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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Chapter 14 The Tanks-in-Series Model"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 14.1 pageno : 329"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"#Original and new length(m)\n",
"import math \n",
"\n",
"# Variables\n",
"L1 = 32. # diameter pipe\n",
"L2 = 50. # pipeline length \n",
"sigma1 = 8. # bottles \n",
"\n",
"# Calculations\n",
"# For small deviaqtion from plug flow,sigma_sqr is directly proportional to L\n",
"sigma2 = sigma1*math.sqrt(L2/L1);\n",
"\n",
"# Results\n",
"print \" No of bottles of rose expected is %.f\"%(sigma2)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" No of bottles of rose expected is 10\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 14.2 pageno : 330"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# Variables\n",
"sigma1 = 14. # hours\n",
"sigma2 = 10.5; # hours\n",
"L1 = 119. # ohio miles apart\n",
"\n",
"# Calculations\n",
"#spread of curve is directly proportional to sqrt of distance from origin\n",
"L = sigma1**2*L1/(sigma1**2-sigma2**2);\n",
"\n",
"# Results\n",
"print \" The dumping of toxic phenol must have occured within %.f miles upstream of cincinnati\"%(L)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" The dumping of toxic phenol must have occured within 272 miles upstream of cincinnati\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 14.3 pageno : 332"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# Variables\n",
"# from figure\n",
"vo = 1.; \n",
"t1 = 1./6;\n",
"t2 = 1.;\n",
"t3 = 11./6;\n",
"w = 1./10;\n",
"\n",
"# Calculations\n",
"A2_by_A1 = 0.5;\n",
"R = A2_by_A1/(1-A2_by_A1);\n",
"#From the location of 1st peak\n",
"V1 = (R+1)*vo*t1;\n",
"#From the time between peaks\n",
"V2 = (R*vo)*((t2-t1)-(t1));\n",
"#From fig 14.3\n",
"N = 1+(2*(t1/w))**2;\n",
"\n",
"# Results\n",
"print \" The reflux ratio is %.f\"%(R)\n",
"print \" The volume of 1st tank is %.3f\"%( V1)\n",
"print \" The volume of 2nd tank is %.3f\"%(V2)\n",
"print \" The number of tanks are %.f \"%(N)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" The reflux ratio is 1\n",
" The volume of 1st tank is 0.333\n",
" The volume of 2nd tank is 0.667\n",
" The number of tanks are 12 \n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 14.4 page no :333"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"%pylab inline\n",
"\n",
"from matplotlib.pyplot import *\n",
"import math \n",
"from numpy import *\n",
"\n",
"# Variables\n",
"# from figure E14.4a\n",
"t2 = 280.\n",
"t1 = 220.\n",
"sigma1_sqr = 100.\n",
"sigma2_sqr = 1000.\n",
"\n",
"# Calculations\n",
"dt = t2-t1;\n",
"dsigma_sqr = sigma2_sqr-sigma1_sqr;\n",
"N = dt**2/dsigma_sqr;\n",
"\n",
"E = zeros(200)\n",
"\n",
"for t in range(200):\n",
" E[t] = ((t**(N-1))*(N**N)*math.exp(-t*N/dt))/((math.factorial(N-1))*(dt**N));\n",
"\n",
"t = zeros(200) \n",
"for i in range(200):\n",
" t[i] = i;\n",
"\n",
"# Results\n",
"plot(t,E)\n",
"xlabel(\"t, s\")\n",
"ylabel(\"E, s**-1\")\n",
"show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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BBx8EnnlG7UqIqDUME5Xt2CEXw6K2PfYYsH498N13aldCRC1RLExMJhNiY2Oh0+mQm5vb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\nfT/rT0BE5BMUuWMXGRkJi8X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"text": [
"<matplotlib.figure.Figure at 0x2625a50>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
}
],
"metadata": {}
}
]
}
|
