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authorkinitrupti2017-05-12 18:53:46 +0530
committerkinitrupti2017-05-12 18:53:46 +0530
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+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:844e839da5b715e4c203153163c0d8334c07e355e78986d7eaa05ddf88c71ee4"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 13:PHASE EQUILIBRIUM:VAPOUR-LIQUID EQUILIBRIUM OF MIXTURES"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Example 13.1, Page No:605"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "#Variable declaration\n",
+ "# Take freon 22 as component 1 and Freon 12 as component 2\n",
+ "# (a). y-x diagram at 40 oC\n",
+ "P1sat=15.335; # Saturation pressure of Freon 22 at 40oC in bar\n",
+ "P2sat=9.607; # Saturation pressure of Freon 12 at 40oC in bar\n",
+ "from pylab import *\n",
+ "figure(1); # For Plotting y-x Diagram\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#Calculation for (a)\n",
+ "a=P1sat/P2sat;\n",
+ "x1=linspace(0,1.0,3);\n",
+ "y1=(a*x1)/(1+x1*(a-1)); # y Function\n",
+ "plt.plot (x1,y1,'-*b',label='y1');\n",
+ "plt.plot (x1,x1,'-<r',label='x1'); # plot comment\n",
+ "plt.title (\"(a).y-x diagram for the mixture at 40 oC\");\n",
+ "plt.xlabel(\" x1 \");\n",
+ "plt.ylabel(\" y1 \");\n",
+ "plt.legend(loc='upper left')\n",
+ "\n",
+ "#Result for (a)\n",
+ "print \"(a). y-x diagram at 40 oC\",\"\\n figure 1\"\n",
+ "plt.show();\n",
+ "\n",
+ "#Calculation for (b)\n",
+ "# (b). p-x-y diagram at 40 oC\n",
+ " # By using the following relation calculate p value for various value of x1,y1\n",
+ " # p=(x1*P1sat)+(1-x1)*P2sat\n",
+ "x1=[0,0.2,0.5,0.8,1];\n",
+ "y1=[0,0.285,0.615,0.865,1];\n",
+ "p=[9.607,10.7526,12.471,14.1894,15.335];\n",
+ "figure(2);\n",
+ "plt.plot (x1,p,'-*b',label='Liquid out')\n",
+ "plt.plot(y1,p,'-<r',label='Vapour');\n",
+ "plt.title (\"(b).P-y-x diagram for the mixture at 40 oC\");\n",
+ "plt.xlabel(\" x1 & y1 \");\n",
+ "plt.ylabel(\" p in bar \");\n",
+ "plt.legend(loc='upper left')\n",
+ "\n",
+ "#Result for (b)\n",
+ "print \"(b). p-x-y diagram at 40 oC\",\"\\n figure 2\"\n",
+ "plt.show();\n",
+ "\n",
+ "#Calculation for (c)\n",
+ "# (c).t-x-y diagram at 10 bar\n",
+ "# for any value of x1 at p=10 bar, the bubble temperature can be found by trial and error from the following relation\n",
+ " # p=10 bar =(x1*P1sat)+(1-x1)*P2sat\n",
+ "T1sat=23.7; # Saturation temperature of Freon 22 at 10 bar in oC\n",
+ "T2sat=41.6; # Saturation temperature of Freon 12 at 10 bar in oC\n",
+ "# Thus, for x1=0.5, we find that t=31 oC. \n",
+ "x1=0.5; # Let assume\n",
+ "P1sat=12.186; # Saturation pressure of Freon 22 at 31oC in bar\n",
+ "P2sat=7.654; # Saturation pressure of Freon 12 at 31oC in bar\n",
+ "a=P1sat/P2sat;\n",
+ "y1=(a*x1)/(1+x1*(a-1)); # y Function\n",
+ "# For different value of x1 the values of t,y1 are calculated by above expression and given below\n",
+ "x1=[0,0.5,1]; \n",
+ "y1=[0,0.614,1]; \n",
+ "t=[41.6,31,23.7];\n",
+ "figure(3);\n",
+ "plt.plot (x1,t,'-*b',label='f');\n",
+ "plt.plot (y1,t,'-<r',label='g');\n",
+ "plt.title (\"(c).t-y-x diagram for the mixture at 10 bar\");\n",
+ "plt.xlabel(\" x1 & y1 \");\n",
+ "plt.ylabel(\" t in oC \");\n",
+ "plt.legend(loc='upper right');\n",
+ "\n",
+ "#Result for (c)\n",
+ "print \"(c).t-x-y diagram at 10 bar\",\"\\n figure 3\"\n",
+ "plt.show();\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "(a). y-x diagram at 40 oC \n",
+ " figure 1\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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I5+7cYc/hly4BCxawayNUu2KaNJrxWEJ8fDzi4+MbpC7OAgOPx6uxH2T58uWL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\nms+Qk5PDr6mMIp4QpTkW4sLDw/0HDx4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+ "text": [
+ "<matplotlib.figure.Figure at 0x4926070>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "(b). p-x-y diagram at 40 oC \n",
+ " figure 2\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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TO3RgJ6twRVDZ9cUuXrzIGjRowN69e8cYY8zFxYWtWbPmP8utXr2affnll+9f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\ne8pkaF7GGCaNHg1BcDBWZWfjg+vK9u1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99+dA2Z15iYlEIrXU1FR9AGjcuPHTkpbJzs7W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\npJajREAIIbUcJQJCCKnl/g8luKJLHwirrwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x4139c50>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "(c).t-x-y diagram at 10 bar \n",
+ " figure 3\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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zgVevJFFsQggBQAGgxmRlAU9PljbI0JANDvL1rWBJYgsLlnzowQNARgawsWFR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\ngg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x4bdcc50>"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Example 13.2, Page No:612"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import math\n",
+ "\n",
+ "#Variable declaration\n",
+ "T=573.15; # Temperature of the water with another liquid in kelvin\n",
+ "R=8.3144/18; # Characteristic gas constant\n",
+ "# (a).4 MPa\n",
+ "P_1=10; # By Method II, The lowest possible pressure at which date available in steam table for 300 oC temperature in kPa\n",
+ "h_i=3076.5; # Specific enthalphy at P_1 in kJ/kg\n",
+ "s_i=9.2813; # Specific entropy at P_1 in kJ/kg K\n",
+ "# from superheat table at p=4 MPa and t=300 oC\n",
+ "hi=2960.7; # Specific enthalphy in kJ/kg\n",
+ "si=6.3615; # Specific entropy in kJ/kg K\n",
+ "\n",
+ "#Calculation for (a)\n",
+ "fi=P_1*math.exp ((((hi-h_i)/T)-(si-s_i))/R); # Standard state fugacity of water\n",
+ "\n",
+ "#Result for (a)\n",
+ "print \"(a).4 MPa\",\"\\nStandard state fugacity of water = \",round(fi,2),\"kPa (round off error)\"\n",
+ "\n",
+ "#Variable declaration for (b)\n",
+ "# (b).equal to saturation pressure at 300 oC\n",
+ "Psat=8.581; # Saturation pressure at 300 oC in MPa\n",
+ "# From steam table at Psat=8.581 MPa and t=300 oC\n",
+ "hi=2749; # Specific enthalphy in kJ/kg\n",
+ "si=5.7045; # Specific entropy in kJ/kg K\n",
+ "\n",
+ "#Calculation for (b)\n",
+ "fi=P_1*math.exp ((((hi-h_i)/T)-(si-s_i))/R); # Standard state fugacity of water\n",
+ "pisat=fi/(Psat*10**3); # fugacity coefficient\n",
+ "\n",
+ "#Result for (b)\n",
+ "print \"\\n(b).Equal to saturation pressure at 300 oC\",\"\\nStandard state fugacity of water = \",round(fi,0),\"kPa\"\n",
+ "print \"fugacity coefficient =\",round(pisat,4)\n",
+ "\n",
+ "#Calculation for (c)\n",
+ "# (c).10 MPa\n",
+ "# Applying Method I \n",
+ "viL=0.001404; # Specific volume at 300 oC in m^3/kg\n",
+ "fi=pisat*Psat*10**3*math.exp ((viL*(P_1-Psat)*10**3)/(R*T)); # Standard state fugacity of water\n",
+ "print \"\\n(c).10 MPa\",\"\\nStandard state fugacity of water = \",round(fi,0),\"kPa\"\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "(a).4 MPa \n",
+ "Standard state fugacity of water = 3591.45 kPa (round off error)\n",
+ "\n",
+ "(b).Equal to saturation pressure at 300 oC \n",
+ "Standard state fugacity of water = 6694.0 kPa\n",
+ "fugacity coefficient = 0.7801\n",
+ "\n",
+ "(c).10 MPa \n",
+ "Standard state fugacity of water = 6745.0 kPa\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Exapmple 13.3, Page No:615"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import math\n",
+ "%matplotlib inline\n",
+ "import math\n",
+ "from __future__ import division\n",
+ "\n",
+ "#Variable declaration\n",
+ "# Let take NH3 as component 1 and H2O as component 2\n",
+ "# (a) & (b)\n",
+ "# Calculation of f1sat = pi1sat*p1sat for ammonia\n",
+ "P_1=50; # low reference state pressure in kPa\n",
+ "P1sat=614.95; # Saturation Pressure of ammonia at 10 oC in kPa\n",
+ "h1sat=1453.3; # Specific enthalpy at 10 oC in kJ/kg\n",
+ "s1sat=5.2104; # Specific entropy at 10 oC in kJ/kg K\n",
+ "R=8.3144/17; # Characteristic gas constant\n",
+ "T=283; # Temperature in kelvin\n",
+ "# At 10 oC and P_1=50 kPa for ammonia\n",
+ "h_1sat=1499.2; # Specific enthalpy in kJ/kg\n",
+ "s_1sat=6.5625; # Specific entropy in kJ/kg K\n",
+ "\n",
+ "#Calculation for (a)&(b)\n",
+ "f1sat=P_1*math.exp ((((h1sat-h_1sat)/T)-(s1sat-s_1sat))/R); # Standard state fugacity of Ammonia\n",
+ "# Calculation of f2sat = pi2sat*p2sat for water\n",
+ "P2sat=1.2276; # Saturation Pressure at 10 oC in kPa for water\n",
+ "pi2sat=1; # At low pressure for water\n",
+ "f2sat = pi2sat*P2sat; # Standard state fugacity of water\n",
+ "# Calulations of ViL/RT\n",
+ "# For ammonia and water at 10 oC\n",
+ "v1L=0.001601; v2L=0.001; # Specific volume in m^3/kg\n",
+ "v1L_RT=v1L/(R*T); v2L_RT=v2L/(R*T);\n",
+ "# Calculations of activity coefficients\n",
+ "# Expression for activity coefficients of ammonia and water become in given by respectively\n",
+ "# r_1=(y1*p/(x1*569.6))*exp (-4.34*10^-6*(p-p1sat)); for ammonia\n",
+ "# r_2=(y2*p/(x2*1.2276))*exp (-7.65*10^-6*(p-p2sat)); for water\n",
+ "# The values thus calculated for r_1,r_2,lny_1,lnr_2 are calculated and plotted in window 1\n",
+ "# Note that the values of pyonting factors are negligibly small\n",
+ "x1=[0,0.2,0.3,0.4,0.5,0.6,0.8,1.0];\n",
+ "y1=[0,0.963,0.986,0.9958,0.9985,0.9993,0.9999,1.0];\n",
+ "lnr_1=[-3.1,-1.845,-1.295,-0.75,-0.33,-0.065,-0.035,-0];\n",
+ "lnr_2=[0,-0.1397,-0.2767,-0.507,-0.709,-0.952,-1.613,-2.2];\n",
+ "# similarly the excess function gE/RT and gE/x1x2RT are also calculated using the following expression respectively\n",
+ "# gE_RT=x1*lnr_1+x2*lnr_2; # the excess function from 12.51\n",
+ " # gE_x1x2RT=(lnr_1/x2)+(lnr_2/x1);\n",
+ "# since gE=0 & x1x2=0 both at x1=0 and x1=1. However its values in between x1=0 & x1=1\n",
+ "# By substituting these values in the above expression and given below\n",
+ "gE_RT=[0,-0.481,-0.582,-0.604,-0.5195,-0.4198,-0.2925,0];\n",
+ "gE_x1x2RT=[-3.1,-2.92,-2.83,-2.74,-2.65,-2.56,-2.38,-2.2];\n",
+ "from pylab import *\n",
+ "figure(1); # For Plotting Diagram\n",
+ "plt.plot (x1,lnr_1,\"-*b\",label='ln r1');\n",
+ "plt.plot (x1,lnr_2,\"-*g\",label='ln r2');\n",
+ "plt.plot (x1,gE_RT,\"r\",label='gE/RT');\n",
+ "plt.plot (x1,gE_x1x2RT,\"-*k\",label='gE/x1x2 RT');\n",
+ "plt.title (\"(a)&(b).Activity coefficients for NH3/H2O at 10 oC\");\n",
+ "plt.xlabel(\" x1 \");\n",
+ "plt.ylabel(\" ln r \");\n",
+ "plt.legend(loc='lower right')\n",
+ "\n",
+ "#Result for (a)&(b)\n",
+ "print \"(a) & (b)\",\"\\nStandard state fugacity of Ammonia = \",f1sat,\"kPa\"\n",
+ "print \"Standard state fugacity of water = \",f2sat,\"kPa\"\n",
+ "print \"v1L/RT = \",v1L_RT,\"(answer mentioned in the textbook is wrong)\",\"\\nv2L/RT = \",v2L_RT\n",
+ "print \"\\n(a)&(b).Activity coefficients for NH3/H2O at 10 oC\"\n",
+ "plt.show();\n",
+ "print \" figure 1 \"\n",
+ "# As x1\u21920,x2\u21921,gE_x1x2RT\u2192A=ln r_1^\u221e\n",
+ "# As x1\u21921,x2\u21920,gE_x1x2RT\u2192B=ln r_2^\u221e\n",
+ "A=-3.1; B=-2.2; # THe Margules constants\n",
+ "print \"The Margules constants \",\"A = \",A,\"B = \",B\n",
+ "print \"From figure 1 for ammonia/water mixture which is characteristic of systems with negative deviation from Roault law.\" \n",
+ "print \"Because \u03b3i<=1 and ln \u03b3i <=0\"\n",
+ "\n",
+ "#Calculations for (c)\n",
+ "# (c).\n",
+ "# Assuming ideal vapour phase, and at low pressures we have \n",
+ "# y1P=\u03b31*x1*p1sat; y2p=\u03b32* x2* p2sat;\n",
+ "# Now the activity coefficients can be found from Margules equations and given below\n",
+ "x1=[0,0.2,0.3,0.4,0.5,0.6,1.0];\n",
+ "y1=[0,0.963,0.986,0.9958,0.9985,0.9999,1.0];\n",
+ "p=[1.2276,8.6597,30.6598,54.6845,150.6458,278.1549,614.95];\n",
+ "# The ideal solution pressure\n",
+ " # PRaoult=x1*P1sat+x2*P2sat;\n",
+ "PRaoult=[1.2276,614.95]; \n",
+ "x_1=[0,1]; # For Ideal solution pressure\n",
+ "from pylab import *\n",
+ "figure(2); # For Plotting Diagram\n",
+ "plt.plot (x1,p,\"-*r\",label='p-x1');\n",
+ "plt.plot (y1,p,\"-*b\",label='p-y1');\n",
+ "plt.plot (x_1,PRaoult,\"g\",label='PRaoult');\n",
+ "plt.title (\"(c).p-x-y diagram of NH3/H2O at 10 oC\");\n",
+ "plt.xlabel(\" x1 & y1 \");\n",
+ "plt.ylabel(\" p, kPa \");\n",
+ "plt.legend(loc='upper left')\n",
+ "\n",
+ "#Result for (c)\n",
+ "print \"\\n\\n(c).p-x-y diagram\"\n",
+ "print \"figure 2\"\n",
+ "plt.show();\n",
+ "print \"From figure 2 The actual pressure p < pRaoult. It is thus seen that the mixture has negative deviation from Raoults law.\"\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "(a) & (b) \n",
+ "Standard state fugacity of Ammonia = 569.617676377 kPa\n",
+ "Standard state fugacity of water = 1.2276 kPa\n",
+ "v1L/RT = 1.15670577403e-05 (answer mentioned in the textbook is wrong) \n",
+ "v2L/RT = 7.22489552801e-06\n",
+ "\n",
+ "(a)&(b).Activity coefficients for NH3/H2O at 10 oC\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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HDrnm5OS0q8/1+RJKSkrF1X/TBQUFKjU9FAp1dujQIVm4bWhomMLj8aTfvn2rpaGh8T4z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\nuvkAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x4926030>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " figure 1 \n",
+ "The Margules constants A = -3.1 B = -2.2\n",
+ "From figure 1 for ammonia/water mixture which is characteristic of systems with negative deviation from Roault law.\n",
+ "Because \u03b3i<=1 and ln \u03b3i <=0\n",
+ "\n",
+ "\n",
+ "(c).p-x-y diagram"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\n",
+ "figure 2\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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M8oSmsiYujrqItjyJPgaBAJg/HzhyBLhwAbCp3FNcK4uam8hn0fklpGrEZ8dj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\naWl5y9zc/HZ4ePhSjuMQFRU1LSoqapqizqxZszabm5vftrO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+ "text": [
+ "<matplotlib.figure.Figure at 0x4be7150>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "From figure 2 The actual pressure p < pRaoult. It is thus seen that the mixture has negative deviation from Raoults law.\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Example 13.4, Page No:624"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import math\n",
+ "from __future__ import division\n",
+ "\n",
+ "#Variable declaration\n",
+ "x1=0.9; # mole fraction of NH3\n",
+ "x2=0.1; # Mole fraction of H2O\n",
+ "p=490.3; # Pressure in kPa\n",
+ "T=280.1; # Temperature in kelvin\n",
+ "lam12_11=-2131; lam21_22=-2726; # In kJ/kmol\n",
+ "R_1=8.3144; # Universal gas constant in kJ/kmol K\n",
+ "# (a).Enthalpy of saturated liquid Mixture at L/B at bubble temperature\n",
+ "V1L=0.0016; V2L=0.001; #from properties of NH3 and H2O in m^3/kg\n",
+ "\n",
+ "#Calculation for (a)\n",
+ "a=((V2L*18)/(V1L*17)) * exp (-lam12_11/(R_1*T));\n",
+ "b=((V1L*17)/(V2L*18)) * exp (-lam21_22/(R_1*T));\n",
+ "d_a=a*(lam12_11/(R_1*T**2)); d_b=b*(lam21_22/(R_1*T**2));\n",
+ "d_lnr1=(-(a*x2**2*d_a/(x1+(a*x2))**2))-(x2*d_b/(b*x1+x2))+(b*x1*x2*d_b/(b*x1+x2)**2);\n",
+ "d_lnr2=(-b*x1**2*d_b/(b*x1+x2)**2)-(x1*d_a/(x1+a*x2))+(a*x1*x2*d_a/(x1+a*x2)**2);x1=0.728; # By substituting these valuses in equation\n",
+ "h_E=-R_1*T**2*(x1*d_lnr1+x2*d_lnr2); # Heat of mixing\n",
+ "x1=0.9;\n",
+ "M=x1*17+x2*18; # Molecular weight\n",
+ "hE=h_E/M; \n",
+ "h1L=32.5; h2L=29.4; # in kJ/kg\n",
+ "hL=(x1*h1L)+(x2*h2L)+hE;# Specific enthalpy of the liquid mixture\n",
+ "\n",
+ "#Result for (a)\n",
+ "print \"(a).Enthalpy of saturated liquid Mixture at L/B at bubble temperature\",\n",
+ "print \"Specific enthalpy of the liquid mixture = \",round(hL,1),\"kJ/kg\"\n",
+ "\n",
+ "#Variable declaration (b)\n",
+ "# (b).Enthalpy of saturated vapour at V in Equilibrium with liquid at L/B\n",
+ "# From property table of ammonia and water at 0 oC\n",
+ "T1=273.15; # Temperature in kelvin\n",
+ "p1sat=429.4; p2sat=0.6108; # Pressure in kPa\n",
+ "hfg1=1262.4; hfg2=2501.4;# specific enthalpy in kJ/kg \n",
+ "vg1=0.2895; vg2=206.3; # specific volume in m^3/kg\n",
+ "# Referring to fig 13.15 , we have\n",
+ "hb1=1262.4; hb2=2501.4;# specific enthalpy in kJ/kg\n",
+ "M=17; \n",
+ "# The crictical properties \n",
+ "Tc1=405.3; Tc2=647.3;# Temperature in kelvin\n",
+ "pc1=11.28; pc2=22.09; # Pressure in MPa\n",
+ "\n",
+ "#Calculation for (b)\n",
+ "z1=(p1sat*vg1/(R_1*T1/M)); z2=(p2sat*vg2/(R_1*T/M));\n",
+ "A2_1=(0.4278/(pc1*10**3))*(Tc1/T1)**2.5; # Constants\n",
+ "B_1=(0.0867/(pc1*10**3))*(Tc1/T1); # Constants\n",
+ "h1R=R_1*(T1/M)*(((-3/2)*(A2_1/B_1)*log (1+(B_1*p1sat/z1)))+z1-1);\n",
+ "A2_2=(0.4278/(pc2*10**3))*(Tc2/T1)**2.5; # Constants\n",
+ "B_2=(0.0867/(pc2*10**3))*(Tc2/T1); # Constants\n",
+ "h2R=-0.2;\n",
+ "hc1=hb1-h1R; hc2=hb2-h2R; # Enthalpies at 0 oC\n",
+ "Cpo1=14.86; Cpo2=12.92; # In kJ/kg\n",
+ "A2_1=(0.4278/(pc1*10**3))*(Tc1/T)**2.5; # Constants\n",
+ "B_1=(0.0867/(pc1*10**3))*(Tc1/T); # Constants\n",
+ "A2_2=(0.4278/(pc2*10**3))*(Tc2/T)**2.5; # Constants\n",
+ "B_2=(0.0867/(pc2*10**3))*(Tc2/T); # Constants\n",
+ "y1=0.9999; y2=0.0001;\n",
+ "Tc=y1*Tc1+y2*Tc2;\n",
+ "z=0.957;\n",
+ "hR=R_1*(T/M)*(((-3/2)*(A2_1/B_1)*log (1+(B_1*p/z)))+z-1);\n",
+ "hV=y1*(hc1+Cpo1)+y2*(hc2+Cpo2)+hR;\n",
+ "\n",
+ "#Result for (b)\n",
+ "print \"\\n(b).Enthalpy of saturated vapour at V in Equilibrium with liquid at L/B = \",round(hV,1),\"kJ/kg (roundoff error)\"\n",
+ "\n",
+ "#Variable declaration for (c)\n",
+ "# (c).Enthalpy of saturated vapour at D after complete vaporization of liquid at B/L\n",
+ "T=359.15; # In K\n",
+ "Cpo1=192.2; Cpo2=160.9; # In kJ/kg\n",
+ "\n",
+ "#Calculation for (c)\n",
+ "A2_1=(0.4278/(pc1*10**3))*(Tc1/T)**2.5; # Constants\n",
+ "B_1=(0.0867/(pc1*10**3))*(Tc1/T); # Constants\n",
+ "A2_2=(0.4278/(pc2*10**3))*(Tc2/T)**2.5; # Constants\n",
+ "B_2=(0.0867/(pc2*10**3))*(Tc2/T); # Constants\n",
+ "y1=0.9; y2=0.1;\n",
+ "Tc=y1*Tc1+y2*Tc2;\n",
+ "z=0.9768;\n",
+ "hR=R_1*(T/M)*(((-3/2)*(A2_1/B_1)*log (1+(B_1*p/z)))+z-1);\n",
+ "hD=y1*(hc1+Cpo1)+y2*(hc2+Cpo2)+hR;\n",
+ "\n",
+ "#Result for (c)\n",
+ "print \"\\n(c).Enthalpy of saturated vapour at D after complete vaporization of liquid at B/L = \",round(hD,1),\"kJ/kg\"\n",
+ "\n",
+ "#Calculation for (d)\n",
+ "# (d).Latent Heat of Vapourization of this Liquid Mixture\n",
+ "hB=-0.2; \n",
+ "hD_hB=hD-hB; #Latent Heat of Vapourization of this Liquid Mixture\n",
+ "\n",
+ "#Result for (d)\n",
+ "print \"\\n(d). Latent Heat of Vapourization of this Liquid Mixture = \",round(hD_hB,1),\"kJ/kg mixture\"\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "(a).Enthalpy of saturated liquid Mixture at L/B at bubble temperature Specific enthalpy of the liquid mixture = -0.2 kJ/kg\n",
+ "\n",
+ "(b).Enthalpy of saturated vapour at V in Equilibrium with liquid at L/B = 1280.1 kJ/kg (roundoff error)\n",
+ "\n",
+ "(c).Enthalpy of saturated vapour at D after complete vaporization of liquid at B/L = 1581.3 kJ/kg\n",
+ "\n",
+ "(d). Latent Heat of Vapourization of this Liquid Mixture = 1581.5 kJ/kg mixture\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file