnVTtU5XPfT9nUJNB2NoYxtVs/rkELTo6mrp163LkyBHq1q3L7t27ad++fb4xJSQkULeu7hEy\n5cuXByAyMpL/+7//e6x9tLaW3zmEEMYlR5VCuHkThgzRFZc9ex5eYADmzJmTq8AApP+dTqOERvg0\n88lVYMBQHEJDQ9mwYQNt27bl559/1v88IiKCjz/+mJiYGH744Qf954mJiXTu3Fm/XKdOHdatW8eB\nAwews7MjPDxc3++jjz7St7t16xZfffUVR48e5euvv+bWrVuA7okCTz/99KMnp5iRsXcDyYWB5EI7\nciZTgLg46NsX3N1h3z74pyY81P03Vd71sBse7e3tuXbtGlOmTMnz5zVr1uT555/H1tZWX5AAatWq\nxZIlSwBYvHgxnp6e1KpVi379+ul/frdf2bJl9f2eeuopxo4dy9ixY3Nt59ChQ7i7u+e/c0II8Yjk\nTAL5v0gAAAlDSURBVCYfq1bpnjk2YYLuBsuCCgxAmTJl8vz83gP9vd59913WrVv30PXt27ePzp07\nc+DAgQfmaO4WtNq1a3Pz5k127drFhAkTHujXqlWrAuP+/fffeeONNwpsV9zJ2LuB5MJAcqEdeXZZ\nHtLS4P33dY+IWbcOmjZ9eNswqzA8lad+Oa85GScnJ2bPnk23bt3yXMfu3bt5/vnnqVOnzmPF+6T+\n/PNPsrKycHNzM8n2hRDmw9jPLpPhsvucPAlvvAH16sH+/fCo0xR3C8ncuXNJS0ujbNmy/Oc//3lo\ngQEKnOB/UmEFvL+8UaNGmm7fnBSUC0siuTCQXGhHisw9NmyAf/0LPvkE/v1v3UOSC3LvWcxd3bp1\ny7eoCCGEpZDhMiAjAyZOhI0bYe1aKMQUhhBClEgyXGZkZ89C//5QtSocPAjPPmvqiIQQouSw6KvL\ntmyB1q2hd28ICiq5BUbuATCQXBhILgwkF9qxyDOZrCzdvMuKFfDTT9CunakjEkKIksni5mQuXIBB\ng6BUKfjhB6hWzQTBCSGEmZLXLz+BHTugZUvw9ITQUCkwQgihNU2LTGhoKA0aNMDFxYWAgIA824we\nPRoXFxfc3NyIiYkpsO/Vq1fp0qUL9erV45VXXiElJaXAOHJy4IsvdGcwy5bphsoK+2rkkkDGmw0k\nFwaSCwPJhXY0KzLZ2dmMGjWK0NBQ4uLiCAwM5OjRo7nahISEcPLkSeLj41m0aBEjRowosK+/vz9d\nunThxIkTdOrUCX9//3zjuHIFunfXnbns3w9dumizv+YsNjbW1CGYDcmFgeTCQHKhHc2KTFRUFM7O\nzjg4OGBra8uAAQMICgrK1WbTpk34+PgA4O7uTkpKCsnJyfn2vbePj48PGzdufGgMERG6VyM3aQLb\nt0OtWhrtrJkrzNmepZBcGEguDCQX2tGsyCQmJlK7dm39sr29PYmJiYVqk5SU9NC+Fy9exM7ODgA7\nOzsuXrz40Bh69dI9nj8g4NFejSyEEMI4NLuE2aowz2SBQl3FoJTKc31WVlb5bicyEv55p5dFO3Pm\njKlDMBuSCwPJhYHkQjuaFZlatWpx7tw5/fK5c+ewt7fPt8358+ext7cnMzPzgc9r/TPWZWdnR3Jy\nMtWrV+fChQtUe8glYk5OTjg6Fq7QWYLly5ebOgSzIbkwkFwYSC50nJycjLo+zYpMy5YtiY+P58yZ\nM9SsWZM1a9YQGBiYq02PHj2YN28eAwYMIDIykkqVKmFnZ0flypUf2rdHjx4sX76ciRMnsnz5cnr1\n6pXn9k+ePKnVrgkhhCgkzYpMqVKlmDdvHl5eXmRnZ/POO+/g6urKwoULAfD19eXVV18lJCQEZ2dn\nnnrqKZYuXZpvX4BJkybRr18/lixZgoODA2vXrtVqF4QQQjyhEnvHvxBCCNMrFnf8m8tNneZAi1xM\nmDABV1dX3Nzc6N27N9evX9d8P4xBi1zcNWvWLKytrbl69apm8RuTVrmYO3curq6uNG7cmIkTJ2q6\nD8aiRS6ioqJo3bo1zZs3p1WrVkRHR2u+H8bwJLkYNmwYdnZ2NGnSJFf7Rz52KjOXlZWlnJycVEJC\ngsrIyFBubm4qLi4uV5vg4GDl7e2tlFIqMjJSubu7F9h3woQJKiAgQCmllL+/v5o4cWIR7tXj0SoX\nW7duVdnZ2UoppSZOnGjRuVBKqbNnzyovLy/l4OCgrly5UnQ79Zi0ysX27dtV586dVUZGhlJKqUuX\nLhXhXj0erXLRsWNHFRoaqpRSKiQkRHl6ehbhXj2eJ8mFUkrt2rVLHTx4UDVu3DhXn0c9dpr9mYw5\n3NRpLrTKRZcuXbC2ttb3OX/+fNHu2GPQKhcA48aNY/r06UW6P09Cq1x8++23TJ48Gdt/bjKrWrVq\n0e7YY9AqFzVq/H97d+yS7hbGAfxLg9F4aQpUKKshRK0spEmIloaGiBBpqpZoDMeChlRoCGpxkug/\naKnFSFoMrcYgooyWhhIbEjOj5w5xIy7R7+rr8/p6+X5mD5zny+t5eD0eTtfXG/7z8/PXv12tzEgW\nwOe18H/9cP9JrWun5ZuMFQ51WoVWFt8lk0lMTk4qzL6xtLLY39+H3W6Hx+NRrqBxtLK4vr7GyckJ\nAoEAgsEgzs7OlCsxTiuLeDyOlZUVOJ1ORCIRxGIx5UqMM5LFb2pdOy3fZKxwqNMqGpnFTzY2NmCz\n2RAOh+sabyaNLMrlMqLRKNbX1+sa3yxaz8X7+zuKxSJOT0+xubmJ2dnZeqZnKq0sFhYWsL29jfv7\ne2xtbWF+fr6e6Zmq3ixqWQv/y9pp+UvLmn2o00oamcW/x+7u7uLg4ABHR0eKFTSORhY3Nze4u7uD\n1+v9+vzw8DCy2aylnw+t58Jut2N6ehoAMDIygra2NhQKBXR2dmqWY4hWFtlsFqlUCgAwMzODxcVF\nzTIaot4s/vRTYM1rp5GNJTNUq1Xp6emRfD4vlUrlj5tXmUzma/Pqt7GRSETi8biIiMRisZbY7NbK\n4vDwUAYGBuTx8dHcggzQyuK7Vtn418oikUjI2tqaiIhcXV2Jw+Ewsar6aGUxODgo6XRaRERSqZT4\n/X4Tq6qPkSz+kc/nf9z4r2XttHyTEfn8N0d/f7+4XC6JRqMi8vkFSCQSX59ZXl4Wl8slHo9Hzs/P\nfx0rIlIoFGR8fFz6+vpkYmJCisWieQUZoJFFb2+vOJ1O8fl84vP5ZGlpybyCDNDI4rvu7u6WaDIi\nOlm8vb3J3NycuN1uGRoakuPjY9PqMUIji1wuJ6Ojo+L1eiUQCMjFxYV5BRlgJItQKCRdXV1is9nE\nbrdLMpkUkdrXTh7GJCIiNZbf+CciotbFJkNERGrYZIiISA2bDBERqWGTISIiNWwyRESkhk2GiIjU\nsMkQEZEaNhkiE+RyOXi9XlQqFZRKJbjdblxeXjZ7WkTqeOKfyCSrq6t4fX1FuVyGw+FomZsmiYxg\nkyEySbVahd/vR0dHBzKZTEtcL0FkFH8uIzLJ09MTSqUSXl5eUC6Xmz0dIlPwTYbIJFNTUwiHw7i9\nvcXDwwN2dnaaPSUidZa/tIzo/2Bvbw/t7e0IhUL4+PjA2NgY0uk0gsFgs6dGpIpvMkREpIZ7MkRE\npIZNhoiI1LDJEBGRGjYZIiJSwyZDRERq2GSIiEgNmwwREalhkyEiIjV/A+2ZkA6sFarJAAAAAElF\nTkSuQmCC\n",

+       "text": [

+        "<matplotlib.figure.Figure at 0x634bf10>"

+       ]

+      }

+     ],

+     "prompt_number": 20

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.8-1 Page Number 726"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Prediction of Time for Batch Leaching\n",

+      "\n",

+      "#Variable Declaration\n",

+      "Es = 0.2             #Fraction unextracted\n",

+      "d1 = 2.0             #Initial particle diameter, mm\n",

+      "d2 = 1.5             #Initial particle diameter, mm\n",

+      "t1 = 3.11            #Time in hr\n",

+      "#Calculation\n",

+      "# Using figure 5.3-13 page 349 for sphere Es = 0.2 alpha*t/a**2 = 0.112 it is same in both sizes\n",

+      "absc = 0.112\n",

+      "a1 = d1/2\n",

+      "a2 = d2/2\n",

+      "t2 = t1*a2**2/a1**2\n",

+      "\n",

+      "#Results\n",

+      "print \"Time require for 80% separation wich changed size\",round(t2,2),'h'"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Time require for 80% separation wich changed size 1.75 h\n"

+       ]

+      }

+     ],

+     "prompt_number": 21

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.9-1 Page Number 731"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Single Stage Leaching of Flaked Soyabeans\n",

+      "import numpy as np\n",

+      "import matplotlib.pylab as plt \n",

+      "#Variable Declaration\n",

+      "V2 = 100.0        #Fresh Solvent, kg\n",

+      "F = 100.0         #Feed of Soyabeen flakes, kg \n",

+      "xA2 = 0.0         #Compositions in wt fractions\n",

+      "xC2 = 1.0\n",

+      "y0 = 0.20\n",

+      "yA0 = 1.0 \n",

+      "Nn = 1.5          #kg insoluble rtained/ jgsolution retained\n",

+      "\n",

+      "#Calculation\n",

+      "B = F*(1.0-y0)\n",

+      "L0 = F*y0\n",

+      "N0 = B/L0\n",

+      "M = L0 + V2\n",

+      "    # Mxam = L0*y0 + V2*xA2 \n",

+      "xAm = L0/M\n",

+      "    #B = Nm*M\n",

+      "Nm = B/M\n",

+      "xA = np.array([0.0,0.2,0.4,0.6,0.8,1.0])\n",

+      "yA = np.array([0.0,0.2,0.4,0.6,0.8,1.0])\n",

+      "N = np.array([0,1,2,3,4,5])\n",

+      "plt.grid(True)\n",

+      "plt.plot([0,yA0],[0.0,N0])\n",

+      "plt.xlabel(\"$x_A,y_A$\")\n",

+      "plt.ylabel(\"$ N $\")\n",

+      "plt.ylabel(\"$ N $\")\n",

+      "plt.plot([0.,1.0],[Nn,Nn])\n",

+      "plt.plot([xAm,xAm],[0.0,Nn])\n",

+      "plt.text(0.6,0.1,\" $N$ vs $x_A$ \")\n",

+      "plt.text(0.6,1.6,\" $N$ vs $y_A$ \")\n",

+      "plt.text(yA0,N0,\" $L_0$ \")\n",

+      "plt.plot(yA0,N0,'ro')\n",

+      "plt.text(xAm,Nm,\" $M$ \")\n",

+      "plt.plot(xAm,Nm,'ro')\n",

+      "N1 = 1.5\n",

+      "yA1 = xAm\n",

+      "xA1 = xAm\n",

+      "plt.text(xA1,0,\" $V_1$ \")\n",

+      "plt.plot(xA1,N1,'ro')\n",

+      "m = (N[5]-N[0])/(xA[5]-xA[0])\n",

+      "yAm = m*xAm\n",

+      "plt.text(xA1,N1,\" $L_1$ \")\n",

+      "plt.plot(xA1,Nm,'ro')\n",

+      "plt.plot(xA2,0,'ro')\n",

+      "l = (Nn-0.0)\n",

+      "l1 = (yAm-0.0)\n",

+      "l2 = (Nn-yAm)\n",

+      "V1 = M*l1/l\n",

+      "L1 = M*l2/l\n",

+      "\n",

+      "#Results\n",

+      "print 'Equilibrium amounts of L1, V1 are %3.1f and %3.1f kg'%(L1,V1)"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Equilibrium amounts of L1, V1 are 53.3 and 66.7 kg\n"

+       ]

+      },

+      {

+       "metadata": {},

+       "output_type": "display_data",

+       "png": 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+       "text": [

+        "<matplotlib.figure.Figure at 0x65319f0>"

+       ]

+      }

+     ],

+     "prompt_number": 22

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.10-1 Page Number 735"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Counterurrent Extraction of Oil from meal\n",

+      "import numpy as np\n",

+      "from scipy.optimize import curve_fit, root\n",

+      "import matplotlib.pyplot as plt\n",

+      "from scipy.interpolate import interp1d\n",

+      "from scipy.integrate import quad\n",

+      "\n",

+      "#Variable Declaration\n",

+      "X = []\n",

+      "N = np.array([2.00,1.98,1.94,1.89,1.82,1.75,1.68,1.61])   #kg of inert solid B/kg solution\n",

+      "yA = np.array([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7])          #kg of inert oil A/kg solution\n",

+      "FMi = 2000.      #Rate of Mass of inert solid meal, kg/hr\n",

+      "FMo = 800.       #Rate of Mass of oil, kg/hr\n",

+      "FMb = 50.        #Rate of Mass of Benzene with feed, kg/hr\n",

+      "FSb = 1310.      #Rate of Mass of Benzene with fresh solvent, kg/hr\n",

+      "FSo = 20.        #Rate of Mass of oil with fresh solvent, kg/hr\n",

+      "LS = 120.        #Rate of Mass of Leached solids, kg/hr\n",

+      " \n",

+      "#Calculation\n",

+      "f = interp1d(yA,N,kind='quadratic')\n",

+      "L0 = FMo+FMb\n",

+      "yA0 = FMo/L0\n",

+      "B = FMi\n",

+      "N0 = B/L0\n",

+      "Vn1 = FSb + FSo\n",

+      "xAn1 = FSo/Vn1\n",

+      "M = L0 + Vn1\n",

+      "xAM = (L0*yA0 + Vn1*xAn1)/M\n",

+      "NM = B/M\n",

+      "sL0Vn1 = B/LS\n",

+      "\n",

+      "cL0Vn1 = NM - sL0Vn1*xAM\n",

+      "xx = -cL0Vn1/sL0Vn1\n",

+      "\n",

+      "plt.grid(True)\n",

+      "plt.xlabel('$x_A$  $y_A$')\n",

+      "plt.ylabel('$N$')\n",

+      "plt.plot(yA0,N0,'ro')           #plot L0\n",

+      "plt.text(yA0,N0,'$L_0$')\n",

+      "plt.plot(0.0,0.0,'ro')          #plot origin\n",

+      "plt.plot(xAn1,0.0,'bo')         #Plot Vn+1\n",

+      "plt.text(-xAn1-0.06,0.2,'$V_{N+1}$') \n",

+      "plt.plot([xAn1,yA0],[0.0,N0])   #plot line L0 to Vn+1\n",

+      "plt.plot(xAM,NM,'ro')           #plot M\n",

+      "plt.text(xAM-0.05,NM-0.5,'$M$')\n",

+      "plt.plot(yA,N,'b-')             #Plot N vs yAN+1\n",

+      "plt.text(yA[len(yA)-1],N[len(yA)-1],'$N$ $vs$ $y_A$')\n",

+      "\n",

+      "ff= lambda x:f(x)-sL0Vn1*x\n",

+      "sol = root(ff,0.01)\n",

+      "yAn = sol.x[0]\n",

+      "Nn = f(yAn)\n",

+      "\n",

+      "plt.plot([0.0,yAn],[0.0,Nn])\n",

+      "plt.plot([xAn1,yAn],[0.0,Nn])    #Plot Vn+1 to Ln\n",

+      "\n",

+      "s1 = (NM-Nn)/(xAM-yAn)\n",

+      "c1 = NM-s1*xAM\n",

+      "x1 = -c1/s1\n",

+      "X.append(x1)\n",

+      "plt.plot([x1,yAn],[0.0,Nn])      #Plot Ln to V1 \n",

+      "sdL0 = (N0-0.0)/(yA0-x1)\n",

+      "c2 = -sdL0*x1\n",

+      "\n",

+      "s3 = (Nn-0.0)/(yAn-xAn1)\n",

+      "c3 = -s3*xAn1\n",

+      "delx = (c3-c2)/(sdL0-s3)\n",

+      "dely = sdL0*delx+c2\n",

+      "plt.text(delx,dely-0.5,'$\\delta$')\n",

+      "plt.plot([yA0,delx],[N0,dely])     #Draw a line from V1 to delta\n",

+      "plt.plot([xAn1,delx],[0.0,dely])   #Draw a line from Vn+1 to delta\n",

+      "plt.plot([1.0,-0.4],[0.0,0.0])\n",

+      "x = x1\n",

+      "j = 0\n",

+      "while x > xAn1:\n",

+      "    j = j+ 1\n",

+      "    y = f(x)\n",

+      "    plt.plot(x,0.0,'bo')\n",

+      "    plt.text(x,-0.5,'V'+str(j))\n",

+      "    plt.plot([x,x],[0.0,y])            #Move to N vs y curve\n",

+      "    plt.plot(x,y,'bo')\n",

+      "    plt.text(x,y+0.5,'L'+str(j))    \n",

+      "    plt.plot([x,delx],[y,dely])        #from x,y Draw a line joining to delx,dely\n",

+      "    slope = (y-dely)/(x-delx)\n",

+      "    inter = y - slope*x\n",

+      "    xnew = -inter/slope\n",

+      "    X.append(xnew)\n",

+      "    x = xnew\n",

+      "\n",

+      "a = np.array([[1,1], [yAn,X[0]] ])\n",

+      "b = np.array([M,M*xAM])\n",

+      "Ln,V1 = np.linalg.solve(a, b)\n",

+      "\n",

+      "#Results\n",

+      "print 'Rate of leached solution %5.1f kg/h'%Ln\n",

+      "print 'Composition of leached solution %5.4f kg/h'%yAn\n",

+      "\n",

+      "print 'Rate of solvent out %5.1f kg/h'%V1\n",

+      "print 'Composition of solvent out %5.3f kg/h'%X[0]\n",

+      "\n",

+      "print 'Rate of leached solids %8.1f kg solution/h' %(M)\n",

+      "print \"Number of equilibrium stages required are\",j "

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Rate of leached solution 1014.0 kg/h\n",

+        "Composition of leached solution 0.1183 kg/h\n",

+        "Rate of solvent out 1166.0 kg/h\n",

+        "Composition of solvent out 0.600 kg/h\n",

+        "Rate of leached solids   2180.0 kg solution/h\n",

+        "Number of equilibrium stages required are 4\n"

+       ]

+      },

+      {

+       "metadata": {},

+       "output_type": "display_data",

+       "png": 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RcbmJiFh2AAcMN2HMvcZzIjVGc9ertDQEDx5MxsCBUVBTEzMXURP4cfygc8+5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\nCGmECgMhhJBG/j+vXVOxmdQUfQAAAABJRU5ErkJggg==\n",

+       "text": [

+        "<matplotlib.figure.Figure at 0x65e4090>"

+       ]

+      }

+     ],

+     "prompt_number": 23

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.11-1 Page Number 739"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Yield of Crystalization Process\n",

+      "import numpy as np\n",

+      "from numpy.linalg import solve\n",

+      "\n",

+      "#Variable Declaration\n",

+      "F = 10000.           #Weight of salt solution, kg\n",

+      "xFc = 0.30           #Weight percent of salt\n",

+      "Ts = 293.            #Temperature of salt solution, K\n",

+      "S = 21.5             #Unhydrous Solubility, kg/100 kg of water\n",

+      "MW = 106.0           #Molecular wt of Na2CO3\n",

+      "MWh = 180.2          #Molecular wt of 10.H2O\n",

+      "\n",

+      "#Calculation\n",

+      "MWhs = MW + MWh      #Molecular wt of Na2CO3.10.H2O\n",

+      "xFs = 1.0 - xFc\n",

+      "xWs,xWc = 1.0, 0.0\n",

+      "\n",

+      "xCs,xCc = MWh/MWhs,MW/MWhs\n",

+      "xSs,xSc = 100/(S+100),S/(S+100)\n",

+      "\n",

+      "#Part A\n",

+      "W = 0.0\n",

+      "a = np.array([[1,1], [xSs,xCs]])\n",

+      "b = np.array([F-W, F*xFs-W*xWs])\n",

+      "[Sa,Ca] = solve(a,b)\n",

+      "\n",

+      "#Part B\n",

+      "W = F*0.03\n",

+      "a = np.array([[1,1], [xSc,xCc]])\n",

+      "b = np.array([F-W, F*xFc-W*xWc])\n",

+      "[Sb,Cb]= solve(a, b)\n",

+      "\n",

+      "#Results\n",

+      "print \"Answer to PART A\"\n",

+      "print \"kgs of Hydrayred Crystal produced and Saturated solution are\", round(Ca,1),\"&\", round(Sa,1),\"respectively\"\n",

+      "print \"Answer to PART B\"\n",

+      "print \"kgs of Hydrayred Crystal produced and Saturated solution are\", round(Cb,1),\"\", round(Sb,1),\"respectively\"\n",

+      "print 'The results are checked by using tools like calculator and\\nother the results of this code are more correct than book'"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Answer to PART A\n",

+        "kgs of Hydrayred Crystal produced and Saturated solution are 6361.7 & 3638.3 respectively\n",

+        "Answer to PART B\n",

+        "kgs of Hydrayred Crystal produced and Saturated solution are 6636.2  3063.8 respectively\n",

+        "The results are checked by using tools like calculator and\n",

+        "other the results of this code are more correct than book\n"

+       ]

+      }

+     ],

+     "prompt_number": 2

+    },

+    {

+     "cell_type": "heading",

+     "level": 2,

+     "metadata": {},

+     "source": [

+      "Example 12.11-2 Page Number 741"

+     ]

+    },

+    {

+     "cell_type": "code",

+     "collapsed": false,

+     "input": [

+      "#Heat Balance in Crystallization\n",

+      "\n",

+      "from numpy.linalg import solve\n",

+      "\n",

+      "#Variable Declaration\n",

+      "F = 2268.0             #kg of feed solution\n",

+      "TF = 327.6             #Temperature of feed solution, K\n",

+      "CF = 48.2              #kgMgSO4/100 kg water\n",

+      "Tc = 293.2             #Temperature cooled to, K\n",

+      "S = 35.5               #kgMgSO4/100 kg water \n",

+      "cp = 2.93              #Average heat Capcity of Solution at 298.2 K, kJ/kg.K\n",

+      "delHs = -13.31e3       #Heat of solution at 298.2 K, kJ/kmol\n",

+      "MW = 120.368           #MW of MgSO4\n",

+      "MWwh = 126.107         #MW of Water of Hydration 7H2O\n",

+      "\n",

+      "#Calculation\n",

+      "xFs = 100./(100+CF)\n",

+      "xFc = 1.0 - xFs\n",

+      "xSs = 100./(100. + S)\n",

+      "xSc = 1.0 - xSs\n",

+      "xCs = MWwh/(MW + MWwh)\n",

+      "xCc = 1.0 - xCs\n",

+      "\n",

+      "a = numpy.array([[1.,1.], [xSc,xCc]])\n",

+      "b = numpy.array([F, F*xFc])\n",

+      "[So,C]= solve(a, b)\n",

+      "\n",

+      "H1 = F*(TF-Tc)*cp\n",

+      "delHs = delHs/(MW+MWwh)\n",

+      "delHc = -(delHs)\n",

+      "Hc = delHc*C\n",

+      "Q = - Hc - H1\n",

+      "#Results\n",

+      "print 'Mass of Hydrayred Crystal produced and Saturated solution are %5.2f & %5.2f kg'%(C,So)\n",

+      "print \"Total heat Absorbed\", round(Q,2), \"kJ. Negative sign indicates heat must be removed\"\n",

+      "print 'The results are checked by using tools like calculator and\\nother the results of this code are more correct than book'"

+     ],

+     "language": "python",

+     "metadata": {},

+     "outputs": [

+      {

+       "output_type": "stream",

+       "stream": "stdout",

+       "text": [

+        "Mass of Hydrayred Crystal produced and Saturated solution are 633.65 & 1634.35 kg\n",

+        "Total heat Absorbed -262814.27 kJ. Negative sign indicates heat must be removed\n",

+        "The results are checked by using tools like calculator and\n",

+        "other the results of this code are more correct than book\n"

+       ]

+      }

+     ],

+     "prompt_number": 3

+    }

+   ],

+   "metadata": {}

+  }

+ ]

+}
\ No newline at end of file