{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 5:Absorption and Stripping"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.1,Page number:287"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\n",
      "\t# Component 'A' is to be absorbed #\n",
      "y_N1 = 0.018\t\t\t\t\t\t# [mole fraction 'A' of in entering gas]\n",
      "y_1 = 0.001\t\t\t\t\t\t# [mole fractio of 'A'in leaving gas]\n",
      "x_0 = 0.0001\t\t\t\t\t\t# [mole fraction of 'A' in entering \t\t\t\t\t\t\tliquid]\n",
      "m = 1.41\t\t\t\t\t\t# [m = yi/xi]\n",
      "n_1 = 2.115\t\t\t\t\t\t# [molar liquid to gas ratio at bottom, \t\t\t\t\t\t\tL/V]\t\n",
      "n_2 = 2.326\t\t\t\t\t\t# [molar liquid to gas ratio at top, L/V]\n",
      "E_MGE = 0.65 \n",
      "\n",
      "\n",
      "print\"Answer 5.1 (a)\"\n",
      "\t# Solution (a)\n",
      "\n",
      "\n",
      "A_1 = n_1/m\t\t\t\t\t\t# [absorption factor at bottom]\n",
      "A_2 = n_2/m\t\t\t\t\t\t# [absorption factor at top]\n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "A = math.sqrt(A_1*A_2) \n",
      "\t# Using equation 5.3 to calculate number of ideal stages\n",
      "N = (math.log(((y_N1-m*x_0)/(y_1-m*x_0))*(1-1/A) + 1/A))/math.log(A)\t# [number of ideal \t\t\t\t\t\t\t\t\tstages]\n",
      "print\"Number of ideal trays is\",round(N,3)\n",
      "\t# Using equation 5.5\n",
      "E_o = math.log(1+E_MGE*(1/A-1))/math.log(1/A) \n",
      "\t# Therefore number of real trays will be\n",
      "n = N/E_o\n",
      "\n",
      "#Result\n",
      " \n",
      "print\"Number of real trays is\",round(n,2)\n",
      "\n",
      "print\"Since it is not possible to specify a fractional number of trays, therefore number of real trays is\",round(n)\n",
      "\n",
      "\n",
      "print\"\\nAnswer5.1 (b)\"\n",
      "\n",
      "\t# Solution (b)\n",
      "\n",
      "\t# Back checking the answer\n",
      "print\"Back checking the answer\"\n",
      "\n",
      "#Calculation\n",
      "\n",
      "N_o = E_o*n \n",
      "\t# Putting N_o in equation 5.3 to calculate y_1\n",
      "def f16(Z):\n",
      "    return(N_o-(math.log(((y_N1-m*x_0)/(Z-m*x_0))*(1-1/A) + 1/A))/math.log(A)) \n",
      "Z = fsolve(f16,0.001) \n",
      "print\"Mole fraction of A in leaving gas is\",Z[0],\"percent which satisfies the requirement that the gas exit concentration should not exceed 0.1 percent\"\n",
      "\n",
      "\t# For a tower diameter of 1.5 m, Table 4.3 recommends a plate spacing of 0.6 m\n",
      "Z = n*0.6\t\t\t\t\t\t# [Tower height, m]\n",
      "\n",
      "#Result\n",
      "print\"The tower height will be\",round(Z,1),\"m\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Answer 5.1 (a)\n",
        "Number of ideal trays is"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4.647\n",
        "Number of real trays is 7.79\n",
        "Since it is not possible to specify a fractional number of trays, therefore number of real trays is 8.0\n",
        "\n",
        "Answer5.1 (b)\n",
        "Back checking the answer\n",
        "Mole fraction of A in leaving gas is 0.001 percent which satisfies the requirement that the gas exit concentration should not exceed 0.1 percent\n",
        "The tower height will be 4.7 m\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exmaple 5.3,Page number:295"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "\t# For tower diameter, packed tower design program of Appendix D is run using # the data \t\tfrom Example 5.2 and packing parameters from Chapter 4.\n",
      "\n",
      "\t# For a pressure drop of 300 Pa/m, the program converges to a tower diameter \n",
      "Db = 0.641  \t\t\t\t\t\t# [m]\n",
      "\t# Results at the bottom of tower\n",
      "fb= 0.733  \t\t\t\t\t\t# [flooding]\n",
      "ahb = 73.52 \t\t\t\t\t\t# [m**-1]\n",
      "Gmyb = 126  \t\t\t\t\t\t# [mol/square m.s]\n",
      "kyb = 3.417  \t\t\t\t\t\t# [mol/square m.s]\n",
      "klb = 9.74*10**-5  \t\t\t\t\t# [m/s]\n",
      "\n",
      "\t# From equation 2.6 and 2.11\n",
      "\t# Fg = ky*(1-y),   Fl = kx*(1-x)\n",
      "\t# Assume 1-y = 1-y1    1-x = 1-x1\n",
      "\t# let t = 1-y1  u = 1-x1\n",
      "\t# Therefore\n",
      "t = 0.926 \n",
      "u = 0.676 \n",
      "Fgb = kyb*t  \t\t\t\t\t\t# [mol/square m.s]\n",
      "rowlb = 780  \t\t\t\t\t\t# [kg/cubic m]\n",
      "Mlb = 159.12  \t\t\t\t\t\t# [gram/mole]\n",
      "c = rowlb/Mlb  \t\t\t\t\t\t# [kmle/cubic m]\n",
      "Flb = klb*c*u  \t\t\t\t\t\t# [mol/square m.s]\n",
      "\t# From equ 5.19\n",
      "Htgb = Gmyb/(Fgb*ahb)  \t\t\t\t\t# [m]\n",
      "import math\n",
      "from scipy.optimize import fsolve\n",
      "from numpy import *\n",
      "from pylab import *\n",
      "\t# Now, we consider the conditions at the top of the absorber\n",
      "\t# For a pressure drop of 228 Pa/m, the program converges to a tower        # diameter\n",
      "Dt = 0.641  \t\t\t\t\t\t# [m]\n",
      "\t# Results at the top of tower\n",
      "ft = 0.668  \t\t\t\t\t\t# [flooding]\n",
      "aht = 63.31  \t\t\t\t\t\t# [m**-1]\n",
      "Gmyt = 118  \t\t\t\t\t\t# [mol/square m.s]\n",
      "kyt = 3.204  \t\t\t\t\t\t# [mol/square m.s]\n",
      "klt = 8.72*10**-5  \t\t\t\t\t# [m/s]\n",
      "\n",
      "rowlt = 765  \t\t\t\t\t\t# [kg/cubic m]\n",
      "Mlt = 192.7  \t\t\t\t\t\t# [gram/mole]\n",
      "cl = rowlt/Mlt  \t\t\t\t\t# [kmole/cubic m]\n",
      "Fgt = kyt*0.99  \t\t\t\t\t# [mole/square m.s]\n",
      "Flt = klb*cl*0.953  \t\t\t\t\t# [mole/square m.s]\n",
      "\t# From equ 5.19\n",
      "Htgt = Gmyt/(Fgt*aht)  \t\t\t\t\t# [m]\n",
      "Htg_avg = (Htgb+Htgt)/2  \t\t\t\t# [m]\n",
      "Fg_avg = (Fgt+Fgb)/2  \t\t\t\t\t# [mole/square m.s]\n",
      "Fl_avg = (Flb+Flt)*1000/2  \t\t\t\t# [mole/square m.s]\n",
      "\n",
      "\t# The operating curve equation for this system in terms of mole fractions\n",
      "\t# y = \n",
      "\n",
      "\t# From Mathcad program figure 5.3\n",
      "x1 = 0.324 \n",
      "x2 = 0.0476 \n",
      "n = 50 \n",
      "dx = (x1-x2)/n \n",
      "me = 0.136 \n",
      "T = zeros((50,2)) \n",
      "y=zeros((50))\n",
      "x=zeros((50))\n",
      "yint=zeros((50))\n",
      "fd=zeros((50))\n",
      "for j in range(1,51):\n",
      "    x[j-1] = x2+j*dx \n",
      "    y[j-1] = (0.004+0.154*x[j-1])/(1.004-0.846*x[j-1]) \n",
      "    \n",
      "    def f12(yint):\n",
      "        return((1-yint)/(1-y[j-1]) - ((1-x[j-1])/(1-yint/me))**(Fl_avg/Fg_avg)) \n",
      "    yint[j-1] = fsolve(f12,0.03) \n",
      "    fd[j-1] = 1/(y[j-1]-yint[j-1]) \n",
      "    T[j-1][0] = y[j-1] \n",
      "\n",
      "    T[j-1][1] = fd[j-1] \n",
      "\n",
      "\n",
      "#Result\n",
      "\n",
      "a1=plot(T[:,0],T[:,1]) \n",
      "\n",
      "xlabel(\"y\") \n",
      "ylabel(\"f = 1/(y-yint)\") \n",
      "\n",
      "yo = y[0] \n",
      "yn = y[49] \n",
      "\t# From graph between f vs y\n",
      "Ntg = 10.612 \n",
      "\t# Therefore\n",
      "Z = Htg_avg*Ntg\t\t  \t\t\t\t\t# [m]\n",
      "\t\n",
      "print\"The total packed height is\",round(Z),\"m.\"\t\n",
      "deltaPg = 300*Z \t \t\t\t\t\t# [Pa]\n",
      "Em = 0.60 \t\t\t\t\t\t\t # [mechanical efficiency]\n",
      "Qg = 1.0 \n",
      "Wg = (Qg*deltaPg)/Em  \t\t\t\t\t\t# [Power required to force the \t\t\t\t\t\t\t\tgas through the tower, W]\n",
      "L2 = 1.214  \t\t\t\t\t\t\t# [kg/s]\n",
      "g = 9.8  \t\t\t\t\t\t\t# [m/square s]\n",
      "Wl = L2*g*Z/Em  \t\t\t\t\t\t# [Power required to pump the \t\t\t\t\t\t\t\tliquid to the top of the \t\t\t\t\t\t\t\tabsorber, W]\n",
      "\n",
      "\n",
      "print\"The power required to force the gas through the tower is \",round(Wg),\"W.\\n\\n\"\n",
      "print\"The power required to pump the liquid to the top of the absorber is \",round(Wl),\"W.\\n\\n\"\n",
      "\n",
      "show(a1)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total packed height is 6.0 m.\n",
        "The power required to force the gas through the tower is  2996.0 W.\n",
        "\n",
        "\n",
        "The power required to pump the liquid to the top of the absorber is  119.0 W.\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlYlXX+//HnQajJJTOTQ3EqTCA8ooCaOKWFCy7jkpWD\noQnl0qTT9DVt0eaXYn0LrKbJFqemspz5VmjNuGRqZnXMNNMUyyKXEgvZRofQFBWE+/fHneQucpb7\ncHg9rutc4lnu+6UX+uaz2wzDMBARETmLIKsDiIhI/aCCISIitaKCISIitaKCISIitaKCISIitaKC\nISIiteL1glFVVUVCQgKDBg0CoLS0lOTkZKKjo+nTpw9lZWU1783MzCQqKoqYmBiWL1/u7WgiInIO\nvF4wZs6cidPpxGazAZCVlUVycjLbtm2jV69eZGVlAZCbm8vcuXPJzc1l2bJljB8/nurqam/HExGR\nWvJqwdi1axdLlixhzJgxHF0fuGjRItLT0wFIT09nwYIFACxcuJDU1FRCQkKIiIggMjKSdevWeTOe\niIicA68WjHvvvZcnn3ySoKBfb1NSUoLdbgfAbrdTUlICQGFhIQ6Ho+Z9DoeDgoICb8YTEZFz4LWC\nsXjxYkJDQ0lISOB0u4/YbLaarqrTvS4iIv4h2FsXXrNmDYsWLWLJkiUcOnSIffv2MXLkSOx2O8XF\nxYSFhVFUVERoaCgA4eHh5Ofn13x+165dhIeHn3TdyMhIvv/+e2/FFhEJSG3atOG7775z7yKGD7hc\nLmPgwIGGYRjG/fffb2RlZRmGYRiZmZnGgw8+aBiGYXzzzTdGXFyccfjwYWPHjh3GVVddZVRXV590\nrXON/NVXhhEWZhgHD7r5h/CQadOmWR3BLcpvnfqc3TCU32qe+O/eZ+swjnYvTZ48mQ8++IDo6Gg+\n+ugjJk+eDIDT6SQlJQWn00n//v2ZNWuWR7qk2reHTp1gzhy3LyUi0qB5rUvqWDfccAM33HADABdf\nfDErVqw45fseeughHnroIY/ff/JkSE+H0aMh2Cd/YhGRwNMgVnp36wZhYfCvf1mdBJKSkqyO4Bbl\nt059zg7KHwhsv/Rt1Rs2m+20s67O5N13Ydo02LABNPlKRBqauv7feawG0cIAGDAAKirggw+sTiIi\nUj81mIIRFAQPPAC/7EQiIiLnqMEUDIDUVPj+e9COIyIi565BFYyQEJg0CWbMsDqJiEj902AGvY86\ncABat4ZPPoGYGA8GExHxYxr0roMmTeCee+Dxx61OIiJSvzS4FgbA3r3Qpg18/rn5q4hIoFMLo46a\nN4fx4zVjSkTkXDTIFgbAf/8L0dGQkwNXXOGBYCIifkwtDDe0bAljxsATT1idRESkfmiwLQyAkhJo\n2xa++QYuvdQjlxQR8UtqYbjJboe0NHjqKauTiIj4vwbdwgAoKDDPzNi6FVq18thlRUT8iloYHhAe\nDsOGwdNPW51ERMS/NfgWBsAPP0DHjmYr45JLPHppERG/oBaGh1x5JaSkaCxDRORM1ML4RX4+xMfD\nt99CaKjHLy8iYim1MDzo8sth+HCtyxAROR21MI5RWAixsVqXISKBxxP/d6pgnODee8Ew4JlnvHYL\nERGfU8HwguJicDph82Zzyq2ISCDw6zGMQ4cOkZiYSHx8PE6nkylTpgCQkZGBw+EgISGBhIQEli5d\nWvOZzMxMoqKiiImJYfny5d6KdkZhYTB6NGRmWnJ7ERG/5dUWRnl5OY0bN+bIkSN069aNp556ig8/\n/JBmzZoxceLE496bm5vL8OHDWb9+PQUFBfTu3Ztt27YRFHR8TfN2CwNg927zND7tZCsigcKvWxgA\njRs3BqCiooKqqipatGgBcMrQCxcuJDU1lZCQECIiIoiMjGTdunXejHdarVrBH/4Ajz5qye1FRPyS\nVwtGdXU18fHx2O12evToQbt27QB47rnniIuLY/To0ZSVlQFQWFiIw+Go+azD4aCgoMCb8c7o/vth\nwQLYssWyCCIifiXYmxcPCgpi06ZN7N27l759++JyuRg3bhxTp04F4OGHH2bSpEm8+uqrp/y8zWY7\n5fMZGRk1XyclJZGUlOTp6LRoAffdBw8/DG+/7fHLi4h4lcvlwuVyefSaPpsl9eijj3LBBRdw3333\n1Ty3c+dOBg0axObNm8n65bzUyZMnA9CvXz+mT59OYmLi8YF9MIZxVHk5REXBwoXQubNPbiki4hV+\nPYaxZ8+emu6mgwcP8sEHH5CQkEBxcXHNe+bPn0/79u0BGDx4MNnZ2VRUVJCXl8f27dvp0qWLt+LV\nSuPGZgvjoYcsjSEi4he81iVVVFREeno61dXVVFdXM3LkSHr16kVaWhqbNm3CZrPRunVrXnrpJQCc\nTicpKSk4nU6Cg4OZNWvWabukfGn0aHNTwg8/hF69rE4jImIdLdyrhexs87yMzz8HP6hhIiLnzK+7\npAJJSgpUVsL8+VYnERGxjloYtbRsmbnP1ObNEOzVuWUiIp6nFoYP9e1rbhvy2mtWJxERsYZaGOdg\nwwYYNMg8yrVZM0siiIjUiVoYPtapkzlT6sknrU4iIuJ7amGcox9/hIQE+OorbX8uIvWHzsOwyJQp\nUFICs2dbGkNEpNZUMCyydy9cfTW8/z7ExVkaRUSkVjSGYZHmzc0tQ+67zzzOVUSkIVDBqKM774T8\nfLOVISLSEKhg1FFICDzxBEyaBEeOWJ1GRMT7VDDcMGgQXHop/O1vVicREfE+DXq76ZtvoEcPyM2F\nSy6xOo2IyKlplpSfuOcec3NCtTRExF+pYPiJn36CmBhYvlzTbEXEP2larZ9o0QKmTzdbGn5Wy0RE\nPEYFw0PGjjUX9L39ttVJRES8Q11SHrRyJaSlwbffmueBi4j4C3VJ+ZkbboDERJgxw+okIiKepxaG\nh+Xnm7vZrl0LkZFWpxERMamF4YcuvxwefBDuvlsD4CISWFQwvGDCBLOl8e9/W51ERMRz1CXlJZ98\nAiNGmCvAdZyriFjNr7ukDh06RGJiIvHx8TidTqZMmQJAaWkpycnJREdH06dPH8rKymo+k5mZSVRU\nFDExMSxfvtxb0Xzi+uuhZ09zfYaISCDwagujvLycxo0bc+TIEbp168ZTTz3FokWLuOSSS3jggQeY\nMWMGP/30E1lZWeTm5jJ8+HDWr19PQUEBvXv3Ztu2bQQFHV/T6ksLA+A//4HYWPjwQ2jf3uo0ItKQ\n+XULA6DxL4sRKioqqKqqokWLFixatIj09HQA0tPTWbBgAQALFy4kNTWVkJAQIiIiiIyMZN26dd6M\n53WhofDIIzBuHFRXW51GRMQ9Xi0Y1dXVxMfHY7fb6dGjB+3ataOkpAS73Q6A3W6npKQEgMLCQhwO\nR81nHQ4HBQUF3oznE2PHQkUFvP661UlERNwT7M2LBwUFsWnTJvbu3Uvfvn35+OOPj3vdZrNhs9lO\n+/nTvZaRkVHzdVJSEklJSZ6I6xWNGsFLL0HfvjBgAPxSK0VEvMrlcuFyuTx6Ta8WjKOaN2/OgAED\n2LBhA3a7neLiYsLCwigqKiI0NBSA8PBw8vPzaz6za9cuwsPDT3m9YwtGfZCQAKNGwf/8D2RnW51G\nRBqCE3+Ynu6BGThe65Las2dPzQyogwcP8sEHH5CQkMDgwYOZM2cOAHPmzGHIkCEADB48mOzsbCoq\nKsjLy2P79u106dLFW/F8bto02LAB3n3X6iQiInXjtRZGUVER6enpVFdXU11dzciRI+nVqxcJCQmk\npKTw6quvEhERwbx58wBwOp2kpKTgdDoJDg5m1qxZZ+yuqm8uuAD+/ndITzf3nLrwQqsTiYicGy3c\n87GxY+H88+H5561OIiINiU7cq4d++slcmzFvHlx3ndVpRKSh8Pt1GHKyFi3g2WfNlsbhw1anERGp\nPRUMC9x8s3kG+KOPWp1ERKT21CVlkeJiiI83Z01dc43VaUQk0KlLqh4LC4OZM81ZU4cOWZ1GROTs\n1MKwkGFASgpcdZWOdRUR79IsqQCwezd06GAetvTb31qdRkQClbqkAkCrVvDCC3D77VBebnUaEZHT\nUwvDT4wYYW6H/te/Wp1ERAKRuqQCSGmpecjSm2+aW4eIiHiSuqQCyMUXwyuvQFoaHHNqrYiI31AL\nw8/86U+wZ4/Z0gigvRdFxGI+65I6cOAA+fn52Gw2HA4HTZo0ceum7gj0gnHwIHTuDFOmwG23WZ1G\nRAKFVwvGzz//zMsvv0x2djZ79uzBbrdjGAYlJSW0bNmSESNGMHbsWJo2bepWgHMOHOAFA+DLL6F3\nb1i3Dlq3tjqNiAQCr45hDBkyhGbNmrFo0SJ27NjBZ599xtq1a8nLy2Px4sU0adKEG2+80a2by6nF\nxcHkyWYL48gRq9OIiJg0huGnqquhTx9zxtTDD1udRkTqO5/MkurVq1etnhPPCgqCOXPMg5Y++8zq\nNCIiZzii9eDBg5SXl7N7925KS0trnt+3bx8FBQU+CdfQhYebx7qmpsLGjebUWxERq5y2YLz00kvM\nnDmTwsJCOnXqVPN8s2bNuPvuu30STuDGG8HlgjvugAULNNVWRKxz1jGMZ599lnvuucdXec6qoYxh\nHKuiArp1M1sa995rdRoRqY98tg5jzZo17Ny5kyPHTNlJS0tz68Z11RALBkBeHiQmwuLF0KWL1WlE\npL7xxP+dp+2SOuq2225jx44dxMfH06hRo5rnrSoYDVXr1vDiizBsGOTkwEUXWZ1IRBqas7Yw2rZt\nS25uLjY/6TxvqC2Mo/70JygogH/9S+MZIlJ7PplWGxsbS1FRUZ0unp+fT48ePWjXrh2xsbE8++yz\nAGRkZOBwOEhISCAhIYGlS5fWfCYzM5OoqChiYmJYvnx5ne4byJ56Cn74wTzeVUTEl87awkhKSmLT\npk106dKF888/3/yQzcaiRYvOevHi4mKKi4uJj49n//79dOrUiQULFjBv3jyaNWvGxIkTj3t/bm4u\nw4cPZ/369RQUFNC7d2+2bdtGUNCvda2htzDAHM/o2hXefhuuv97qNCJSH/hkDCMjI6POFw8LCyMs\nLAyApk2b0rZt25o1HKcKvnDhQlJTUwkJCSEiIoLIyEjWrVtH165d65whELVuDf/4B9x6K3zxBVx2\nmdWJRKQhOGvBSEpK8siNdu7cSU5ODl27dmX16tU899xz/OMf/6Bz58785S9/4aKLLqKwsPC44uBw\nOLRI8DT69oXx42HoUHOdxnnnWZ1IRALdaQvGddddx+rVq2natOlJA942m419+/bV+ib79+9n6NCh\nzJw5k6ZNmzJu3DimTp0KwMMPP8ykSZN49dVXT/nZUw22H9vqSUpK8lhRq28eegjWr4eJE80tRERE\njnK5XLhcLo9e0+ubD1ZWVjJw4ED69+/PhAkTTnp9586dDBo0iM2bN5OVlQXA5MmTAejXrx/Tp08n\nMTHx18AawzjO3r1wzTXw//6feVqfiMip+GSW1MSJE/nmm2/qdHHDMBg9ejROp/O4YnHsrKv58+fT\nvn17AAYPHkx2djYVFRXk5eWxfft2umiV2hk1bw7//jdMmmSuzxAR8ZazjmG0bduWO++8k8rKSkaN\nGkVqairNmzev1cVXr17N//3f/9GhQwcSEhIAePzxx3nrrbfYtGkTNpuN1q1b89JLLwHgdDpJSUnB\n6XQSHBzMrFmz/Gb9hz+LjYVZs2DIEPPQJbvd6kQiEohq3SW1ZcsWXn/9dd588026devG2LFj6dGj\nh7fznURdUqeXkQHLl8NHH8FvfmN1GhHxJz7pkgKoqqpiy5YtfPvtt7Rq1Yq4uDiefvpphg0b5tbN\nxbOmTjW3RP/DH0A1VUQ87awtjHvvvZd3332Xnj17MmbMmOPGFK6++mq2bt3q9ZDHUgvjzA4cgO7d\nzZ1t77/f6jQi4i98slvta6+9RkpKCk2aNDnptbKyMi7y8S54Khhnt2uXubPtSy/BwIFWpxERf+Cz\n7c2PuvPOO/n73//u1g3dpYJRO59/DoMGmeMZsbFWpxERq/lsDOOo9evXu3Uz8Z3ERPjrX82iUce9\nI0VEjnPWabXHCg0N9VYO8YIRI8yNCgcOhJUroWlTqxOJSH121i6pRYsWMXDgwON2jLWSuqTOjWHA\n2LFmK2PhQgg+px8RRCRQ+KRLau7cuURGRvLAAw+wZcsWt24mvmezwd/+BlVV8Mc/arqtiNRdrQa9\n9+7dy1tvvcXrr7+OzWbjjjvuIDU1lWbNmvki43HUwqibn382z84YNgx+2apLRBoQnw16N2/enKFD\nhzJs2DAKCwuZP38+CQkJNSfoif9r1gzee89sbbz5ptVpRKQ+OmsLY+HChbz++uts376dtLQ0br/9\ndkJDQykvL8fpdLJz504fRTWpheGer7+GXr3gn/+EPn2sTiMivuKTdRjp6emMHj2a609xFuiKFSvo\n3bu3WwHOlQqG+z79FG66Cd591zzqVUQCn1cLhmEYZ90ptjbv8TQVDM9YsgRGjYIPP4R27axOIyLe\n5tUxjKSkJJ588km2bdt20mtbt25lxowZ3HDDDW7dXKzzu9/B009Dv37g415FEamnTtvCOHz4MG+8\n8QZvvfUWX3/9Nc2aNcMwDPbv309sbCwjRoxg+PDhnOfjw6TVwvCs554zH6tW6RwNkUDms72kqqqq\n2LNnDwCXXHIJjRo1cuum7lDB8LyMDFi0yNx3ysd7SYqIj3i1YHTq1Ilu3brRv39/kpKS+I2fnMij\nguF5hgETJpgbFi5fDhdeaHUiEfE0rxaMyspKPv30U5YtW4bL5eLiiy+mX79+9O/fn+joaLdu6g4V\nDO8wDBg/3px2u3Sp9p0SCTQ+3d68oKCAZcuW8f777/Pdd9/RtWtXZs2a5dbN60IFw3uqq819p/Ly\nYPFiaNzY6kQi4ik+Pw/jqKqqKtauXct1113n1s3rQgXDu6qq4Pbb4T//MTcr9JOeSBFxk8/Pwzhq\n3LhxlhQL8b5GjeC116B5cxg6FCoqrE4kIv7itC2M0tLSU37AMAw6dOhAQUGBV4OdjloYvlFZaW5U\nWFkJ77wD559vdSIRcYdXu6SCgoK48sorT/mhgoICKiz60VMFw3cqK2H4cHOn2/nz4YILrE4kInXl\n1S6pq666CpfLRV5e3kkPey1XeOXn59OjRw/atWtHbGxsze62paWlJCcnEx0dTZ8+fSgrK6v5TGZm\nJlFRUcTExLB8+XK3/nDinpAQeOstaNkSBgyAAwesTiQiVjptwZgwYQI//fTTKV+7//77a3XxkJAQ\n/vrXv/LNN9+wdu1aXnjhBb799luysrJITk5m27Zt9OrVi6ysLAByc3OZO3cuubm5LFu2jPHjx1Nd\nXV2HP5Z4SnAw/OMfcOWV0L+/2doQkYapTrOk6mrIkCHcfffd3H333axcuRK73U5xcTFJSUls2bKF\nzMxMgoKCePDBBwHo168fGRkZdD1mS1V1SVmjutpcp/Hll+Y6Da0IF6lfLJslVRc7d+4kJyeHxMRE\nSkpKarq17HY7JSUlABQWFuJwOGo+43A4LBtcl+MFBZmHL11zjXmexu7dVicSEV8L9sVN9u/fzy23\n3MLMmTNPOtbVZrOdcYv0U72WkZFR83VSUhJJSUmeiipnYLPBzJkwdSp06wYffABXXGF1KhE5FZfL\nhcvl8ug1vV4wKisrueWWWxg5ciRDhgwBqOmKCgsLo6ioiNDQUADCw8PJz8+v+eyuXbsIDw8/6ZrH\nFgzxLZsNHn3UHAjv1g2WLQOn0+pUInKiE3+Ynj59utvXrFWX1L59+wDYu3fvOV3cMAxGjx6N0+lk\nwoQJNc8PHjyYOXPmADBnzpyaQjJ48GCys7OpqKggLy+P7du306VLl3O6p/jGhAnw2GPQs6e5aaGI\nBL5aDXp37NiRjRs3kpCQQE5OTq0v/umnn3L99dfToUOHmq6lzMxMunTpQkpKCj/++CMRERHMmzeP\ni34ZRX388ceZPXs2wcHBzJw5k759+x4fWIPefmXxYrjjDnjzTUhOtjqNiJyOz/aSOloozrVgeIMK\nhv9ZtQpuucUc30hNtTqNiJyKJ/7v9MmgtwS27t3Ns8EHDIAffoAHHzTHOkQksPhsWq0Etvbt4bPP\nIDsb7roLjhyxOpGIeJoKhnhMeLjZPfXDDzB4sFaFiwQaFQzxqGbN4N13zeJxww1QWGh1IhHxlFoV\njLlz5wKQnZ3t1TASGEJC4O9/N8/T6NoVNm60OpGIeIJP95LyBM2Sql/eeQfGjYNZs+D3v7c6jUjD\nZdkRrVZSwah/cnJgyBBzvcbUqea+VCLiWyoYUm8UF8PNN8Nll8GcOdCkidWJRBoWr+5WO3LkSACe\neeYZt24gAhAWBh9/DE2bmntQ/fCD1YlE5FydtmBs2LCBwsJCZs+eTWlp6UkPkXN1/vnw2mswciQk\nJpq73YpI/XHaLqlnn32Wv/3tb+zYsYPLLrvs+A/ZbOzYscMnAU+kLqnAsHKluY3I3XfD5Mka1xDx\nNp+MYdx11128+OKLbt3Ek1QwAkdBgTlzqlUrc1xDp/iJeI8GvaXeq6iAiRPh/ffh3/82txgREc+r\nV0e0ipzKeefB88/DtGnm2RqzZ4N+HhDxT2phiN/IzYVhw6BDB3jxRXObERHxDLUwJKA4nebpfU2a\nQKdO5oI/EfEfKhjiVxo3NveheuQR6NvX7K5Sg1LEP6hLSvzWd9/BrbeaO9++8oo5m0pE6kZdUhLQ\nIiNhzRpo2xbi4mDJEqsTiTRsamFIvfDJJ5CWBv37w1NPaS8qkXOlFoY0GNdfD19+CQcOQMeOsH69\n1YlEGh61MKTemTsX/vQn85yNP//ZXMshImemFoY0SMOGmVNuN2yAzp11op+Ir3i1YIwaNQq73U77\nY/Z7yMjIwOFwkJCQQEJCAkuXLq15LTMzk6ioKGJiYli+fLk3o0k9Fx5unh1+//3Qrx88/DAcPmx1\nKpHA5tUuqVWrVtG0aVPS0tLYvHkzANOnT6dZs2ZMnDjxuPfm5uYyfPhw1q9fT0FBAb1792bbtm0E\nnbCNqbqk5ERFRXDXXbBjh7l9eufOVicS8T9+3yXVvXt3WrRocdLzpwq9cOFCUlNTCQkJISIigsjI\nSNatW+fNeBIgLr0UFiyAKVNgwAC47z7Yv9/qVCKBx5IxjOeee464uDhGjx5NWVkZAIWFhTgcjpr3\nOBwOCgoKrIgn9ZDNBsOHw+bNUFICsbHw3ntWpxIJLMG+vuG4ceOYOnUqAA8//DCTJk3i1VdfPeV7\nbTbbKZ/PyMio+TopKYmkpCRPx5R6KjQU/vlP8zS/cePMczZmzjRbISINicvlwuVyefSaPi8YoaGh\nNV+PGTOGQYMGARAeHk5+fn7Na7t27SI8PPyU1zi2YIicSnKy2dr43/81d7995BG4805o1MjqZCK+\nceIP09OnT3f7mj7vkioqKqr5ev78+TUzqAYPHkx2djYVFRXk5eWxfft2unTp4ut4EkAuuAAeeww+\n/hjefNM8R3ztWqtTidRfXm1hpKamsnLlSvbs2cPll1/O9OnTcblcbNq0CZvNRuvWrXnppZcAcDqd\npKSk4HQ6CQ4OZtasWaftkhI5F7Gx5tYib7wBt9xi7oKblWV2X4lI7WmltzQo+/aZ3VNz5sDUqeY4\nR7DPO2ZFfE9neovUUW6uub3I7t3w9NPQu7fViUS8SwVDxA2GYa7fuP9+iImBJ580t1IXCUR+v3BP\nxJ/ZbHDTTWZro2dPc0fcu++GPXusTibin1QwpME77zyYOBG+/RaCgsxWxpNPwqFDVicT8S8qGCK/\nuOQSePZZWLUKVq+G6GiYPRuOHLE6mYh/0BiGyGl89hlMnmwOjD/2GAwZYnZjidRHGvQW8TLDgGXL\nzI0Nf/MbyMyEHj2sTiVy7lQwRHykuhqys821G1dcAdOnQ/fuVqcSqT3NkhLxkaAgczfcLVtg5EhI\nTzfXbqxebXUyEd9RwRA5B8HBcMcdsHUrpKbCiBHmViPao0oaAhUMkToICYHRo2HbNnN/qltvNVsc\nH31kjnuIBCKNYYh4QGWluSNuZia0aAEPPQQDB2pWlfgPDXqL+JmqKpg/35yGW11tzq4aOlQbHIr1\nVDBE/NTR6biPPQaFhXDvvebYR9OmVieThkqzpET8lM0G/fvDp5+a53CsXAmtW5tdVcecISZSr6hg\niHjZb38L77xjzqTatw+cThg1yjxCVqQ+UcEQ8ZE2beD55+G77+Cqq8zpuD17mlusV1VZnU7k7DSG\nIWKRigr417/MDQ+LiuCPfzSn6l58sdXJJBBpDEOkHjvvPHPx32efwdtvm11UbdrAnXdCTo7V6URO\nphaGiB8pLoZXXoGXX4awMLjrLhg2DBo3tjqZ1HeaVisSoKqqYOlSePFFswVy223whz+YA+YidaEu\nKZEA1aiRuVJ88WLYuBGaNYNevaBbN/NQp/37rU4oDZFaGCL1RGUlLFliFoxPPjH3sBo1ypy2qy1I\n5Gz8voUxatQo7HY77du3r3mutLSU5ORkoqOj6dOnD2VlZTWvZWZmEhUVRUxMDMuXL/dmNJF6JyQE\nbrwRFi6E3FyIijJXjzud8MQTUFBgdUIJdF4tGHfccQfLli077rmsrCySk5PZtm0bvXr1IisrC4Dc\n3Fzmzp1Lbm4uy5YtY/z48VRXV3sznki9deml8OCD5vkcL79s7prbvj306QP//Ke6rMQ7vFowunfv\nTosWLY57btGiRaSnpwOQnp7OggULAFi4cCGpqamEhIQQERFBZGQk69at82Y8kXrPZjPHNV55xWxh\njB5tngzocEBaGqxYoUWB4jk+H/QuKSnBbrcDYLfbKSkpAaCwsBCHw1HzPofDQYHa2CK1dsEF5hTc\n994zD3jq2NFshTgc8D//Y25NouE/cYelmy7bbDZsZxitO91rGRkZNV8nJSWRlJTk4WQi9ZvdDhMm\nmI+tW81Wx+23w+HD5mFPt94KHTposDyQuVwuXC6XR6/p84Jht9spLi4mLCyMoqIiQkNDAQgPDyc/\nP7/mfbt27SI8PPyU1zi2YIjImV19NUybBlOnwpdfwltvweDB5mLA3//ePK+jfXsVj0Bz4g/T06dP\nd/uaPu8lvFbGAAAL80lEQVSSGjx4MHPmzAFgzpw5DBkypOb57OxsKioqyMvLY/v27XTp0sXX8UQC\nls0G8fEwYwbk5ZnTc8vLzeJx9dXm1usbN6rbSk7Pq+swUlNTWblyJXv27MFut/PII49w4403kpKS\nwo8//khERATz5s3joosuAuDxxx9n9uzZBAcHM3PmTPr27XtyYK3DEPEow4ANG8wt2N9+2/z9LbfA\nkCHQtau5iFDqP20NIiIeZRiwaZN5zOzChebeVoMHm+s/eveG3/zG6oRSVyoYIuJVO3aYhWPBArOQ\nJCfDoEHmaYK/DD9KPaGCISI+s2cPvPuuub/Vhx9CTIy539WAAebYiAbN/ZsKhohY4vBhWLXKXPOx\neDEcPAi/+5356NkTLrzQ6oRyIhUMEfEL27aZhWPZMnM79o4doV8/8xja+HgI0r7YllPBEBG/U14O\nK1eaxWPZMigrMwtHcrK5Rftll1mdsGFSwRARv5eXB++/b+5r9dFH5saJvXubjxtuUPeVr6hgiEi9\nUlVlnle+YoX5+Pxzc4uSnj2hRw/zbI8LLrA6ZWBSwRCReu3gQfj0U/j4Y3C54KuvoFMnSEoyC0jX\nrlr74SkqGCISUPbvh9WrzQLy8cfwzTdmAene3Xxce615XK2cOxUMEQlo+/aZs65WrTKPpd240Vz/\ncbSAdOumBYS1pYIhIg3K4cOwfr1ZQFatgjVroFUrs+Vx7bVw3XXmkbWaxnsyFQwRadCqq83zzdes\n+fXxn/+YYx+//a35a5cucMLBnw2SCoaIyAl27za7sVavNmdhbdhgrv1ITPz10aEDnHee1Ul9SwVD\nROQsjhwxWyGff24eU/v55+bakLg46NzZfFxzDURHB/ZW7ioYIiJ1sG+fOYD+xRfmmMgXX5gtk44d\nfy0iHTtCZGTgjIeoYIiIeEhpqdl9dbSA5OTAf/9r7oWVkGAWkI4doW1bCPb54dbuU8EQEfGi0lKz\ncGzc+OuvP/4I7dqZXVrx8eavHTpA8+ZWpz0zFQwRER/7+WfYvBm+/NI8VOrLL+Hrr831IHFxvxaQ\n2Fho08Z/xkVUMERE/EBVFXz3nVk8vvzS3OLk66/NKb5t20L79uYjNtb8NSzM9wdOqWCIiPixffvM\n7U02b/718f33sHOn71seKhgiIlIrnvi/M0AmjImIiLdZNjksIiKCCy+8kEaNGhESEsK6desoLS1l\n2LBh/PDDD0RERDBv3jwuuugiqyKKiMgxLGth2Gw2XC4XOTk5rFu3DoCsrCySk5PZtm0bvXr1Iisr\ny6p4XuNyuayO4Bblt059zg7KHwgs7ZI6sT9t0aJFpKenA5Cens6CBQusiOVV9f2bTvmtU5+zg/IH\nAktbGL1796Zz5868/PLLAJSUlGC32wGw2+2UlJRYFU9ERE5g2RjG6tWrufTSS9m9ezfJycnExMQc\n97rNZsPm64nKIiJyWn4xrXb69Ok0bdqUl19+GZfLRVhYGEVFRfTo0YMtW7Yc997IyEi+//57i5KK\niNRPbdq04bvvvnPrGpYUjPLycqqqqmjWrBkHDhygT58+TJs2jRUrVtCyZUsefPBBsrKyKCsrC8iB\nbxGR+siSgpGXl8dNN90EwJEjRxgxYgRTpkyhtLSUlJQUfvzxR02rFRHxM37RJSUiIv7Pr1Z6L1u2\njJiYGKKiopgxY8Yp33PPPfcQFRVFXFwcOTk5Nc+PGjUKu91O+/btfRX3JHXNn5+fT48ePWjXrh2x\nsbE8++yzvowN1D37oUOHSExMJD4+HqfTyZQpU3wZu4Y73zsAVVVVJCQkMGjQIF/EPYk7+SMiIujQ\noQMJCQl06dLFV5GP407+srIyhg4dStu2bXE6naxdu9ZXsWvUNf/WrVtJSEioeTRv3tzn/37d+bvP\nzMykXbt2tG/fnuHDh3P48OEz38zwE0eOHDHatGlj5OXlGRUVFUZcXJyRm5t73Hvee+89o3///oZh\nGMbatWuNxMTEmtc++eQTY+PGjUZsbKxPcx/lTv6ioiIjJyfHMAzD+Pnnn43o6OiTPuuv2Q3DMA4c\nOGAYhmFUVlYaiYmJxqpVq3yW3TDcz28YhvGXv/zFGD58uDFo0CCf5T7K3fwRERHGf//7X59mPpa7\n+dPS0oxXX33VMAzze6isrMx34Q3PfP8YhmFUVVUZYWFhxo8//uiT3IbhXva8vDyjdevWxqFDhwzD\nMIyUlBTj9ddfP+P9/KaFsW7dOiIjI4mIiCAkJIRbb72VhQsXHveeYxf2JSYmUlZWRnFxMQDdu3en\nRYsWPs99VF3zl5SUEBYWRnx8PABNmzalbdu2FBYW1ovsAI0bNwagoqKCqqoqLr74Yp9l90T+Xbt2\nsWTJEsaMGWPJxpbu5oeTF8H6kjv59+7dy6pVqxg1ahQAwcHBNPfxSUSe+PsHWLFiBW3atOHyyy+v\nF9kvvPBCQkJCKC8v58iRI5SXlxMeHn7G+/lNwSgoKDjuL9rhcFBQUHDO77FKXfPv2rXruPfs3LmT\nnJwcEhMTvRv4LLnOJXtVVRXx8fHY7XZ69OiB0+n0TfAzZDuX7517772XJ598kiCLDm92N/+pFsH6\nkjvfP3l5ebRq1Yo77riDjh07MnbsWMrLy32W/XTZ6vJvNzs7m+HDh3s37Anc+d65+OKLmTRpEldc\ncQWXXXYZF110Eb179z7j/fymYNR2kd6JP0n5y+I+T+Tfv38/Q4cOZebMmTRt2tSj+c7E3eyNGjVi\n06ZN7Nq1i08++cTnWyjUNb9hGCxevJjQ0FASEhIs+ym9rvmP+vTTT8nJyWHp0qW88MILrFq1ypPx\nzsqd758jR46wceNGxo8fz8aNG2nSpInPp9J74t9uRUUF7777Lr///e89mu1s3Pne+f7773nmmWfY\nuXMnhYWF7N+/nzfeeOOM1/GbghEeHk5+fn7N7/Pz83E4HGd8z65du87ahPIVd/NXVlZyyy23cNtt\ntzFkyBDfhD5Nrrr+3Tdv3pwBAwbwxRdfeDfwCdzJv2bNGhYtWkTr1q1JTU3lo48+Ii0tzWfZT5Xt\nXP/+L7vsMgBatWrFTTfdVLOZp6+4k9/hcOBwOLjmmmsAGDp0KBs3bvRN8NNkq8v3/9KlS+nUqROt\nWrXyfuAz5DqX7F988QXXXnstLVu2JDg4mJtvvpk1a9ac+YYeGntxW2VlpXHVVVcZeXl5xuHDh886\nePPZZ5+dNPCUl5dn2aC3O/mrq6uNkSNHGhMmTPB5bsNwL/vu3buNn376yTAMwygvLze6d+9urFix\not7kP5bL5TIGDhzok8zHcif/gQMHjH379hmGYRj79+83rr32WuP999+vN/kNwzC6d+9ubN261TAM\nw5g2bZrxwAMP+C684Znvn2HDhp11wNgb3Mmek5NjtGvXzigvLzeqq6uNtLQ04/nnnz/j/fymYBiG\nYSxZssSIjo422rRpYzz++OOGYRjGiy++aLz44os17/njH/9otGnTxujQoYOxYcOGmudvvfVW49JL\nLzXOO+88w+FwGLNnz643+VetWmXYbDYjLi7OiI+PN+Lj442lS5fWi+xfffWVkZCQYMTFxRnt27c3\nnnjiCZ/mdjf/sVwulyWzpAyj7vm///57Iy4uzoiLizPatWtX89n6kt8wDGPTpk1G586djQ4dOhg3\n3XSTz2dJuZt///79RsuWLWsKt6+5k33GjBmG0+k0YmNjjbS0NKOiouKM99LCPRERqRW/GcMQERH/\npoIhIiK1ooIhIiK1ooIhIiK1ooIhIiK1ooIhIiK1ooIhIiK1ooIhIiK1ooIh4qZp06Yxc+bMmt//\n+c9/tuQQLBFv00pvETf98MMP3HzzzWzYsIHq6mqio6NZv369peeziHhDsNUBROq7K6+8kpYtW7Jp\n0yaKi4vp2LGjioUEJBUMEQ8YM2YMr732GiUlJTWnx4kEGnVJiXhAZWUlsbGxVFVVsX37dr852EvE\nk9TCEPGAkJAQevbsSYsWLVQsJGCpYIh4QHV1NWvXruWdd96xOoqI12harYibcnNziYqKonfv3rRp\n08bqOCJeozEMERGpFbUwRESkVlQwRESkVlQwRESkVlQwRESkVlQwRESkVlQwRESkVv4/Qk4uQeqi\n2poAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x79c25f8>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.4,Page number:299"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "m = 0.57 \n",
      "D = 0.738  \t\t\t\t# [tower diameter, m]\n",
      "G = 180.0  \t\t\t\t# [rate of gas entering the tower, kmole/h]\n",
      "L = 151.5  \t\t\t\t# [rate of liquid leaving the tower, kmole/h]\n",
      "\t# Amount of ethanol absorbed \n",
      "M = G*0.02*0.97  \t\t\t# [kmole/h]\n",
      "\n",
      "#Calculation\n",
      "\n",
      "import math\n",
      "\t# Inlet gas molar velocity\n",
      "Gmy1 = G*4/(3600*math.pi*D**2)  \t# [kmole/square m.s]\n",
      "\t# Outlet gas velocity\n",
      "Gmy2 = (G-M)*4/(3600*math.pi*D**2)  \t# [kmole/square m.s]\n",
      "\t# Average molar gas velocity\n",
      "Gmy = (Gmy1+Gmy2)/2  \t\t\t# [kmole/square m.s]\n",
      "\n",
      "\t# Inlet liquid molar velocity\n",
      "Gmx2 = L*4/(3600*math.pi*D**2)  \t# [kmole/square m.s]\n",
      "\t# Outlet liquid molar velocity\n",
      "Gmx1 = (L+M)*4/(3600*math.pi*D**2)  \t# [kmole/square m.s]\n",
      "\n",
      "\t# Absorption factor at both ends of the column:\n",
      "A1 = Gmx1/(m*Gmy1) \n",
      "A2 = Gmx2/(m*Gmy2) \n",
      "\t# Geometric average\n",
      "A = math.sqrt(A1*A2) \n",
      "\n",
      "y1 = 0.02 \n",
      "\t# For 97% removal of the ethanol\n",
      "y2 = 0.03*0.02 \n",
      "\t# Since pure water is used \n",
      "x2 = 0 \n",
      "\t# From equation 5.24\n",
      "Ntog = math.log((y1-m*x2)/(y2-m*x2)*(1-1/A)+1/A)/(1-1/A) \n",
      "\n",
      "\t# From example 4.4\n",
      "\t# ky*ah = 0.191 kmole/cubic m.s\n",
      "\t# kl*ah = 0.00733 s**-1\n",
      "kyah = 0.191  \t\t\t\t# [kmole/cubic m.s]\n",
      "klah = 0.00733  \t\t\t# [s**-1]\n",
      "rowl = 986  \t\t\t\t# [kg/cubic m]\n",
      "Ml = 18.0  \t\t\t\t# [gram/mole]\n",
      "c = rowl/Ml  \t\t\t\t# [kmole/cubic m]\n",
      "kxah = klah*c  \t\t\t\t# [kmole/cubic m.s]\n",
      "\n",
      "\t# Overall volumetric mass transfer coefficient\n",
      "Kyah = (kyah**-1 + m/kxah)**-1  \t# [kmole/cubic m.s]\n",
      "\n",
      "\t# From equation 5.22\n",
      "Htog = Gmy/Kyah  \t\t\t\t# [m]\n",
      "\t# The packed height is given by equation 5.21,\n",
      "Z = Htog*Ntog  \t\t\t\t\t# [m]\n",
      "\n",
      "#Result\n",
      "print\"The packed height of an ethanol absorber is\",round(Z,2),\"m.\\n\\n\" "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The packed height of an ethanol absorber is 5.66 m.\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.5,Page number:302"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variable declaration\n",
      "# a = CH4 b = C5H12\n",
      "Tempg = 27 \t\t\t\t\t\t\t# [OC]\n",
      "Tempo = 0 \t\t\t\t\t\t\t# [base temp,OC]\n",
      "Templ = 35 \t\t\t\t\t\t\t# [OC]\n",
      "xa = 0.75 \t\t\t\t\t\t\t# [mole fraction of CH4 in gas]\n",
      "xb = 0.25 \t\t\t\t\t\t\t# [mole fraction of C5H12 in gas]\n",
      "M_Paraffin = 200 \t\t\t\t\t\t# [kg/kmol]\n",
      "hb = 1.884 \t\t\t\t\t\t\t# [kJ/kg K]\n",
      "\n",
      "\n",
      "Ha = 35.59 \t\t\t\t\t\t\t# [kJ/kmol K]\n",
      "Hbv = 119.75 \t\t\t\t\t\t\t# [kJ/kmol K]\n",
      "Hbl = 117.53 \t\t\t\t\t\t\t# [kJ/kmol K]\n",
      "Lb = 27820 \t\t\t\t\t\t\t# [kJ/kmol]\n",
      "\t\t# M = [Temp (OC) m]\n",
      "import math\n",
      "import matplotlib\n",
      "%matplotlib inline\n",
      "from scipy.optimize import fsolve\n",
      "from numpy import *\n",
      "from pylab import *\n",
      "\n",
      "#Calculation\n",
      "\n",
      "M = matrix([[20,0.575],[25,0.69],[30,0.81],[35,0.95],[40,1.10],[43,1.25]])\n",
      "\t# Basis: Unit time\n",
      "GNpPlus1 = 1.0 \t\t\t\t\t\t\t# [kmol]\n",
      "yNpPlus1 = 0.25 \t\t\t\t\t\t# [kmol]\n",
      "HgNpPlus1 = ((1-yNpPlus1)*Ha*(Tempg-Tempo))+(yNpPlus1*(Hbv*(Tempg-Tempo)+Lb)) # [kJ/kmol]\n",
      "L0 = 2.0 \t\t\t\t\t\t\t# [kmol]\n",
      "x0 = 0 \t\t\t\t\t\t\t\t# [kmol]\n",
      "HL0 = ((1-x0)*hb*M_Paraffin*(Templ-Tempo))+(x0*hb*(Templ-Tempo))\t # [kJ/kmol]\n",
      "C5H12_absorbed = 0.98*xb \t\t\t\t\t# [kmol]\n",
      "C5H12_remained = xb-C5H12_absorbed \n",
      "G1 = xa+C5H12_remained \t\t\t\t\t\t# [kmol]\n",
      "y1 = C5H12_remained/G1 \t\t\t\t\t\t# [kmol]\n",
      "LNp = L0+C5H12_absorbed \t\t\t\t\t# [kmol]\n",
      "xNp = C5H12_absorbed/LNp \t\t\t\t\t# [kmol]\n",
      "\t# Assume:\n",
      "Temp1 = 35.6 \t\t\t\t\t\t\t# [OC]\n",
      "Hg1 = ((1-y1)*Ha*(Temp1-Tempo))+(y1*(Hbv*(Temp1-Tempo)+Lb)) \t# [kJ/kmol]\n",
      "Qt = 0 \n",
      "def f30(HlNp):\n",
      "    return(((L0*HL0)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(G1*Hg1)+Qt)) \n",
      "HlNp = fsolve(f30,2) \n",
      "\n",
      "def f31(TempNp):\n",
      "    return(HlNp-(((1-x0)*hb*M_Paraffin*(TempNp-Tempo))+(x0*hb*(TempNp-Tempo)))) \n",
      "TempNp = fsolve(f31,35.6) \n",
      "\t# At Temp = TempNp:\n",
      "mNp = 1.21 \n",
      "yNp = mNp*xNp \t\t\t\t\t\t\t# [kmol]\n",
      "GNp = G1/(1-yNp) \t\t\t\t\t\t# [kmol]\n",
      "HgNp = ((1-yNp)*Ha*(TempNp-Tempo))+(yNp*(Hbv*(TempNp-Tempo)+Lb)) # [kJ/kmol]\n",
      "\t# From equation 5.28 with n = Np-1\n",
      "def f32(LNpMinus1):\n",
      "    return(LNpMinus1+GNpPlus1-(LNp+GNp)) \n",
      "LNpMinus1 = fsolve(f32,2) \t\t\t\t\t# [kmol]\n",
      "\n",
      "\t# From equation 5.29 with n = Np-1\n",
      "def f33(xNpMinus1):\n",
      "    return(((LNpMinus1*xNpMinus1)+(GNpPlus1*yNpPlus1))-((LNp*xNp)+(GNp*yNp))) \n",
      "xNpMinus1 = fsolve(f33,0) # [kmol]\n",
      "\n",
      "\t# From equation 5.30 with n = Np-1\n",
      "def f34(HlNpMinus1):\n",
      "    return(((LNpMinus1*HlNpMinus1)+(GNpPlus1*HgNpPlus1))-((LNp*HlNp)+(GNp*HgNp))) \n",
      "HlNpMinus1 = fsolve(f34,0) # [kJ/kmol]\n",
      "def f35(TempNpMinus1):\n",
      "    return(HlNpMinus1-(((1-xNpMinus1)*hb*M_Paraffin*(TempNpMinus1-Tempo))+(xNpMinus1*hb*(TempNpMinus1-Tempo)))) \n",
      "TempNpMinus1 = fsolve(f35,42) # [OC]\n",
      "\n",
      "\t# The computation are continued upward through the tower in this manner until the gas \tcomposition falls atleast to 0.00662.\n",
      "\t# Results = [Tray No.(n) Tn(OC) xn yn]\n",
      "Results = matrix([[4.0,42.3,0.1091,0.1320],[3,39.0,0.0521,0.0568],[2,36.8,0.0184,0.01875],[1,35.5,0.00463, 0.00450]]) \n",
      "figure(1)\n",
      "a1=plot(Results[:,0],Results[:,3]) \n",
      "xlabel(\"Tray Number\") \n",
      "ylabel(\"mole fraction of C5H12 in gas\") \n",
      "show(a1)\n",
      "figure(2)\n",
      "a2=plot(Results[:,0],Results[:,1]) \n",
      "\n",
      "xlabel(\"Tray Number\") \n",
      "ylabel(\"Temparature( degree C)\") \n",
      "\n",
      "show(a2)\n",
      "# For the cquired y1\n",
      "Np = 3.75\n",
      " \n",
      "\n",
      "#result\n",
      "print\"The No. of trays will be\",round(Np)\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQTWVrgp1O2cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4995200e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEPCAYAAACgFqixAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4977599c90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The No. of trays will be 4.0\n"
       ]
      }
     ],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
 ]
}