diff options
Diffstat (limited to 'backup/Mass_-_Transfer_Operations_version_backup/Chapter13.ipynb')
| -rwxr-xr-x | backup/Mass_-_Transfer_Operations_version_backup/Chapter13.ipynb | 443 |
1 files changed, 443 insertions, 0 deletions
diff --git a/backup/Mass_-_Transfer_Operations_version_backup/Chapter13.ipynb b/backup/Mass_-_Transfer_Operations_version_backup/Chapter13.ipynb new file mode 100755 index 00000000..225bff8b --- /dev/null +++ b/backup/Mass_-_Transfer_Operations_version_backup/Chapter13.ipynb @@ -0,0 +1,443 @@ +{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:b841fc17f95d9198998020044b36e625586e7f0a22a266fb56d5482aed19bd4a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 13: Leaching"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.1: Page 722"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.1\n",
+ "# Page: 722\n",
+ "\n",
+ "print'Illustration 13.1 - Page: 722\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import numpy as np\n",
+ "import math\n",
+ "#***Data***#\n",
+ "Density_L = 1137.0;# [kg/cubic m]\n",
+ "Density_S = 960.0;# [kg/cubic m]\n",
+ "Density_p = 1762.0;# [kg/cubic m]\n",
+ "A_prime = 16.4;# [square m/kg]\n",
+ "g = 9.81;# [square m/s]\n",
+ "sigma = 0.066;# [N/m]\n",
+ "Z = 3.0;# [m]\n",
+ "dia = 1.0;# [m]\n",
+ "#**********#\n",
+ "\n",
+ "e = 1-(Density_S/Density_p);# [fraction void]\n",
+ "ap = A_prime*Density_S;# [square m/cubic m]\n",
+ "# By Eqn. 6.67:\n",
+ "dp = 6*(1-e)/ap;# [m]\n",
+ "# By Eqn. 13.6:\n",
+ "K = dp**2*e**3.0*g/(150.0*(1-e)**2);# [cubic m/s]\n",
+ "check = K*Density_L*g/(g*sigma);\n",
+ "if (check<0.02):\n",
+ " # By Eqn. 13.3: \n",
+ " So = 0.075;\n",
+ "else:\n",
+ " # By Eqn. 13.4:\n",
+ " So = 0.0018/(check)\n",
+ "\n",
+ "# By Eqn. 13.2:\n",
+ "ZD = (0.275/g)/((K/g)**0.5*(Density_L/sigma));# [m]\n",
+ "# By Eqn. 13.1:\n",
+ "Sav = ((Z-ZD)*So/Z)+(ZD/Z);\n",
+ "# VolRatio=Vol liquid retained/Vol bed.\n",
+ "VolRatio = Sav*e;\n",
+ "print\"Vol liquid retained/Vol bed : \",round(VolRatio,4),\" cubic m/cubic m\\n\"\n",
+ "Mass = VolRatio*math.pi*dia**2*Z*Density_L/4;# [kg]\n",
+ "# Mass ratio=Mass Liquid/Mass dry solid\n",
+ "MassRatio = VolRatio*Density_L/(Density_S);\n",
+ "print\"Mass liquid/Mass dry solid: \",round(MassRatio,4),\" kg/kg\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.1 - Page: 722\n",
+ "\n",
+ "\n",
+ "Vol liquid retained/Vol bed : 0.058 cubic m/cubic m\n",
+ "\n",
+ "Mass liquid/Mass dry solid: 0.0687 kg/kg\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.2: Page 749"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.2\n",
+ "# Page: 749\n",
+ "\n",
+ "print'Illustration 13.2 - Page: 749\\n\\n'\n",
+ "\n",
+ "# Solution \n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#***Data***#\n",
+ "# Eqb=[x(Wt fraction NaOH in clear solution) N(kg CaCO3/kg soln in settled sludge) y*(wt fraction NaOH in soln of settled sludge)]\n",
+ "# a=H2O b=CaCO3 c=NaOH\n",
+ "Eqb = np.array([[0.090 ,0.495, 0.0917],[0.0700, 0.525, 0.0762],[0.0473, 0.568, 0.0608],[0.0330, 0.600, 0.0452],[0.0208, 0.620, 0.0295],[0.01187 ,0.650, 0.0204],[0.00710, 0.659, 0.01435],[0.00450, 0.666, 0.01015]]);\n",
+ "def f80(x):\n",
+ " return x\n",
+ "x = np.arange(0,0.12,0.01);\n",
+ "Mass_c = 0.1;# [kg]\n",
+ "Mass_b = 0.125;# [kg]\n",
+ "Mass_a = 0.9;# [kg]\n",
+ "#**************#\n",
+ "\n",
+ "\n",
+ "plt.plot(x,f80(x),label=\"N Vs x\")\n",
+ "plt.plot(Eqb[:,2],Eqb[:,1],label=\"N Vs Y\");\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x,y Wt. fraction of NaOH in loquid\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.legend('upper left');\n",
+ "plt.title(\"Equilibrium Plot\")\n",
+ "plt.show()\n",
+ "# Basis: 1 kg soln in original mixture.\n",
+ "# As in Fig. 13.27 (Pg 750)\n",
+ "# The original mixture corresponds to M1:\n",
+ "NM1 = 0.125;# [kg CaCO3/kg soln]\n",
+ "yM1 = 0.1;# [kg NaOH/kg solution]\n",
+ "# The tie line through M1 is drawn. At point E1 representing the settled sludge:\n",
+ "N1 = 0.47;# [kg CaCO3/kg soln]\n",
+ "y1 = 0.100;# [kg NaOH/kg solution]\n",
+ "E1 = Mass_b/N1;# [kg soln. in sludge]\n",
+ "Ro = 1-E1;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 2:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M2 = E1+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM2 = Mass_b/(E1+Ro);# [kg CaCO3/kg soln]\n",
+ "# M2 is located on line RoE1. At this value of N, and the tie line through M2 is drawn. At E2:\n",
+ "N2 = 0.62;# [kg CaCO3/kg soln]\n",
+ "y2 = 0.035;# [kg NaOH/kg solution]\n",
+ "E2 = Mass_b/N2;# [kg soln. in sludge]\n",
+ "Ro = 1-E2;# [kg clear soln drawn]\n",
+ "\n",
+ "# Stage 3:\n",
+ "xo = 0;# [kg NaOH/kg soln]\n",
+ "# By Eqn. 13.11:\n",
+ "M3 = E2+Ro;# [kg liquid]\n",
+ "# By Eqn. 13.12:\n",
+ "NM3 = Mass_b/M3;# [kg CaCO3/kg soln]\n",
+ "# Tie line E3R3 is located through M3.At E3:\n",
+ "N3 = 0.662;# [kg CaCO3/kg soln]\n",
+ "y3 = 0.012;# [kg NaOH/kg solution]\n",
+ "# By Eqn. 13.8:\n",
+ "E3 = Mass_b/N3;# [kg soln. in sludge]\n",
+ "print\"The fraction of original NaOH in the slurry: \",round(E3*y3/Mass_c,4),\" \\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.2 - Page: 749\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7c610f0>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "The fraction of original NaOH in the slurry: 0.0227 \n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.3: Page 754"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.3\n",
+ "# Page: 754\n",
+ "\n",
+ "print'Illustration 13.3 - Page: 754\\n\\n'\n",
+ "\n",
+ "# Solution (a)\n",
+ "import numpy as np\n",
+ "from scipy import interp\n",
+ "import matplotlib.pyplot as plt\n",
+ "#***Data***#\n",
+ "# a=H2O b=CaCO3 c=NaOH \n",
+ "mass_c = 400;# [kg/h]\n",
+ "x1 = 0.1;# [wt fraction NaOH in overflow]\n",
+ "#**********#\n",
+ "\n",
+ "Mb = 100.0;# [kg/kmol]\n",
+ "Mc = 40.0;# [kg/kmol]\n",
+ "rate_c = mass_c/Mc;# [kmol/h]\n",
+ "rate_b = rate_c/2;# [kmol/h]\n",
+ "mass_b = rate_b*Mb;# [kg/h]\n",
+ "# After trial calculations:\n",
+ "y3 = 0.01;# [kg NaOH/kg solution]\n",
+ "N3 = 0.666;# [kg CaCO3/kg solution]\n",
+ "E3 = mass_b/N3;# [kg/h]\n",
+ "lost_c = E3*y3;# [kg/h]\n",
+ "sludge_a = E3-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg NaOH/kg solution]\n",
+ "R1 = overflow_c/x1;# [kg overflow/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# For purpose of calculation, it may be imagined that agitators are not present in the flowsheet and the first thickner is fed with the dry mixture of the reaction products, CaCO3 and NaOH, together with overflow from the second thickner.\n",
+ "F = 400.0;# [kg NaOH/h]\n",
+ "NF = mass_b/F;# [kg CaCO3/kg NaOH]\n",
+ "yF = 1.0;# [wt fraction NaOH in dry solid, CaCO3 free basis]\n",
+ "# Points R1, E3, RNpPlus1 and F are plotted as in Fig 13.30 (Pg 755) and locate the point deltaR at the intersection of lines FR1 and E3RNpPlus1 extended. The coordinates of point deltaR are NdeltaR=-0.1419, ydeltaR=-0.00213. Further computation must be done on enlarged section of the equilibrium diagram (Fig 13.31 (Pg 755)). Point deltaR is plotted and the stages stepped off in a usual manner. The construction are projected on the xy diagram. Three stages produce a value: y3=0.001\n",
+ "print\"The NaOH lost in sludge: \",round((lost_c/mass_c)*100,2),\"%\\n\"\n",
+ "print\"\\n\"\n",
+ "\n",
+ "# Solution (b)\n",
+ "#*** Data***#\n",
+ "lost_c = 0.001*mass_c;# [kg/h]\n",
+ "#***********#\n",
+ "\n",
+ "NNp_by_yNp = mass_b/lost_c;# [kg CaCO3/kg NaOH in final sludge]\n",
+ "# In order to determine the liquid content of the final sludge:\n",
+ "# Eqb=[N y_star]\n",
+ "Eqb = np.array([[0.659 , 0.01435],[0.666, 0.01015],[0.677, 0.002],[0.679, 0.001],[0.680 ,0.0005]]);\n",
+ "N_by_ystar = zeros(5);\n",
+ "for i in range(0,5):\n",
+ " N_by_ystar[i] = Eqb[i,0]/(Eqb[i,1]);\n",
+ "\n",
+ "plt.plot(Eqb[:,0],Eqb[:,1]);\n",
+ "plt.grid('on');\n",
+ "plt.xlabel(\"x Wt fraction of NaOH\");\n",
+ "plt.ylabel(\"N kg CaCO3 / kg solution\");\n",
+ "plt.title(\"Equilibrium plot\")\n",
+ "# By Interpolation, for N_by_ystar=NNp_by_yNp:\n",
+ "NNp = interp(NNp_by_yNp,N_by_ystar,Eqb[:,0]);# [kg CaCO3/kg soln]\n",
+ "yNp = NNp/NNp_by_yNp;# [wt fraction NaOH in the liquid of the final sludge]\n",
+ "ENp = mass_b/NNp;# [kg/h]\n",
+ "ENp_a = ENp-lost_c;# [kg/h]\n",
+ "overflow_c = mass_c-lost_c;# [kg/h]\n",
+ "R1 = overflow_c/0.1;# [kg/h]\n",
+ "R1_a = R1-overflow_c;# [kg/h]\n",
+ "RNpPlus1 = R1_a+sludge_a;# [kg/h]\n",
+ "# On the operating diagram (Fig 13.32 (Pg 757)) point deltaR is located and stages were constructed. \n",
+ "# Beyond the fourth stage, the ratio of the overflow to the liquid in the sludge become substantially constant.\n",
+ "R_by_E = RNpPlus1/ENp;\n",
+ "# This is the initial slope of the operating line on the lower part of the figure.\n",
+ "# From Illustration 13.2:\n",
+ "m = 0.01015/0.00450;\n",
+ "Value1 = R_by_E/m;\n",
+ "xNpPlus1 = 0;# [kg NaOH/kg solution]\n",
+ "y4 = 0.007;# [wt fraction NaOH in the liquid]\n",
+ "Value2 = (yNp-(m*xNpPlus1))/(y4-(m*xNpPlus1));\n",
+ "# From Fig 5.16: (Pg 129):\n",
+ "# An Additional 2.3 stages beyond 4 are computed graphically are required.\n",
+ "# An additional two stage will make yNp/y4=0.099:\n",
+ "yNp = 0.099*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,2),\"kg NaOH was lost if 6 thickners were used\\n\"\n",
+ "# An additional three stage will make yNp/y4=0.0365:\n",
+ "yNp = 0.0365*y4;# [wt fraction NaOH in the liquid]\n",
+ "print round(yNp*ENp,3),\"kg NaOH was lost if 7 thickners were used\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.3 - Page: 754\n",
+ "\n",
+ "\n",
+ "The NaOH lost in sludge: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " 1.88 %\n",
+ "\n",
+ "\n",
+ "\n",
+ "0.51"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " kg NaOH was lost if 6 thickners were used\n",
+ "\n",
+ "0.188 kg NaOH was lost if 7 thickners were used\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEZCAYAAABFFVgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XecHVX9//HXm4QgfQkWugEBFURCSwJSgoCGIEGp36Bo\nQCEaQ/mKSpMiFkCKEhANaEhASVCkfqWXRQQJJLKhhRIhPwho6EiTEPL5/XFmc2+uW+5u7tyy9/18\nPO4jOzNnZj73w7Bn55wzZxQRmJmZ5WGZWgdgZmZ9lysZMzPLjSsZMzPLjSsZMzPLjSsZMzPLjSsZ\nMzPLjSsZsxKS1pP0hiRly62Svp79/GVJNxWVXSRpgx4ce4n9a0XSoCx2/w6wXPkCs4Ylaa6kt7MK\nof0zYWmPGxHPRMTKUXiILLIPEfH7iPj8Uhx7qfavBUmnSLq01nFYY+pf6wDMlkIAX4iI22sdSDkk\n9YuI92sdh1k1+U7G+iRJy0g6S9KLkv4h6dvFzUPZXdAuReUX/7XeVVOSpDGS7ipZvUd2jhcl/ayo\nmW2MpLslnSPpJeCU4v07Ok9J01zx/q9KmiNpO0kHS3pG0nxJX+0iB62STpM0XdLrkq6WtFonZdeS\ndK2klyU9Kekb2foRwHHAAdmd4gPl5N+snSsZa3TqZP1hwB7AYGBrYF+yJq9MdLDcW18EtgK2BPYC\nDinaNgT4B/Bh4CdlHKs0riHALGAgMBX4Q3aejwFfAc6XtEIXxzsIOBhYE1gIdNacOA14Jiu3L/BT\nSTtHxI3AT4FpWRPiFmV8B7PFXMlYIxNwdfZXfvvn69m2/YGfR8RzEfEq6RdlZxUS3WzrzhkR8VpE\nPAv8AhhdtO35iPhlRCyKiP/04thPR8SUrH/oD8BawKkR8V5E3AIsADbsZN8ALomIRyPibeBEYP/2\nO612ktYFtgOOiYgFETEL+A3Qfpckli4/1sTcJ2ONLIC9OumTWRN4tmj5mRzjKD3PWp1s6435RT+/\nAxARL5asW6kHsS0LfLCkzFrAKxHxVknZrXscrVkJ38lYX/VPYL2i5fVKtr8FrFi0vMZSnKv0PM8V\nLXfVDNf+S724uWtp4uhIaWzvAS+VlHkeGChppZKy87KfPVW79ZorGWt0nTXj/AE4QtLaWWf3sSz5\ny7IN+B9J/SVtDexD73+ZfldSS9bsdARweTk7ZXckzwEHSeon6RBSX0ulCPiKpE9m/TanAn8sGprd\nHsezwD3AaZKWk/RpUr/S77Ii/wIGlTazmZXDlYw1uutKnpP5U7b+IuAmUqf5DOBPLFkhnUj6hf4q\ncArw+5LjdlbhlHbMA1wDzAQeAP4P+G0XZUvXHQp8j3R3sQlwdzfn6klFGMClwGTSnd0AUiXY0bFG\nA4NIdzVXAicVNUP+Mfv3ZUkzenB+M5TnS8uy4Y+/APoBv4mIMzooMwHYHXgbGBMRD2TrJ5FGB70Q\nEZuV7HM4MA54H/hzRByT25ewPkHSIOApoH9ELKptNNUh6Q7g0oiYVOtYrHnldicjqR9wPjCC9Bfa\naEmfLCkzEtgwIjYiDTn9VdHmi7N9S4+7MzAK+HREfAo4K59vYNYnuInLairP5rIhwJyImBsR75HG\n4e9VUmYUMAUgIqYDLZLWyJbvIjVllPoWcFp2zNKRNmZdacYO7Gb8zlZH8qxk1mbJ4ZPzsnU9LVNq\nI2BHSfdmTzR7mKV1K/tjp1+zNJUBRMTObiqzWsvzOZly/4IqvZ3vbr/+wGoRMUzSNqRRRGXPgmtm\nZtWTZyXzHLBu0fK6FMbdd1ZmHZZ8xqAj80ijX4iI+7O5n1aPiJeLC0lyM4GZWS9ERMX68vJsLpsB\nbJRNAjgAOAC4tqTMtWRTV0gaBrwWEfPp2tXAZ7N9NgYGlFYw7SLCnwhOPvnkmsdQLx/nwrlwLrr+\nVFpulUxELATGk55VeBS4PCJmSxoraWxW5nrgKUlzgImkYckASJpKekBsY0nPSjo42zQJ2EDSQ6QJ\nAzudhdaSuXPn1jqEuuFcFDgXBc5FfnKduywibgBuKFk3sWR5fCf7ju5k/XukmWXNzKzO+Yn/JjBm\nzJhah1A3nIsC56LAuchPrk/815Kk6KvfzcwsL5KIBun4tzrR2tpa6xDqhnNR4FwUOBf5cSVjZma5\ncXOZmZkt5uYyMzNrGK5kmoDbmwuciwLnosC5yI8rGTMzy437ZMzMbDH3yZiZWcNwJdME3N5c4FwU\nOBcFzkV+XMmYmVlu3CdjZmaLuU/GzMwahiuZJuD25gLnosC5KHAu8uNKxszMcuM+GTMzW8x9Mk1s\n0aJaR2Bm1jOuZBrEzJmw1VYwfXrP93V7c4FzUeBcFDgX+cm1kpE0QtJjkp6UdEwnZSZk22dJ2qJo\n/SRJ8yU91Ml+R0taJGlgXvHXky23hO99D774RRg/Hl5/vdYRmZl1L7c+GUn9gMeBXYHngPuB0REx\nu6jMSGB8RIyUNBQ4NyKGZdt2AN4ELomIzUqOvS5wEfBxYKuIeKWD8/fJPplXXoFjj4Xrr4df/AL2\n2QdUsdZTM2t2jdQnMwSYExFzI+I9YBqwV0mZUcAUgIiYDrRIWiNbvgt4tZNjnwN8P5eo69zAgXDh\nhTBtGpx8Muy5J8ydW+uozMw6lmclszbwbNHyvGxdT8ssQdJewLyIeLASQTaq7beHBx6A7baDrbeG\ns86ChQs7Luv25gLnosC5KHAu8pNnJVNuW1XpbVmn+0laATgeOLmL/ZvGgAFw/PFw771w882psunN\nwAAzs7z0z/HYzwHrFi2vS7pT6arMOtm6znwMGATMUuqIWAeYKWlIRLxQWnjMmDEMGjQIgJaWFgYP\nHszw4cOBwl8ufWF5ww3huONaue02+OIXh7PPPjBiRCsrrZS2Dx8+vK7i9XL9LLerl3hqtdy+rl7i\nqeZya2srkydPBlj8+7KS8uz470/q+N8FeB64j647/ocBv2jv+M+2DwKuK+34L9r+NE3W8d8dDwww\ns6XRMB3/EbEQGA/cBDwKXB4RsyWNlTQ2K3M98JSkOcBEYFz7/pKmAvcAG0t6VtLBHZ0mr/gbVUcD\nA6ZNa611WHWj9C/4ZuZcFDgX+cmzuYyIuAG4oWTdxJLl8Z3sO7qM42+wVAH2Ye0DA846C8aOhXnz\n4KijoH+u/8XNzJbkucuawJw5MG4cvPACTJwIQ4fWOiIzq1cN01xm9WPDDeGmm+D73/eMAWZWXa5k\nmkBraysSHHggPPIILFgAm24KV1wBzXaz57b3AueiwLnIjyuZJuMZA8ysmtwn08QWLEgDA845Jw17\n9sAAM6t0n4wrGfPAADNbzB3/1mPdtTc308AAt70XOBcFzkV+XMkYgAcGmFku3FxmHfrrX9NDnOuv\nD+efDzlMaWRmdcjNZVYVPXmVgJlZZ1zJNIHetjcP6IOvEnDbe4FzUeBc5MeVjHWrmQYGmFlluU/G\nesSvEjDr2/ycTJlcyeTLAwPM+iZ3/FuP5dHe3KgDA9z2XuBcFDgX+XElY73WFwcGmFllubnMKiIC\npk6Fo49O/TQ/+QmsumqtozKznnJzmdUlzxhgZh1xJdMEqtneXO+vEnDbe4FzUeBc5KfbSkbSByR9\nWdIJkk7OPieVewJJIyQ9JulJScd0UmZCtn2WpC2K1k+SNF/SQyXlz5Q0Oyt/pSQ3zNSZRh0YYGaV\n1W2fjKSbgNeAmcD77esj4uxuDy71Ax4HdgWeA+4HRkfE7KIyI4HxETFS0lDg3IgYlm3bAXgTuCQi\nNivaZzfgtohYJOn0LJ5jS87tPpk64VcJmDWOSvfJlPOKqrUj4vO9PP4QYE5EzAWQNA3YC5hdVGYU\nMAUgIqZLapG0RkT8KyLukjSo9KARcUvR4nRgn17GZ1XQPmPA1KlpxgAPDDBrHuX0ydwj6dO9PP7a\nwLNFy/OydT0t05VDgOt7FV2TqIf25noZGFAPuagXzkWBc5Gfcu5kdgAOlvQ08G62LiKinIqn3F8h\npbdmZe0n6QRgQURc1tH2MWPGMCh7FL2lpYXBgwczfPhwoHBRebn6yxdeCOed18p3vwuTJw/n/PNh\n7tzqnL9dPeWjVsttbW11FU8tl9va2uoqnmout7a2MnnyZIDFvy8rqZw+mfazthcUQHsTWDf7DgNO\niYgR2fJxwKKIOKOozK+B1oiYli0/BuwUEfOLzn9dcZ9Mtn4McCiwS0T8p4Nzu0+mzi1YkAYEnHNO\nmg/tqKOgfzl/9phZbqr+nExWmbSQ+k72BFYtp4LJzAA2kjRI0gDgAODakjLXAl+FxZXSa+0VTGck\njQC+B+zVUQVjjWGAZwww6/PKGcJ8JPA74EPAR4DfSTqinINHxEJgPHAT8ChweUTMljRW0tiszPXA\nU5LmABOBcUXnngrcA2ws6VlJB2ebzgNWAm6R9ICkC8r7us2ptKmo3lTzVQL1notqci4KnIv8lNM4\n8Q1gaES8BZANGb4XmFDOCSLiBuCGknUTS5bHd7Lv6E7Wb1TOua1xtA8MGDEiNZ1tuqlfJWDWF5TT\nJ/MQMCQi3smWlwfuK+0jqTfuk2lsfpWAWW3UYu6yi4Hpkk6R9EPSXcykSgVg1hHPGGDWN5TT8X8O\ncDDwKvAyMCYifp53YFY5jdrenMfAgEbNRR6ciwLnIj+d9slIWiUi/i1pIPA0MDfbFJIGRsQr1QjQ\nzDMGmDWuTvtkJP05IvaQNJcOHo6MiPVzjm2puE+mb3rllTQw4PrrPTDALA+V7pPxS8usIXlggFk+\nqt7xL+m2ctZZ/eqL7c29HRjQF3PRW85FgXORn04rGUnLS1od+JCkgUWfQfRsAkuzXHjGALP611Wf\nzFHAkcBawPNFm94ALoyI8/MPr/fcXNZcItLAgKOP9sAAs6VR9T4ZSYdHxHmVOmG1uJJpTh4YYLZ0\navEw5r8lfbX0U6kALH/N1N48cCBceCFMmwYnnwx77glz5xa2N1MuuuNcFDgX+Smnktmm6LMjcApp\nRmazuuUZA8zqQ4+HMEtqIc2m3NtXMleFm8us3Zw5MG4cvPACTJwIQ4fWOiKz+lWL5rJSbwN1/SCm\nWbFqvkrAzJZUznMy1xV9/gw8DlyVf2hWKW5vLrxKYOLEVhYsSK8SuOKKNCqtWfm6KHAu8lPO+2TO\nLvp5IfD/IuLZnOIxy9Uqq6SBAe0zBkye7BkDzPLkaWWsaS1YkAYEnHNOGvZ81FHQv5w/u8z6sKo9\nJyPpTTqYGDMTEbFKpYLIgysZK5cHBpgVVK3jPyJWioiVO/nUdQVjS3J7c0FHuWjWgQG+Lgqci/yU\nNbpM0uaSDpc0XtLm5R5c0ghJj0l6UtIxnZSZkG2fJWmLovWTJM3PXv9cXH6gpFskPSHp5mxItdlS\naR8Y8MgjeGCAWQWVM63MkcChwJWAgC8CF0XEhG7260caibYr8BxwPzA6ImYXlRkJjI+IkZKGAudG\nxLBs2w7Am8AlEbFZ0T4/A16KiJ9lFddqEXFsB+d3c5n1WvGrBC64ANZbr9YRmVVHLZ6T+QYwNCJO\niogTgWGkSqc7Q4A5ETE3It4DpgF7lZQZBUwBiIjpQIukNbLlu0ivfC61eJ/s3y+WEYtZj7TPGLDt\ntjBkCNx4Y60jMmtM5T6MuaiTn7uyNlA81Hke//2KgHLKlPpIRMzPfp4PfKTMeJqW25sLepKLAQPg\nhBPgj3+Eb3wjzYX2/vv5xVZtvi4KnIv8lDNg82JguqTi5rJJZexXbltV6W1Z2W1cERGSOi0/ZswY\nBmUPQLS0tDB48GCGDx8OFC4qLzfXcrue7L/DDjBhQiunngr33juc3/8eHn64Pr7P0iy3tbXVVTy1\nXG5ra6ureKq53NrayuTJkwEW/76spLKek5G0FbA9qQK4KyIeKGOfYcApETEiWz4OWBQRZxSV+TXQ\nGhHTsuXHgJ3a71SyF6RdV9In8xgwPCL+JWlN4I6I+EQH53efjFXUwoXwgx+k99b84Q8e6mx9Uy1e\nv/wx4JGIOBd4CNihzBFdM4CNJA2SNAA4ALi2pMy1wFez8wwDXitqCuvMtcDXsp+/BlxdRixmS61/\nfzj9dJgwIb1C4IILPPrMrDvl9MlcCSyUtCEwEVgXuKy7nSJiITAeuAl4lDRz82xJYyWNzcpcDzwl\naU527HHt+0uaCtwDbCzpWUkHZ5tOB3aT9ATw2WzZulDaVNTMKpGLvfaCv/0tTU/zla/AW28tfVy1\n4OuiwLnITzl9MosiYqGkvYHzIuI8Sd02lwFExA3ADSXrJpYsj+9k39GdrH+FNCzarGY+9rFU0Xz7\n22n02Z/+BJ/4r0ZbMyvnOZnpwLnA8cCeEfG0pIcj4lPVCLC33Cdj1fLb36a5zy64APbbr9bRmC2d\nqs1dVnTCTYGxwN8iYqqk9YH9izvw65ErGaumv/8d9t03NaX97Gew7LK1jsisd6re8R8Rj0TEEREx\nNVt+ut4rGFuS25sL8srFllvCzJlpss2dd4bnnsvlNBXl66LAuchPb96MaWYdWG01uOYaGDkSttkG\nbr+91hGZ1Z7fJ2OWg9tuSyPPjjwyze68jP+cswZR9T6ZRuVKxmpt3jzYf3/44AdhypR0p2NW72rx\nMOZ1kq7N/m3/+XeSjpT0gUoFYvlxe3NBNXOxzjrQ2gobbABbb50m3Kwnvi4KnIv8lHMT/zRpyv0L\ngYuAN7LPxtmymXViwAD4xS/gtNPgc5+DSeXM+mfWh5QzhHlGRGzd0TpJj0TEprlG2EtuLrN6M3s2\n7LNPen3A+efD8svXOiKz/1aL98msKOmjRQF8FFgxW1xQqUDM+rpPfhLuuw/eeQe22w6eeqrWEZnl\nr5xK5mjgLkmtklqBu4DvSVqRwsvDrI65vbmg1rlYaSX4/e/h61+HYcPguutqF0utc1FPnIv8lDN3\n2Y2k/pdPkKb6f4L0Kpf/AL/IMTazPkmC8ePTYID9909zoJ16aprl2ayvKadP5uKIOLhoeSXg2oj4\nbN7BLQ33yVgjePFFOPDA9MbNqVPhI37Pq9VYLfpk5km6IDv5asDNwKWVCsCsmX3oQ3DjjbD99unO\n5u67ax2RWWWVM3fZicBbkiYCtwDnRMTFuUdmFeP25oJ6zEW/fqm57Ne/hr33TkOeq3ETXo+5qBXn\nIj+dVjKS9sk+ewP3AkOBB4DI1plZBe2xB0yfDr/7HRxwALzxRq0jMlt6nfbJSJpM6uhfvKp4ubif\nph65T8Ya1X/+k+Y8u/PO9DK0TevySTTrqzx3WZlcyVijmzIFvvtdOPfcNDjArBpq0fFvDc7tzQWN\nlIuvfS3N5nzyyWnI87vvVvb4jZSLvDkX+cm1kpE0QtJjkp6UdEwnZSZk22dJ2qK7fSUNkXSfpAck\n3S9pmzy/g1ktffrTMGNGegnajjvCM8/UOiKznumqT2Y70iuXe9XmJKkf8DiwK/AccD8wOiJmF5UZ\nCYyPiJGShgLnRsSwrvbNZh04LSJukrQ78P2I2LmD87u5zPqMCDj7bDjrLLjkkjTZplkeqtlc9lXg\n75IulzRG0ho9PPYQYE5EzI2I94BpwF4lZUaRTU0TEdOBluw8Xe37T2DV7OcWUiVk1qdJqX/m8svh\n4IPTkOdFi2odlVn3Oq1kIuKbEbEFcAowEJgs6V5JP5W0Y3a30ZW1gWeLludl68ops1YX+x4LnC3p\nGeBM4Lhu4mh6bm8uaPRc7LRTaj679Vb4whfg5Zd7f6xGz0UlORf56Xa2pKx5azZwjqQVgJ2B/YGf\nA1t1tWuZMfT0tuy3wBERcZWk/YBJwG4dFRwzZgyDBg0CoKWlhcGDBzN8+HCgcFF5ubmW29VLPL1Z\nXnNNOOmkVi66CLbaajh//CO89VbPj9fW1lYX36celtva2uoqnmout7a2MnnyZIDFvy8rKbchzJKG\nAadExIhs+ThgUUScUVTm10BrREzLlh8DdgLW72xfSf+OiFWy9QJei4hVKeE+GWsGV14J3/wm/OhH\ncNhhqVnNbGk00hDmGcBGkgZJGgAcAFxbUuZaUt9Pe6X0WkTM72bfOZJ2yn7+LGlWaLOmtPfe8Ne/\nwi9/mYY8v/12rSMyW1JulUxELATGAzcBjwKXZ6PDxkoam5W5HnhK0hxgIjCuq32zQx8G/ExSG/Dj\nbNm6UNpU1Mz6Yi423hjuvTf9PHQoPFHmn119MRe95VzkJ9c3WETEDcANJesmliyPL3ffbP0M0jxq\nZpZZYYU0Q8CFF6YZndsn2zSrta6ek9kcOBt4iTSCaxKwJfAgcHBEzKlWkL3hPhlrVjNmwH77wT77\nwGmnwbLL1joiayTV7JP5NXAucA1wD3AhsBrwM+CCSgVgZpW19daponn0UdhlF/jnP2sdkTWzriqZ\nD0TEdRExFXgrIqZGxKKIuA74UJXiswpwe3NBs+Ri9dXh//4PdtstVTp33vnfZZolF+VwLvLTVSVT\n/LDlOSXbfANuVueWWQZOPBEuvji9n+bMM6vzMjSzYl31yXwT+H1EvFGyfkPSfGNHVSG+XnOfjFnB\nM8/A/vvDmmvC5Mmw6n89WWaW+H0yZXIlY7akBQvg6KPhxhvhiitg881rHZHVo6o+jClppKS/SHo5\n+9wpaY9Kndyqw+3NBc2ciwED4Lzz4Ic/hF13hWOPba11SHWjma+LvHX6nIykQ4GxwPeBmdnqrYDT\nJa1T+ryLmTWGAw9MdzG77w6vvprevPmBD9Q6KuuruuqTmQ1sHxEvl6xfHbg7Ij5Rhfh6zc1lZl17\n4w34+tfhH/9IzWfrr1/riKweVLW5rLSCKVrn395mDW7lldP7aQ46CIYNg+uvr3VE1hd1Vcn8W9Lg\n0pXZTABvdFDe6pTbmwuci4LW1lYkOOqoNJvz2LFpyPP779c6surzdZGfriqZo4FrJJ0iaU9JoyT9\nkDQb8tHVCc/MquEzn4GZM+Huu2HECHjxxVpHZH1Fl0OYs1chfxvYJFv1KPDLiPhXFWJbKu6TMeu5\nhQvhpJPgd79LTWnbblvriKzaqvacjKQPAx+KiEdK1m8KvBARdf23jisZs9677ro0KODEE2H8eL8M\nrZlUs+P/POCDHaxfnTRxpjUItzcXOBcFXeVizz3TO2ouvjgNeX7zzerFVQu+LvLTVSWzYUT817R6\nEfEXwM8Km/VxG2yQ+mhWXBGGDIHZs7vfx6xUV81lT0TExj3dVi/cXGZWOZMmwTHHwPnnp8k2re+q\nZnPZnI6mkJE0EvhHpQIws/p3yCFw881w/PFw5JFpHjSzcnRVyRwF/FzSZEmHSzpC0hRSf0xdz8Bs\nS3J7c4FzUdDTXGyxRRrm/PTTMHw4zJuXS1g14esiP51WMhHxBPBp4C/A+sBHgTuBT0fE4+UcXNII\nSY9JelLSMZ2UmZBtnyVpi3L2zSq92ZIelnRGObGY2dJraYGrr4ZRo2CbbeC222odkdW73Kb6l9QP\neBzYFXgOuB8YHRGzi8qMJL2bZqSkocC5ETGsq30l7QwcD4yMiPckfaij4dTukzHL1x13wJe/DN/+\nNhx3XHpJmjW+qs5dlp1wW0n3S3pT0nuSFkn6dxnHHgLMiYi5EfEeMA3Yq6TMKGAKQERMB1qyB0C7\n2vdbwGnZeur9eR2zvmrnneH++9OcZ6NGpRmdzUqV87fH+cCBwJPAB4CvAxeUsd/awLNFy/OydeWU\nWauLfTcCdpR0r6RWSVuXEUtTc3tzgXNRUIlcrL02tLbCxhvDVlulPptG5OsiP52+T6ZYRDwpqV9E\nvA9cLKkNOLa73cqMoae3Zf2B1bJmtW2APwAbdFRwzJgxDBo0CICWlhYGDx7M8OHDgcJF5eXmWm5X\nL/HUcrmtra0ix1t2WRg1qpVVVoERI4Zz2mnwsY+lyTfr6ft2tdzW1lZX8VRzubW1lcmTJwMs/n1Z\nSd32yUj6C7Ab8Bvgn8C/gK9FRJcPZEoaBpwSESOy5eOARRFxRlGZXwOtETEtW34M2Ik00KDDfSXd\nAJze/qCopDnA0A7ee+M+GbMqe/xx2Hvv9PDmL38JK6xQ64isp6reJwN8NSs3HngbWAfYp4z9ZgAb\nSRokaQBwAGkG52LXZsdvr5Rei4j53ex7NfDZbJ+NgQEdvffGzKrv4x+H6dPTczTbbQdz5tQ6Iqu1\nTisZSR+WtGnW+f5ORLweEacAvwVe7+7AEbGQVDHdRJq9+fJsdNhYSWOzMtcDT2V3IxOBcV3tmx16\nErCBpIeAqWSVlHWutKmomTkXBXnlYqWV0izOhx2WKpprrsnlNBXl6yI/XfXJnEfHHfyrAyeQBgN0\nKSJuAG4oWTexZHl8uftm698DDuru3GZWOxKMG5cGA+y/P9xzD/zkJ9C/rF5g60u6mrtsZkRs1cm2\nRyJi01wjW0rukzGrDy+9lJ6nWbAApk6FNdaodUTWlWr2yazcxbZlKxWAmfVtH/xgepZmxx1h663h\nrrtqHZFVkyfIbAJuby5wLgqqmYt+/eCHP4Tf/Ab22w/OOQfqqaHB10V+umohPQr4P0n7ATNJz7Ns\nBWwHfKEKsZlZHzNiRBp9tu++qZ9m0iRYZZVaR2V56vI5GUkfIHXwt/e/PAJcFhH/qUJsS8V9Mmb1\n69134aij4Pbb4YorYLPNah2Rtat0n0xuE2TWmisZs/p36aXwne/Az38OX/lKraMxqM3DmNbg3N5c\n4FwU1EMuDjoo3c2cemoa8vzuu7WJox5y0Ve5kjGzmtpsszSb8/z5sMMO8P/+X60jskpyc5mZ1YWI\nNOrszDNh8uQ0SMCqr+p9Mtn0LcGSsyW/TnqR2I/rdd4wVzJmjemuu2D0aPjGN+DEE9PwZ6ueWvTJ\n3Aj8mTTK7MvAdaQJLOcDkysViOXH7c0FzkVBveZihx1gxoz0npqRI9OMAXmr11z0BeVUMrtGxHER\n8VBEPBgRxwM7RcTpwKB8wzOzZrTGGnDrrTB4cJr/7L77ah2R9VY5zWUPAodmr0dG0hDgoojYXNID\nEbFFFeLsMTeXmfUNV1+dZnQ+5RT41rfS5JuWn1r0yWwDXAyslK16g/QK5keAPSLiD5UKppJcyZj1\nHXPmwD77pJFoEyfCiivWOqK+qxZ9Mg9FxKeAwcDgiNgMmBMRb9VrBWNLcntzgXNR0Ei52HBD+Nvf\n0qsChg6EQvQeAAATiUlEQVRNb+CspEbKRaMpp5K5UtKyEfFaRLwmaU3g1rwDMzMrtsIKcPHFcOSR\naXDAFVfUOiIrRznNZYcCI4F9gXVJr0H+bkTcnH94vefmMrO+a+bMNMnml74EZ5wBy/rlIxVTk7nL\nJI0HRgAfBb4ZEXdXKoC8uJIx69teeSVNS/P663D55bD22rWOqG+oWp+MpKOzz3eA5Uh3MbOAYdk6\naxBuby5wLgoaPRcDB8J118Huu8M228Add/T+WI2ei3rW3ZsxVyr69yrgyaJ13ZI0QtJjkp6UdEwn\nZSZk22dJ2qLcfbMKcJGkgeXEYmZ9zzLLwAknwCWXwIEHwumnw6JFtY7KiuU2d5mkfsDjwK7Ac6Rp\naEZHxOyiMiOB8RExUtJQ4NyIGNbdvpLWBS4CPg5sFRGvdHB+N5eZNZFnn4X994cPfximTIGWllpH\n1Jgaaar/IaShznMj4j1gGrBXSZlRwBSA7GHPFklrlLHvOcD3c4zdzBrMuuvCnXfCRz+aZgloa6t1\nRAb5VjJrA88WLc/L1pVTZq3O9pW0FzAvIh6sdMB9ldubC5yLgr6YiwEDYMIE+MlPYLfd0pDncvTF\nXNSL/jkeu9y2qrJvyyQtDxwP7FbO/mPGjGHQoEEAtLS0MHjwYIYPHw4ULiovN9dyu3qJp5bLbW1t\ndRVPJZfXWKOVs86C008fzt13w377tbLccp2Xb8tue+ol/mout7a2MnnyZIDFvy8rqdM+GUknd7JP\nAETEqV0eWBoGnBIRI7Ll44BFEXFGUZlfA60RMS1bfgzYCVi/o31Js0HfBrydHWIdUp/NkIh4oeT8\n7pMxa3JvvAGHHgpPPJEe3txgg1pHVP+q2SfzFvBmySdI85Z1OFKsxAxgI0mDJA0ADiA9yFnsWuCr\nsLhSei0i5ne2b0Q8HBEfiYj1I2J9UjPalqUVjJkZwMorw9SpcPDBMGxYGvJs1dVpJRMRZ0XE2RFx\nNmkk1/LAwaRO+PW7O3BELATGAzcBjwKXR8RsSWMljc3KXA88JWkOMBEY19W+HZ2m7G/axEqbipqZ\nc1HQLLmQ4PDD4ZprYNy4NOT5/feXLNMsuaiFLvtkJK0O/C/pZWWXkO4aXi334BFxA3BDybqJJcvj\ny923gzK++TWzsmy7bZqO5sAD4XOfS3c4H/5wraPq+7rqkzkL+BJwIXBBRLxRzcCWlvtkzKwj778P\nJ5+cnqWZNg0+85laR1RfqjZ3maRFwALgvQ42R0SsUqkg8uBKxsy68uc/wyGHwPHHwxFH+GVo7arW\n8R8Ry0TEByJi5Q4+dV3B2JLc3lzgXBQ0ey722APuvTdNSfPZz7byRkO11TSOPB/GNDOra+uvD3ff\nnd60uc028MgjtY6o78lt7rJac3OZmfXE5Mnwve+lGQNGj651NLVTk/fJNCJXMmbWU7NmwT77pNcH\nnH12mqam2TTSBJlWJ5q97b2Yc1HgXBS052LzzWHGjDSj8447pn9t6biSMTMr0tICV10Fe++d+mlu\nrusXzdc/N5eZmXWitTU9vPmtb6WZApZpgj/L3SdTJlcyZlYJzz8PBxyQ5kG79FJYffVaR5Qv98lY\nj7ntvcC5KHAuCrrKxVprwe23wyabwNZbpz4bK58rGTOzbiy7LJx1VvrsvjtMnAhuKCmPm8vMzHrg\niSfSMOctt4Rf/QpWWKHWEVWWm8vMzGpo443TdDSLFqV31Dz5ZK0jqm+uZJqA294LnIsC56Kgp7lY\nccU059m4cWkW56uuyieuvsCVjJlZL0jwzW+m2Zz/93/TlDQLF9Y6qvrjPhkzs6X08svwla/A22+n\nd9SsuWatI+o998mYmdWZ1VdPdzS77JKGOd95Z60jqh+5VzKSRkh6TNKTko7ppMyEbPssSVt0t6+k\nMyXNzspfKWnVvL9HI3Pbe4FzUeBcFFQiF8ssAyedBJMmpYc3zzzTw5wh50pGUj/gfGAEsAkwWtIn\nS8qMBDaMiI2Aw4BflbHvzcCmEbE58ARwXJ7fw8ysXJ//PNx3H1xxRRrq/PrrtY6otvK+kxkCzImI\nuRHxHjAN2KukzChgCkBETAdaJK3R1b4RcUtELMr2nw6sk/P3aGjDhw+vdQh1w7kocC4KKp2L9daD\nv/wl9c1svTU8+GBFD99Q8q5k1gaKJ8uel60rp8xaZewLcAhw/VJHamZWQcstB7/8JZxySuqrueSS\nWkdUG3lXMuW2SPZqJIOkE4AFEXFZb/ZvFm57L3AuCpyLgjxz8eUvwx13wE9+AoccAv/6V26nqkv9\ncz7+c8C6Rcvrku5IuiqzTlZm2a72lTQGGAns0tnJx4wZw6BBgwBoaWlh8ODBi2+L2y8qLzfXcrt6\niaeWy21tbXUVTy2X29racj3+Sy+1cs45cOutw9lkExgxopX/+R8YNar237+1tZXJkycDLP59WUm5\nPicjqT/wOKkieB64DxgdEbOLyowExkfESEnDgF9ExLCu9pU0Ajgb2CkiXurk3H5Oxszqzrx58KMf\nwZ/+BEcckR7kXHnlWkdV0FDPyUTEQmA8cBPwKHB5VkmMlTQ2K3M98JSkOcBEYFxX+2aHPg9YCbhF\n0gOSLsjze5iZVco666RZnKdPT/OebbghnH02vPNOrSPLh5/4bwKtra2Lb5ObnXNR4FwU1DIXDz+c\nnq+57z74wQ9Sv82AATUJBWiwOxkzM+vapz4FV16ZJtm86ir4xCfSSLT33691ZJXhOxkzszpy551w\nwgnw6qtw6qmw995pMs5qqfSdjCsZM7M6EwE33pgqGykNf/7856tT2bi5zHqsdPhuM3MuCpyLgnrL\nhZRe8zxjBhx3HHznO7DjjmkWgUbjSsbMrE4tswzsuy889BAceiiMGQMjRqTKp1G4uczMrEEsWAC/\n/S38+McwdGh63mbTTSt7DjeXmZk1qQED4Fvfgjlz0mufP/tZOOgg+Mc/ah1Z51zJNIF6a2+uJeei\nwLkoaLRcLL88HH10ephzo43SXc03v5lmE6g3rmTMzBrUKqukBzkffxxWXRU23zwNEnjxxVpHVuA+\nGTOzPuKf/4Sf/hQuuwzGjUt3Oy0tPTuG+2TMzKxDa64J550HM2fCc8+lprTTT4e33qpdTK5kmkCj\ntTfnybkocC4K+louBg2CSZPgrrvggQfSJJznnQfvvlv9WFzJmJn1UZ/4BFx+OdxwA9x0E2y8cRoC\nvXBh9WJwn4yZWZO45540Vc3zz8MPfwj7758e+CzmucvK5ErGzOy/RcBtt6XK5j//SQ92fuELhXnR\n3PFvPdbX2puXhnNR4FwUNFMuJNh1V7j33jRjwPHHw7bbwu2353O+/vkc1szM6pkEo0alu5jLL4ex\nY2G99XI4T19tUnJzmZlZ+d57D265BfbYw30yZXElY2bWcw3VJyNphKTHJD0p6ZhOykzIts+StEV3\n+0oaKOkWSU9IullSD59nbT7N1N7cHeeiwLkocC7yk1slI6kfcD4wAtgEGC3pkyVlRgIbRsRGwGHA\nr8rY91jglojYGLgtW7YutLW11TqEuuFcFDgXBc5FfvK8kxkCzImIuRHxHjAN2KukzChgCkBETAda\nJK3Rzb6L98n+/WKO36FPeO2112odQt1wLgqciwLnIj95VjJrA88WLc/L1pVTZq0u9v1IRMzPfp4P\nfKRSAZuZWWXlWcmU2+teTgeTOjpe1rPv3v1uzJ07t9Yh1A3nosC5KHAu8pPnczLPAesWLa9LuiPp\nqsw6WZllO1j/XPbzfElrRMS/JK0JvNBZAFLFBkg0vClTpnRfqEk4FwXORYFzkY88K5kZwEaSBgHP\nAwcAo0vKXAuMB6ZJGga8FhHzJb3cxb7XAl8Dzsj+vbqjk1dyCJ6ZmfVObpVMRCyUNB64CegH/DYi\nZksam22fGBHXSxopaQ7wFnBwV/tmhz4d+IOkrwNzgf3z+g5mZrZ0+uzDmGZmVnsNMUFmmQ91Dpf0\ngKSHJbUWrZ8r6cFs231F60+RNC9b/4CkEVX4KkttKXPRIukKSbMlPZo1UTbsA64VzsXQbH1TXReS\nPl70XR+Q9LqkI7JtTXVddJOLprousvXHSXpE0kOSLpO0XLa+Z9dFRNT1h9RcNgcYRBoQ0AZ8sqRM\nC/AIsE62/MGibU8DAzs47snAd2r9/aqciynAIdnP/YFVs59/Bnw/+/kY4PRaf9ca5qLprouiMssA\n/wTWbdbrootcNNV1ke3zFLBctnw58LXeXBeNcCdTzkOdBwJ/ioh5ABHxUsn2zgYBNNrggF7nQtKq\nwA4RMSlbvzAiXs/2acQHXPPKBTTRdVFiV+AfEdH+jFpTXRclSnMBzXVd/Bt4D1hBUn9gBQojfHt0\nXTRCJVPOQ50bAQMl3SFphqSDirYFcGu2/tCS/Q5XmjPttw3SFLA0uVgfeFHSxZL+LukiSStk2xrx\nAde8cgHNdV0U+x/gsqLlZrsuipXmAprouoiIV4CzgWdII3xfj4hbs316dF00QiVTzsiEZYEtgZHA\n54ETJW2Ubds+IrYAdge+LWmHbP2vSL9sBpNui8+uaNT5WJpc9M/WXxARW5JG8/3XvG+R7oEbYTRI\nXrlotusCAEkDgD2BP3Z4gua4LoBOc9FU14WkjwFHkZrN1gJWlPTl/zpBGddFI1Qy5TzU+Sxwc0S8\nExEvA38BNgeIiOezf18EriLdQhIRL0QG+E37+jrX21x8Ols/LyLuz8r9iXRxQfaAK4C6ecC1jlQy\nF1eQ5aLJrovNi7bvDszM/j9p10zXRZe5aMLrYivgnoh4OSIWAlcC22X79Oi6aIRKZvFDndlfGAeQ\nHsgsdg2wvaR+WbPHUOBRSStIWhlA0orA54CHsuU1i/b/Uvv6OtfbXMzObm+flbRxVm4XUocfFB5w\nhS4ecK0zlczFrmS5aLLr4tGi7aOBqSX7NNN10WUumvC6eBwYJml5SSL9P9Keo55dF3mObqjUh/SX\nxeOkkRLHZevGAmOLynyX9IviIeCIbN0GpBEVbcDD7ftm2y4BHgRmZUn6SK2/Z565yNZvDtyffecr\nKYyoGgjcCjwB3Ay01Pp71jAXzXhdrAi8BKxccsxmvC46y0UzXhffL1o/BVi2N9eFH8Y0M7PcNEJz\nmZmZNShXMmZmlhtXMmZmlhtXMmZmlhtXMmZmlhtXMmZmlhtXMtbQJP1c0pFFyzdJuqho+WxJ/yvp\no5JK38xafJwzs6nOz6hATEdJWr5o+c+SVlna43Zxvg9Jmi5ppqTPlGxrlXR/0fLWku4o45ibSrpd\naZr4JyT9oGjbGEnndXCerSrxfaxvcSVjje6vZNNdSFoGWB3YpGj7tsDdpHmnDuziOIcCm0XEEu/c\nkNSvFzEdSZq1FoCI2CMi/t2L45RrF+DBiNgqIu7uYPuH1IP3n2QV5DXATyPiE6QHV7eTNC4r0tHD\ndY0yt5lVmSsZq1uStslmvV1O0orZncYmJcX+RqpIADYlzezwhtJLyZYDPgk8QHpt9w5KL2c6svgA\nkq4FVgL+Lml/SZMl/VrSvcAZWRz3ZDM2390+HU02FcdZSi91miVpvKTDSRMK3iHptqzcXEkDs5+/\nk5V/qD2ObNqP2ZIuzL7jTZI+0EE+BmV3F7Mk3SppXUmDgTOAvbLvVrpfAGcBJ3RyvL9kd0AzJbXn\n8UDgr5HNuhsR7wDjKUwi2mhT3lsN9a91AGadiYj7swrgx8DywKUR8WhJmeclLZS0Lqmy+RtpOvNt\nSe/EeCgi3lN6K+B3I2LPDs4zStIbkWbrRtLupIpi24iIbP67HSLifUm7Aj8F9gUOA9YDNo+IRZJW\ni4hXJX0HGB5punTI/sLPmpPGkCZXXAaYLulO4DVgQ+CAiDhM0uXAPsDvS0I9D7g4Ii6VdDAwISK+\nJOkkYKuIOKKTVP4N+JKk4cAbRevnA7tFxLtKsxBfBmxDqqxnluToKUkrSVopW3WApO2LimzYybmt\nybmSsXp3Kmmiv3eAwzspcw+pyWw74BxSJbMd8DqpOQ16/tf3H6Mw51ILcImkDUkVRvv/N7sAv4qI\nRQAR8WoXxxOwPXBldmeApCuBHUgTDj4dEQ9mZWeSplgvNYzCC6J+R3pDYfuxu/t+PwZ+QHqTYbsB\nwPmSNgfeJ71bhOw7dne8acWVWjn9PNac3Fxm9e6DpEkLVyLdzXTkbuAzwGakyfzupVDp3NPL875d\n9POPgNsiYjPSWwGL4+hJ5VX6y1sU+jHeLVr/Pp3/AdibpqqIiDtIcQ8rWv+/wD8j4tPA1sBy2fpH\nSVO9F04qbQC8GRFvLkUc1oRcyVi9m0j6C/wyUt9DR+4BvgC8HMmrpLuPbSlUMv8GVu5lDKuQ3g4I\nqbmr3S3A2PbBAZJWy9a/ke1TLIC7gC9m06evSLoruYvyf2HfQ3pjI8CXSe/+6Ikfk+5k2iu2VYB/\nZT9/lfROeEi53l7SLrB4IMAEOs+/WadcyVjdkvRV4N2ImEbquN8m61co9TBpVNm9ReseBF4r6hd5\nEHhfUltpx3+mdGRU8fLPgNMk/Z30i7h9229Ir6d9UFIb6T0kABcCN7Z3/C8+YMQDwGTgvizWiyJi\nVhnnb3c4cLCkWaRK5siist2O7IqIG1jyBVMXAF/LYv848GZW7h3Su+B/IOkxUu6mR8Qve3I+M8BT\n/ZuZWX58J2NmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrlxJWNmZrn5/4wM\nQj9yu23UAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7a1c780>"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Ex13.4: Page 758"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "# Illustration 13.4\n",
+ "# Page: 758\n",
+ "\n",
+ "print'Illustration 13.4 - Page: 758\\n\\n'\n",
+ "\n",
+ "# Solution\n",
+ "import numpy\n",
+ "from scipy import interp\n",
+ "#***Data***#\n",
+ "# a:oil b:soyabean c:hexane\n",
+ "# Data=[100y*(Wt % oil in soln) 1/N(kg soln retained/kg insoluble solid)]\n",
+ "Data = numpy.array([[0 ,0.58],[20 ,0.66],[30 ,0.70]]);\n",
+ "# Soyabean feed:\n",
+ "percent_b = 20.0;# [soluble]\n",
+ "yF = 1.0;# [mass fraction oil,solid free basis]\n",
+ "# Solvent:\n",
+ "RNpPlus1 = 1.0;# [hexane,kg]\n",
+ "xNpPlus1 = 0;# [mass fraction oil]\n",
+ "# Leached Solids:\n",
+ "leached = 0.005;# [fraction of oil to be leached]\n",
+ "# Miscella:\n",
+ "percent_miscella = 10.0;# [percent of insoluble solid]\n",
+ "#**********#\n",
+ "\n",
+ "N = zeros(3);\n",
+ "ystar_By_N = zeros(3);\n",
+ "for i in range(0,3):\n",
+ " N[i] = 1/Data[i,1];# [kg insoluble solid/kg soln retained]\n",
+ " ystar_By_N[i] = Data[i,0]/(100*N[i]);# [kg oil/kg insoluble solid]\n",
+ "\n",
+ "# Basis: 1 kg flakes introduced\n",
+ "# Soyabean feed:\n",
+ "mass_b = 1-(percent_b/100.0);# [insoluble,kg]\n",
+ "F = 1.0-mass_b;# [kg]\n",
+ "NF = mass_b/F;# [kg insoluble solid/kg oil]\n",
+ "\n",
+ "# Leached Solids:\n",
+ "Ratio = leached/(1-leached);# [kg oil/kg insoluble solid]\n",
+ "# By interpolation:\n",
+ "Np = interp(Ratio,ystar_By_N,N);\n",
+ "miscella_b = (percent_miscella/100.0)*mass_b;# [Insoluble solid lost to miscella,kg]\n",
+ "leached_b = (1-(percent_miscella/100.0))*mass_b;# [Insoluble solid in miscella,kg]\n",
+ "ENp = leached_b/Np;# [kg soln retained]\n",
+ "retained_a = Ratio*leached_b;# [oil retained,kg]\n",
+ "retained_c = ENp-retained_a;# [Hexane retained,kg]\n",
+ "yNp = retained_a/ENp;# [mass fraction of oil in retained liquid]\n",
+ "\n",
+ "# Miscella:\n",
+ "mass_c = 1.0-retained_c;# [kg]\n",
+ "mass_a = F-retained_a;# [kg]\n",
+ "R1 = mass_c+mass_a;# [clear miscella,kg]\n",
+ "x1 = mass_a/R1;# [mass fraction of oil in the liquid]\n",
+ "NR1 = miscella_b/R1;# [kg insoluble solid/kg soln]\n",
+ "\n",
+ "# The operating diagram is shown in Fig 13.33 (Pg 759).\n",
+ "# Point R1 represents the cloudy miscella and is therefore is displaced from the axis of he graph at NR1. Point deltaR is located as usual and the stages determined with the N=0 axis for all the stages but the first.\n",
+ "print\"Between 4 and 5 stages are required\\n\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Illustration 13.4 - Page: 758\n",
+ "\n",
+ "\n",
+ "Between 4 and 5 stages are required\n",
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file |