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{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Chapter 5: Interphase Mass Transfer"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Ex5.1: Page 114"
]
},
{
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"collapsed": false,
"input": [
"\n",
"\n",
"# Illustration 5.1\n",
"# Page: 114\n",
"\n",
"print'Illustration 5.1 - Page: 114\\n\\n'\n",
"import matplotlib.pyplot as plt\n",
"import numpy\n",
"%matplotlib inline\n",
"# solution\n",
"\n",
"#***Data***#\n",
"# a = NH3, b = H2O\n",
"d = 2.54*10**(-2);# [m]\n",
"Yag = 0.80;\n",
"Xal = 0.05;\n",
"T = 273+26.7;# [K]\n",
"Kl = 2.87*10**(-5);# [kmol/square m.s.(kmol/cubic m)]\n",
"Sh = 40;\n",
"Da = 2.297*10**(-5);# [square m.s]\n",
"P = 1.0133*10**(5);# [N/square m]\n",
"Xbm = 1.0;\n",
"#*********#\n",
"\n",
"Ma = 18.0;# [kg/kmol]\n",
"# Liquid:\n",
"# Because of large conc. of ammonia in gas F's rather than k's are used.\n",
"# Molecular weight of water and ammonia are nearly same.\n",
"# The density of the solution is practically that of water.\n",
"MolarDensity1 = 1000/Ma;# [kmol/cubic m]\n",
"# Kl is determined for dilute soln. where Xbm is practically 1.0\n",
"Fl = Kl*Xbm*MolarDensity1;# [kmol/square m.s]\n",
"Ma = 18;# [kg-/kmol]\n",
"# Gas:\n",
"MolarDensity2 = (1/22.41)*(273/(273+26.7));# [kmol/cubic m]\n",
"Fg = Sh*MolarDensity2*Da/d;# [kmol/square m.s]\n",
"\n",
"# Mass Transfer Flux\n",
"# Th eqb. distribuion data for NH3 from \"The Chemical Engineers Handbook\" 5th Edt. p3-68:\n",
"# Data = [Xa,pa]\n",
"# Xa = NH3 mole fraction in gas phas\n",
"# pa = NH3 partial pressure in N/square m\n",
"Data = [(0 ,0),(0.05 ,7171),(0.10, 13652),(0.25 ,59917),(0.30 ,93220)];\n",
"\n",
"X = numpy.zeros(5);\n",
"for i in range(1,5) :\n",
" X[i]=Data[i][0]\n",
" \n",
"\n",
"# Ya_star = mole fraction of NH3 in gas phase at eqb.\n",
"Ya_star = numpy.zeros(5);\n",
"for i in range(0,5) :\n",
" Ya_star[i] = (Data[i][1])/P\n",
"\n",
"# For transfer of only one component\n",
"Na_by_SummationN = 1.0;\n",
"Ya = numpy.zeros(5);\n",
"for i in range(0,5):\n",
" Ya[i] = 1-((1-Yag)*(1.0-Xal)/(1-Data[i][0]));\n",
"\n",
"plt.plot(X,Ya_star,'g',label='Equilibrium Line')\n",
"plt.plot(X,Ya,'r',label='Operating Line')\n",
"ax = pylab.gca()\n",
"ax.grid('on')\n",
"ax.set_xlabel('Xa = mole fraction of NH3 in liquid phase');\n",
"ax.set_ylabel('Ya = mole fraction of NH3 in gas phase');\n",
"pylab.legend(loc='lower right')\n",
"plt.title('Ya Vs Xa');\n",
"plt.show()\n",
"\n",
"# From intersection of operating line & Eqb. line\n",
"Xai = 0.274;\n",
"Yai = 0.732;\n",
"\n",
"# From Eqn.5.20\n",
"Na = Na_by_SummationN*Fg*log((Na_by_SummationN-Yai)/(Na_by_SummationN-Yag));# [kmol NH3 absorbed/square m.s]\n",
"print\"Local mass transfer flux for ammonia is \",round(Na,6),\" kmol/square m.s\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Illustration 5.1 - Page: 114\n",
"\n",
"\n"
]
},
{
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KV4tImZHqk2gHvAA0BcrgXM56IJLPufZ6krB236LLy7FBgPGlpgZ/Fdq+fVC6dHB9NVn9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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7765208>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Local mass transfer flux for ammonia is 0.00043 kmol/square m.s\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Ex5.2: Page 130"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Illustration 5.2\n",
"# Page: 130\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import numpy\n",
"\n",
"print'Illustration 5.2 - Page: 130\\n\\n'\n",
"\n",
"\n",
"# solution\n",
"\n",
"#****Data***#\n",
"# Eqb. data\n",
"# Data = [Wt% of moisture in the soap,Partial pressure of water in air(mm Hg)]\n",
"Data = [(0,0),( 2.40, 9.66),(3.76 ,19.20),(4.76 ,28.4),(6.10, 37.2),(7.83, 46.4),(9.90, 55.0),(12.63, 63.2),(15.40, 71.9),(19.02 ,79.5)];\n",
"P = 760.0;# [mm Hg]\n",
"# Initial air\n",
"p1 = 12;# [mm Hg]\n",
"T = 273+75.0;# [K]\n",
"#******#\n",
"\n",
"# Y = kg water/kg dry air\n",
"# X = kg water/kg dry soap\n",
"# E = Air water phase\n",
"# R = Soap water phase\n",
"Y = numpy.zeros(10);\n",
"X = numpy.zeros(10);\n",
"for i in range(1,10):\n",
" Y[i] = Data[i][1]/(P-Data[i][1])*(18.02/29);\n",
" X[i] = Data[i][0]/(100.0-Data[i][0]);\n",
"\n",
"\n",
"print'Illustration 5.2 (a)\\n\\n'\n",
"\n",
"import pylab\n",
"# Soln. (a)\n",
"# First operation\n",
"Y1 = p1/(P-p1);# [kg water/kg dry soap]\n",
"# Initial Soap\n",
"S1 = 16.7/(100-16.7);# [kg water/kg dry soap]\n",
"# Final soap\n",
"S2 = 13.0/(100-13);# [kg water/kg dry soap]\n",
"Rs = 10.0*(1-0.167);# [kg dry soap]\n",
"# Using ideal gas law\n",
"Es = 10.0*((760-p1)/760.0)*(273.0/T)*(29.0/22.41);# [kg dry air]\n",
"slopeOperat = -Rs/Es;\n",
"\n",
"def f2(x):\n",
" return slopeOperat*(x-S1)+Y1\n",
"x = numpy.arange(S1,S2,-0.01);\n",
"X1=S2;\n",
"def f3(S):\n",
" return slopeOperat*(S-X1)+Y1\n",
"S=numpy.arange(0,S1,0.01);\n",
"\n",
"plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
"plt.plot(x,f2(x),'g',label='First Process')\n",
"plt.plot(S,f3(S),'r',label='Second Process')\n",
"ax = pylab.gca()\n",
"plt.title(\"Illustration 5.2(a)\")\n",
"ax.set_autoscale_on('False')\n",
"pylab.axis([0.0,0.24, 0,0.08])\n",
"plt.grid(b=None, which='major', axis='both')\n",
"ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
"pylab.legend(loc='upper left')\n",
"ax.set_xlabel('kg water / kg dry soap')\n",
"ax.set_ylabel('kg water / kg dry air')\n",
"plt.show()\n",
"\n",
"# Results for First Process\n",
"# The condition at abcissa S2 correspond to the end of first operation\n",
"print \"Conditions corresponding to First Operation \\n\"\n",
"print \"X = kg water/kg dry soap\\n\",S2\n",
"print \"Y = kg water/kg dry air\\n\",f2(S2)\n",
"\n",
"# Results for Second Process\n",
"# The point at which the line meets the equilibrium line corresponds to the final value\n",
"X2 = 0.103;\n",
"Y2 = (X2/(1+X2));\n",
"print\"Final moisture content of soap is \",round(Y2*100,3),'%'\n",
"\n",
"\n",
"print'\\n\\n Illustration 5.2 (b)\\n\\n'\n",
"\n",
"# Solution (b)\n",
"\n",
"Rs = 1*(1-0.167);# [kg dry soap/h]\n",
"# Entering soap\n",
"X1 = 0.20;# [kg water/kg dry soap]\n",
"# Leaving soap\n",
"x = 0.04;\n",
"X2 = x/(1-x);# [kg water/kg dry soap]\n",
"# Entering air\n",
"Y2 = 0.00996;# [from Illustration 5.2(a), kg water/kg dry air]\n",
"# The operating line of least slope giving rise to eqb. condition will indicate least amount of air usable.\n",
"# At X1 = 0.20; the eqb. condition:\n",
"Y1 = 0.0675;# [kg water/kg dry air]\n",
"\n",
"def f4(x):\n",
" return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
"x = numpy.arange(X2,0.24,0.01);\n",
"plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
"plt.plot(x,f4(x),'g',label='Operating line')\n",
"ax = pylab.gca()\n",
"ax.set_xlabel('kg water / kg dry soap')\n",
"ax.set_ylabel('kg water / kg dry air')\n",
"ax.set_autoscale_on('False')\n",
"pylab.axis([0.0,0.24, 0,0.08])\n",
"plt.title(\"Illustration 5.2(b)\")\n",
"plt.grid(b=None, which='major', axis='both')\n",
"ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
"pylab.legend(loc='upper left')\n",
"plt.show()\n",
"# By Eqn. 5.35\n",
"\n",
"Es = Rs*(X1-X2)/(Y1-Y2);# [kg dry air/h]\n",
"Esv = (Es/29)*22.41*(P/(P-p1))*(T/273.0); #[cubic m/kg dry soap]\n",
"print\"Minimum amount of air required is\",round(Esv,4),\" cubic m/kg dry soap\\n\\n\"\n",
"\n",
"print'Illustration 5.2 (c)\\n\\n'\n",
"\n",
"# solution (c)\n",
"\n",
"Esnew = 1.30*Es;# [kg dry air/h]\n",
"Y1 = Rs*((X1-X2)/Esnew)+Y2;\n",
"\n",
"def f5(x):\n",
" return ((Y1-Y2)/(X1-X2))*(x-X1)+Y1\n",
"x = numpy.arange(X2,0.24,0.01);\n",
"plt.plot(X,Y,'blue',label='Equilibrium Line')\n",
"plt.plot(x,f5(x),'g',label='Operating line')\n",
"ax = pylab.gca()\n",
"ax.set_xlabel('kg water / kg dry soap')\n",
"ax.set_ylabel('kg water / kg dry air')\n",
"ax.set_autoscale_on('False')\n",
"pylab.axis([0.0,0.24, 0,0.08])\n",
"plt.title(\"Illustration 5.2(c)\")\n",
"plt.grid(b=None, which='major', axis='both')\n",
"ax.grid(color='Black', linestyle='--', linewidth=0.5)\n",
"pylab.legend(loc='upper left')\n",
"plt.show()\n",
"# with final coordinates X = X1 & y = Y1\n",
"# From figure, Total number of eqb . stages = 3\n",
"N = 3;\n",
"print\"Moisture content of air leaving the drier is \",round(Y1,4),\" kg water/kg dry air\\n\"\n",
"print\"Total number of eqb. stages = \",N\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Illustration 5.2 - Page: 130\n",
"\n",
"\n",
"Illustration 5.2 (a)\n",
"\n",
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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Qd7lcOn/+/KyRwvr167POBTJS8PYm7q0db3rVrFlTVVX79OmjAwYMOOs+u3bt\nOmtk44vvvvtOixUrpps3bz7j+L59+zQhIUGHDh2a4z0i7XlxgkOHVF94QfX881Wvu071669VMzKc\n1sqSV/AzUgimT2E5UFdEXCJSFLgN+DybzOfAXQAi0go4rKp7VXUPsF1E6rnl2gNR4THcsGEDr776\nKjt37gRg+/btTJ8+ncsvvxyA++67j+eee461a9cCZM2NA3Tq1Indu3czevRoTp06xbFjx1i2bBlg\n6h+PGDGC/fv3s3//fp555hnuvPPOHPUpXLgwN910E8OHD+fPP/9k7dq1TJkyJeC6zznhTa8ePXoA\n0Lt3byZNmsSCBQvIyMhg586dbNiwgapVq3LdddcxcOBAjh07RkZGBps3b84q+/nRRx+xY8cOwLzl\nZpbvXL58OUuXLiU1NZXixYufUfLTkje2bzeF7WvVgtWrTVGbefOgfXvrPI5afFmLgtgwS003AJuA\nJ93H+gB9PGT+4z6/CrjU4/jFwI/u458CZbzc358VDEt27typ3bp10/j4eC1RooTGx8frfffdp8eO\nHcuSee+997RJkyZaunRprV69uvbu3Tvr3K+//qrXXHONxsXFaZUqVfTFF19UVdW//vpL+/fvr1Wr\nVtWqVavqQw89lDW3v3DhQq1evfoZemSOFFRV//jjD+3UqZOWLl1aW7ZsqUOHDtW2bdt61X/r1q1a\nqFAhnyOF7O3400tV9b///a9edNFFWqpUKa1Tp45+9dVXqqp65MgR7du3r1arVk3LlCmjl1xyiX7w\nwQeqqvrYY49pfHy8lixZUmvXrq3jx49XVdX58+frRRddpCVLltQKFSpojx499MSJEzn+n4Tz8+IU\nq1ap9uihGhenOmCA6rZtTmtkKUjwM1KwqbMtMY99XgyqMH++CTRbvRr69zeBZra2cfRhU2dbLBaf\npKXBRx8ZY/DXX6aGweef2xoGsYpfn4IYqvuTsVgskcnx4/DoKysoldiRt96CZ56BX3+Ff/7TGoRY\nTnMRiKN5TtC1sFgsIWPPHnjqKVPMZsvSRhSRpTz+zmw6dbJFbTKxRsEH7gn7n0SkRYj0sVgsQWL9\nerjnHhNpfPgwLFkCn35YjBsadKT/nP78lfaX0ypawoBA3gtaAT+IyBYRWe3efgm2YhaLJf+ou4ZB\nly5w5ZUQH29qGLz+OtSubWTqlq9Lk8pNGLl4pLPKWsKCQBzNHXIWsVgs4UR6OsycCSNHwr59MHAg\nTJ8OxYvZ15mEAAAgAElEQVR7l3+tw2s0e7sZPS7qgausK6S6WsILn0ZBREqr6lHgaAj1KTAKKvjK\nYokkTp6EKVPg1VehfHmThqJrV8gphs9V1sWAVgMYMG8A/73tv6FR1hKW+Js+mu7+dwXwk5ctbPEV\nlBEr28KFCx3XIRy3hQsXor/9hpYvj27ffsa5SGffPnj6aXC5TMTxpEnwww9w8805G4TM3DiPXvEo\nq/euZs5vdm2Jt9xCsUJUBq9ZLH4ZNsyUAPvgA6c1yTcbN5pRwQcfQLduZpqofv2832/Ob3PoP7c/\nq/uuptg5xQpOUUtY4S94LaAFaCISJyItROTKzK1gVbRYQsgTT5giAN9847Qmeeb77+HGG01Fs0qV\njI0bNy5/BgGgY92ONK7Y2DqdY5gcRwoicg/QH6gO/Ix7NZKqXh189fxjRwqWPDNzpjEOq1aBR6rv\ncMbTebx3rxkV9OwJJUoUbDsph1No/nZzlt+73Dqdo5T8jhQewlRRS1HVdsAlQFRUQbPEMJ07m9Sf\no0Y5rUmOnDwJb74JDRrASy+ZrKUbN5rSlwVtEMA4nR9q+RAD5g0o+Jtbwp5AjMJfqvongIgUU9X1\nQD4HqRaLw4jA6NHmV9adhjvcyI/zOL8Maj3IOp1jlECMwnYRiQM+A74Wkc+BlKBqZckXsRyi74+z\n+qVOHbj/fvPqHUZs3Giyk9avb1JSLFoEn31m/AfBWGnt7Xkpdk4xxnYcS/+5/TmVdqrgGw1zYvk7\nlKNRUNUbVfWQqg4HhgLvAF2DrZgl78TyA+0Pr/2S6XSePz/k+mQnWM7jnPD1vMSy0zmWv0O5Sn+l\nqsmq+rmqng6WQhZLSCle3PgV+vWD06F/rNPT4dNP4Yor4K67TEWzrVtNxtJKlUKuzlmMShzFa0te\nY9vhbU6rYgkRNieixdK5s0kZOnp0yJoMtfM4r1inc+xhjYLFIgJjxsCLLwbd6fzHHzB8uHEez50b\nWudxXhnUehC/7P2FuZvmOq2KJQTkaBREpL/b0WyxRC916kDfvqbsWBDIdB7Xqwe7dxvn8cyZwXMe\nFyTFzinGmI5jeHDOgzHpdI41AhkpVAZ+FJEPRSRRcpFpzi2/XkR+E5HHfciMcZ9fJSKXeBxPEZFf\nRORnEVkWaJuW2M7b4o8c++XJJ2Hp0gJ1Omc6j1u3Nj6C9etD4zzODYE8L0l1k2LK6RzL36GAch+J\nSCHgOqAn0Bz4EJigqpv9XFMY2AC0B3YCPwLdVXWdh0wS0E9Vk0SkJTBaVVu5z20FmqnqQT9t2Ihm\nS8Eyc6YxDitX5jnSOVSRx6Em5XAKzd5uxop7V3BB2QucVseSD/Kd+0hVM4A9wF4gHYgDPhaRl/1c\n1gLYpKopqpoKzAC6ZJPpDExxt7EUKCsilT11D0Q/i6XA6NzZTPjnwens6Tx+8UVjDMLReZxXXGVd\nPNzyYet0jnIC8Sk8JCI/AS8B3wMXqmpfoBlwk59L44HtHvs73McClVHgGxFZ7s6/ZLEEnzw4nbM7\njydONKUub7klfJ3HecU6naOfQCqvlQNuUtUzFiqraoaI3ODnukDndXyNBtqo6i4RqYiJpF6vqouy\nC3nO/blcLlwuFwkJCV7nBJOTk70GpVh5K3+WfKbTecYMn/IHDpiVQ+vXJ3DHHQl8+60ZJYSF/kGU\n7127Nw/OeZBf+/7Kueec67g+Vj5n+czzgeDXpyAi5wBrVDXXbjERaQUMV9VE9/6TQIaqvugh8xaQ\nrKoz3PvrgatUdW+2ez0NHFfVV7Idtz4FS3A4eRIaN4YJE+DqMxMCL14ML79sah/37WumhypX9nGf\nKKXrjK5cdv5lPHXlU06rYskDefYpqGoasF5E8uJVWg7UFRGXiBQFbgM+zybzOXCXW8lWwGFV3Ssi\nxUWklPt4CYyTe3UedIhJYjlE3x+56pfixeG117IinT0jj++800Qep6SYyONINwh5eV6iPdI5lr9D\ngTiaywFrRGSBiHzh3rL/uJ+F26D0A+YBa4EPVHWdiPQRkT5umdnAFhHZBIwD7ndfXgVYJCIrgaXA\nLFX9KtefLkaJ5QfaH7nuly5dSK/u4ofbR0et8xjy9rxEe6RzLH+HAvEpDM3rzVV1DjAn27Fx2fb7\nebluC9A0r+1aLPlFFT7+WHjt5zHMPdKKqdO60+KmamEfaBZKBrUexIVvXMjcTXNJrJPotDqWAiJH\no6CqySHQw2IJG3btMiOBDRtgwsw6lJ7dl5YfPQo3z8j54hii2DnFGJ042qvT2RK5+Jw+EpHjInLM\nx3Y0lEpaLKFAFd55B5o2hSZN4Oef4fLLMcFsS5bAggVOqxh2XF/vehpVbMQrP7ySs7AlIvA5UlDV\nkgAiMgLYBUx1n7oDOD/4qlksoWPzZrj3Xjh6FL75Bi66yOOkZ3rtfEQ6RyujOozisvGXcUeTO2yk\ncxQQiKO5s6q+oapH3dubnB2ZbAkjYjlviz+89Ut6Orz6KrRsCUlJJu7gDIOQSZcuJjptzJhgqxly\n8vu81IyrSf+W/Rn41cCCUSgMiOXvUI65j0TkB+B1YLr70O3AA6p6RZB1yxEbp2DJD7/+Cr17m4HA\n+PEmUapfNm2CVq1g1SqIzx6cH9v8lfYXF75xIa8nvU6HOh2cVseSA/nNffQPoBsm79Fe99//KDj1\nLJbQcuqUSUvRrh383/8ZV0GOBgGCnl47kvF0Otv02pFNQFlSwxU7UrDkliVLzOigTh144408vPCf\nPAmNGpnqOO3aBUXHSKbLjC60jG/J4LaDnVbF4gd/IwVrFCwxwYkTMGSISWU0ejTcems+itt89hkM\nHmymkYoUKVA9I52th7Zy2fjLWNFnBTXK1HBaHYsP8p0622KJZL75xiwxPXDA+BG6dctntbMuXeCC\nC0Ja0zlSyHQ6R2ukcyxgjUIUEssh+p4cOmSminr3NlNF//xnMuXLF8CNM9Nrv/AC7NxZADd0loJ+\nXh5r/Rgr96xk3qZ5BXrfUBLL36FA6ik8IiID3f9m/t1bRGwaijAllh/oTD79FC680Kws+vVXSEws\n4H6pW9cUXY4Cp3NBPy/FzinGmMTIrukcy9+hQEYKzYD7MAFr8UAfoCMw3lfdZYvFKfbsMcVtBg+G\nDz6AsWOhVKkgNTZ4sAlsWLgwSA1ELtfXu54GFRrYSOcIJBCjUB24VFUfUdWBGCNRCbgKU7PZYnEc\nVZg8GS6+2BS6WbkS2rQJcqOe6bVTU4PcWOQxOnE0r/7wKr8f+d1pVSy5IBCjUBE47bGfClRW1ZPA\nX0HRymLJBVu3QocOZlQwbx6MGAHFioWo8a5doUaNqIx0zi8142ryYIsHrdM5wgjEKLwPLBWRp0Vk\nOLAYmOYufrM2mMpZLP5ITzcLgC67zBS9WbrUJLMLKZlO5+efN+lVLWcQDU7nWCNHo6CqzwL3AkeA\nQ0AfVf2Xqp5Q1TuCraAl98RC3pa1a8300KefmvKYjz0G5+SQCD5o/RLhTudgPi/nFTkvIiOdY+E7\n5ItAch/1VtUJ2Y69oKpPBFWzALDBa7HH6dOmAtqYMWaa6J57oFA4LKy2kc5+6Ty9M5dXu5wn2z7p\ntCoW8h+8douI9PC42esYR7PFElJ+/BGaNzfTRD//DH36hIlBAOt0zoFRiaMY+cNI63SOAAL5St0E\n3C0i3UXkXSBNVf8ZZL0slixOnjQzMzfcYOrdfPEFVKvmtFZesE5nn9SKq0X/Fv0ZOC960mtHK/4q\nr5UTkXLAecD/AY8DR4F/uY/niIgkish6EfnNV0yDiIxxn18lIpdkO1dYRH4WkS8C/kSWqGLhQlPf\nYPduWL0aunfPZ4qKYGKdzn55rPVjrNi9wjqdwxyfPgURSQE8T4rHvqpqLb83FikMbADaAzuBH4Hu\nqrrOQyYJ6KeqSSLSEhitqq08zmfGRZRS1c5e2rA+hSjl8GHjPJ47F958E66/3mmNcsGQIbBlC0yb\n5rQmYcesjbMYOG8gq/uutjWdHSRPPgVVdalqTY/Nc9+vQXDTAtikqimqmgrM4OyKbZ2BKe72lgJl\nRaSyW+lqQBLwDsYgWQIk0kP0Z882KSrOOcekqCgogxCyfhk82CyJipBI51A+L53qdaJ+hfq8+sOr\nIWszL0T6dyg/BNNNFw9s99jf4T4WqMxrwCAgI1gKRiuR+kCrwksvmVrJ779vktiVLl1w9w9Zv0SY\n0znUz8voxNFh73SO1O9QQZDDyu58Eei8TvZRgIhIJ2Cfqv4sIgn+LvZcT+xyuXC5XCQkJHhdZ5yc\nnOz1P9vKOy9/xRUJ3HefWVW0ZIlxJEeS/mfJd+1K8vPPk9ypE1x+ufP6+JFPSUk561gw9cl0Ot/5\n6p20k7OX74ZD/yQnJzN8+PCw0Se/8pnnA0JVvW5AEV/nAtmAVsBcj/0ngcezybwF3O6xvx6oAjyH\nGUFsBXYDJ4B3vbShlrN5+umnnVYhV/zxh+qVV6p27ap67Fjw2gl5v2zcqFq+vOrOnaFtN5c48byc\nPH1Sa46qqfM2zQt524EQad+h3OL+7fT62+1v+ugHEZkpIveJiCswE3MGy4G6IuISkaLAbcDn2WQ+\nB+4CEJFWwGFV3aOqg1W1uqrWBG4HFqjqXXnQwRLmrF8PrVqZl+lPPoGSJZ3WqACpW9cEU0RopHMw\nOa/IeYzpGNnptaMVf47m5sDDmOmdUSKyXEReE5HrRCTHZQOqmgb0A+ZhciR9oKrrRKSPiPRxy8wG\ntojIJmAccL+v2+XqU1kigm++gauugqeeMvVqwiYQrSAZPBi+/x5ieI7aF53qdaJe+Xph73SOOXwN\nIbJvQFHgGuBlYBnwZaDXBmvDTh95ZeHChU6rkCNvvqlaubJqcnLo2nSsXz75RLVRI9XTp51pPwec\nfF42H9ys5V8sr9sOb3NMB29EwncoP+Bn+ijH3Ee+EJFqqrqjYExT3rBxCpFHejo88oiJP5g1C+rU\ncVqjEKAKHTvCddfBQBvRm53hycP5dd+vfNztY6dViRn8xSnk2SiEA9YoRBZHj5qI5FOn4KOPIC7O\naY1CyMaNcMUV8MsvcP75TmsTVvyZ+ieN32jMW53e4rra1zmtTkyQ34R4Fku+SUmB1q2henWYMyfG\nDAJAvXrW6eyDSE2vHa34NQru3EMjQ6WMJTr54Qfzkvx//2dSVhQp4rRGDmGdzj65of4N1Ctfj9eW\nvOa0KjGPX6OgqulAG5GwTUFmCXOmTYPOnWH8eHjooTBOZhcKSpQwkc4PPBARkc6hZnTiaEYuDu9I\n51ggkOmjlcBMEblTRG52bzcFWzFL3gmHEP2MDBg2zLwcL1gQHgntwqFfuPFGE649dqzTmmQRFv2C\niXTu16Ifj3z1iNOqhE2fOEEgRqEYcBC4Gujk3m4IplKW/OH0A/3nn8ah/PXXpiBOkyaOqpOF0/0C\nmKHS2LHw3HNhk147LPrFzeOtH+enXT/x9eavHdUjnPok1OSY+0hVe4ZAD0uUsGcPdOkCtWubJKHF\nijmtURhSr57J+jdokMn8Z8ki0+ncb04/frnvF5te2wFyHCmISH0RmS8ia9z7F4nIkOCrZok0Vq2C\nli3NVNH771uD4JennoLvvrNOZy9Yp7OzBDJ9NB4YDJx2768GugdNI0tE8sUX0L69SX09bFiMO5QD\nwTqd/ZLpdN5+ZHvOwpYCJRCjUFxNARzAHRsN9im2ACZY95VXzBL8WbPgttuc1iiCCEOnc7iQ6XQe\n+JWNAA81gRiFP0QkKxmBiNyCSWdtCVO85VoPBqdPm6nxd981NRBatgxJs3kmVP0SMGHidA67fnHj\npNM5XPskFOSY5kJEagNvA5cDhzE1Du5Q1ZSga5cDNs2Fcxw8CLfcYmZBpk2DUqWc1iiCGTwYtm2z\nTmcvfLHhCwZ9PYhf+v5C0cJFnVYnashvmosMVb0GqAQ0UNXW2JrJMc3GjaYGwqWXwmefWYOQb6zT\n2Sc31L+BuuXr8toP1ukcKgIxCp8CqOpxVT3qPmbTGcYoCxZA27ZmNeXIkVC4sNMaRQElSsCrr1qn\nsw9GJ47m5cUvW6dziPBpFESkoYjcDJQRkZsyI5lFpCcmoM0SY4wfb4LSpk+He+5xWpso46abrNPZ\nB9bpHFp8+hREpAtwIyZ62bOM5jFghqouDr56/rE+hdCQng6PPWaWnc6aZWKvLEHAptf2SWZ67bdv\neJv2tdo7rU7EkyefgqrOdEcz36CqvTy2/uFgECy+KcgQ/fR0uOMOWLHCrDCKZIMQ9qkLPCOdQ0jY\n9wsekc6z+3E6/XTOF+STSOiTYBGIT+FnEeknIm+IyCQRmSgiE4OumSXPFNQDnZFh0l0fOGBqIJQr\nVyC3dYyI+KI/9RQsWhRSp3NE9AvG6VynXJ2QOJ0jpU+CQSBG4T2gMpAIJAPVgeOB3FxEEkVkvYj8\nJiKP+5AZ4z6/SkQucR8rJiJLRWSliKwVkecD+jSWAkMV+veH334zK4xsyooQkRnp3K+fdTp7wTqd\ng08gRqGOqg4FjqvqFCAJyDFMSUQKA//BGJNGQHcRaZhNJsl9/7rAvcCbAKr6F9BOVZsCFwHtRKRN\n4B/Lkh9U4YknzHTRl1+a3ylLCLnpJuNTsE7ns6hdrjYPXPZAWKTXjlYCMQqZE3hHRKQJUBaoGMB1\nLYBNqpqiqqnADKBLNpnOwBQAdyqNsiJS2b1/0i1TFCiMSd9tCQEjRsDs2TBvHpQp47Q2MUiYRDqH\nK0+0eYLlu5bzzZZvnFYlKgkoIZ6IlAOGYFYhrQVeCuC6eMBzjLfDfSwnmWqQVQp0JbAXWKiqawNo\n05JPXn0V3nvP1EIoX95pbWKY+vXNut8QO50jgfOKnMeoxFEhczrHGoHUUxjv/vN/QM1c3DvQtaLZ\nl0Wpu910oKmIlAHmiUiCqiZnv9gzR4nL5cLlcpGQkOA1d0lycrJXB1K0yWf+ndv7P/JIMhMmJNOr\nF7z1lnP6B0s++zVO65OjvAjMmkXCqFEkPPxw0PQpW7bsWccK8v7BkFdVMn7N4PoV1/PUXU8V+P1T\nUlIYPnx40PQPtXzm+UAIJPfRZmAJsAhYpKprArqxSCtguKomuvefxKTMeNFD5i0gWVVnuPfXA1ep\n6t5s9xoK/KmqI7Mdt3EKBcTUqcaPkJwMderkKG4JFR9/DMOHw88/Q5EiTmsTVmw+uJmW77Rk5X0r\nqVa6mtPqRBT5zX3UGJMQrzwwUkS2iMhnAVy3HKgrIi4RKQrcxplBcLj373Ir2Qo4rKp7RaSCiJR1\nHz8PuBb4OYA2LXng00/h0UeND8EahDDj5puN0/k//3Fak7DDOp2DQyBGIQ1TPyEdyAD2Yeb5/aKq\naUA/YB7GD/GBqq4TkT4i0sctMxvYIiKbgHHA/e7LqwIL3D6FpcAXqjo/V5/MEhBz58J99xnHcuPG\nTmtjOYtMp/O//w27bcb67DzR5gmW7Vxmnc4FSCDTRycx1dZeBear6v5QKBYIdvoof/zvfyb99cyZ\nJruCJYx58knYvt3M81nO4PMNn/P4N4+z6r5VNr12gPibPgrEKHQB2gKXYUYMi4FvVdVx02yNQt5Z\nuhQ6dYIZM+Caa5zWxpIjJ05Aw4ZmadhVVzmtTVihqnSa3omrLriKx1o/5rQ6EUG+fAruHEiPAn2A\n2UBPYFaBamgpUHJaZbBqFXTuDJMnx5ZBiOjUBZnptYMQ6RzR/YL5gRuTOIaXvn+JHUd3FMg9I71P\n8kOORkFEPnGvQBoDFAfuBOKCrZgl7/h7oNevh44djd/y+utDp1M4EPFf9JtvhipVCtzpHPH9gnE6\n33/Z/QXmdI6GPskrgTiaXwDqq+p1qjpCVf+nqn8GWzFLwbNlC1x7LTz/PNx6q9PaWHKNdTr7xTqd\nC4ZApo9+dK8kskQwO3ZA+/bGX3n33U5rY8kzDRqY1LU20vksihcpzqgOo3hwzoM20jkfBDJSsEQ4\n+/YZg9C3L9x/f87yljBnyBD49luzWc6gc/3O1Iqrxaglo5xWJWKxRiHKOXjQTBnddpt9uYwaSpaE\nV16xNZ29ICKMThxdoE7nWCMQR3MzEbk021ZbRHLMm2RxhsxcJ8eOGady+/YmU0Ks4y1fTMRyyy0F\n5nSOqn4B6pSrk2+nc7T1SW4IJE5hCdAM+MV9qAmwBigD9FXVeUHV0L9uNk7BBydPGoPQsCG8+abx\nUVqijPXroU0bWL0aqlZ1Wpuw4mTqSRq/0Zh3bniHa2rF0LrrAMlv7qNdQFNVbaaqzYCmwBZMPqJA\nUmhbQsypU2b1Yo0a8MYb1iBELdbp7JNMp3O/OTa9dm4JxCjU98yM6q5r0EBVNxN4emxLiEhLg+7d\n4bzzYNIkKGS9RtHNkCEmX4l1Op+FdTrnjUB+MtaIyJsicpWIJIjIG8BaETkXk/bCEkYMGmSmjqZP\nh3Os1yf6KVnSRDpbp/NZWKdz3gjEp1Ack720tfvQ98AbwF9ACVU9FlQN/etmfQoeLFwIPXrAL7/Y\nqmkxhSpcd50JUfdSjCfWGbZwGBsPbGTGLTOcViVsyK9PoaGqjlTVG93bSOBqVc1w0iBYzuTYMfjn\nP+Htt2H16mSn1QlLojZ1QWak84gReYp0jtp+cfNEmydYunMp87cEnn0/2vvEH4HWaG6SuSMi3YFh\nwVPJkhceecQkt7v++th+oP0R1f2S6XR+LPdZQqO6X8hbpHO094k/AjEKtwBTRKSBiNyDmUq6Nrhq\nWXLDnDnw1VdmatkSwwwZYuqpWqfzWXSu3xlXWRejl4x2WpWwJ5DcR1uA7sB/gZuBDqp6JNiKWQLj\n4EG45x6z0qh0aae1sTiKdTr7REQY03EML37/onU654BPoyAiqzM34GOgHFATWCoiv/i6zhJaHnzQ\nxCS0a+e0Jpaw4JZboHJleP11pzUJO+qUq0Pf5n159KtHnVYlrPG3aPGGkGlhyRMffww//ggrVzqt\niSVsEDGpL9q2NQmvbKTzGTzZ9kkav9GYBVsXcHXNq51WJyzxOVJQ1RR/W6ANiEiiiKwXkd9E5HEf\nMmPc51eJyCXuY9VFZKGIrBGRX0Wkf64/XRSzd68pwjVlChQvfua5WM7b4o+Y6ZcGDcxStACdzjHT\nLxin82sdXqPfbP+RzrHUJ9nJMU4hXzcXKQxsANoDO4Efge6qus5DJgnop6pJItISGK2qrUSkClBF\nVVeKSEngJ6BrtmtjMk5BFW680eQ1ev55p7WxhCXHj5sH5P334corndYmrFBVrp92Pe1c7RjUOjZT\nhOQ3TiE/tAA2uUcXqcAMoEs2mc7AFABVXQqUFZHKqrpHVVe6jx8H1gHnB1nfiOC990wVNZv51OKT\nzPTa/fqZ3CeWLKzT2T/BNgrxwHaP/R3uYznJVPMUEBEXcAmwtMA1jDC2b4dHH4V334Vzz3VaG0tY\nc+utUKmSdTp7wTqdfRPs7DiBzu1kH8ZkXeeeOvoYeMg9YjgDz7k/l8uFy+UiISHB65xgcnKy16CU\nSJFfuDCZ//u/ZC68ED77zGyRpL+VD7F8ZqSz2+mcvH59ZOkfZPnsTmen9QmmfOb5QAi2T6EVMFxV\nE937TwIZqvqih8xbQLKqznDvrweuUtW9IlIEmAXMUdWzUh3Gmk/hrbdg4kRYvNgmu7PkgscfN+kv\n3n3XaU3Cjs/Wf8bg+YNZed9KihYu6rQ6IcNJn8JyoK6IuESkKHAb8Hk2mc+BuyDLiBx2GwQBJgBr\nvRmEWGPzZhg61HyvczIIsRyi74+Y7ZehQ022xEWLvJ6O2X4ButTvgqusizFLx5xxPJb7JKhGQVXT\ngH7APGAt8IGqrhORPiLSxy0zG9giIpuAcZg0GmCysvYA2onIz+4tMZj6hivp6dCzJwwebFYb5kQs\nP9D+iNl+8azp7MXpHLP9wt9O5xe+e4GdR3dmHY/lPgl6CRZVnaOq9VW1jqo+7z42TlXHecj0c5+/\nWFVXuI99p6qFVLWpql7i3uYGW99wZNQoUyznoYec1sQSsVins0+ynM5fW6czhMAoWPLH2rUmFsFW\nUbPkC8/02nv2OK1N2PFk2yf5YfsPLNy60GlVHMf+zIQxqalw113w739DrVpOa2OJeBo2zFWkcyxR\nvEhxRiWO4oHZD5CaHtvJBK1RCGOefx4qVIB773VaE0vUkIPTOZbpUr8LF5S9gNFLYzu9tjUKYcqK\nFSav2YQJZuSfG2I5b4s/bL/g1els+8UgIoxJNE7nxpc1dlodxwhqnEKwidY4hVOnoFkzeOIJU3PZ\nYilQVKF9e+jSBfrbPJPZGbJgCJsPbWb6zdOdViVo+ItTsEYhDBk4EFJS4JNPcj9KsFgCYt06kyhv\n9WqoUsVpbcKKk6knafR6IyZ1mUS7mtFZqMTJ4DVLLnnjDZg1C95+2xoESxBp2BB69TLRzpYzKF6k\nOOM6jSMtIzYTCdqRQhjx6aemktqiRXa1kSUEZKbXnj4d2rRxWhtLCLEjhQjgu+/gvvvgiy+sQbCE\niJIlYeRIn5HOltjEGoUwYO1aU2f5/ffh0kvzf79YDtH3h+0XL3TrRnLhwmbe0pJFLD8r1ig4zI4d\n0LGjeWG79tqCuWcsP9D+sP3iBRGSL78cnn3W1Hi1ALH9rFij4CCHDxuDcP/9cOedTmtjiVkqVjRO\nZxvpbMEaBcc4dcrUWW7Xzn4XLWHA0KGwYIFxblliGmsUHCAjw+Q0qlABXnvNLj21hAGlSlmnswWw\nRiHkqMIjj5hEle+9B4ULO62RxeKmWzfzpmKdzjGNNQoh5pVX4OuvTX3lYsWC04bNZeMd2y/eyeoX\nEZNwyzqdY/pZscFrIWTaNJPPaPFiqFbNaW0sFh889pgxClOmOK2JJUjY3EdhwPz58I9/mH8vvNBp\nbSwWPxw7ZiKdZ8ywkc5RiqMRzSKSKCLrReQ3EfGaaEVExrjPrxKRSzyOTxSRvSKyOth6BpOVK6F7\nd/joI2sQLBFAqVJ+azpbopugGgURKQz8B0gEGgHdRaRhNpkkoI6q1gXuBd70OD3JfW3EkpIC119v\nfDwvDHEAAA28SURBVHdXXum0NhZLgGQ6nd98M2dZS1QR7JFCC2CTqqaoaiowA+iSTaYzMAVAVZcC\nZUWkint/EXAoyDoGjQMHIDHR+BFuucVpbSyWXJDpdH7mmZh3OscawTYK8cB2j/0d7mO5lYk4Tp6E\nG26Arl1N5tNQEssh+v6w/eIdn/0Sw+m1Y/lZCbZRCNQLnN3hERneYx+kpRkfQp06ps5yqInlB9of\ntl+847dfhg6Fb76B778PmT7hQCw/K+cE+f47geoe+9UxIwF/MtXcxwLCcz2xy+XC5XKRkJDgdZ1x\ncnKy1//sgpRfuDCZWbNMXqN//AP+9a+Cvb+Vt/IFLZ+SknLWsTPkW7UyaXzvvRcKFQo7/YMhn5yc\nzPDhw8NGn/zKZ54PCFUN2oYxOpsBF1AUWAk0zCaTBMx2/90KWJLtvAtY7eP+Gm4884zqpZeqHj3q\nnA5PP/20c42HMbZfvJNjv2RkqF59teqYMSHRJxyI9mfF/dvp9Xc7qNNHqpoG9APmAWuBD1R1nYj0\nEZE+bpnZwBYR2QSMA+7PvF5EpgOLgXoisl1EegVT3/wyYQJMngxffmlW9VksUYEIjB1rnc4xQrCn\nj1DVOcCcbMfGZdvv5+Pa7kFUrUD58ksYMgT+9z9bB90ShTRqBD17Gqfz5MlOa2MJIjb3UQGwdKlZ\npPHZZ1CvntPaxHbeFn/YfvFOwP0ybFjMOJ1j+VmxaS7ygSpMnGjiECZNgk6dHFPFYgkNH3xgltQt\nXw7nBH2iwRIkHE1zEa1s3WrKZ775pnl5sgbBEhN06wbly8NbbzmtiSVIWKOQS9LTYfRouOwyuO46\nWLIELr7Yaa0slhCR6XT+17+s0zlKsdNHuWDdOujd24ya33knPPwHFosjDBoE+/ebeVNLxGGnj/JJ\nair8+98mod2dd0JysjUIlhhn2DBTLWrxYqc1sRQw1ijkwIoVZqrou+/gp5+gb18oFOa9Fssh+v6w\n/eKdPPVLlNd0juVnJcx/3pzjzz/NqqKOHU1N5dmzoUYNp7UKjFh+oP1h+8U7ee6X226DuLiodDrH\n8rNijYIXvvsOmjaFLVvgl1/MlJF4nX2zWGKYzPTa1ukcVVij4MGxY9Cvn3kBeuEF+PBDqFzZaa0s\nljAmM9L5iSec1sRSQFij4GbePGjSxNRB+PVXuPFGpzWyWCIE63SOKmI+JPHgQRg40OQsGj/eBKRZ\nLJZc4Ol0/vFHG+kc4cT0SOGTT+DCC6F0aVi9OnoMQiznbfGH7RfvFEi/RJnTOZaflZgMXtuzx7zU\nrFlj0l23bh0E5SyWWGPNGkhIMP9WquS0NhY/2OA1N6om6+9FF0GDBrBypTUIFkuB0bgx3H13TNZ0\njiZiZqSwbRv06WNWzk2cCJdcEmTlLJZY5NgxaNjQLN274gqntbH4IKZHChkZZil1s2Zw1VWwbJk1\nCBZL0ChVCl5+OWojnWOBqB4pbNhgEtipGt9BgwYhVM5iiVVU4eqr4eabTeCPJeyIuZFCaqoJPmvd\n2iyKWLQotgxCLIfo+8P2i3cKvF88I5337SvYe4eIWH5WgmoURCRRRNaLyG8i4tX7JCJj3OdXicgl\nubnWGytXQsuWsGCBKQ714IPhn8CuoInlB9oftl+8E5R+iXCncyw/K0H7uRSRwsB/gESgEdBdRBpm\nk0kC6qhqXeBe4M1Ar83OX3/BU0+Zwjf9+5sIZZeroD9VZJCSkuK0CmGJ7RfvBK1fnn4avvoqIiOd\nY/lZCeY7dAtgk6qmqGoqMAPokk2mMzAFQFWXAmVFpEqA12axeLFxHq9bB6tWmVQssZzALpYfaH/Y\nfvFO0PrFM9I5PT04bQSJWH5WgmkU4oHtHvs73McCkTk/gGsBeOghuOUWePZZ+PRTqFo133pbLJaC\n4vbboUyZqIl0jgWCmaQk0GVN+XqnP3zYpKgoXz4/d7FYLEFBBF5/3UQ633qrjXSOAIJpFHYC1T32\nq2Pe+P3JVHPLFAngWgDefVd499186xp1SCzPn/nB9ot3QtIvEZaHPlaflWAaheVAXRFxAbuA24Du\n2WQ+B/oBM0SkFXBYVfeKyIEArvW5ztZisVgseSNoRkFV00SkHzAPKAxMUNV1ItLHfX6cqs4WkSQR\n2QScAHr5uzZYulosFovFENERzRaLxWIpWMI2rMuJwLdIIJ/9kiIiv4jI/7d3/jFyVVUc/3xrgUJr\nDTX4IyG2pcZCTY38aIilCGI0aEEiVqMWMUAa1Kg1lkRNQGOsCQZj/EOlUKytAWpEC9QIMVjA1krd\nlG3ZdRNUmlJjAVNJxZamUuPxj3Nm9nWY6c7s7O7MvD2f5Gbuu++d9+49e/ed++Pdc3dJ6pu4XI8v\nI+lE0tmSnpB0VNKqVmR7mTb1Usq6Ak3pZXn87wxI2i7pHc3KlgIz67qADxk9A8zBJ513A+fUXPNB\n4KGIXwjsaFa2V0M7eonjvcCsTpejAzo5A7gAWA2sakW2V0M7eilrXWlBL+8CXhfxyyfDu6UYurWn\nMGEL33qM0eql+NlH2SbnR9SJmR0ws53AsVZle5h29FKhbHUFmtPLE2b2Uhz+Ef8qsinZMtCtRmFC\nFr71IO3oBXztyG8l7ZS0YtxyObE0o5PxkO122i1bGesKtK6XG4CHRinbk3TrDtsTsvCtB2lXL0vM\n7DlJZwCPSHrazLaNUd46RTtfSpT5K4t2y3aRmT1fsroCLehF0nuA64HK/oxlri9VurWn0M7Ct2Zk\ne5XR6mU/gJk9F78HgPvx7nCv087fe7LXlYaY2fPxW6a6Ak3qJSaX1wIfMrODrcj2Ot1qFKoL3ySd\njC9e21xzzWbgWoDiwrcmZXuVUetF0mmSXhvp04H3A4MTl/Vxo5W/d20ParLXlQrH6aXEdQWa0Iuk\ntwCbgGvM7JlWZEtBp2e6GwXgA8Cf8dn+r0XajcCNhWt+EOefAs47kWxZwmj1ApyFfy2xG/hTmfQy\nkk6AN+FjwS8BB4G/ATMme11ppJcy15Um9XIX8CKwK0LfiWTLFnLxWpIkSVKlW4ePkiRJkg6QRiFJ\nkiSpkkYhSZIkqZJGIUmSJKmSRiFJkiSpkkYhSZIkqZJGIekYsQhowhdFSbpK0jljdK+dkk6qSXtW\n0qwxuv/hsbhPkjRLGoVkMvJhYEErApJeUydtLrDf3GNmkbFc/POqe0nqVp9lSQlIo5B0BZLOktQv\n6fxws/BzSUOSNknaIen8musXSfplxK+SdETSVEnTJO2J9BWS+iTtlvQLSadKWgxcCdwWG8jMlTRP\n0sPR6t8qaX7Ir5e0RtIO4Dt1sn058PAJynRq3PeGOL4lNmjZJune2o1t4pq5sfHNgKTVhfRLQ+5B\nYEjSNyWtLJz/tqQv1txruqRfR/kHJX0s0t8buh6Q9ONw2VDJX19ce0fhPo9L+n7oa1DSokZlTkpA\np5dUZ5i8Ad+sZBCYD/QDCyP9JuD2iL8d9/d/Xo3sVGBPxL+L+71fDFwC3BPpswrXfwv4fMR/Alxd\nOLcFeGvELwS2RHw97ttGDfL/ADCnTvpeYDbwCO4/B2AR7jLhZNyVxF+AL9eR3VyQ+RxwKOKXAoeB\n2XE8G3gy4lNwtwun19zrI8CdheOZwDTcnUWlvBuAlRE/vXDtT4ErIv4YcEfELwYGO113MoxfyJ5C\n0mnegL9cP2lmlfmFi/ANTDCzIWCgVsjM/gvskXQ2/sL9HvBuYAlQcfG8MFrXA8Byjh8yEoCkGfhO\nW/dJ2gWswX0CgQ/d3GfxNiwSreszzezZOmUS8CCwzszuLpTpATN7xcwOA7+q5KGGxcDGiN9dc67P\nzPZF+fcBL0p6J+6wrt+GvXlWGADeJ+lWSUvM7N+4Ad5rw47eNuB6A7gsemUDwGUcr6+N8dxtwExJ\nM+vkPSkBOTaZdJp/AfvwFujThfRm9srYim8/egxv7W/AW803xfn1uOvjQUmfxlvbFSov+im4J9lz\nqc+RBukXM2x8ajHg97jztI2FtGKZRrMXyMs1x3cB1wFvBNa9KhNmf5Xv0b0UWC1pC26silSM4ynA\nj/Ae2X5J38B7FY1Ip2klJXsKSad5BbgauFbSJyJtO1AZ/14ALGwguw34EvAHM/sn8HpgfvQuwIdp\nXoivg65h+EV2CB9KIVrPeyUti+dJhY3aT8AJ5xOArwMHJf2wUKYrJZ0SvZOl1H+xbgc+HvHlI+Th\n/sjHBcBvak9KejNw1MzuwYfYzsU9fM6RNC8u+xTwOG4ADO99zAA+WrwV7iYaSUtwI3pohLwlPUr2\nFJJOY2Z2RNIV+A5fh/AW6wZJQ3jvYQh371xLHz78tDWOn8JbzRVuwecaDsTvjEj/GbBW0heAZfjL\n93ZJN+Mbsm9keMiqUYv4EuDmRmWKgq2UtE7SrWb2VUmb477/wOdS6pVpJXCvpK/grfri84/Li5kd\nk/QocLDeEBduTG+T9D+8N/UZM/uPpOvw4bKpuA7XxL3W4q6yX8D1VXzuUUn9+Dvj+gblTkpAus5O\nug5JU4CT4gU2D5+wfVvMI3QcSWfiE69LW5SbbmYvSzoN+B2wwsx2t5GPKcCTwDIz2zPa+zTxnMeA\nVWbWP17PSLqH7Ckk3ch04NEY9hHw2W4xCABm9nd8+KdV7ozhsGnA+jYNwgJ8snrTeBqEZPKRPYUk\nSZKkSk40J0mSJFXSKCRJkiRV0igkSZIkVdIoJEmSJFXSKCRJkiRV0igkSZIkVf4PB798eo0nwAQA\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7a26828>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Conditions corresponding to First Operation \n",
"\n",
"X = kg water/kg dry soap\n",
"0.149425287356\n",
"Y = kg water/kg dry air\n",
"0.0586080045715\n",
"Final moisture content of soap is 9.338 %\n",
"\n",
"\n",
" Illustration 5.2 (b)\n",
"\n",
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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kzes0Hj/yCOTJ4/v4E9c9+Gj1RwxrNoyih4ry3tvv+ez4aZWPiYlJmjo72P5/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\nxUkNBVmMbwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7c30128>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Minimum amount of air required is 2.2941 cubic m/kg dry soap\n",
"\n",
"\n",
"Illustration 5.2 (c)\n",
"\n",
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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GpENkJEyYAB9/DDfe6Iw8fvJJCMo6Tdp+7+CZg/Re1Ju5u+fSL6QfL9V4KcOW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\nVkkn5tJ2S5o0TPkfGo58gqBWwigEo5EvATPqEZB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"text": [
"<matplotlib.figure.Figure at 0x7e5c940>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Moisture content of air leaving the drier is 0.0542 kg water/kg dry air\n",
"\n",
"Total number of eqb. stages = 3\n"
]
}
],
"prompt_number": 1
}
],
"metadata": {}
}
]
}
|