path: root/Principles_And_Modern_Applications_Of_Mass_Transfer_Operations/chapter8.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 8: Humidification Operations "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.1,Page number:479"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "#Variable declaration\n",
      "\n",
      "P_total = 1  \t\t\t\t\t\t# [bar]\n",
      "T1 = 320.0  \t\t\t\t\t\t# [K]\n",
      "T_c = 562.2  \t\t\t\t\t\t# [K]\n",
      "P_c = 48.9  \t\t\t\t\t\t# [bar]\n",
      "A = -6.983 \n",
      "B = 1.332 \n",
      "C = -2.629 \n",
      "D = -3.333 \n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "from pylab import *\n",
      "\n",
      "x1 = 1-(T1/T_c) \n",
      "def f12(P1):\n",
      "    return(math.log(P1/P_c)-(A*x1+B*x1**1.5+C*x1**3+D*x1**6)/(1-x1)) \n",
      "P1 = fsolve(f12,0.01) # [bar]\n",
      "print\"Vapor pressure of benzene at 320 K is\",round(P1[0],2),\"bar\"\n",
      "\n",
      "M_benzene = 78.0 # [gram/mole]\n",
      "print\"\\nSolution 8.1 (a)\"\n",
      "\n",
      "\t# Solution (a)\n",
      "\t# For nitrogen\n",
      "M_nitrogen = 28.0  \t\t\t\t# [gram/mole]\n",
      "\t# From equation 8.2\n",
      "Y = P1/(P_total - P1)  \t\t\t\t#[mole C6H6/ mole N2]\n",
      "Y_s1 = Y*(M_benzene/M_nitrogen)  \t\t# [gram C6H6/gram N2]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"Absolute humidity of mixture of benzene and nitrogen is\",round(Y_s1[0],2),\" gram C6H6/gram N2\\n\\n\"\n",
      "\n",
      "print\"\\nSolution 8.1 (b)\\n\"\n",
      "\t# Solution (b)\n",
      "\t# For carbon dioxide\n",
      "M_carbondioxide = 44.0  \t\t\t\t# [gram/mole]\n",
      "\t# From equation 8.2\n",
      "Y = P1/(P_total - P1)  \t\t\t\t#[mole C6H6/ mole C02]\n",
      "Y_s2 = Y*(M_benzene/M_carbondioxide)  \t\t# [gram C6H6/gram CO2]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"\\nAbsolute humidity of mixture of benzene and carbon dioxide is\",round(Y_s2[0],3),\"gram C6H6/gram CO2\\n\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Vapor pressure of benzene at 320 K is 0.32 bar\n",
        "\n",
        "Solution 8.1 (a)\n",
        "Absolute humidity of mixture of benzene and nitrogen is 1.31  gram C6H6/gram N2\n",
        "\n",
        "\n",
        "\n",
        "Solution 8.1 (b)\n",
        "\n",
        "\n",
        "Absolute humidity of mixture of benzene and carbon dioxide is 0.833 gram C6H6/gram CO2\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.2,Page number:480"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\t# A - water vapor    B - air\n",
      "\t# REference state is air\n",
      "\n",
      "T_ref = 273  \t\t\t\t\t# [Reference temperature, K]\n",
      "T = 303  \t\t\t\t\t# [K]\n",
      "P_total = 1  \t\t\t\t\t# [atm]\n",
      "P_A = 4.24  \t\t\t\t\t# [Vapor pressure of water at 303K, kPa]\n",
      "M_A = 18.0  \t\t\t\t\t# [gram/mole]\n",
      "M_B = 29.0  \t\t\t\t\t# [gram/mole]\n",
      "C_A = 1.884  \t\t\t\t\t# [kJ/kg.K]\n",
      "C_B = 1.005  \t\t\t\t\t# [kJ/kg.K]\n",
      "lamda = 2502.3  \t\t\t\t# [Latent heat of Vaporization at 273K, kJ/kg]\n",
      "\n",
      "#Calculation\n",
      "\n",
      "P_total = P_total*101.325  \t\t\t# [kPa]\n",
      "\n",
      "\t# From equation 8.2\n",
      "Y_s = P_A/(P_total - P_A)*(M_A/M_B)  \t\t#[kg H2O/ kg dry air]\n",
      "\n",
      "\n",
      "\n",
      "print\"Absolute humidity of mixture of water vapor and air is\",round(Y_s,3),\"kg H2O/kg dry air\" \n",
      "\t# From equation 8.3\n",
      "H_s = C_B*(T-T_ref) + Y_s*(C_A*(T-T_ref) + lamda)  \t# [kJ/kg dry air]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"Enthalpy per unit mass of dry air of a saturated mixture at 303 K and 1 atm is\",round(H_s,1),\"kJ/kg dry air\" \n",
      "print\"\\n\\nFollowing graph shows the result of similar calculations form 273 to 333 K\"\n",
      "x=[273,283,293,303,313,323,333]\n",
      "y=[9.48,29.36,57.57,99.75,166.79,275.58,461.50]\n",
      "a=plot(x,y)\n",
      "xlabel('$Temperature,K$')\n",
      "ylabel('$Hs,kJ/kg dry air$')\n",
      "show(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Absolute humidity of mixture of water vapor and air is 0.027 kg H2O/kg dry air\n",
        "Enthalpy per unit mass of dry air of a saturated mixture at 303 K and 1 atm is 99.5 kJ/kg dry air\n",
        "\n",
        "\n",
        "Following graph shows the result of similar calculations form 273 to 333 K\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HEBVljjZuvtncUlbEE9Tle6fLTKKLNJTSUliwwBxtFBSYq+Nu2QIBAVYnE3Ev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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x768af60>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.3,Page number:482"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\t# A - water vapor   B - air\n",
      "\n",
      "T = 328  \t\t\t\t\t# [dry bulb temperature, K]\n",
      "P_total = 1.0  \t\t\t\t\t# [atm]\n",
      "H = 30.0  \t\t\t\t\t# [relative humidity, %]\n",
      "\n",
      "P_vapA = 15.73  \t\t\t\t# [vapor pressure of water, kPa]\n",
      "P_total = P_total*101.325  \t\t\t# [kPa]\n",
      "M_A = 18.0  \t\t\t\t\t# [gram/mole]\n",
      "M_B = 29.0  \t\t\t\t\t# [gram/mole]\n",
      "\n",
      "P_A = (H/100)*P_vapA \t\t\t\t# [partial pressure of A,kPa]\n",
      "\n",
      "#Calculation\n",
      "\n",
      "\n",
      "print\"Solution 8.3 (a)\"\n",
      "\t# At dew point partial pressure is equal to vapor pressure\n",
      "\t# Using Antonnie equation we can find dew point temperature\n",
      "\n",
      "\n",
      "\n",
      "print\"Dew point temperature is 304.5 K\\n\"\n",
      "\n",
      "\t# From equation 8.1\n",
      "Y_s = P_A/(P_total-P_A)*(M_A/M_B) \n",
      "\n",
      "\n",
      "print\"Absolute humidity of air-water mixture at 328 K is\",round(Y_s,2),\"kg H2O/kg dry air\\n\\n\"\n",
      "\n",
      "print\"\\n Solution8.3 (b)\"\n",
      "\n",
      "\t#soluton (b)\n",
      "T_ref = 273  \t\t\t\t\t# [K]\n",
      "C_A = 1.884  \t\t\t\t\t# [kJ/kg.K]\n",
      "C_B = 1.005  \t\t\t\t\t# [kJ/kg.K]\n",
      "lamda = 2502.3  \t\t\t\t# [Latent heat of Vaporization at 273 K, kJ/kg]\n",
      "\n",
      "\t# From equation 8.3\n",
      "H_s = C_B*(T-T_ref) + Y_s*(C_A*(T-T_ref) + lamda) \n",
      "\n",
      "\n",
      "print\"Enthalpy per unit mass of dry air of a saturated mixture relative to 273 K is\",round(H_s,1),\"kJ/kg dry air\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Solution 8.3 (a)\n",
        "Dew point temperature is 304.5 K\n",
        "\n",
        "Absolute humidity of air-water mixture at 328 K is 0.03 kg H2O/kg dry air\n",
        "\n",
        "\n",
        "\n",
        " Solution8.3 (b)\n",
        "Enthalpy per unit mass of dry air of a saturated mixture relative to 273 K is 134.3 kJ/kg dry air\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.4,Page number:483"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\n",
      "\t# a - water vapor    b - air\n",
      "T_G1 = 356  \t\t\t\t\t\t# [K]\n",
      "P_total = 101.325  \t\t\t\t\t# [kPa]\n",
      "Y_1 = .03  \t\t\t\t\t\t# [kg water/kg dry air]\n",
      "\n",
      "\n",
      "C_pa = 1.884 \t \t\t\t\t\t# [kJ/kg.K]\n",
      "C_pb = 1.005  \t\t\t\t\t\t# [kJ/kg.K]\n",
      "\n",
      "C_s1 = C_pb + Y_1*C_pa \t\t\t\t\t# [kJ/kg.K]\n",
      "\n",
      "T_1 = 373.15  \t\t\t\t\t\t# [K]\n",
      "T_c = 647.1  \t\t\t\t\t\t# [K]\n",
      "M_a = 18.02  \t\t\t\t\t\t# [gram/mole]\n",
      "M_b = 28.97  \t\t\t\t\t\t# [gram/mole]\n",
      "lamda_1 = 2256  \t\t\t\t\t# [Latent Heat of Vaporizarion at T_1, \n",
      "#Calculation\n",
      "\t\t\t\t\t\t\t\n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "from pylab import *\n",
      "\n",
      "def f12(T_as):\n",
      "    return(T_as - T_G1 + ((math.exp(16.3872 - (3885.7/(T_as - 42.98)))/(P_total - (math.exp(16.3872 - (3885.7/(T_as - 42.98))))))*(M_a/M_b) - Y_1)*(lamda_1*((1-T_as/T_c)/(1-T_1/T_c))**.38/C_s1)) \n",
      "T_as = fsolve(f12,310)  # [K]\n",
      "\n",
      "\n",
      "print\"Adiabatic Saturation Temperature is\",round(T_as[0]),\"K\"\n",
      "\n",
      "\t# Now using equation 8.2\n",
      " \n",
      "P_a = math.exp(16.3872-(3885.7/(T_as-42.98)))  \t\t# [kPa]\n",
      "Y_as = P_a/(P_total-P_a)*M_a/M_b  \t\t\t# [kg water/kg dry air]\n",
      "\n",
      "#Result\n",
      "print\"Absolute humidity is\",round(Y_as,3),\"kg water/kg dry air\\n\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Adiabatic Saturation Temperature is 313.0 K\n",
        "Absolute humidity is 0.049 kg water/kg dry air\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.5,Page number:487"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\n",
      "T_w = 320  \t\t\t\t\t\t# [K]\n",
      "T_g = 340  \t\t\t\t\t\t# [K]\n",
      "lambda_w = 2413  \t\t\t\t\t# [Latent Heat of Vaporization at 320K, \t\t\t\t\t\t\tkJ/kg]\n",
      "Y_w1 = 0.073  \t\t\t\t\t\t# [kg water/kg dry air]\n",
      "\n",
      "A = 0.95  \t\t\t\t\t\t# [For air water system,A, kJ/kg.K]\n",
      "\n",
      "#Calculation\n",
      "\n",
      "\t#    here A = hg/ky, psychrometric ratio\n",
      "\t#    Air-water mixture is saturated at 320K and 1 atm\n",
      "\t#    Using equation 8.15\n",
      " \n",
      "Y_w2 = Y_w1 - ((T_g-T_w)*A/lambda_w)  \t\t\t# [kg water/kg dry air]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"Absolute humidity of air-water mixture at 340 K and 1 atm is\",round(Y_w2,3),\" kg water/kg dry air\\n \""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Absolute humidity of air-water mixture at 340 K and 1 atm is 0.065  kg water/kg dry air\n",
        " \n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.6,Page number:487"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "#Variable declaration\n",
      "\t# a - toluene    b - air\n",
      "\n",
      "T_G1 = 333  \t\t\t\t\t\t# [K]\n",
      "P_total = 101.325  \t\t\t\t\t# [kPa]\n",
      "Y_1 = 0.05  \t\t\t\t\t\t# [kg vapor/kg dry air]\n",
      "\n",
      "\n",
      "C_pa = 1.256  \t\t\t\t\t\t# [kJ/kg.K]\n",
      "C_pb = 1.005  \t\t\t\t\t\t# [kJ/kg.K]\n",
      "\n",
      "C_s1 = C_pb + Y_1*C_pa\n",
      "\n",
      "T_1 = 383.8  \t\t\t\t\t\t# [K]\n",
      "T_c = 591.8  \t\t\t\t\t\t# [K]\n",
      "M_a = 92  \t\t\t\t\t\t# [gram/mole]\n",
      "M_b = 28.97  \t\t\t\t\t\t# [gram/mole]\n",
      "\n",
      "#Calculation\n",
      "lamda_1 = 33.18*1000/92  \t\t\t\t# [Latent heat of vaporization at T_1, \t\t\t\t\t\t\tkJ/kg]\n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "\t# Constants of antoine equation\n",
      "A = 13.9320 \n",
      "B = 3057  \t\t\t\t\t\t# [K]\n",
      "C = -55.52  \t\t\t\t\t\t# [K]\n",
      "\n",
      "print \"\\nSolution 8.6 (a)\"\n",
      "\n",
      "\t# Solution (a)\n",
      "\n",
      "def f12(T_as):\n",
      "    return(T_as - T_G1 + ((math.exp(13.9320 - (3057/(T_as - 55.52)))/(P_total - (math.exp(13.9320 - (3057/(T_as - 55.52))))))*(M_a/M_b) - Y_1)*(lamda_1*((1-T_as/T_c)/(1-T_1/T_c))**0.38/C_s1))\n",
      "T_as = fsolve(f12,273)   \t\t\t\t# [K]     \n",
      "print\"Adiabatic Saturation Temperature is\",round(T_as),\"K\"\n",
      "\n",
      "\t# Now using equation 8.2\n",
      " \n",
      "P_a = math.exp(13.9320-(3057/(T_as-55.52)))  \t\t# [kPa]\n",
      "Y_as = P_a/(P_total-P_a)*M_a/M_b  \t\t\t# [kg vapor/kg dry air]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"Absolute humidity is\",round(Y_as,3),\"kg vapor/kg dry air\"\n",
      "\n",
      "\n",
      "\n",
      "print\"\\nSolution 8.6 (b)\"\n",
      "\n",
      "# Solution (b)\n",
      "\n",
      "# Thermodynamic properties of mixture of toluene and air\n",
      "row = 1.06  \t\t\t\t\t\t# [kg/cubic m]\n",
      "u = 19.5*10**-6  \t\t\t\t\t# [P]\n",
      "Pr = 0.7 \n",
      "Dab = 0.1  \t\t\t\t\t\t#[From Wilke-Lee equation, square cm/s]\n",
      "Sc = u/(row*Dab*10**-4) \n",
      "\n",
      "# Using equation 8.16\n",
      "\n",
      "A_1 = C_s1*(Sc/Pr)**0.567  # [kJ/kg.K]\n",
      "\t# here A_1 = hg/ky, psychrometric ratio\n",
      "\n",
      "\t# Using equation 8.15\n",
      "\t#    T_w = T_G1 - (Y_w-Y_1)*lamda_w/(hg/ky)\n",
      "\t#   where lamda_w = lamda_1*((1-T_w/T_c)/(1-T_1/T_c))**.38\n",
      "\t#   Y_w = P_a/(P_total-P_a)*M_a/M_b\n",
      "\t#   P_a = math.exp(A-B/(T+c))\n",
      "\n",
      "def f15(T_w):\n",
      "    return(T_w - T_G1 + ((math.exp(13.9320 - (3057/(T_w - 55.52)))/(P_total - (math.exp(13.9320 - (3057/(T_w - 55.52))))))*(M_a/M_b) - Y_1)*(lamda_1*((1-T_w/T_c)/(1-T_1/T_c))**.38/A_1)) \n",
      "T_w = fsolve(f15,273)  # [K]\n",
      "print\"Wet bulb Temperature is\",round(T_w),\" K\\n\"\n",
      "\n",
      "# Now using equation 8.2\n",
      " \n",
      "P_a = math.exp(13.9320-(3057/(T_w-55.52)))  \t\t# [kPa]\n",
      "Y_w = P_a/(P_total-P_a)*M_a/M_b  \t\t\t# [kg vapor/kg dry air]\n",
      "\n",
      "print\"Absolute humidity is\",round(Y_w,3),\"kg vapor/kg dry air\\n\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Solution 8.6 (a)\n",
        "Adiabatic Saturation Temperature is 300.0 K\n",
        "Absolute humidity is 0.136 kg vapor/kg dry air\n",
        "\n",
        "Solution 8.6 (b)\n",
        "Wet bulb Temperature is 305.0  K\n",
        "\n",
        "Absolute humidity is 0.177 kg vapor/kg dry air\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.7,Page number:493"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "L_min = 2.27 # [kg/square m.s]\n",
      "G_min = 2 # [kg/square m.s]\n",
      "L2_prime = 15 # [kg/s]\n",
      "Templ2 = 318 # [K]\n",
      "Tempg1 = 303 # [Entering air dry bulb, K]\n",
      "Tempw1 = 297 # [ Entering air wet bulb, K]\n",
      "Kya = 0.90 # [kg/cubic m.s]\n",
      "\n",
      "import math\n",
      "from pylab import *\n",
      "from scipy.optimize import fsolve\n",
      "from numpy import*\n",
      "\n",
      "H1_prime = 72.5 # [kJ/kg dry air]\n",
      "Y1_prime = 0.0190 # [kg water/kg dry air]\n",
      "Templ1 = 302 # [K]\n",
      "Cal = 4.187 # [kJ/kg]\n",
      "\n",
      "# Equilibrium Data:\n",
      "# Data  = [Temp.(K),H_star(kJ/kg)]\n",
      "Data_star =matrix([[302,100],[305.5,114],[308,129.8],[310.5,147],[313,166.8],[315.5,191],[318,216]]) \n",
      "\n",
      "# The operating line for least slope:\n",
      "H2_star = 210 # [kJ/kg]\n",
      "Data_minSlope =matrix([[Templ1,H1_prime],[Templ2,H2_star]]) \n",
      "def f14(Gmin):\n",
      "    return(((L2_prime*Cal)/Gmin)-((H2_star-H1_prime)/(Templ2-Templ1))) \n",
      "Gmin = fsolve(f14,2) # [kg/s]\n",
      "Gs = 1.5*Gmin # [kg/s]\n",
      "\n",
      "# For the Operating Line:\n",
      "def f15(H2):\n",
      "    return(((H2-H1_prime)/(Templ2-Templ1))-((L2_prime*Cal)/Gs)) \n",
      "H2 = fsolve(f15,2) # [kJ/kg dry air]\n",
      "Data_opline =matrix([[Templ1,H1_prime],[Templ2,H2]]) \n",
      "\n",
      "\n",
      "a1=plot(Data_star[:,0],Data_star[:,1],label='$Equilibrium line$')\n",
      "a2=plot(Data_minSlope[:,0],Data_minSlope[:,1],label='$Minimum Flow Rate Line$')\n",
      "a3=plot(Data_opline[:,0],Data_opline[:,1],label='$Operating Line$') \n",
      "legend(loc='upper right')\n",
      "title('Operating Diagram')\n",
      "xlabel(\"$Liquid Temperature, K$\") \n",
      "ylabel(\"$Enthalphy Of Air Water vapour, kJ / kg dry air$\") \n",
      "show(a1)\n",
      "show(a2)\n",
      "show(a3)\n",
      "\n",
      "# Tower cross section Area:\n",
      "Al = L2_prime/L_min # [square m]\n",
      "Ag = Gs/G_min # [square m]\n",
      "A = min(Al,Ag) # [square m]\n",
      "print\"Cross sectional is\",round(A[0],2),\" square m\\n\"\n",
      "\n",
      "# Data from operating line:\n",
      "# Data1 = [Temp.(K),H_prime(kJ/kg)]\n",
      "Data1 =matrix([[302,72.5],[305.5,92],[308,106.5],[310.5,121],[313,135.5],[315.5,149.5],[318,164.2]]) \n",
      "\n",
      "# Driving Force:\n",
      "Data2 = zeros((7,2)) \n",
      "# Data2 = [Temp[K],driving Force]\n",
      "for i in range(0,7):\n",
      "    Data2[i][0] = Data1[i,0] \n",
      "    Data2[i,1] = 1/(Data_star[i,1]-Data1[i,1]) \n",
      "\n",
      "\n",
      "# The data for operating line as abcissa is plotted against driving force \n",
      "Area = 3.28 \n",
      "# From Eqn. 7.54\n",
      "def f16(Z):\n",
      "    return(Area-(Kya*Z/G_min)) \n",
      "Z = fsolve(f16,2) \n",
      "print\"The height of tower is\",round(Z[0],2)\n",
      "NtoG = 3.28 \n",
      "HtoG = G_min/Kya # [m]\n",
      "\n",
      "# Make up water\n",
      "# Assuming the outlet air is essentially saturated:\n",
      "Y2_prime = 0.048 # [kg water/kg dry air]\n",
      "H2 = 164.2  # [kJ/kg dry air]\n",
      "# This corresponds to an exit-air temperature of 312.8 K\n",
      "\n",
      "# Approximate rate of evaporation \n",
      "R = Gs*(Y2_prime-Y1_prime) \n",
      "print\"Rate of evaporation is\",round(R[0],3),\"kg/s\\n\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LFsLCwjh06BCfffaZLJ8rSW+xe/duVqxYwd69e1m9ejVz585V7ZWU3/KiNOu7\nyr/mVTuS+rI1BtKuXTvVtF1ra2t27NjBrl27mDx5smozNHW0bduWiIiILMeqV6/Oo0ePAEhMTFRN\nD96zZw9OTk7o6+tjYmKCubk5QUFB2Nraqt2uJBVlQgg+/fRTOnTowNixY1XHZ8+ezaJFi1SbFWpS\nx44dOXz4sKoqn7u7e67PaWNjQ6lSpRg+fLhq19mX5UU7kvqy1QOZOHEiI0eOJD4+njlz5rBkyRLu\n3LnDoUOHuHHjRp4E4u7uzjfffEPt2rWZNGkSbm5uAMTExGRJUsbGxkRHR+dJm5JUlLi7u1OiRAkc\nHR2zHLe2tmb79u0ab1+dkq7qKCrlX4uibP2l+/TpA0DDhg1V2xwnJSURHBzMmTNn8mQl+kcffcTS\npUvp378/27Ztw9XVFR8fn9feV0fn9aVDZ86cqfrZ3t4+y377kqRxb3hfqi0HA5qJiYm4u7tz/vz5\nV267f/8+qampXLhwgZCQEK5evUrr1q2Ji4ujRIkSDB8+/JXSqL169SI8PJzff/+d/v37s2bNGvbs\n2UOtWrWylHXdtGkTQ4YMoUSJEllKuvbs2TPbZXOfy8jIwN3dnQYNGhAbG0twcDBeXl5vLP8KhbAE\nbAHg6+uLr69v3pzsXSULr1y5IsLDw3Nc8vBNbt26JRo1aqT6vVy5cqqfMzMzhYGBgRBCCDc3N+Hm\n5qa6rWvXrqoSky/KxlORpFwpyO+x/fv3CxMTk9feNmjQIDFlyhRx+PBh8ffffwtHR0chhFKi1cLC\nQsTFxb1SGvWff/4R58+fFx07dhRCCJGcnKx6zJvKur5Y0lWd0qzPTZ06VVVmdv369WLhwoVCiDeX\nf1W3HXVLwBY1b3r/5uZ9/c5LWGZmZty+fZvly5fz22+/cebMmbzJXC8xNzfHz88PgGPHjlGvXj1A\n6f1s3ryZ1NRUbt26xbVr17CxsdFIDJJUWKWmplK1atVXjl+8eJFTp04xadIkunTpgre3t6qq37lz\n56hYsSLbtm2jdu3aWUqjNmrUiCNHjjB48GAASpYsCby5rKt4qaSrOqVZQanSt2LFClXJWV9fXzp3\n7gy8ufyruu0UhBKwRY66Gef06dPCw8NDLFu2TPj4+Ii0tDS1s9bQoUNF9erVhb6+vjA2Nhaenp4i\nODhY2NjYiCZNmghbW1tx9uxZ1f1//vlnYWZmJurXry8OHTr02nPm4KlIkloK8nssMTFRmJiYiMTE\nRNWx2NiPXh9zAAAgAElEQVRY0bZtWxEUFKQ61rp1a3Hnzh0hhBAff/yx2LFjh1i1alWWXn5MTIxI\nSUkR3bp1Ezdv3szSzsKFC8X8+fOFEEL89ddfYty4ceLgwYPi0qVLon///kIIITZt2iSEEMLGxkYk\nJiaKffv2CSGEaNWqlSq+sWPHiiNHjoiDBw+q4n9+RSIlJUXY2NgIIYRIS0sT5cuXF/fu3VPFsGnT\nJnH69GnV79lt53XPMzU1VZ2XuVB70/s3N+9rtUe7bGxsVD2Aq1evsmrVKlJTU6lZsyZdu3alTJky\n7zzHpk2bXnv89OnTrz0+bdo0pk2bpm6okvTeKF++PFu3buWHH36gRYsWpKenExUVxebNm1VjB48e\nPeLhw4ccO3aM1NRUWrVqxYABA0hOTn5tadR///2XunXrZmnHycnptWVdmzZtmqWkK2S/NOvz+Pv2\n7cu2bdu4dOkSDRo0yFb5V3XaKQglYIuabK1Ev3nzJqampm+9T0xMDP7+/qouaH6Tq4QlTSvs77Fd\nu3YRGBjI3LlztR3KK+7du4ehoSElS5Zk7ty5WFhYMGDAAG2HVaRoraRthw4dGDduHHZ2dlSrVi1H\nDWlaYf/HLRV8hfk9duXKFUaPHo25uTlLlizBwMBA2yFl8fHHH9O8eXMMDQ2JiYnJlzUr7xutJZBh\nw4ZRu3ZtAgMDiYuLw8rKitatW9OrV6939kzyS2H+xy0VDvI9JhVmWksgz/epAmWudmhoKAEBAfj4\n+NC/f3+GDx+eo8bzkvzHLWmafI9JhZnWEsjbrFy5ko8//jg3p8gT8h+3pGnyPSYVZgViN94X2dvb\n8/jx49ycQpIkSSqkctUDuXnzJkZGRhgZGeVlTDkivx1KmibfY1JhViB6IM/rgYAysyMsLCxHDUuS\nJEmFm9oLCV+sB3L27FmSk5P54IMPNBGbJBUoRkZGb9zIU5IKOk1cKcrWJawX64E0a9aM4ODgLPVA\nKlSokOeBqUteXpCkgk8ImLXmBD+fGU9lw9JsHrmYdubW2g7rvabxWVh79+7FwsKCgIAAgoKCVJet\nevXqhYODQ55s555bMoFIUsEWcOUWA3+fzP3iQfxgN4/v+znKHl0BoJVpvM/rgVy5coVPP/00R43n\nJZlAJKlgepT8hKHL3Tgcv4L2xceza9JEDMuW0nZY0v9odR1IQSETiCQVLJkik/nea/nB7zvK3e/I\nljFudGxZU9thSS/R2jqQW7duYWZmhq+vL4cPH87NqSRJKkK8r/yN8Wwbvt+1gk+NdhL7+1qZPIqg\nXPdAoqOjqVlT+28M2QORJO27Gnsbp1VTuPDwFLZJ7qyb7ISpqRznKMjytQfi7++f5feCkDwkSdKu\nxKdJ9F40HcvFzUm63YCTzpc5+buzTB5FnNoJZPny5aSkpGgiFkmSCpmMzEzGrVxL5VkNOB1+k22d\nzhO+Yia2zd9dWE4qAJ4+zdXD1U4ghoaG+Pn5kZaWlquGJUkq3JbuDKD8BDs8Ly5nnvU2Yj02MKBj\nLW2HJb2LEODvD66ukMsrSGqPgUyZMgUDAwPOnDlDSkoKLVq04Mcff8xVEHlBjoFIUv7Y6xvJmG1T\niC97gjFmbiz5aBjF9HI1H0fKD9HRsHYteHmBvr6SQD78EJ1q1fJvDKR3794MGjSIXbt2MX369ByV\nsHV1daVq1apYWVllOf7rr7/SsGFDGjVqxJQpU1TH3dzcsLCwoEGDBnh7e6vdniRJuXfmwlMafDqT\n/oebYm1qzv0frrJ8jItMHgVZSgps3w49eoCVFdy+DevWwcWL8M03ULVqrk6v9l5Yv//+O+XKlaN1\n69Y0b96co0eP0qhRI7XOMWrUKMaNG5elENXx48fZu3cvoaGh6Ovrc//+fQDCwsLYsmULYWFhREdH\n06lTJ8LDw9HVlW9aScoPt24Jhv+ykVOlpmJV8wPCPj5L/Wp1tB2W9DYXLig9jQ0blMTh6qokktKl\n87QZtRPI+vXruXXrFqdOneK3336jWDG1T0Hbtm2JiIjIcuy3337j22+/RV9fH4DKlSsDsGfPHpyc\nnNDX18fExARzc3OCgoKwtbVVu11JkrIvNhbGzT3NzqfjqVItjQNDNtO1odw4tcB6+BA2bQJPT7h/\nH0aNgtOnQYNlx9X+Gh8YGMi9e/cYNmwYy5Yto23btnkSyLVr1zhx4gS2trbY29tz5swZAGJiYjA2\nNlbdz9jYmOjo6DxpU5KkVz16BF9+H03t8S4cKDeARc6fEDUjSCaPgigjA7y9YehQJVH8/Te4u8Ot\nWzBrlkaTB+SgB3LkyBH09fVZvHgxpUqVolatWgwcODDXgaSnp5OQkEBgYCDBwcE4Ojpy8+bN1973\nTRuwzZw5U/Wzvb099vb2uY5Lkt4XT5/Cwl+f4ub3CxnWSxjt+AnuPa9StnhZbYcmvezmTVi9Wvmv\nShXlEtVvv0E2tmz39fXF19c3T8JQO4H07duXJ0+eZBnkzgvGxsYMGDAAgJYtW6Krq8uDBw+oWbMm\nkZGRqvtFRUW9cfHiiwlEkqTsSUuDVasE0zZu4VnbKbQb2IrfB4ZgYmii7dCkFz19Cjt2KGMbFy+C\nszP89Rc0aaLWaV7+cj1r1qwch6T2JaxixYqxadMmfvjhB65du5bjhl/Wr18/jh07BkB4eDipqalU\nqlSJPn36sHnzZlJTU7l16xbXrl3DxsYmz9qVpPdVZqZyydy0bTBTr7WhSv95HBqzjkMfbZXJo6AQ\nAgIDYexYMDaGzZvh888hMhIWL1Y7eeQ1tXsg+/fv59NPP+X27du4u7szaNAgunfvrtY5nJyc8PPz\nIz4+nlq1ajF79mxcXV1xdXXFysqK4sWLs3btWgAsLS1xdHTE0tKSYsWK4eHhIWsISFIuCAEHDsDk\nH2OIs5qG6OvNwm4/MaLJCPR09bQdngTKDIZ165QB8fR05RLVP//keuFfXlN7IeGaNWsYMWLEG3/X\nFrmQUJLe7e+/YfJ3yVyvvJDkpgv5rNVovms3DYMSBtoOTUpLUzK7lxf4+UH//kri+OAD0OCX5tx8\ndqrdA6lYsSJDhw5l2LBh1K5dm7i4uBw1LElS/jl/HqZ9Jwj+dxui82TaWbTgly7BmBppdpaOlA1h\nYUrSWLcOLCyUpLF+PZQt+JMXcrSde3h4OGvWrCElJYXRo0dTv359TcSmFtkDkaRXXb8O06eDz8Wz\nGA4dT5kKj1nSfTH2JvbaDu399ugRbNmiJI47d2D4cGXdRr16+R5KvvZAFixYwIEDB7h37x59+vTB\nVMPzjCVJUl90NPz4I2w9cI+6H0+jWIuDTHGYjWszVznOoS2ZmXDihDKusXcvdOqkZPcuXSAHC7IL\nArVnYdWvX5+jR49y8eJFOnXqVCA2UpQkSREfD5Mng1WzZ1yu6I7O543oaFeJq19cYXSL0TJ5aMOd\nO0o2NzeHL7+E5s3h2rX/9qgqpMkDctADuXfvHgcOHKBdu3Z07NiRp7ncT16SpNxLSlJmdS5aLGjx\n4U7KTZ2EUY3GnO4SiHkFc22H9/559gz27FF6G2fOKCvFt21TkkcRmkWqdgKJjIwkMTERLy8v4uPj\nSU9P59GjR0RHR+f54kJJkt4uJQX++APmzIGm3c5j/uN47vEQz65/0tG0o7bDe78IAefOKUlj82Yl\nWYwaBbt3Q6lS2o5OI9QeRD979izJycl88IGyL86NGzc4deoUK1euxM/PTyNBZoccRJfeJxkZykSd\nmTPBrHEsBv2/59SDv5hlP4uPmn9EMd3Ce1mk0HnwQNn11stLGRwfNQpGjIA6hWPH4tx8duZoFtbr\n3L17l+rVq+fFqXJEJhDpfSCE8oX2++/BsGIKjccsYVvMPEY0GcH09tMxLGmo7RDfD883MfT0BB8f\n6N1bmX7bvj0UslIT+ToLa+XKlTRq1IhmzZpx5swZ7t69y6BBg7SaPCTpfXDsGHz7LTxLEQyYtodN\n8ROJ1rPk1EenqFcx/6d/vpeuXVN6GmvXKqvCXV1h5UooX17bkWmF2gkkLi4OPz8/li5dypMnTzAz\nM2PQoEGaiE2SJCA4GKZNU3boHv19KId1xrPrURy/9fyNzmadtR1e0ZeUpMyY8vSEq1fBxQUOH4b/\n+z9tR6Z1aicQY2NjVSXB1NRU9uzZk+dBSZIEly8rl6pOn4bx390nvOZ0FobvYkb7GYxpMUaOc2iS\nEHDqlJI0du6Edu2UErA9eij1xCUgBwmkePHijBo1it69e1O/fn2ioqI0EZckvbfu3FEGx/ftg68n\nptLyy1+ZG+TOsOLDuPL5FYxKvbvmg5RDMTH/bWKoq6tcorp8GapV03ZkBZLaoz0HDx7ExMSE77//\nnsWLF9OmTRtNxCVJ7524OBg/Hpo1g+o1BIsP7cWz1P/hH30M/1H+LO62WCYPTUhNVXoZvXpBo0Zw\n44ZSqCksDCZNksnjLdSehRUREYGJiQkAwcHBJCYm0rmz9q/DyllYUmF16xYsXKjMBP3wQxjw6UV+\nPvM10Y+jWdh1Id3Mu2k7xKLpn3+UnsaGDWBpqfQ2Bg6EMmW0HVm+ys1np9o9kDNnzrB+/Xri4+Np\n2bIljx49ylHDkvS+CwlRFii3bKlsvHoi5AHpXT/DcV8H+tTrw4VPLsjkkdcSE5XSry1bKuMZZctC\nQAD4+iobGr5nySO31E4gUVFR6Orq8umnn9K+fXsuXLigibgkqUgSAg4dgo4dlXIPNjYQfj2Nqn0X\n47C9IcV0i3HliyuMazUOfT05WJsnMjPhyBGlBKyJiVJr46efICJC2aPKzEzbERZaal/CunHjBnFx\ncdjZ2WkqphyRl7CkgiwtTdndYv585fdJk2DIEIFPxAG+8f4GE0MTFnZdiGVlS+0GWpRERChjGatX\nQ4UKyiUqZ2flZ0klXxcSxsTE0LZt2xw1JknvmydP4M8/YdEipdTDvHnQtStcfhBGn60TiEiMYGHX\nhXQ37y5LNeeF5GRlQNzTE0JDlYSxezc0bartyIoktS9hLV++nJSUFE3EIklFxt27yqrxunUhKEj5\nDDt6FFq2i+fLg+Nov7o93c2788+n/9DDoodMHrkhhPIif/IJGBsrm4R98glERcGSJTJ5aJDaCcTQ\n0BA/Pz/S0tJy3KirqytVq1bFysrqldsWLFiArq4uDx8+VB1zc3PDwsKCBg0a4O3tneN2JUnTrlyB\njz9WFiknJSmfa5s3Q+OmaSw9vZSGyxuSKTK5/PllvrL9So5z5EZcnDJ9zcpK6WnUrg0XLsDBgzB4\nMJQooe0Iizy1L2EZGhoSHBzMb7/9RkpKCi1atFC7qNSoUaMYN26cakX7c5GRkfj4+FDnhV0sw8LC\n2LJlC2FhYURHR9OpUyfCw8PRLWQblklFlxBw8qQyvhEYCF98oWyZVLGicvuh64eYcHgCxgbGHBtx\njEZVGmk34MIsPV1JEF5eyuZg/fqBhwe0bVuk6mwUFmonkF69elG5cmW+++47hBDcuXNH7Ubbtm1L\nRETEK8cnTJjAvHnz6Nu3r+rYnj17cHJyQl9fHxMTE8zNzQkKCsLW1lbtdiUpL2VkKJVJ582D+/dh\n4kSlt/G89MOVB1f4xvsbrsVfY0GXBfSq10teqsqpK1f+28TQ1FQZEF+zBsqV03Zk7zW1E4i5uTlJ\nSUkA3L9/n2p5tEpzz549GBsb07hx4yzHY2JisiQLY2NjoqOj86RNScqJ5GTlc2zBAjAyUkrI9usH\nev+rFpuQnMAsv1ls+GcD37b5ll1DdlFcr7h2gy6MHj+GrVuVAfFbt5QaG8ePQ4MG2o5M+h+1E8iO\nHTto2LAhkZGRtG3bli1btjBs2LBcBfH06VPmzJmDj4+P6tjbppW96VvczJkzVT/b29tjb2+fq7gk\n6UUPHypXS5YtA2trZRfvF6+cpGems+LMCmafmE3/Bv0J+yyMymUqazfowkYI8PdXksbu3dChg7IV\ncbduhbp2eEHi6+uLr69vnpxL7b9IamoqHTp0YN++fRQrVgxDw9wXsLlx4wYRERE0adIEUBYrtmjR\ngtOnT1OzZk0iIyNV942KiqJmzZqvPc+LCUSS8kpEhDINd9066NtXmU318k7e3je8+frw11QrWw0f\nFx8aV2382nNJbxAVpVyS8vKCkiXho4+Ua4NVqmg7siLn5S/Xs2bNyvG51E4gDRo0oG3btlhYWJCe\nnk5oaCg9e/bMcQAAVlZWxMbGqn6vW7cuISEhVKhQgT59+uDs7MyECROIjo7m2rVr2NjY5Ko9ScqO\nc+eUgfHDh5WZVf/8o9QQelF4fDjfeH/D5fuXWdBlAX3q95HjHNmVkgJ79ii9jaAgGDIENm1Sunfy\nNSwU1J7K1KxZM9avX0+TJk24c+cOX331ldqNOjk50bp1a8LDw6lVqxZeXl5Zbn/xH6ClpSWOjo5Y\nWlrSvXt3PDw85D9QSWOEUCqVdu6sVClt3hxu3oS5c7Mmj8RniUw4PIHWq1rTrnY7Ln12ib4N+sr3\nZnacPw9ffqms2fjjD2UPqujo//aokq9hoZHtrUzmzJlDs2bNiIqKYvTo0YCyG29SUhIODg4aDTI7\n5FYmUm6kpSnjtfPnKzNFJ00CJyco/tLYd3pmOivPrmSm70z61O/Djw4/UrVsVe0EXZjEx8PGjUpv\nIyEBRo5U/vvfzt6S9uTmszPbCeTy5cscP36cVatWUaNGDapVq4aNjQ3R0dEFYuxBJhApJ5KSlMHw\nRYuU2aGTJkH37q//Enz05lG+Pvw1FUpVYHG3xTStJlc4v1VGBvj4KOMahw9Dz57K9FsHB6VYk1Qg\n5EsCee7QoUN069aNe/fuERwcTI0aNWjRokWOGs9LMoFI6rh3D379FVasUCb6TJqkXD15nesPrzPR\neyKhsaH80uUX+jfoLy9Vvc3168oGhmvWQPXqMGqUsm+9kSyGVRDlaz2QqKgoAgMDqVChAhUqVODm\nzZs5aliStOHqVRgzRqkflJio1BvfuvX1yePRs0dM8p6E7UpbbI1tCfs8jAENB8jk8Tr//qskDHt7\naN0anj6FAweUwfFPP5XJo4hSexZWXFwcfn5+LF26lCdPnmBmZsbgwYM1EZsk5ZlTp5RZoadOwWef\nKYmk8huWaGRkZrDq3Cp+OP4DPS16cvGzi1QrK8uavkIIZe8WT0/Yvh3atIGvvlIuVb08eCQVSWon\nEGNjY9UeVqmpqezZsyfPg5KkvJCZCX/9pSSOe/dgwgRlHLd06Tc/xjfCl/GHxlOuRDkODDtA8+rN\n8y/gwuLuXWVRjJeX8iK7usKlS1CjhrYjk/KZ2glEX1+fkSNH0qdPH+rXr09UVJQm4pKkHHv2TNnR\n+5dflK2SJk2CAQPevpD5ZsJNJvlMIiQmhPmd5zPIcpC8VPWitDTYv1/pbfj7K7XDV60COzs57fY9\nlq1B9LCwMCwt/6uUdvXqVdavX09iYiLDhw+n5ZtGH/ORHESXEhLg999h6VJo1kzZo6p9+7d/vj1O\necwc/zmsPLuSCXYT+Nr2a0rpl8q/oAu6S5eUpLF+vbIH1ahRMGiQUktcKhI0PgurdevW/PXXX1R8\nvj91ASQTyPvrzh1lGu6aNcriv4kTlRIRb5ORmcHq86uZfnw6Xcy6MKfjHGqUk5dgAHj0SNlW2NNT\nWeA3YoSyZsPCQtuRSRqg8ZK248ePJzw8nPj4eD744AOM5IwKqQC4cEFZ+HfwoPLF+MIFqFXr3Y87\ncfsE4w+Np7R+afY67cW6hrXmgy3oMjPB11dJGvv2QZcuMHOm8v/n2wxL0kvUXgdy6tQpHj58SJs2\nbfJkI8W8Insg74e0NGX7pOXLITxcmfQzdiyUL//ux95KuMXkI5MJig5iXqd5OP6foxznuH1bWbOx\nejUYGCibGDo7Q6VK2o5Myicav4S1adMmnJycAGXr9YSEBPbt28fTp0/56KOPMDAwyFHjeUkmkKIt\nNhb+/FMZ46hbV6n6179/9maLPkl5gtvfbqwIWcH4VuOZ2Hri+z3OkZysbJXu6ansGOnkpHThmjWT\nA+LvIY0nkLJly1K6dGlKlChB2bJlMTQ0xMjICENDQywsLHK1HXBekQmk6BFCWei3bJkyAWjwYPj8\nc/jfrv/vlCkyWXthLd8d+46OdTsyp+McjA2MNRt0QSUEhIQoSWPLFmXHW1dXZX/6kiW1HZ2kRRof\nA1m1ahVdunThwIEDVKxYkW7duuWoMUnKjuRkZQx3+XJlZtXnnyvbjqgz9Pb3nb8Zf2g8+nr67HTc\nSSvjVpoLuCC7fx82bFASx7//Kj2Nc+egdm1tRyYVAdnqgTx9+pTS/1t9FRMTw+7du6lTp06u64Dk\nJdkDKfwiIpQdvT09la1FvvhCKUSnzr57txNvM+XIFE5FnsK9kztOjZzev3GO9HRl80JPT6X6VZ8+\nSm+jXTu5iaH0Co1fwhoyZAh9+/bN0siVK1c4ceIEkyZNolevXjlqPC/JBFI4ZWYqn3HLlsHJk8qM\n0U8/BXNz9c6TlJrE3L/n4nHGg3E245jUehJlipfRTNAFVXi4sjp8zRqoU0dJGo6O2ZthIL23NH4J\nKyQkBEA17mFkZISxsTGfffYZpUq9x4ORUo49fqx8zi1fDiVKKL2NjRuhjJqf+Zkik/Wh65l2dBrt\nTdpzfux5apXPxlzeouLJE9i2TeltXL+uFGc6ehQaNtR2ZNJ7IFs9kNDQUBo3Vmo8+/v707ZtW40H\npi7ZAykcLl1SksbmzcoSg88/V/bgy8lVpoDIAMYfHo8QgiXdlmBXyy7vAy6IhIC//1aSxu7dynJ7\nV1elkIm+vrajkwoZjfdAnicPgOXLl2NjY0OJEiVy1KD0/klPh717lctUly8r6zYuXsz53nuRjyKZ\nenQqfhF+uHV0Y1jjYejqvAfX9qOjYe1aJXHo6ytrNtzdoaqsiChph9qbKRoaGuLn54eDgwP68tuO\n9BZxcUq1v99+Uy7Jf/GFsqlhTnf6fpr2lHkn5/Fr0K983vJzVvRaQdniRXxPppQUZUthT09l6/TB\ng5V9qWxs5JoNSevU/tpmaGhIcHAwjo6O9OjRg+nTp6vdqKurK1WrVsXqhQ2LJk2aRMOGDWnSpAkD\nBgzg0aNHqtvc3NywsLCgQYMGeHt7q92elH+er91wcYH69eHWLeXz7++/laJ0OUkeQgg2hG6g/rL6\nXHlwhbNjzjLbYXbRTh4XLsD48WBsDB4eyurwqCilhGKrVjJ5SAWDUJO/v7+4cuWKEEKIzMxMERER\noe4pxIkTJ8TZs2dFo0aNVMe8vb1FRkaGEEKIKVOmiClTpgghhLh06ZJo0qSJSE1NFbdu3RJmZmaq\n+70oB09FykPJyUKsXi2EtbUQdesK8csvQsTH5/68gZGBwnalrWixooX4+/bfuT9hQRYfL8SyZUI0\nby5ErVpCTJ8uxI0b2o5KKuJy89mp9iWsNm3aqH7W0dGhTp06aiettm3bEhERkeVY586dVT+3atWK\nHTt2ALBnzx6cnJzQ19fHxMQEc3NzgoKCsLW1VbtdKe/dvq1sL7JqFbRooey/161b7vffi34czdSj\nUzl26xhzOszBpYlL0RznyMhQZk15esKhQ8pAuLu7UqhdbmIoFXDZ+hf5fB8sgO3bt7Nx40aSkpI4\ndeoUx48fz/OgPD096dGjB6AsXDQ2/m/7CWNjY6Kjo/O8TSn7hIAjR6BfP2jeXLlMf/Kksituz565\n+9x7mvaU2X6zafx7Y2ob1ObqF1cZ0XRE0UseN2/CDz8oG3tNm6Ys8rt1CzZtgs6dZfKQCoVs9UDW\nrl2r+jkmJoaKFSvi6uqKjo4OVapUwcHBIc8C+vnnnylevDjOzs5vvM+bVhbPnDlT9bO9vT329vZ5\nFpekrN1Yu1aZhquvrwyKb9ig/tqN1xFCsOXSFqYcmUKrmq04M/oMdY3q5v7EBcnTp7Bjh7LY7+JF\nZVzjr7+yv7mXJOUBX19ffH198+Rc2UogL8626tmzJ7GxsWzdupUnT57w7NmzPAkEYPXq1Rw4cICj\nR4+qjtWsWZPIyEjV71FRUdSsWfO1j38xgUh55/JlJWls3AidOinjuG3b5t04bnB0MOMPjyc5LZl1\n/dfRrk67vDlxQfB8VoGXl7Lgz85OWfzSq5eyglKS8tnLX65zsxmu2mMgNWvWpFixYpw5c4bY2Fi2\nbNmSpYeSU4cOHWL+/Pn4+flR8oXdQfv06YOzszMTJkwgOjqaa9euYWNjk+v2pLdLT1e+HC9bBmFh\nMGYM/PMPvCF350jMkximHZ2G9w1vfurwEyOajEBPt4hcuomNhXXrlLGNtDRloV9ev4CSpGXZSiAf\nfvghgYGBJCUlUapUKSpVqsSzZ89o2bIl165dU7tRJycn/Pz8ePDgAbVq1WLWrFm4ubmRmpqqGky3\ns7PDw8MDS0tLHB0dsbS0pFixYnh4eLx/m+Plo/v3/1u7UauWcplq4MCcr914neS0ZBYGLGRh4EJG\nNx/NlS+uYFBC+zVlci0tDQ4cUHobfn5KwZI//oAPPpDTbqUiKVtbmaSmprJlyxYyMzNxdHSkVKlS\nrFixgrFjx3L+/HmaNm2aH7G+ldzKJHeCgpTLVHv3Kov9Pv9cGSDPS0IItoVtY7LPZFrUaMH8zvMx\nNTLN20a0ISxMSRrr1il1w11dlQV/ZYvwOhWpyND4brzP/fvvv2zYsIHixYvz6NEjvvrqqxw1qgky\ngajv2TPYulW5THX/vpI0Ro2CihXzvq2zd88y/tB4Hqc8ZnG3xdib2Od9I/np0SOlMJOXlzKXecQI\n5cWrV0/bkUmSWvItgTz34MED/vjjD+rVq0fFihXzdBZWTskEkn137ihrN1auVHoZX3yhLD/QxMzR\ne0n3+O7odxy4foDZ9rNxbeZaeMc5MjPhxAllXGPvXmVGgaursitkMbWHEyWpQMj3BPLcnTt36NWr\nF6GhoTk9RZ6RCeTthIDjx5Xehp+fstXIZ59p7gvzs/RnLA5czC+nfsG1mSvftf2O8iULaV2KO3eU\nvWUPF0IAACAASURBVOe9vJTLUq6uMGwYVK6s7cgkKdc0vhvv7t27adq0KSYmJlmO165dm8WLF+eo\nYUnzoqOVRc5Hjij/Vayo9DbWrtXc5XkhBDsv72SSzyQaV21M4MeBmFdQszpUQfDsmbJVupcXnDmj\nbOS1bZvSZZMD4pIEZDOB+Pn5YWxsjImJCXv27KFv376q2zp06KCx4CT1PH6s9C6eJ4x795QdMTp1\nghkzwNRUs5995++dZ/yh8TxMfsifvf+ko2lHzTWmCUIo9cI9PZWCJc2bK+Mau3eDLJwmSa/IVgLp\n3bs3P//8M8+ePSM5OZnw8HCsrKywsrJ646I+SfPS0pQ1as8TxoULykatnTopV1yaNcufHTFik2L5\n/tj3/BX+F7PsZ/FR848opluIxgQePFCW1Ht5KYPjo0ZBSIiyB70kSW+k9hjIggULsLa25tKlS1y8\neFG1V9W4ceOoX7++puJ8p/dhDEQIZcbo84Rx4gSYmSlbJ3XqpCw3KF06/+JJSU9h6emlzD05lxFN\nRjC9/XQMSxrmXwC5kZEB3t5Kb8PHB3r3VhKHvT3oFrF9tyTpLbQ2iP7c5s2biYyMZNKkSbk9VY4V\n1QTy8jhGiRL/JYwOHaBSpfyPSQjBnqt7mOg9EcvKlvzS5RfqVSwk01evXVN6GmvXKqvCXV2V8Y3y\nhXSAX5JySeOD6O9SvHhxGjRokBeneu9pexzjXUJjQ/n68NfEJsXi0dODLmZdtBdMdiUlwfbtSm/j\n6lVlCtrhw/B//6ftyCSpUMt2D+Thw4cEBASQnp6Ora0tVQtYHebC2gN52zhGp075N47xLvf/vc/0\n49PZdWUXM9rPYEyLMQV7nEMIOHVKSRo7dyq7P7q6KvvNy1LMkqSi8UtY+/fvx8vLCzMzM2JiYjh9\n+jStWrViyZIlVKhQIUcN57XCkkAK2jjGu6RmpPLr6V9xP+nOMKthzGg/A6NSRtoO681iYv7bxFBX\nV0kaLi5QrZq2I5OkAknjl7Bu377N9u3bVb8LIfD29mb06NH88ccfVNTE3hdFSEzMfwnjxXEMFxfl\ncrw2xjHeRQjBvvB9fOP9DRYVLfAf5U+DSgX0MmVqKuzbpySNU6dg0CBYvRpsbeWaDUnSoGz1QHbt\n2kX//v1fOf748WNWrFih1cHz5wpSD+TJE/D1ff04RqdO2h/HeJeLcReZcHgCkY8jWdR1Ed3Mu2k7\npNf75x8laWzYAJaWSm9j4MC8qXAlSe8JjfdAwsPDefbsWZY6HQAGBgZZys2+rwrKeozcevD0ATOO\nz2Bb2Damt5vOJ9afoK9XwMYLEhOVsq+enkpmHjkSAgKU64CSJOWrbCUQJycnnJ2d+eGHH17Zuv32\n7dsaCawgE0Kp0ufj8+o4xowZBW8c413SMtJYHrycn/1/xqmRE5c/v0zF0gXosmRmJhw7piSNAweg\na1f46SclQxeGzCxJRVS2Z2HdvHkTV1dXEhMTsbOzw8jIiNDQUHr16sUnn3yi6TjfSdOXsN40jqHN\n9Ri5JYTgwLUDfOP9DSaGJizsuhDLypbaDus/ERHKWMbq1VChgnKJytlZ+VmSpDyRrwsJz549y8mT\nJ1XVAxs3bpyjhvNaXieQl8cx7t5VEsXzpFHQxzHeJex+GBMOTyAiMYKFXRfS3bx7waj0mJysTLv1\n9ITQUHByUlaIN2um7cgkqUjK14WES5cupXLlyrRu3brArQXJjZfHMc6fV8YxOncuXOMY7/Iw+SEz\njs9g86XNfN/2ez5r+Zn2xzmEgOBgJWls3aq88J98An36KF09SZIKpBxtZXLlyhUCAwMJCAggJCQE\nR0dHJk6ciK4W9xBSN4u+aRyjUyclaRS2cYx3SctI4/czv/PjiR8ZbDmYWQ6zqFRay9fd4uJg/Xol\ncTx7pvQ0hg9XirFLkpQvcnX1RqgpICBAnDp1SvX71q1bxdWrV8Wff/6Z7XOMGjVKVKlSRTRq1Eh1\nLD4+XnTq1ElYWFiIzp07i4SEBNVtc+bMEebm5qJ+/fri8OHDrz1ndp5KdLQQa9YI4eIiRPXqQtSp\nI8THHwuxZYsQcXHZDr/QOXjtoGi4rKHotLaT+Cf2H+0Gk5YmxN69QvTrJ0T58kKMGCGEn58QmZna\njUuS3lM5SAMqavdAfvrpJ/T19Tl79iylS5emdu3a2Nv/f3v3HlZVlf9x/I2KiqImIBdF01BR8EKo\nIFCG9zsp3sdJzbQpm9+o04hNPTXUlKLpjM3veRydcTQGs9QaLz91TDMwHAWOkKCigkoiyMG8oNzk\n5vr9sceTpBAcz4GNfV/Pw/NEnr3X56DrfNl7rb1WMAUFBYwfP75G54iNjcXe3p5Zs2Zx8uRJAMLC\nwnByciIsLIwVK1Zw8+ZNIiIiSE1N5Re/+AUGg4Hs7GyGDRtGWlraA1c7D6uij/s4xk85e+0srx94\nnfTr6awesZpx3cfV3zjH2bM/LGL41FPagPjUqdCqVf3kEUIAdXwFcurUKRUfH6+UUioxMVGVlZWp\nv//972r//v21Ok9GRkalKxBPT09lNBqVUkrl5OQoT09PpZR29REREWF63ciRI9WxY8ceOB+gSkuV\nio1V6g9/UCooSKmWLZUaMkSp5cuVMhiUKi+v7bttmG4U3VAL/71QOa10UquPrlYl5SX1E+TWLaX+\n/nelAgKUcnVVKixMqTNn6ieLEOKhzCgDJrUeRE9OTiYhIYHU1FQCAwPZtm0b8+bNM6963Sc3N9c0\nKO/i4kJubi4AV65cYeDAgabXubu7k52d/dBzODn9MI7REJ/HeFTld8tZf3w9733zHhN7TCR1QSrt\nWtbxvt1KQWysNq6xc6d2yffmmzBqFDTR8eKLQohaq3WPbty4Me+88w5xcXH86U9/ol07y39A2djY\nVHurpao/e/nlcNMqFra2wbRoEWzxbHp18MJBFn+5GBd7Fw6+cJA+LnU8vTorS5uutmkTNG8OL70E\nK1eCs3Pd5hBCVCsmJoaYmBiLnKtGBSQoKAg/Pz/69+9PdnY2d+/eZcyYMYwZM8YiIUC76jAajbi6\nupKTk4Pzfz94OnTowOXLl02vy8rKqnIb3Q8/DLdYnoYi7XoavzvwO1K/T2XViFU87/l83Y1zlJTA\nrl3a1UZCAkybpi0z0r//4z24JEQDFhwcTHBwsOn7d9991+xz1Wje7dKlS3n55ZcpKSnh/PnzhIaG\nMmHCBFasWEFCQoLZjd8vJCSEyMhIACIjI5kwYYLp/3/22WeUlpaSkZFBeno6fn5+FmmzIcu7k8fr\nX75O4D8CebbTs5xecJoJPSbUTfE4cQJ+8xttR7+//U1bVjgrC/76VxgwQIqHED8X5g6eFBQUqOjo\naLV27dpaHzt9+nTl5uambG1tlbu7u9q4caO6fv26Gjp06EOn8X7wwQfKw8NDeXp6VjlY/whvpUEp\nqyhTfzX8Vbl86KLm756vjPnGumn42jWl/vIXpXx8lOrUSZupcPFi3bQthLCaR/nsrPU03tWrV7Nv\n3z6MRiPPP/884eHhNG3a1DrVrRb0tJy7tRy6eIjFXy7Gwc6BNaPW4OPq89MHPYqKCu1Jy02btC1g\nx47VHvYbMkTbrEkI0eDV6VpYe/bsYdy4cSil+Prrrzl8+DDvvfeeWY1b0uNcQM7fOM/vDvyOlNwU\nVo1YxcQeE617q+r8eW0Bw8hIcHPTisb06dBWxzsRCiHM8iifnbX+NdJoNLJv3z4KCwsZOnQoAwYM\nMKth8dNu3bnFkgNLGLhhIAPdB5L6WiqhPUOtUzwKC7WCERwMgYFQVKQtnZ6QAK++KsVDCPGAWk/j\nvXz5Mnl5eWzatInr169TXl7OrVu3yM7OZunSpdbI+LNTcbeCjd9u5J2YdxjTdQwnXz2JWys3yzek\nFMTFabOoPv9ce3DmN7+BceNAB7clhRD6ZtZy7sXFxQQFBQFw4cIFjh49yoYNGzh8+LBVQtbE43IL\nK+a7GBbtX0SrZq1YM3IN/dr3s3wjOTkQFaWNbdy9qy0r8sIL0L695dsSQuia1cdA3n77bQYOHIi/\nvz9O9+2cFB0dTd++fXFwcCAnJwc3Nyv8llxDDb2AXLx5kSUHl5B4JZEPh3/IZK/Jlr1VVVYGe/dq\nVxuxsdre4XPnQkCATLsV4mfM6vuBFBcXk5mZyeeff87Vq1dp27at6cHCDRs2EBYWVq/FoyG7XXKb\nZbHL2JC0gd8G/JbNEzdjZ2tnuQZOn9aKxubN4OmpFY0tW8De3nJtCCF+lszaD+TWrVsYDAYSExPx\n8PBg8uTJ1shWKw3tCqTibgUfn/iYt6PfZoTHCJYNXUb7Vha6hZSXB599pt2iysqCOXO0r27dLHN+\nIcRjo06n8epVQyogsZdiWbh/IXa2dnw06iP6t+//6Ce9e1dbu37jRtizR1uzfu5cGDHi8dhKUQhh\nFXVaQDZs2ECvXr3w9fXFYDCQk5MjVyA19F3ed4QdDCM+O54Vw1YwzXvao49zXLqkPbPx8cfQurW2\niOEvfqEtTSyEED+hTvdEv3r1KocPH+Yvf/kL+fn5urmFpWcFpQUsj13OusR1LPJfxMcTPqaF7SOs\nM19crC2VvnEjfPstzJgBX3yhbdwuA+JCiDpS6wLi7u7OrFmzACgtLWXXrl0WD/W4uKvu8s/kf/LW\n128xtMtQkl9Jxr21u3knUwoSE7WisXWrtuLtvHnw/PPa8ulCCFHHal1AbG1tmTNnDiEhIXh6epKV\nlWWNXA3ekcwjLNq/CNvGtvxr6r/wd/c370Tff6/NoNq0SXta/MUXtauOTp0sG1gIIWrJrEH0c+fO\nsXnzZvLy8pg1a5YuljPRyxjIpbxLLP1qKUcvHyViWAQzes2o/ThHebm2eOHGjXDoEISEaAPigwbJ\nIoZCCIuqt1lYSUlJ9OnThyY62Kq0vgtIYWkhEUciWHt8Lf/j9z8sCVxCy6Yta3eStDTtSiMyEp58\nUisaU6dCmzbWCS2E+Nmr00H0LVu2kJCQgI+PD4GBgWzdupWZM2ea1fjj4K66yycpn/D7Q7/nuc7P\nceJXJ+jYpmPNT5CfD9u3a1cb589rS4p89RV4eVkvtBBCWIAu90RvKI5dPsaiLxehlGL7lO0EdAyo\n2YFKwZEjWtHYsUNbATcsDEaPBltbq2YWQghLqdEtrB/viT537txKa2LpQV3ewrp86zJvHHqDw98d\nZvnQ5czsM5NGNjUYm8jOhn/+UysctrbaMxu//CW4uFg/tBBCPITVx0B2795Nt27dOHbsGPHx8Zw5\ncwYHBwcCAgIYPHiwLvYor4sCUlRWxMr/rOR/E/6X1wa8RlhQGPZNf2JNqZIS+L//04pGXBxMmaKN\nbfj5yTMbQoh6V6eD6EajEVdXVwoKCjh8+DCZmZm8+uqrZjX+Y8uXL2fz5s00atSI3r17s2nTJgoL\nC5k2bRqXLl2ic+fObNu2jSeeeOLBN2LFAqKUYsvJLbxx6A2COgaxYtgKnnziyeoPSk7WBsQ/+QR6\n99aKRmgotHiEBwiFEMLC6qSALFu2jKeffpqsrCzmz58PgMFgID8/nyFDhpjV+P2+++47hgwZwpkz\nZ2jWrBnTpk1jzJgxnD59GicnJ8LCwlixYgU3b94kIiLiwTdipQKSkJ3Awv0LKasoY82oNTzT6Zmq\nX3zjBnz6qXa18f33Pyxi+NRTFs8lhBCWUCdb2k6cOJGMjAzWrVvH+PHjmT9/PidOnOCbb74xq+Ef\na926Nba2thQVFVFeXk5RURHt27dn9+7dzJ49G4DZs2ezc+dOi7T3U7JvZzNrxywmbp3Ir/r9ioT5\nCQ8vHhUVcOCAtmf4U09pg+MREZCRAe+9J8VDCPHYqvEsrJ49e9KzZ0+6dOnC6NGjMRqNGAwGfH19\nLRLEwcGB119/nU6dOmFnZ8fIkSMZPnw4ubm5uPx3kNnFxYXc3FyLtFeV4rJiVh1dxZr4NbzS7xXO\nvnaWVs1aPfjCixd/WMTQ2Vl7QnztWnBwsGo+IYTQi1pP4x09ejQArq6uODk54ezsbJEgFy5cYM2a\nNXz33Xe0adOGKVOmsHnz5kqvsbGxsewuffdRSrH19FaWfrUU/w7+HJ9/nC5tu1R+UVGRtmjhxo1w\n6hTMnKkNkPfta5VMQgihZ7UuIO+//z7p6ek0adKE4cOHk5CQwMKFCx85yPHjxwkMDMTR0RGA0NBQ\njh07hqurq2ngPicnp9qCFR4ebvrv4OBggoODa9S2IdvAoi8XUVxWTNTEKAY9OeiHP1QK4uO1AfHt\n27UtYF97DcaPh2bNzHmrQghRb2JiYoiJibHIuWo9C2vHjh1MnDiRW7dusW/fPlq1asW4ceMeOUhy\ncjIzZ87EYDDQvHlz5syZg5+fH5cuXcLR0ZGlS5cSERFBXl6exQbRr+Rf4c1Db3LgwgHeH/I+s/vO\npnGj/26+lJsLUVHa1UZZmTaLatYs6NDhkd+rEELoRZ1O492xYwfu7u5WWUBx5cqVREZG0qhRI3x9\nfdmwYQP5+flMnTqVzMxMi03jLS4r5s9xf+ZPx/7EPN95vPnsm7Ru1lorFPv2aVcbhw/DxIla4QgK\nkmc2hBCPpTotIIsWLQK0MYvmzZvz3HPP8etf/9qsxi2pJj8EpRSfp35O2Fdh+Lr5snLYSjwcPCA1\nVSsaUVHQtatWNKZMgVYPGTwXQojHiNUXU3zppZcYP348/v7+TJo0CaUUAQEBVFRUcOrUKbMarmtJ\nOUks2r+I2yW32RiykcEOvtrGTBtnQmYmzJ6tXXV4etZ3VCGEaBBqVEBcXV2ZMGECAG5ubpSUlHDo\n0CHS09Pp2rWrVQM+KmOBkbcOvcW+8/t4b1A4c/O70vgPm2D3RBg2DN55B0aMAB0sSS+EEA1JjT41\nu3TRprPu3buX1NRU/Pz8GDZsGCNGjGDIkCGmqb16cqf8Dmvi1rDq6Cp+22EyGbfn0nzaCrC3125R\nrV4NP+OVhIUQ4lHVqIDcuz82duxYoqOj8fT0RClFo0aNmDx5slUD1pZSih1nd/Dm3t8yL9OJzFM9\naZG8XXtSfNs26NdPBsSFEMICalRA3nzzTWJiYggKCqJNmzY4OzvT6L9bq9rZ2Vk1YG2cyPmWtX+b\nz+DoDFJSymnarxvMnwsTJoCOcgohxOOgRrOw1q9fj7+/P3FxcRgMBlJSUmjcuDF9+/blxo0bbN++\nvS6yVsvGxoaTbk3oZNOGli//msZzXtS2hRVCCFGletkTPT8/H4PBwEcffcSuXbvMatySbGxsyP/3\nLuxHjINGNV4jUgghftbqpYDcYzAYrPJQYW3V5Y6EQgjxuKjXAqIXUkCEEKL26mQ/ECGEEOJ+UkCE\nEEKYRQqIEEIIs0gBEUIIYRYpIEIIIcwiBUQIIYRZpIAIIYQwixQQIYQQZpECIoQQwixSQIQQQphF\ndwUkLy+PyZMn07NnT7y8vIiPj+fGjRsMHz6c7t27M2LECPLy8uo7phBC/OzproAsXLiQMWPGcObM\nGVJSUujRowcREREMHz6ctLQ0hg4dSkRERH3HNFtMTEx9R6iRhpCzIWQEyWlpklM/dFVAbt26RWxs\nLHPnzgWgSZMmtGnTht27dzN79mwAZs+ezc6dO+sz5iNpKP+oGkLOhpARJKelSU790FUBycjIoF27\ndrz44ov4+voyf/58CgsLyc3NxcXFBQAXFxdyc3PrOakQQghdFZDy8nKSkpJYsGABSUlJtGzZ8oHb\nVTY2NtjInuZCCFH/lI7k5OSozp07m76PjY1VY8aMUT169FA5OTlKKaWuXLmiPD09HzjWw8NDAfIl\nX/IlX/JViy8PDw+zP7OboCOurq507NiRtLQ0unfvzldffYW3tzfe3t5ERkaydOlSIiMjmTBhwgPH\nnj9/vh4SCyHEz5fudiRMTk5m3rx5lJaW4uHhwaZNm6ioqGDq1KlkZmbSuXNntm3bxhNPPFHfUYUQ\n4mdNdwVECCFEw6CrQfSq3LlzB39/f3x8fPDy8uL3v/89QJUPGB48eJD+/fvTp08f+vfvT3R0tC5z\n3pOZmYm9vT2rV6/Wbc6UlBQCAgLo1asXffr0oaSkRHc579y5w4wZM+jTpw9eXl518rxQVRm3b9+O\nt7c3jRs3JikpqdIxy5cvp1u3bvTo0YMDBw5YPWNNcyYmJpper7c+VN3PE/TTh6rLqac+VFXOWvch\ns0dP6lhhYaFSSqmysjLl7++vYmNj1ZIlS9SKFSuUUkpFRESopUuXKqWU+vbbb02D7qdOnVIdOnTQ\nZc57Jk2apKZOnapWrVqly5xlZWWqT58+KiUlRSml1I0bN1RFRYXucm7atElNnz5dKaVUUVGR6ty5\ns7p06VK9ZDxz5ow6d+6cCg4OVomJiabXnj59WvXt21eVlpaqjIwM5eHhUa8/y6py6q0PVZXzHr30\noapy6q0PVZWztn2oQVyBALRo0QKA0tJSKioqaNu2bZUPGPr4+ODq6gqAl5cXxcXFlJWV6S4nwM6d\nO3nqqafw8vKqk3zm5Dxw4AB9+vShd+/eALRt25ZGjermn05tcrq5uVFYWEhFRQWFhYU0bdqU1q1b\n13lGBwcHevToQffu3R947a5du5gxYwa2trZ07tyZrl27kpCQYPWMtc2ppz5UXU7QTx+qLqee+lB1\nOWvbhxpMAbl79y4+Pj64uLgwePBgvL29a/SA4RdffEG/fv2wtbXVXc6CggJWrlxJeHh4nWQzN2da\nWho2NjaMGjWKfv368eGHH+oy58iRI2ndujVubm507tyZJUuW1Mlkix9nrO6D7MqVK7i7u5u+d3d3\nJzs72+oZoXY571fffai6nHrqQ9XlTE9P100fqi5nbftQgykgjRo14sSJE2RlZfHNN988cE/2YQ8Y\nnj59mjfeeIP169frMmd4eDiLFy+mRYsWqDqey1CbnOXl5Rw5coQtW7Zw5MgRduzYwddff627nJs3\nb6a4uJicnBwyMjJYtWoVGRkZdZ6xtktY1NWDsebk1EMfqi6nnvpQdTnLysp004eqy1nbPtRgCsg9\nbdq0YezYsSQmJuLi4oLRaAQgJycHZ2dn0+uysrIIDQ0lKiqKLl266DJnQkICYWFhdOnShY8++ohl\ny5axdu1a3eXs2LEjgwYNwsHBATs7O8aMGfPQgcz6znn06FEmTpxI48aNadeuHUFBQRw/frzOM1bX\nZocOHbh8+bLp+6ysLDp06FAX8UxqkhP004eqy6mnPlRdTj31oepy1rYPNYgCcu3aNdNMm+LiYg4e\nPMjTTz9NSEgIkZGRAJUeMMzLy2Ps2LGsWLGCgIAA3eb85ptvyMjIICMjg0WLFvHWW2+xYMEC3eUc\nMWIEJ0+epLi4mPLycg4fPoy3t7fucvbo0cP0W11hYSFxcXH07NmzXjLe7/7fjENCQvjss88oLS0l\nIyOD9PR0/Pz8rJrRnJx660NV5dRbH6oq58iRI3XVh6rKWes+ZKWBf4tKSUlRTz/9tOrbt6/q3bu3\nWrlypVJKqevXr6uhQ4eqbt26qeHDh6ubN28qpZT64x//qFq2bKl8fHxMX99//73uct4vPDxcrV69\n2uoZzc25efNm5e3trXr16vXALDK95Lxz546aOXOm6tWrl/Ly8qqTGTlVZfzXv/6l3N3dVfPmzZWL\ni4saNWqU6ZgPPvhAeXh4KE9PT7V//36rZzQnp976UHU/z3v00Ieqy6mnPlRVztr2IXmQUAghhFka\nxC0sIYQQ+iMFRAghhFmkgAghhDCLFBAhhBBmkQIihBDCLFJAhBBCmEUKiBBCCLNIARFCCGEWKSCi\nQVi3bh2Ojo6sXbuWa9euAdoCdTNmzKj1uR52XHp6Or179+b69eukpaUxevRo1q9fz7Bhw3jppZdY\nv349/fr14+7duxZ5P3q0atUq3NzciIqKArS1sHr27Mm6devqOZnQLSs/SS+ERRgMBjVp0iSrtjFh\nwgSllFJbt25VpaWlSimlRo4cqc6ePauUUurTTz+1avs1kZqaqj744AOrnDs6OlrNmTNHKaVURUWF\n2rp1q7p165ZV2hKPhyb1XcCEqIn4+HirLjpYVFRk2jinW7dupr0v0tLS8PT0BLSF5upbdHT0A4vh\nWUp8fDz+/v6UlJSwY8cOQkNDadq0qVXaEo8HKSCiQTAYDMyZM8f0/cWLF9mzZw/t27dn8uTJAGzZ\nsoWysjJKSkq4c+cOt2/fZtiwYQwcOJAXXniBqKgoLly4wN69e03H3Tvm/PnzDBgwAMD0AZ2eno6H\nh4epzZycHKKjo2natCmTJk0iPT2dL774gueeew6lFDExMYwaNcp0i23WrFkkJSWxa9cuOnbsiKur\nK+fOneP111/n3//+N2fPnjWd6/z58+zZs4e8vDzy8vJ47bXXyMrKoqysjKysLJydnXF3d+cf//gH\nr7zyCkajkatXr7J79+4H3mNsbCx79+6tdK6CgoJK7d3bbfDHP+MFCxYwefJk3n33XSke4ifJGIho\nEBITE+nfv7/pe6PRiKOjI6WlpQCcO3eOAwcOMHv2bK5du0ZBQQG2trYopcjIyMDe3h6A3NxcHB0d\nKSkpqXSMvb09/v7+ldpMSEgwXfVcunSJZcuWsXjxYnr27ElBQYFpIyh3d3dCQ0NJSUlh0KBBjBs3\nzrTXQ3FxMa1ataJ9+/aMGzeOffv2kZmZ+cC52rVrR6tWrQgNDSUyMhJnZ2e+/PJLZs2aRePGjenV\nqxejRo2iffv2zJ8/H1dX1yrfo7Ozc6VzderU6YH2HsZgMHD9+nVCQkL45JNPLPi3Jx5XUkCE7t2+\nfRvA9AFZWlrK7du32bVrFyEhIYC2k9q9/05KSmLx4sUkJSUREBDA0aNHCQwMBCAwMNB03P3HpKSk\n4OPjU6ldg8HAwIEDAW3f7W7durFnzx5sbGzo2rUrzzzzDBcuXGDAgAEUFRXh6OiIvb09cXFxpnMF\nBQURHx/PoEGDUEphNBrZuXMnXbt2rXQuT09Pjh8/zuDBg2nWrFmlbMnJyfj6+mI0GitdOQQGtGkV\nPgAAAqpJREFUBj70Pf74XA/L/mNGoxE3NzemTJnClClT2LlzZ53v8CcaHikgQvcMBkOlq4+oqCi8\nvb2xsbEhJSUF0DZA8vT0pLS0lKKiIuzs7LCzswPg2LFj+Pr6Eh8fT35+PjY2Npw8ebLSMQUFBcTF\nxT3Q7r3bWnZ2doSEhDBu3DieffZZrl69SnFxMc2bNwfg+PHjpquV3bt38+yzz5qyXb9+HXt7e77+\n+mtCQkJo3rw5zz//fKVzKaUoKSkxjb3cny0/Px+DwYDBYMDPzw+DwUBRUZEp14/f44/P9bDsQKWt\nSuPj403F8oknnmDAgAEcPHjQIn9/4vHVOLw+dqMXooYMBgMrV66koqKC27dvExUVxZEjR5g+fToJ\nCQl0796dLl264OTkRHR0NHFxcdja2jJq1CgSExO5efMmWVlZ5OXlMXLkSMrLy03HDRgwgOjoaHJy\ncigoKMDR0REvLy+Sk5OJiopi27ZtdOzYkSeffJK+ffuye/duCgoKSExMxNfXl1OnTtGkSRP8/PzY\nu3cvQUFBdOjQgcTERIqLixk6dCgXL15kx44dODk5cfToUcLDw/H09HzgXNnZ2RiNRoYMGQJAu3bt\nKmVr27YtnTp14vjx47i5udG9e3eAh75Ho9FY6Vxdu3Z9oL3c3FwGDx7MwoUL+c9//sOqVauoqKgg\nICCAJk2a8Omnn7J3714GDRqEo6Njvf39C32TDaXEY+Xjjz/Gzs6OadOm1XcUQLtasrGx4Ze//GV9\nR3lATEwMwcHB9R1DNGByC0s8Nk6cOMH69evJysqq7yiANmtrw4YNZGdn13eUhyopKanvCKKBkysQ\nIYQQZpErECGEEGaRAiKEEMIsUkCEEEKYRQqIEEIIs0gBEUIIYRYpIEIIIcwiBUQIIYRZpIAIIYQw\ny/8DTPMaHFV/PpQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7e784a8>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cross sectional is 5.48  square m\n",
        "\n",
        "The height of tower is 7.29\n",
        "Rate of evaporation is 0.318 kg/s\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 8.8,Page number:495"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "print\"Solution8.8 (a)\"\n",
      "\n",
      "#Variable declaration\n",
      "\t# a - water vapor   b - air\n",
      "\n",
      "T_L2 = 314  \t\t\t\t# [inlet water temperature, K]\n",
      "T_L1 = 303  \t\t\t\t# [outlet water temperature, K]\n",
      "T_d = 306  \t\t\t\t# [dry bulb temperature ,K]\n",
      "T_w1 = 298  \t\t\t\t# [wet bulb temperature, K]\n",
      "Z = 3 \t\t \t\t\t# [packed tower depth, m]\n",
      "G_x = 3  \t\t\t\t# [mass velocity, kg/square m.s]\n",
      "G_s =2.7  \t\t\t\t# [mass velocity, kg/square m.s]\n",
      "\n",
      "import math\n",
      "from numpy import *\n",
      "from pylab import *\n",
      "T_o = 273  \t\t\t\t# [reference temperature, K]\n",
      "C_al = 4.187  \t\t\t\t# [kJ/kg.K]\n",
      "C_pb = 1.005  \t\t\t\t# [kJ/kg.K]\n",
      "C_pa = 1.884  \t\t\t\t# [kJ/kg.K]\n",
      "P_total = 101.325  \t\t\t# [kPa]\n",
      "lamda_0 = 2502.3  \t\t\t# [kJ/kg]\n",
      "M_a = 18.02  \t\t\t\t# [gram/mole]\n",
      "M_b = 28.97  \t\t\t\t# [gram/mole]\n",
      "\n",
      "\t# Equilibrium Data:\n",
      "\t# Data  = [Temp.(K),H_eqm(kJ/kg)],[H_eqm - Equilibrium gas enthalpy]\n",
      "Data_eqm =matrix([[273,9.48],[283,29.36],[293,57.8],[303,99.75],[313,166.79],[323,275.58],[333,461.5]]) \n",
      "\n",
      "\n",
      "a1=plot(Data_eqm[:,0],Data_eqm[:,1],label='$Equilibrium line$') \n",
      "\n",
      "legend(loc='upper right') \n",
      "xlabel(\"Liquid Temperature, K\") \n",
      "ylabel(\"Enthalphy Of Air Water vapour, kJ / kg dry air\") \n",
      "\n",
      "P_a = math.exp(16.3872-(3885.7/(T_w1-42.98)))  \t\t\t\t# [kPa]\n",
      "Y_m1 = P_a/(P_total-P_a)*(M_a/M_b)  \t\t\t\t\t# [kg water/kg dry air]\n",
      "H_g1 = C_pb*(T_w1-T_o) + Y_m1*(C_pa*(T_w1-T_o)+lamda_0)  \t\t# [Enthalpy of saturated \t\t\t\t\t\t\t\t\tmixture, kJ/kg \t\t\t\t\t\t\t\t\tdry air]\n",
      "\n",
      "\t# From overall energy balance\n",
      "H_g2 = H_g1 + G_x*C_al*(T_L2-T_L1)/G_s  \t\t\t\t# [Enthalpy of exit air, \t\t\t\t\t\t\t\t\tkJ/kg]\n",
      "\n",
      "\t\t# For calculation of mass transfer unit, Ntog\n",
      "\t\t# Data1 = [T_L1 H_g1,.....,T_L2 H_g2]\n",
      "\n",
      "deltaT = (T_L2-T_L1)/9 \n",
      "\n",
      "\t# Data for enthalpy of exit air at different temperature varying from T_L1 to T_L2, \t\t\toperating line\n",
      "Data1=matrix([[303,76.17],[304.22,81.85],[305.44,87.53],[306.67,93.22],[307.89,98.91],[309.11,104.59],[310.33,110.28],[311.56,115.96],[312.78,121.65],[314,127.35]]) \n",
      "\n",
      "\t# Data of equilibrium gas enthalpy at different temperature varying from T_L1 to T_L2 \t\tfrom the above equilibrium graph \n",
      "\n",
      "Data2=matrix([[303,100],[304.22,107.93],[305.44,116.12],[306.67,124.35],[307.89,132.54],[309.11,140.71],[310.33,148.89],[311.56,157.14],[312.78,165.31],[314,177.67]]) \n",
      "\n",
      "\t# Driving force \n",
      "Data3 = zeros((10,3)) \n",
      "\t# Data3 =[Equilibrium gas enthalpy, driving force]\n",
      "for i in range(0,10):\n",
      "    Data3[i][0] = Data1[i,1] \n",
      "    Data3[i][1] = 1/(Data2[i,1]-Data1[i,1]) \n",
      "\n",
      "\n",
      "\t# The data for Equilibrium gas enthalpy as abcissa is plotted against driving force\n",
      "Area = 1.642 \n",
      "N_tog = 1.642 \n",
      "H_tog = Z/N_tog  \t\t\t\t# [m]\n",
      "\n",
      "\t# Overall volumetric mass-transfer coefficient, K_ya\n",
      "K_ya = G_s/H_tog \n",
      "print\"Overall volumetric mass-transfer coefficient is\",K_ya,\"kg/cubic m.s\\n\\n\" \n",
      "\n",
      "print\"\\nSolution (b)\"\n",
      "#Illustration 8.8 (b) \n",
      "\n",
      "T_w2 = 288  \t\t\t\t\t# [New entering-air wet-bulb temperature, K]\n",
      "P_a2 = math.exp(16.3872-(3885.7/(T_w2-42.98)))  # [kPa]\n",
      "Y_m2 = P_a2/(P_total-P_a2)*(M_a/M_b)  \t\t# [kg water/kg dry air]\n",
      "H_g11 = C_pb*(T_w2-T_o) + Y_m2*(C_pa*(T_w2-T_o)+lamda_0)  \t# [Enthalpy of saturated mixture, \t\t\t\t\t\t\t\tkJ/kg dry air]\n",
      "\n",
      "\t# the change in water temperature through the tower must remain the same as in part (a), \t\tnamely T_L2b-T_L1b = 11K \n",
      "\t# Since N_tog is a function of both water temperatures(T_L1',T_L2'), this provides the \t\t\tsecond relation needed to calculate the values of T_L2b and T_L1b\n",
      "\t# The two equations are solved simultaneously by trial and error method, from above we \t\tget T_L1'= 297K\n",
      "T_L1b = 297  \t\t\t\t\t# [K]\n",
      "T_L2b = T_L1b + 11  \t\t\t\t#[K]\n",
      "S =  T_L1b - T_w2  \t\t\t\t# [wet bulb temperature approach, K]\n",
      "\n",
      "#Result\n",
      "\n",
      "print\"The outlet water temperature and wet bulb temperature approach is\",T_L1b,\" K and \",S,\" K respectively \"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Solution8.8 (a)\n",
        "Overall volumetric mass-transfer coefficient is"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1.4778 kg/cubic m.s\n",
        "\n",
        "\n",
        "\n",
        "Solution (b)\n",
        "The outlet water temperature and wet bulb temperature approach is 297  K and  9  K respectively \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4u7sTEhJCRkaG4ZjJkyfTtGlTPDw8iI2NNfHbEkKUtu+/h7ff1tZvcne3dBpR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\nJua+jlVKsXXrVnr37k2VKlWoUqUKzz7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       "text": [
        "<matplotlib.figure.Figure at 0x7cb6240>"
       ]
      }
     ],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}