path: root/Introduction_To_Chemical_Engineering/ch6.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 6 : Mass Transfer"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.3 page number 215"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "D_AB=6.75*10**-5   #in m2/s\n",
      "Z=0.03   #in m\n",
      "R=8314\n",
      "p_A1=5.5*10**4   #in Pa\n",
      "p_A2=1.5*10**4   #in Pa\n",
      "T=298   #in K\n",
      "\n",
      "N_A=D_AB*(p_A1-p_A2)/(R*T*Z);\n",
      "print \"flux = %f kmol/sq m s\"%(N_A)\n",
      "\n",
      "Z=0.02;   #in m\n",
      "p_A2=p_A1-((N_A*R*T*Z)/D_AB);\n",
      "print \"pressure = %f Pa\"%(p_A2)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "flux = 0.000036 kmol/sq m s\n",
        "pressure = 28333.333333 Pa\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.4 page number 216\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "Z=0.15   #in m\n",
      "P=1.013*10**5    #in Pa\n",
      "p_A1=1.5*10**4    #in Pa\n",
      "p_A2=5*10**3    #in Pa\n",
      "\n",
      "p_B1=P-p_A1;\n",
      "p_B2=P-p_A2;\n",
      "\n",
      "D_AB=2.30*10**-5   #in m2/s\n",
      "R=8314.\n",
      "T=298.   #in K\n",
      "\n",
      "p_BM=(p_B2-p_B1)/math.log (p_B2/p_B1);\n",
      "print p_B1, p_B2\n",
      "N_A=D_AB*(p_A1-p_A2)*P/(R*T*Z*p_BM);\n",
      "print \"flux = %.4e kmol/sq m s\"%(N_A)\n",
      "\n",
      "N_A=D_AB*(p_A1-p_A2)/(R*T*Z);\n",
      "print \"flux = %.4e kmol/sq m s\"%(N_A)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "86300.0 96300.0\n",
        "flux = 6.8736e-07 kmol/sq m s\n",
        "flux = 6.1889e-07 kmol/sq m s\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.6 page number 218\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "M_A=36.5    #molar mass of HCl\n",
      "M_B=18.      #molar masss of water\n",
      "w_A1=12.;    #weight % of HCL\n",
      "w_A2=4.      #weight % of HCL\n",
      "\n",
      "x_A1=(w_A1/M_A)/((w_A1/M_A)+((100-w_A1)/M_B));\n",
      "print 'x_A1 =%f'%(x_A1)\n",
      "\n",
      "x_B1=1.-x_A1;\n",
      "M1=100./((w_A1/M_A)+((100-w_A1)/M_B));\n",
      "print \"molar mass at point 1 = %f kg/kmol\"%(M1)\n",
      "\n",
      "x_A2=(w_A2/M_A)/((w_A2/M_A)+((100-w_A2)/M_B));\n",
      "x_B2=1-x_A2;\n",
      "M2=100/((w_A2/M_A)+((100-w_A2)/M_B));    #avg molecular weight at point 2\n",
      "print \"molar mass at point 2 = %f Kg/kmol\"%(M2)\n",
      "\n",
      "density_1=1060.7;    #in kg/m3\n",
      "density_2=1020.15;   #in kg/m3\n",
      "C_av=((density_1/M1)+(density_2/M2))/2;\n",
      "print \"C_av = %f kmol/cubic m\"%(C_av)\n",
      "\n",
      "x_BM=(x_B2-x_B1)/(math.log (x_B2/x_B1));\n",
      "Z=0.004    #in m\n",
      "D_AB=2.5*10**-9;\n",
      "N_A=(D_AB*C_av*(x_A1-x_A2))/(x_BM*Z);\n",
      "print \"flux = %f kmol/sq m-s\"%(N_A)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x_A1 =0.063011\n",
        "molar mass at point 1 = 19.165694 kg/kmol\n",
        "molar mass at point 2 = 18.372483 Kg/kmol\n",
        "C_av = 55.434825 kmol/cubic m\n",
        "flux = 0.000002 kmol/sq m-s\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.8 page number 229\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "Gs=700/22.4    #in kmol of dry air/hr\n",
      "Ls=1500./18     #in kmol of dry air/hr\n",
      "y1=0.05\n",
      "Y1=y1/(1-y1);\n",
      "Y2=0.02*Y1;\n",
      "X2=0\n",
      "X1=(Gs/Ls)*(Y1-Y2);\n",
      "m=Gs*(Y1-Y2);\n",
      "\n",
      "delta_Y1=Y1-1.68*X1;\n",
      "delta_Y2=Y2-1.68*X2;\n",
      "delta_Y=(delta_Y1-delta_Y2)/(math.log (delta_Y1/delta_Y2));\n",
      "print \"driving force = %f kmol acetone/kmol dry air\"%(delta_Y)\n",
      "\n",
      "K_G=0.4    #in kmol acetone/kmol dry air\n",
      "A=m/(K_G*delta_Y);\n",
      "print \"area = %f sq m\"%(A)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "driving force = 0.006466 kmol acetone/kmol dry air\n",
        "area = 623.154093 sq m\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.9 page number 229\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "G1=(855/22.4)*(106.6/101.3)*(273/299.7);\n",
      "y1=0.02;\n",
      "Y1=y1/(1-y1);\n",
      "Gs=G1*(1-y1);\n",
      "\n",
      "Y2=0.05*Y1;\n",
      "x2=0.005;\n",
      "X2=x2/(1-x2);\n",
      "Y=0.204;\n",
      "X1=0.176;    #in kmol bgenzene/kmol benzene free oil\n",
      "\n",
      "Ls_molar=(Gs*(Y1-Y2))/(X1-X2);\n",
      "Ls=Ls_molar*260;\n",
      "\n",
      "print \"minimum oil circulation rate = %f kg/hr\"%(Ls)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "minimum oil circulation rate = 1057.149516 kg/hr\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.10 page number 231\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "P_M = 53.32   #kPa\n",
      "P_W = 12.33   #in kpA\n",
      "P = 40  #IN K pA\n",
      "\n",
      "x = (P - P_W)/(P_M-P_W);\n",
      "\n",
      "print \"liquid phase composition = %f\"%(x)\n",
      "\n",
      "y = P_M*x/P;\n",
      "print \"vapor phase composition = %f\"%(y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "liquid phase composition = 0.675043\n",
        "vapor phase composition = 0.899832\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.12 page number 231\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "from numpy import *\n",
      "\n",
      "%pylab inline\n",
      "\n",
      "x = [1,0.69,0.40,0.192,0.045,0];\n",
      "y = [1,0.932,0.78,0.538,0.1775,0];\n",
      "plot(x,y)\n",
      "x = linspace(0,1,10)\n",
      "y = linspace(0,1,10)\n",
      "plot(x,y)\n",
      "x = [0.5,0.31];\n",
      "y = [0.5,0.7];\n",
      "plot (x,y)\n",
      "\n",
      "xlabel(\"x\")\n",
      "ylabel(\"y\")\n",
      "suptitle(\"distillation curve\")\n",
      "Z=0.5;\n",
      "y_D=0.69;\n",
      "x_W=0.31;\n",
      "\n",
      "show()\n",
      "\n",
      "print \"composition of top product = %f mole percent of hexane\"%(y_D*100)\n",
      "print \"composition of bottom product = %f mole percent of hexane\"%(x_W*100)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['draw_if_interactive', 'new_figure_manager']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEhCAYAAAB7mQezAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x27f1450>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "composition of top product = 69.000000 mole percent of hexane\n",
        "composition of bottom product = 31.000000 mole percent of hexane\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.13 page number 237\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%pylab inline\n",
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "F = 100.   #moles\n",
      "xf = 0.4;\n",
      "D = 60.   #moles\n",
      "W = 40.   #moles\n",
      "\n",
      "x = linspace(0.2,0.45,6)\n",
      "y = zeros(6)\n",
      "z = zeros(6)\n",
      "for i in range(6):\n",
      "    y[i] = 2.16*x[i]/(1+1.16*x[i]);\n",
      "    z[i] = (y[i]-x[i])**-1;\n",
      "z = z.T / 10\n",
      "plot(x,z)\n",
      "suptitle('Batch Distillation Curve')\n",
      "xlabel('x')\n",
      "ylabel('y')\n",
      "xw = 0.22;   #from the graph\n",
      "yd = (F*xf-W*xw)/D;\n",
      "show()\n",
      "\n",
      "print \"composition of distillate = %f\"%(yd)\n",
      "print \"composition of residue = %f\"%(xw)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEhCAYAAACHjCx5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuE3JQ0RE3KbkISIiblPyEBERt/1/DZ660K/RnHYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1c46110>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "composition of distillate = 0.520000\n",
        "composition of residue = 0.220000\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.15 page number 249\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "x=0.4;\n",
      "y=0.8;\n",
      "x_D=y;\n",
      "x_W=0.135;   #bottom concentration\n",
      "\n",
      "D=(100*x-100*x_W)/(y-x_W);     #distillate amount\n",
      "print \"amount of distillate =%f moles/h\"%(D)\n",
      "\n",
      "alpha=6;   #relative volatility\n",
      "x_R=y/(y+(alpha*(1-y)));    #liquid leaving partial condensor\n",
      "print \"liquid leaving partial condenser = %f\"%(x_R)\n",
      "\n",
      "y1=(1./3)*y+(2./3)*x;\n",
      "x1=y1/(y1+(alpha*(1-y1)));\n",
      "y_W = (1./3)*x_D+(2./3)*x1;\n",
      "x_W=y_W/(y_W+(alpha*(1-y_W)));\n",
      "D=(100*(x-x_W))/(y-x_W);\n",
      "\n",
      "print \"amount of distillate = %f moles/h\"%(D)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "amount of distillate =39.849624 moles/h\n",
        "liquid leaving partial condenser = 0.400000\n",
        "amount of distillate = 43.636364 moles/h\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.16 page number 264\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "x=0.01;    #% of nicotine\n",
      "X0 = x/(1-x);\n",
      "w=150.    #weight of nicotine water solution\n",
      "\n",
      "A0=w*(1-X0);\n",
      "B0=250.;    #kg keroscene\n",
      "X1 = A0*X0/(A0+B0*0.798);\n",
      "print \"final concentration of nicotine = %f\"%(X1)\n",
      "\n",
      "c=A0*(X0-X1);\n",
      "print \"amount of nicotine removed = %f kg\"%(c)\n",
      "\n",
      "percentage = (c*100)/(A0*x);\n",
      "print \"percentage recovery = %f percent\"%(percentage)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "final concentration of nicotine = 0.004310\n",
        "amount of nicotine removed = 0.859863 kg\n",
        "percentage recovery = 57.909174 percent\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.17 page number 264\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "x=0.01   #mole fraction of nicotine\n",
      "yN = 0.0006;   #mole fraction in solvent\n",
      "xN = 0.001;     #final mole fraction in water\n",
      "\n",
      "X0=x/(1.-x);    #in kg nicotine/kg water\n",
      "YN =yN/(1.-yN);   #in kg nicotine/kg keroscene\n",
      "XN = xN/(1.-xN);\n",
      "A0=100.*(1.-X0);    #kgwater/h\n",
      "B0=150.*(1.-YN);   #in kg kerosene/h\n",
      "\n",
      "Y1=((A0*(X0-XN))/B0)+YN;    #in kg nicotine/kg kerosene\n",
      "print \"Y1 = %f kg nicotine/kg kerosene\"%(Y1)\n",
      "\n",
      "number_of_stages = 8.4;\n",
      "print \"numnber of stages = %f\"%(number_of_stages)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Y1 = 0.006609 kg nicotine/kg kerosene\n",
        "numnber of stages = 8.400000\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.18 page number 274\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "P = 101.3    #in kPa\n",
      "pA = 3.74    #in kPa\n",
      "p_AS = 7.415  #in kPa\n",
      "\n",
      "H = (18.02/28.97)*(pA/(P-pA));\n",
      "print \"humidity = %f kg H2O/kg air\"%(H)\n",
      "\n",
      "Hs = (18.02/28.97)*(p_AS/(P-p_AS));\n",
      "print \"Saturated humidity = %f kg H2O/kg air\"%(Hs)\n",
      "\n",
      "humidity = 100*(H/Hs);\n",
      "print \"percentage humidity = %f percent\"%(humidity)\n",
      "\n",
      "relative_humidity = 100*(pA/p_AS);\n",
      "print \"percentage relative humidity = %f percent\"%(relative_humidity)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "humidity = 0.023845 kg H2O/kg air\n",
        "Saturated humidity = 0.049127 kg H2O/kg air\n",
        "percentage humidity = 48.538334 percent\n",
        "percentage relative humidity = 50.438301 percent\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.19 page number 264\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from numpy import *\n",
      "\n",
      "S=425.6   #in kg/h\n",
      "X1 = 0.035   #in kgwater/kg dry solid\n",
      "t_s1=25.   #in degree C\n",
      "X2 = 0.017   #in kg H2O/kg dry air\n",
      "t_s2=60.   #in degree C\n",
      "H2 = 0.0175    #in kg H2O/kg dry air\n",
      "t_G2 = 84.2   #in degree C\n",
      "t_G1= 32.8   #in degree C\n",
      "C_pS = 1.465   #in kJ/kg dry solid\n",
      "C_pA = 4.187   #in kg/ kg H2O K\n",
      "\n",
      "H_G2=(1.005+1.88*H2)*(t_G2-0)+H2*2501;\n",
      "H_S1 = C_pS*(t_s1-0)+X1*C_pA*(t_s1-0);    #in kJ/kg\n",
      "H_S2 = C_pS*(t_s2-0)+X2*C_pA*(t_s2-0);   #in kJ/kg\n",
      "Q=9300.;  #in kJ/h\n",
      "\n",
      "print \"Latent heat of water at 0C HG2 = %f kJ/kg dryair\"%(H_G2)\n",
      "print \"Enthalpy of entering solid HS1 = %f kJ/kg dryair\"%(H_S1)\n",
      "print \"Enthalpy of exit solid HS2 = %f kJ/kg dryair\"%(H_S2)\n",
      "\n",
      "A = array([[0.0175, -1],[98.194, -2562.664]]);\n",
      "b = array([[-14.17248],[29745.398]]);\n",
      "x = linalg.solve(A,b)\n",
      "G = x[0]\n",
      "H1 = x[1]/G;\n",
      "print \"Air flow rate G = %f kg dryair/hr\"%(G)\n",
      "print \"Humidity H1 = %f kg dryair/hr\"%(H1)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Latent heat of water at 0C HG2 = 131.158680 kJ/kg dryair\n",
        "Enthalpy of entering solid HS1 = 40.288625 kJ/kg dryair\n",
        "Enthalpy of exit solid HS2 = 92.170740 kJ/kg dryair\n",
        "Air flow rate G = 1238.387008 kg dryair/hr\n",
        "Humidity H1 = 0.028944 kg dryair/hr\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.20 page number 291\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from numpy import *\n",
      "M_Na2CO3 = 106\n",
      "M_10H2O = 180.2\n",
      "M_Na2CO3_10H2O = 286.2;\n",
      "w_Na2CO3 = 5000.   #in kg\n",
      "water = 0.05   #% of water evaporated\n",
      "\n",
      "W = water*w_Na2CO3;\n",
      "\n",
      "A = array([[0.8230, 0.6296],[0.1769, 0.3703]])\n",
      "b = array([[3500],[1250]])\n",
      "x = linalg.solve(A,b);\n",
      "L = x[0]\n",
      "C = x[1];\n",
      "\n",
      "print \"L = %f kg solution\"%(L)\n",
      "print \"C = %f kg of Na2CO3.10H2O crystals\"%(C)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "L = 2632.372855 kg solution\n",
        "C = 2118.102193 kg of Na2CO3.10H2O crystals\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 6.21 page number 291\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "from numpy import *\n",
      "A = array([[0.7380, 0.5117],[0.2619, 0.4882]])\n",
      "b = array([[1400],[600]])\n",
      "\n",
      "x = linalg.solve(A,b)\n",
      "L = x[0]\n",
      "C = x[1];\n",
      "print \"L = %f kg solution\"%(L)\n",
      "print \"C = %f kg of MgSO4.7H2O crystals\"%(C)\n",
      "\n",
      "F = 2000   #in kg/h\n",
      "cv = 2.93   #in kJ/kg K\n",
      "H1 = F*cv*(330-293);\n",
      "print \"enthalpy of feed = %f kJ\"%(H1)\n",
      "\n",
      "wt = 246.49   #molar mass MgSO4.7H2O\n",
      "heat_soln = -13.31*10**3;   #in kJ/kg mol\n",
      "heat = heat_soln/wt;\n",
      "heat_crystallization = abs(heat);\n",
      "H2 = heat_crystallization*C;   #total heat\n",
      "q = -H1-H2;\n",
      "print \"heat absorbed = %f kJthus heat shall be removed\"%(q)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "L = 1663.710339 kg solution\n",
        "C = 336.489681 kg of MgSO4.7H2O crystals\n",
        "enthalpy of feed = 216820.000000 kJ\n",
        "heat absorbed = -234989.814805 kJthus heat shall be removed\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}