{
 "metadata": {
  "name": "",
  "signature": "sha256:f28b10e862601fa7c238c02abbd33f9e5034b01e48fba8ea70e2e847c96ba3bd"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 9: Drying of Process Material"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.3-1 Page Number 526"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Humidity from Vapor-pressure Data\n",
      "\n",
      "#Variable declaration\n",
      "Pas = 3.5                   #Vapor pressure of water at 26.7 deg C (kPa)\n",
      "Pa = 2.76                   #Partial pressure of water vapor (kPa)\n",
      "P = 101.3                   #Pressure at room temperature (kPa)\n",
      "\n",
      "#Calculation\n",
      "        #Calculation for part (a)\n",
      "H = 18.02*Pa/(28.97*(P - Pa))\n",
      "        #Calculation for part (b)\n",
      "Hs = 18.02*Pas/(28.97*(P - Pas))\n",
      "Hp = 100.*H/Hs       \n",
      "    #Calculation for part (c)\n",
      "Hr = 100.*Pa/Pas\n",
      "#Result\n",
      "print \"(a) The Humidity is\",round(H,5),\"kg H2O/(kg dry air)\"\n",
      "print \"(b) The saturation humidity is\",round(Hs,5),\"kg H2O/(kg dry air)\"\n",
      "print \"(b) The percentage humidity is\",round(Hp,1),\"%\"\n",
      "print \"(c) The precentage relative humidity is\",round(Hr,1),\"%\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(a) The Humidity is 0.01742 kg H2O/(kg dry air)\n",
        "(b) The saturation humidity is 0.02226 kg H2O/(kg dry air)\n",
        "(b) The percentage humidity is 78.3 %\n",
        "(c) The precentage relative humidity is 78.9 %\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.3-2 Page Number 528"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Use of Humidity Chart\n",
      "\n",
      "#Variable declaration\n",
      "T = 60.                        #Dry bulb temperature of air (deg C)\n",
      "T_F = 140.                     #Temperature in FPS units (deg F)\n",
      "Td = 26.7                      #Dew point of air (deg C)\n",
      "\n",
      "#Calculation\n",
      "H = 0.0225                     #Actual humid heat (Value determined from the Psychrometric chart)\n",
      "\n",
      "Cs_SI = 1.005 + 1.88*H\n",
      "Cs_Eng = 0.24 + 0.45*H\n",
      "Vh_SI = (2.83e-3 + 4.56e-3*H)*(T + 273.)\n",
      "Vh_Eng = (0.0252 + 0.0405*H)*(T_F + 460.)\n",
      "\n",
      "#Result\n",
      "print 'Actual humidity determine using Humidity chart= %5.4f kgH2O/kgdry air'%H\n",
      "print \"The calculated Humid Heat in SI units is\",round(Cs_SI,3),\"kJ/(kg dry air.K)\"\n",
      "print \"The calculated Humid Heat in English units is\",round(Cs_Eng,3),\"btu/(lbm dry air.\u00b0C)\"\n",
      "print \"The calculated Humid Volume in SI units is \",round(Vh_SI,3),\"m3/kg dry air\"\n",
      "print \"The calculated Humid Volume in English units is\",round(Vh_Eng,2),\"ft3/lbm dry air\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Actual humidity determine using Humidity chart= 0.0225 kgH2O/kgdry air\n",
        "The calculated Humid Heat in SI units is 1.047 kJ/(kg dry air.K)\n",
        "The calculated Humid Heat in English units is 0.25 btu/(lbm dry air.\u00b0C)\n",
        "The calculated Humid Volume in SI units is  0.977 m3/kg dry air\n",
        "The calculated Humid Volume in English units is 15.67 ft3/lbm dry air\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.3-3 Page Number 531 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Adiabatic Saturation of Air\n",
      "\n",
      "#Variable declaration\n",
      "Ha = 0.030                        #Humidity of dry air (kg H2O/kg dry air)\n",
      "\n",
      "#Calculation\n",
      "    #Calculation for (a)\n",
      "Hb = 0.0500       #Value determined from Humidity chart for 90 % saturation (kg H2O/kg dry air)\n",
      "T = 42.5          #Value determined from Humidity chart for 90 % saturation (deg C)\n",
      "    #Calculation for (b)\n",
      "Hb = 0.0505       #Value determined from Humidity chart for 100 % saturation (kg H2O/kg dry air)\n",
      "Tb = 40.5         #Value determined from Humidity chart for 90 % saturation (deg C)\n",
      "#Result\n",
      "print \"(a) The value of humidity at 90 % saturation is \",Ha,\"(kg H2O/kg dry air)\"\n",
      "print \"    The value of temperature at 90 % saturation is \",T,\"deg C\"\n",
      "print \"(b) The value of humidity at 100 % saturation is \",Hb,\"(kg H2O/kg dry air)\"\n",
      "print \"    The value of temperature at 100 % saturation is \",Tb,\"deg C\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(a) The value of humidity at 90 % saturation is  0.03 (kg H2O/kg dry air)\n",
        "    The value of temperature at 90 % saturation is  42.5 deg C\n",
        "(b) The value of humidity at 100 % saturation is  0.0505 (kg H2O/kg dry air)\n",
        "    The value of temperature at 100 % saturation is  40.5 deg C\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.3-4 Page Number 532 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Wet Bulb Temperature and Humidity\n",
      "\n",
      "#Variable declaration\n",
      "T = 60.               #Dry bulb temperature of vapor-air mixture (deg C)\n",
      "Tw = 29.5             #Obtained wet bulb temperature (deg C)\n",
      "\n",
      "#Calculation\n",
      "H = 0.0135   #Humidity obtained from the adiabatic saturation curve (kg H2O/kg dry air)\n",
      "\n",
      "#Result\n",
      "print \"The humidity obtained from the adiabatic curve is\",H,\"(kg H2O/kg dry air)\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The humidity obtained from the adiabatic curve is 0.0135 (kg H2O/kg dry air)\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.6-1 Page Number 541"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Time of Drying from Drying Curve\n",
      "\n",
      "#Variable declaration\n",
      "X1 = 0.38                      #Initial Moisture content (kg H2O/kg dry solid)\n",
      "X2 = 0.25                      #Final Moisture content (kg H2O/kg dry solid)\n",
      "t1 = 1.28                      #Time required for drying solid (h) (Value determined from fig. 9.5-1A)\n",
      "t2 = 3.08                      #Time required for drying solid (h) (Value determined from fig. 9.5-1A)\n",
      "\n",
      "#Calculation\n",
      "t = t2 - t1      \n",
      "#Result\n",
      "print \"The time required is \",t,\"h\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The time required is  1.8 h\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.6-2 Page Number 541"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Time of Drying from Drying Curve\n",
      "\n",
      "#Variable declaration\n",
      "X1 = 0.38                      #Initial Moisture content (kg H2O/kg dry solid)\n",
      "X2 = 0.25                      #Final Moisture content (kg H2O/kg dry solid)\n",
      "LsbyA = 21.5                   #From Fig.9.5-1b\n",
      "Rc = 1.51                      #From Fig.9.5-1b\n",
      "#Calculation\n",
      "t = LsbyA*(X1 - X2)/Rc\n",
      "#Result\n",
      "print \"The time required is \",round(t,2),\"h\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The time required is  1.85 h\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.6-3 Page Number 543 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Prediction of Constant-Rate Drying\n",
      "\n",
      "#Varialbe declaration\n",
      "A_eng = 1.5*1.5                   #Area of pan (ft2)\n",
      "H = 0.01                          #Humidity of air stream (kg H2O/kg dry air) or (lbm H2O/lbm dry air)\n",
      "Hw = 0.026                        #Saturated Humidity (kg H2O/kg dry air) or (lbm H2O/lbm dry air)\n",
      "\n",
      "#Variable declaration SI units\n",
      "A = 0.457*0.457                   #Area of pan (m2)\n",
      "T = 65.6                          #Dry bulb temperature (deg C)\n",
      "Tw = 28.9                         #Wet bulb temperature (deg C) \n",
      "v = 6.1                           #Velocity of air stream flowing parallel to the surface (m/s)\n",
      "lambdaw = 2433.                   #Value determined from steam table (kJ/kg)\n",
      "lambdaw_eng = 1046.               #Value determined from steam table (btu/lbm) \n",
      "\n",
      "#Calculation\n",
      "Vh = (2.83e-3 + 4.56e-3*H)*(T + 273.)\n",
      "Rho = (1. + H)/0.974\n",
      "G = v*3600.*Rho\n",
      "h = 0.0204*G**0.8\n",
      "Tw = 28.9\n",
      "Rc_SI = (h/(lambdaw*1000.))*(T - Tw)*3600.\n",
      "R = Rc_SI*A\n",
      "\n",
      "print \"Results in SI units\"\n",
      "print \"The calculated total rate of evaporation in SI Units \",round(R,3),\"kg H2O/h\"\n",
      "\n",
      "#Variable declaration English units\n",
      "T_eng = 150.                      #Dry bulb temperature (F)\n",
      "Tw = 28.9                         #Wet bulb temperature (deg C) \n",
      "v_eng = 20.                       #Velocity of air stream flowing parallel to the surface (ft/s)\n",
      "lambdaw_eng = 1046.               #Value determined from steam table (btu/lbm) \n",
      "Tw_eng = 84.\n",
      "\n",
      "#Calculation\n",
      "Vh_eng = (0.0252 + 0.0405*H)\n",
      "Rho_eng = 0.0647\n",
      "G_eng = v_eng*3600.*Rho_eng\n",
      "h_eng = 0.0128*G_eng**0.8\n",
      "Rc_Eng = h_eng*(T_eng - Tw_eng)/lambdaw_eng\n",
      "R_eng = Rc_Eng*A_eng\n",
      "\n",
      "#Result\n",
      "print \"Results in English units\"\n",
      "print \"The calculated total rate of evaporation in English Units\",round(R_eng,3),\"lbm H2O/h\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Results in SI units\n",
        "The calculated total rate of evaporation in SI Units  0.708 kg H2O/h\n",
        "Results in English units\n",
        "The calculated total rate of evaporation in English Units 1.563 lbm H2O/h\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.7-1 Page Number 545"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Graphical Integration in Falling-Rate Drying Period\n",
      "import numpy as np\n",
      "from scipy.interpolate import interp1d\n",
      "import scipy.integrate as integrate\n",
      "from matplotlib.pylab import plot,fill_between\n",
      "\n",
      "#Variable declaration\n",
      "X1 = 0.38             #Free moisture content (kg H2O/kg dry soild)\n",
      "X2 = 0.04             #Dry solid (kg H2O/kg dry soild)\n",
      "Ls = 399.             #Weight of dry solid (kg)\n",
      "A = 18.58             #Area of top drying surface (m2)\n",
      "\n",
      "Xc = 0.195            #Critical free moisture content (kg H2O/kg dry soild)\n",
      "Rc = 1.51             \n",
      "#Calculation\n",
      "LsbyA = Ls/A\n",
      "tc = LsbyA*(X1 - Xc)/Rc                     #Time required for drying under constant rate period\n",
      "\n",
      "X = np.array([0.195,0.150,0.1,0.065,0.05,0.04])\n",
      "R = np.array([1.51,1.21,0.9,0.71,0.37,0.27])\n",
      "InvR = 1/R\n",
      "plot(X,InvR,'ro-')\n",
      "f = interp1d(X,InvR)\n",
      "AUC = -integrate.simps(InvR,X)        #Time required for drying under falling rate period\n",
      "tf = AUC*LsbyA\n",
      "\n",
      "tfs = str(round(tf,2))\n",
      "plot([Xc,Xc],[0.,InvR[0]])\n",
      "plot([X2,X2],[0.,InvR[len(X)-1]])\n",
      "xlabel(\"Moisture content, Kg H2O/kg Dry solid\")\n",
      "ylabel(\"Drying Rate, kg H2O/(h.m2)\")\n",
      "fill_between(X,InvR,0,color='0.8')\n",
      "#Result\n",
      "print \"Total drying time:\", round(tc+tf,3), \"h\"\n",
      "title('Area under curve in falling rate period')\n",
      "text(0.1,0.5,'Area='+tfs)\n",
      "\n",
      "print 'Because of numerical integration answer is different than graphical'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total drying time: 6.536 h\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.text.Text at 0x63fdd70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6370f70>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.7-2 Page Number 547 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Approximation of Straight Line for Falling-Rate Period\n",
      "\n",
      "#Variable declaration\n",
      "Rc = 1.51\n",
      "Xc = 0.195            #Critical free moisture content (kg H2O/kg dry soild)\n",
      "X2 = 0.040            #Dry solid (kg H2O/kg dry soild)\n",
      "Ls = 399.             #Weight of dry solid (kg)\n",
      "A = 18.58             #Area of top drying surface (m2)\n",
      "LsbyA = Ls/A\n",
      "\n",
      "#Calculation\n",
      "t = LsbyA*Xc*log(Xc/X2)/(Rc)\n",
      "#Result\n",
      "print \"The falling rate period is\",round(t,2),\"h\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The falling rate period is 4.39 h\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.8-1 Page Number 550"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Constant-Rate Drying When Radiation and Convention are Present  \n",
      "import numpy as np\n",
      "from scipy.interpolate import interp1d\n",
      "from scipy.optimize import root\n",
      "\n",
      "#Variable declaration\n",
      "t2 = np.array([293,298,303,308,313,318,323])\n",
      "Hs = np.array([14.8,20.2,27.6,36.,49.,65.,87.])\n",
      "\n",
      "t1 = np.array([280,285,290,295,300,305,310,315,320,325,330,335,340])\n",
      "lambds = np.array([2485.4,2473.9,2462.2,2450.3,2438.4,2426.3,2414.3,2402.0,2389.8,2377.6,2365.3,2353.0,2340.5])\n",
      "\n",
      "T = 65.6                #Temperature of the bottom metal surface (deg C)\n",
      "Zs = 0.0254             #Depth of the pan (m)\n",
      "Km = 43.3               #Thermal conductivity of pan (W/m.K)\n",
      "Ks = 0.865              #Thermal conductivity of material (W/m.K)\n",
      "Zm = 0.00061            #Thickness of the metal pan (m)\n",
      "e = 0.92                #Emissivity of the solid \n",
      "H = 0.010               #Humidity of dry air (kg H2O/kg dry air)\n",
      "Tw = 28.9               #Wet bulb temperature (deg C)\n",
      "Ts = 32.2\n",
      "Tr = 93.3               #Temperature of the surface (deg C)\n",
      "lams = 2424.            #Value determined from steam table (kJ/kg)\n",
      "tsg = 32.2\n",
      "u = 6.1                 #m/s\n",
      "\n",
      "#Calculation\n",
      "fHs = interp1d(t2,Hs)\n",
      "flambd = interp1d(t1,lambds)\n",
      "T1 = Tr + 273.2\n",
      "T2 = Ts + 273.2\n",
      "cs = (1.005 + 1.88*H)*1e3\n",
      "Rho = (1. + H)/0.974\n",
      "G = u*3600.*Rho\n",
      "hc = 0.0204*G**0.8\n",
      "UK = 1/(1/hc+Zm/Km+Zs/Ks)\n",
      "er = 1.02\n",
      "Tsg = 303.5\n",
      "while er >= 0.1:\n",
      "    print \"Watch function value displyed to fall near to zero\"\n",
      "    Tsg = float(raw_input(\"Enter the guess temperature  \")) + 273.2\n",
      "    hr = e*5.676*((T1/100)**4-((Tsg)/100)**4)/(T1-(Tsg))\n",
      "    Hs = fHs(Tsg)*1e-3\n",
      "    lambdas = flambd(Tsg)*1e3\n",
      "    k1 = (Hs-0.01)*lambdas/cs\n",
      "    k2 = 1+UK/hc\n",
      "    k3 = hr/hc\n",
      "    f = lambda tsc: -k1 + k2*(T-tsc) + k3*(Tr-tsc)\n",
      "    sol = root(f,T)\n",
      "    tsc = sol.x[0]\n",
      "    er = abs(Tsg-(tsc+273.2))\n",
      "    print er\n",
      "    if er <= 0.01: break\n",
      "    Tsg = tsc+273.2\n",
      "\n",
      "Rc = ((hc+UK)*(T-tsc)+hr*(Tr-tsc))*3600./lambdas\n",
      "#Result\n",
      "\n",
      "print \"Rate of constant drying to \", round(Rc,2),\"kg/(h.m2)\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Watch function value displyed to fall near to zero\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Enter the guess temperature  34.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "6.79133441651\n",
        "Watch function value displyed to fall near to zero\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Enter the guess temperature  32.5\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0923167102895\n",
        "Rate of constant drying to  4.86 kg/(h.m2)\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.9-1 Page Number 553"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Drying slabs of wood when Diffusion of Moisture Controls\n",
      "\n",
      "#Variable declaration\n",
      "Dl = 2.97e-6         #Experimental average diffusion cofficient (m2/h)\n",
      "X_ = 0.04             #Moisture content of wood (kg H2O/kg dry wood)\n",
      "Xt1 = 0.29           #Initial average moisture content \n",
      "Xt = 0.09            #Final average moisture content \n",
      "d = 25.4             #Thickness of the wooden plank (mm)\n",
      "#Calculation\n",
      "X1 = Xt1 - X_         #The free moisture content \n",
      "X = Xt - X_\n",
      "x1 = d/(2*1000.)\n",
      "t = 4*(x1**2)*log(8*X1/((pi**2)*X))/((pi**2)*Dl)\n",
      "Ea = X/X1\n",
      "Dltbyx12 = 0.56\n",
      "t_g = x1**2*Dltbyx12/Dl\n",
      "#Result\n",
      "print \"The time needed for drying by calculation is\",round(t,1),\"h\"\n",
      "print \"The time needed for drying by graph is\",round(t_g,1),\"h\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The time needed for drying by calculation is 30.8 h\n",
        "The time needed for drying by graph is 30.4 h\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.9-2 Page Number 555"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Diffusion Coefficient in the Tapoica Root\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from scipy.interpolate import interp1d\n",
      "from scipy.optimize import root\n",
      "#Variable declaration\n",
      "XbyXc = np.array([1.,0.8,0.63,0.55,0.4,0.3,0.23,0.18])\n",
      "t = np.array([0.,0.15,0.27,0.40,0.6,0.8,0.94,1.07])\n",
      "xbyxc = 0.2\n",
      "\n",
      "#Calculation\n",
      "x1 = 3./2.\n",
      "plt.gca().set_yscale('log')\n",
      "f = interp1d(t,XbyXc)\n",
      "f1 = lambda tt: f(tt)-xbyxc\n",
      "sol = root(f1,.97)\n",
      "txbyxc = sol.x[0]\n",
      "\n",
      "plot(t,XbyXc,'bo-')\n",
      "plot([0.0,txbyxc,txbyxc],[0.2,0.2,0.1],'k')\n",
      "#plot([txbyxc,txbyxc],[0.2,0.1],'k')\n",
      "xlabel('$Time,  h$')\n",
      "ylabel('$X/X_c$')\n",
      "# for xbyxc = 0.2 from fig 9.9-1 Dl*t/x**2 = 0.56\n",
      "absc = 0.56\n",
      "DL =absc*x1**2/(txbyxc*3600)\n",
      "#Result\n",
      "print \"Time required for drying to\", xbyxc,\"is \", round(txbyxc,2),\"hr\"\n",
      "print 'Average moisture diffusivity %6.2e' %(DL),'m2/s'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Time required for drying to 0.2 is  1.02 hr\n",
        "Average moisture diffusivity 3.44e-04 m2/s\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkQAALKFIAACWUCQAAEv+H0HymmNUDGStAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6406150>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.10-1 Page Number 559"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Through Circulation Drying in a Bed\n",
      "import numpy as np\n",
      "from scipy.interpolate import interp1d\n",
      "\n",
      "#Variable declaration\n",
      "Xt1 = 1.                  #Initial total moisture content (kg H2O/kg dry air)\n",
      "Xs = 0.01                 #The equilibrium moisture content (kg H2O/kg dry air)\n",
      "Rho = 1602.               #Density of dry solid (kg/m3)\n",
      "Rhos = 641.               #Density of dry solid (kg/m3)\n",
      "H1 = 0.04                 #Inlet air humidity (kg H2O/kg dry air)\n",
      "T1 = 121.1                #Temperature of air (deg C)\n",
      "v = 0.811                 #Gas superficial velocity (m/s)\n",
      "Xtc = 0.5                 #The total critical moisture content \n",
      "Xt = 0.1                  #Final total moisture content (kg H2O/kg dry air) \n",
      "x1 = 0.0508               #Depth of packed cylinders\n",
      "mu = 2.15e-5              #Viscosity of air, kg/(m.s)        \n",
      "\n",
      "T = np.array([280,285,290,295,300,305,310,315,320,325,330,335,340])\n",
      "lambds = np.array([2485.4,2473.9,2462.2,2450.3,2438.4,2426.3,2414.3,2402.0,2389.8,2377.6,2365.3,2353.0,2340.5])\n",
      "\n",
      "#Calculation\n",
      "\n",
      "flambd = interp1d(T,lambds)\n",
      "lambdaw = flambd(320.2)\n",
      "X1 = Xt1 - Xs\n",
      "Xc = Xtc - Xs\n",
      "X = Xt - Xs\n",
      "Vh = (2.83e-3 + 4.56e-3*H1)*(T1 + 273.)\n",
      "RhoH = (1. + H1)/Vh\n",
      "Ga = v*RhoH*3600.*(1./(1.+ H1))\n",
      "H = 0.05                  #Approximate average \n",
      "Gt = (2459. + 2459.*H)/3600.\n",
      "e = 1 - Rhos/Rho          #Void fraction (m3)\n",
      "h = 0.0254                #The length of solid cylinder (m)\n",
      "Dc = 0.00635              #Diameter \n",
      "a = 4*(1 - e)*(h + 0.5*Dc)/(Dc*h)\n",
      "Dp = (Dc*h + 0.5*Dc**2)**0.5\n",
      "Nre = Dp*Gt/mu\n",
      "h = 0.151*(Gt*3600)**0.59/Dp**0.41\n",
      "Tw = 47.2\n",
      "Cs = (1.005 + 1.88*H)*1000.\n",
      "G = Ga/3600.\n",
      "tc = Rhos*lambdaw*1000.*x1*(X1 - Xc)/(G*Cs*(T1 - Tw)*(1 - exp(-h*a*x1/(G*Cs))))\n",
      "tf = Rhos*lambdaw*1000.*x1*Xc*log(Xc/X)/(G*Cs*(T1 - Tw)*(1 - exp(-h*a*x1/(G*Cs))))\n",
      "t = tc+tf\n",
      "\n",
      "#Result\n",
      "print \"Total drying time\", round(t/3600,3),\"h or\", round(t),\"s\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total drying time 0.628 h or 2260.0 s\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.10-2 Page Number 562"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Heat Balance on a Dryer\n",
      "from scipy.optimize import root\n",
      "\n",
      "#Variable declaration\n",
      "Ls = 453.6       #Feed rate,kg of dry solids/h\n",
      "X1 = 0.04        #moisture content in feed,kg of moisture/kg dry solids\n",
      "X2 = 0.002       #moisture content in feed,kg of moisture/kg dry solids\n",
      "cpA = 4.187      #Specific Heat of moisture, kJ/(kg water.K)\n",
      "cpS = 1.465      #Specific Heat of solid, kJ/(kg dry solid.K)\n",
      "Ts1 = 26.7       #Temperature of feed at inlet, \u00b0C \n",
      "Ts2 = 62.8       #Temperature of product at outlet, \u00b0C\n",
      "Tg2 = 93.3       #Temperature of air at inlet, \u00b0C \n",
      "Tg1 = 37.8       #Temperature of air at outlet, \u00b0C\n",
      "H2 = 0.01        #Humidity of incomming air, kg water/kg dry air\n",
      "Tref = 0.0       #Reference Temperature, \u00b0C\n",
      "\n",
      "#Calculation\n",
      "#Enthalpy of Entering Air, \n",
      "#Latent heat of water from steam table = 2501 kJ/kg @93.3\u00b0C \n",
      "lambda0 = 2501.\n",
      "cs2 = 1.005 + 1.88*H2\n",
      "Hg2 = cs2*(Tg2-Tref) + lambda0*H2 \n",
      "Hs1 = cpS*(Ts1-Tref)+X1*cpA*(Ts1-Tref)\n",
      "Hs2 = cpS*(Ts2-Tref)+X2*cpA*(Ts2-Tref)\n",
      "We = Ls*(X2-X1)               #Water Transfered to air\n",
      "delHs = Ls*(Hs1-Hs2)          #Enthalpy change of solid\n",
      "a = 37.99     #Constants in simplified relation for Humidity dependence of Enthalpy of leaving gas\n",
      "b = 2572.\n",
      "er = 5.\n",
      "h = 0.011\n",
      "while er>=0.001:\n",
      "    g = We/(H2-h)\n",
      "    hc = ((g*Hg2+delHs)/g-a)/b\n",
      "    gc = We/(H2-hc)    \n",
      "    er = (abs(h-hc)+abs(g-gc))/2 \n",
      "    h = hc\n",
      "    g = gc\n",
      "\n",
      "#Results\n",
      "print \"Air flow rate\",round(g,2), \"kg Dry Air/h\"\n",
      "print \"Humidity of air leaving\",round(h,4), \"kg of Water/kg Dry Air\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Air flow rate 1170.92 kg Dry Air/h\n",
        "Humidity of air leaving 0.0247 kg of Water/kg Dry Air\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.12-1 Page Number 573"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sterlization of Cans\n",
      "\n",
      "#Variable declaration\n",
      "t1 = 20.\n",
      "t2 = 40. - 20.\n",
      "t3 = 73. - 40.\n",
      "T1 = 160.\n",
      "T2 = 210.\n",
      "T3 = 230.\n",
      "T1_C = 71.1\n",
      "T2_C = 98.9\n",
      "T3_C = 110.\n",
      "z = 18.\n",
      "z_SI = 10.\n",
      "#Calculation\n",
      "t0_eng = t1*10.**((T1 - 250.)/z) + t2*10.**((T2 - 250.)/z) + t3*10.**((T3 - 250.)/z)\n",
      "t0_SI = t1*10.**((T1_C - 121.1)/z_SI) + t2*10.**((T2_C - 121.1)/z_SI) + t3*10.**((T3_C - 121.1)/z_SI)\n",
      "\n",
      "#Result\n",
      "print \"The calculated time of sterlization in English units is \",round(t0_eng,2),\"min\"\n",
      "print \"The calculated time of sterlization in SI units is \",round(t0_SI,2),\"min\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The calculated time of sterlization in English units is  2.68 min\n",
        "The calculated time of sterlization in SI units is  2.68 min\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.12-2 Page Number 574"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Thermal Process Evaluation by Graphical Integration\n",
      "import numpy as np\n",
      "from scipy.integrate import quad\n",
      "from scipy.interpolate import interp1d\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "#Variable declaration\n",
      "t = np.array([0.,15.,25.,30.,40.,50.,64.])\n",
      "T = np.array([80.,165.,201.,212.5,225.,230.5,235.])\n",
      "F0 = 2.45\n",
      "z = 18\n",
      "#Calculation\n",
      "y = 10**((T-250.)/z)\n",
      "f = interp1d(t,y,'cubic')\n",
      "plt.plot(t,y)\n",
      "plt.fill_between(t,y,0,color='0.8')\n",
      "xlabel('$Time, min$')\n",
      "ylabel('$10^(T-250)/z$')\n",
      "I = quad(f,0,64)\n",
      "title('Figure showing area by integration')\n",
      "txt = 'Area='+str(round(I[0],1))+' m2'\n",
      "text(45,0.02,txt)\n",
      "#Result\n",
      "\n",
      "print \"The process time required is \",round(I[0],1),\"is greater than\", F0,\"hence sterilization is adequet\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The process time required is  2.5 is greater than 2.45 hence sterilization is adequet\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEdCAYAAADJporJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCGk0SiZEEIIaTRKJoQQQhrt/wEuIli7RcUxrAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x66e1310>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.12-3 Page Number 576"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Pasteurization of Milk\n",
      "#Variable declaration\n",
      "F150 = 9.              #Typical value (min)\n",
      "D150 = 0.6\n",
      "#Calculation\n",
      "N0byN = 10.**(F150/D150)\n",
      "#Result\n",
      "print 'The reduction in number of viable cells is %10.3e'%(N0byN) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The reduction in number of viable cells is  1.000e+15\n"
       ]
      }
     ],
     "prompt_number": 30
    }
   ],
   "metadata": {}
  }
 ]
}