1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
|
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Chapter 11 : Properties of solutions"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.1 Page No : 378"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"%matplotlib inline\n",
"import math \n",
"from numpy import *\n",
"from matplotlib.pyplot import *\n",
"\n",
"\n",
"# Variables\n",
"antoine_const_benzene = [6.87987,1196.760,219.161]\n",
"antoine_const_toluene = [6.95087,1342.310,219.187]\n",
"t = 95.\n",
"P = 101.325\n",
"\n",
"# Calculations\n",
"P1_s = 10**(antoine_const_benzene[0]-(antoine_const_benzene[1]/(t+antoine_const_benzene[2])))\t\t\t \n",
"P2_s = 10**(antoine_const_toluene[0]-(antoine_const_toluene[1]/(t+antoine_const_toluene[2])))\t\t\t\n",
"x1 = linspace(0,1,10)\n",
"i = 0;\t\t\t #iteration parameter\n",
"n = len(x1);\t\t\t #iteration parameter\n",
"P_tot = zeros(10)\n",
"y1 = zeros(10)\n",
"\n",
"while i<n :\n",
" P_tot[i] = P2_s+((P1_s-P2_s)*x1[i])\n",
" y1[i] = (x1[i]*P1_s)/(P_tot[i])\t\n",
" i = i+1;\n",
"\n",
"#T-x-y diagram:\n",
"P = 760.\n",
"t1_s = ((antoine_const_benzene[1])/(antoine_const_benzene[0]-math.log10(P)))-antoine_const_benzene[2]\n",
"t2_s = ((antoine_const_toluene[1])/(antoine_const_toluene[0]-math.log10(P)))-antoine_const_toluene[2];\n",
"x1_initial = 0.0;\n",
"y1_initial = 0.0;\n",
"x1_final = 1.0;\n",
"y1_final = 1.0;\n",
"\n",
"T = linspace(85,105,5)\n",
"k = 0;\n",
"l = len(T)\n",
"P1s = zeros(5)\n",
"P2s = zeros(5)\n",
"X1 = zeros(5)\n",
"Y1 = zeros(5)\n",
"while k<l:\n",
" P1s[k] = 10**((antoine_const_benzene[0])-((antoine_const_benzene[1])/(T[k]+antoine_const_benzene[2])))\n",
" P2s[k] = 10**((antoine_const_toluene[0])-((antoine_const_toluene[1])/(T[k]+antoine_const_toluene[2])))\n",
" X1[k] = (P-P2s[k])/(P1s[k]-P2s[k]);\t\n",
" Y1[k] = (X1[k]*P1s[k])/P;\t\t\t # Calculations of mole fraction of Benzene in vapour phase (no unit) \n",
" k = k+1;\n",
"\n",
"temp = zeros(l+2)\n",
"x1_benzene = zeros(l+2)\n",
"y1_benzene = zeros(l+2)\n",
"j = 0;\n",
"\n",
"while j<l+2:\n",
" if j == 0:\n",
" temp[j] = t1_s;\n",
" x1_benzene[j] = x1_final;\n",
" y1_benzene[j] = y1_final;\n",
" elif j == l+1:\n",
" temp[j] = t2_s;\n",
" x1_benzene[j] = x1_initial;\n",
" y1_benzene[j] = y1_initial;\n",
" else:\n",
" temp[j] = T[j-1];\n",
" x1_benzene[j] = X1[j-1];\n",
" y1_benzene[j] = Y1[j-1];\n",
" j = j+1;\n",
"\n",
"# Results\n",
"print 'P-x-y results ';\n",
"\n",
"for i in range( n):\n",
" print 'x1 = %f \\t y1 = %f\\t P = %f Torr '%(x1[i],y1[i],P_tot[i]);\n",
"\n",
"print 'T-x-y results t = %f degree celsius\\t P1_s = 760.0 Torr \\t P2_s = -) Torr \\t\\t x1 = 1.0 \\t y1 = 1.0 '%(t1_s);\n",
"for k in range(l):\n",
" print 't = %f degree celsius\\t P1_s = %f Torr \\t P2_s = %f Torr \\t x1 = %f \\t y1 = %f '%(T[k],P1s[k],P2s[k],X1[k],Y1[k]);\n",
"\n",
"print 't = %f degree celsius\\t P1_s = -)Torr \\t\\t P2_s = 760.0 Torr \\t x1 = 0.0 \\t y1 = 0.0 '%(t2_s);\n",
"subplot(2,1,1)\n",
"plot(x1,P_tot,y1,P_tot)\n",
"suptitle('P-x-y diagram for benzene-toluene system at 95 degree celsius')\n",
"xlabel('x1,y1')\n",
"ylabel('P(Torr)')\n",
"subplot(2,1,2)\n",
"plot(x1_benzene,temp,y1_benzene,temp)\n",
"xlabel(\"x1,y1\")\n",
"ylabel(\"t (degree celsius)\") \n",
"suptitle('T-x-y diagram for benzene-toluene sytem at 760 Torr')\n",
"\n",
"show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"P-x-y results \n",
"x1 = 0.000000 \t y1 = 0.000000\t P = 477.025655 Torr \n",
"x1 = 0.111111 \t y1 = 0.235600\t P = 554.713037 Torr \n",
"x1 = 0.222222 \t y1 = 0.413315\t P = 632.400419 Torr \n",
"x1 = 0.333333 \t y1 = 0.552144\t P = 710.087801 Torr \n",
"x1 = 0.444444 \t y1 = 0.663592\t P = 787.775183 Torr \n",
"x1 = 0.555556 \t y1 = 0.755031\t P = 865.462564 Torr \n",
"x1 = 0.666667 \t y1 = 0.831407\t P = 943.149946 Torr \n",
"x1 = 0.777778 \t y1 = 0.896158\t P = 1020.837328 Torr \n",
"x1 = 0.888889 \t y1 = 0.951751\t P = 1098.524710 Torr \n",
"x1 = 1.000000 \t y1 = 1.000000\t P = 1176.212092 Torr \n",
"T-x-y results t = 80.099595 degree celsius\t P1_s = 760.0 Torr \t P2_s = -) Torr \t\t x1 = 1.0 \t y1 = 1.0 \n",
"t = 85.000000 degree celsius\t P1_s = 881.542536 Torr \t P2_s = 345.216082 Torr \t x1 = 0.773380 \t y1 = 0.897062 \n",
"t = 90.000000 degree celsius\t P1_s = 1020.650885 Torr \t P2_s = 406.866584 Torr \t x1 = 0.575338 \t y1 = 0.772657 \n",
"t = 95.000000 degree celsius\t P1_s = 1176.212092 Torr \t P2_s = 477.025655 Torr \t x1 = 0.404719 \t y1 = 0.626363 \n",
"t = 100.000000 degree celsius\t P1_s = 1349.471568 Torr \t P2_s = 556.502216 Torr \t x1 = 0.256628 \t y1 = 0.455673 \n",
"t = 105.000000 degree celsius\t P1_s = 1541.703421 Torr \t P2_s = 646.141534 Torr \t x1 = 0.127136 \t y1 = 0.257903 \n",
"t = 110.614326 degree celsius\t P1_s = -)Torr \t\t P2_s = 760.0 Torr \t x1 = 0.0 \t y1 = 0.0 \n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEhCAYAAABoTkdHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x3802f10>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.2 Page No : 384"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# Variables\n",
"T = 95. \t\t \t #temperature of the equimolar vapour mixture of benzene and toluene in degree celsius\n",
"y1 = 0.5\t \t\t #mole fraction of benzene in vapour phase (no unit)\n",
"y2 = 0.5 \t\t\t #mole fraction of toluene in vapour phase (no unit)\n",
"P1_s = 1176.21\t\t\t #saturation pressure of benzene at T, taken from Example 11.1 in Torr\n",
"P2_s = 477.03\t\t\t #saturation pressure of toluene at T, taken from Example 11.1 in Torr\n",
"\n",
"# Calculations\n",
"P = 1./((y1/P1_s)+(y2/P2_s))\n",
"x1 = (y1*P)/P1_s;\t\t\t \n",
"x2 = 1-x1\n",
"\n",
"# Results\n",
"print 'The composition of the liquid which is in equilibrium with the equimolar vapour mixture of\\\n",
" benzene and toluene at 95 degree celsius is mole fraction of benzene\\\n",
" in liquid phase x1 = %f mole fraction of toluene in liquid phase x2 = %f '%(x1,x2);\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The composition of the liquid which is in equilibrium with the equimolar vapour mixture of benzene and toluene at 95 degree celsius is mole fraction of benzene in liquid phase x1 = 0.288542 mole fraction of toluene in liquid phase x2 = 0.711458 \n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.5 page no : 386"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Variables\n",
"n_pentaneA = 6.87632 # Antoine constants\n",
"n_hexaneA = 6.91058\n",
"n_heptanA = 6.89386\n",
"n_pentaneB = 1075.780 \n",
"n_hexaneB = 1189.640\n",
"n_heptanB = 1264.370\n",
"n_pentaneC = 233.205\n",
"n_hexaneC = 226.205\n",
"n_heptanC = 216.640\n",
"pressure = 90 # pressure\n",
"\n",
"# Calculations\n",
"logP1 = n_pentaneA - (n_pentaneB/(90 + n_pentaneC))\n",
"P1 = round(10**logP1 *133.322 /1000,1)\n",
"logP2 = n_hexaneA - (n_hexaneB/(90 + n_hexaneC))\n",
"P2 = round(10**logP2 *133.322 /1000,2)\n",
"logP3 = n_heptanA - (n_heptanB/(90 + n_heptanC))\n",
"P3 = round(10**logP3 *133.322 /1000,2)\n",
"P = 200\n",
"K1 = round(P1/P,3)\n",
"K2 = round(P2/P,2)\n",
"K3 = P3/P\n",
"\n",
"# assume L/F = 0.4\n",
"L_F = 0.4\n",
"x1 = 0.3/(L_F + (1-L_F) * K1)\n",
"x2 = 0.3/(L_F + (1-L_F) * K2)\n",
"x3 = 0.4/(L_F + (1-L_F) * K3)\n",
"sum_x = x1+x2+x3\n",
"\n",
"# assume L/F = 0.752\n",
"L_F = 0.752\n",
"x1 = 0.3/(L_F + (1-L_F) * K1)\n",
"x2 = 0.3/(L_F + (1-L_F) * K2)\n",
"x3 = 0.4/(L_F + (1-L_F) * K3)\n",
"sum_x = round(x1+x2+x3,1) # which is equal to 1 \n",
"\n",
"# Hence, L/F = 0.752 is correct\n",
"y1 = K1*x1\n",
"y2 = K2*x2\n",
"y3 = K3*x3\n",
"\n",
"# Results\n",
"print \"Hence, fraction vaporized, V/F = 1-(L-F) = %.3f\"%(1-L_F)\n",
"print \"Compositions of liquid and vapor leaving the flash unit are :\"\n",
"print \"x1 = %.4f y1 = %.4f\"%(x1,y1)\n",
"print \"x2 = %.4f y2 = %.4f\"%(x2,y2)\n",
"print \"x3 = %.4f y3 = %.4f\"%(x3,y3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hence, fraction vaporized, V/F = 1-(L-F) = 0.248\n",
"Compositions of liquid and vapor leaving the flash unit are :\n",
"x1 = 0.2246 y1 = 0.5285\n",
"x2 = 0.3045 y2 = 0.2863\n",
"x3 = 0.4709 y3 = 0.1851\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.7 Page No : 397"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"# Variables\n",
"T = 45.\t \t\t #temperature of the mixture in degree celsius\n",
"A = 2.230\t\t\t #van laar constant for the system at T (no unit)\n",
"B = 1.959\t\t\t #van laar constant for the system at T (no unit)\n",
"n1 = 30. \t\t #mole percentage of nitromethane in the mixture ( in percentage)\n",
"\n",
"# Calculations\n",
"n2 = 100-n1\n",
"x1 = n1/100\n",
"x2 = 1-x1\n",
"gaamma1 = math.exp(A/(1+((A/B)*(x1/x2)))**2)\n",
"gaamma2 = math.exp(B/(1+((B/A)*(x2/x1)))**2)\n",
"\n",
"# Results\n",
"print 'The activity coefficients for the system using van laar equation is : gamma1 \\\n",
" = %f, gamma2 = %f \\t '%( gaamma1,gaamma2);\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The activity coefficients for the system using van laar equation is : gamma1 = 2.738343, gamma2 = 1.234443 \t \n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.8 Page No : 397"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"# Variables\n",
"n_azeo = 44.8;\t\t\t #azeotropic composition given as mole percentage\n",
"Tb = 68.24;\t\t\t #boiling point of mixture in degree celsius\n",
"P = 760.;\t\t\t #pressure in Torr\n",
"P1_s = 506.;\t\t\t #saturation pressure of ethanol at Tb in Torr\n",
"P2_s = 517.;\t\t\t #saturation pressure of benzene at Tb in Torr\n",
"n1 = 10.;\t\t\t #mole percentage of ethanol in the mixture (in percentage)\n",
"\n",
"# Calculations\n",
"x1 = n_azeo/100\n",
"x2 = 1-x1;\t\t\n",
"gaamma1 = P/P1_s\n",
"gaamma2 = P/P2_s\n",
"A = math.log(gaamma1)*(1+((x2*math.log(gaamma2))/(x1*math.log(gaamma1))))**2\n",
"B = math.log(gaamma2)*(1+((x1*math.log(gaamma1))/(x2*math.log(gaamma2))))**2\n",
"x1 = n1/100\n",
"x2 = 1-x1;\t\n",
"gaamma1 = math.exp(A/(1+((A/B)*(x1/x2)))**2)\n",
"gaamma2 = math.exp(B/(1+((B/A)*(x2/x1)))**2)\n",
"\n",
"# Results\n",
"print 'The van Laar constants for the system are : A = %f \\t B = %f '%(A,B)\n",
"print 'The activity coefficients for the system using van laar equation are\\\n",
" : gamma1 = %f \\t gamma2 = %f \\t '%( gaamma1,gaamma2);\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The van Laar constants for the system are : A = 1.910203 \t B = 1.328457 \n",
"The activity coefficients for the system using van laar equation are : gamma1 = 4.137783 \t gamma2 = 1.025531 \t \n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.9 Page No : 399"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"# Variables\n",
"\n",
"T = 45. \t\t\t #temperature of the system in degree celsius\n",
"A_12 = 0.1156;\t\t\t #Wilson's parameter for the system at T (no unit)\n",
"A_21 = 0.2879;\t\t\t #Wilson's parameter for the system at T (no unit)\n",
"x1 = 0.3;\t \t\t #mole fraction of nitromethane in the liquid mixture (no unit)\n",
"\n",
"# Calculations\n",
"x2 = 1-x1\n",
"ln_gaamma1 = -math.log(x1+(A_12*x2))+(x2*((A_12/(x1+(A_12*x2)))-(A_21/((A_21*x1)+x2))));\t\t\t\n",
"gaamma1 = math.exp(ln_gaamma1)\n",
"ln_gaamma2 = -math.log(x2+(A_21*x1))-(x1*((A_12/(x1+(A_12*x2)))-(A_21/((A_21*x1)+x2))));\t\t\t # Calculations of ln(activity coefficient) using Eq.(11.90) (no unit)\n",
"gaamma2 = math.exp(ln_gaamma2)\n",
"\n",
"# Results\n",
"print 'The activity coefficients for the system using Wilsons parameters are : gamma1 = %f \\\n",
" \\t gamma2 = %f \\t '%( gaamma1,gaamma2);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The activity coefficients for the system using Wilsons parameters are : gamma1 = 2.512605 \t gamma2 = 1.295788 \t \n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.10 Page No : 401"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"# Variables\n",
"T = 345.\t\t\t #temperature of the mixture in K\n",
"x1 = 0.8\t\t\t #mole fraction of ethanol in the liquid phase (no unit)\n",
"\n",
"nu_ki = [1,1,1,6]\t\t\t #number of groups of type: CH3, CH2, OH and ACH respectively (no unit)\n",
"R_k = [0.9011,0.6744,1.0000,0.5313];\t\t\t #Group volume parameter for CH3, CH2, OH and ACH respectively (no unit)\n",
"Q_k = [0.848,0.540,1.200,0.400];\t\t\t #Area parameter for CH3, CH2, OH and ACH respectively (no unit)\n",
"R = 8.314;\t\t\t #universal gas constant in J/molK\n",
"u12_u22 = -241.2287;\t\t#UNIQUAC parameter for the system in J/molK\n",
"u21_u11 = 2799.5827;\t\t#UNIQUAC parameter for the system in J/molK\n",
"z = 10. \t\t\t #co-ordination number usually taken as 10 (no unit)\n",
"\n",
"# Calculations\n",
"x2 = 1-x1\n",
"r1 = (nu_ki[0]*R_k[0])+(nu_ki[1]*R_k[1])+(nu_ki[2]*R_k[2]);\t\t\t \n",
"r2 = (nu_ki[3]*R_k[3])\n",
"phi1 = (x1*r1)/((x1*r1)+(x2*r2));\t\t\t \n",
"phi2 = (x2*r2)/((x2*r2)+(x1*r1));\t\t\t \n",
"q1 = (nu_ki[0]*Q_k[0])+(nu_ki[1]*Q_k[1])+(nu_ki[2]*Q_k[2])\t\t\t \n",
"q2 = (nu_ki[3]*Q_k[3])\t\t\t\n",
"theta1 = (x1*q1)/((x1*q1)+(x2*q2))\n",
"theta2 = (x2*q2)/((x1*q1)+(x2*q2))\n",
"l1 = ((z/2)*(r1-q1))-(r1-1);\t\t\n",
"l2 = ((z/2)*(r2-q2))-(r2-1);\t\t\n",
"tau_12 = math.exp(-(u12_u22)/(R*T));\t\t\n",
"tau_21 = math.exp(-(u21_u11)/(R*T));\t\t\n",
"tau_11 = 1.0;\t\t\t \n",
"tau_22 = 1.0;\t\t\t \n",
"\n",
"ln_gaamma1_c = math.log(phi1/x1)+((z/2)*q1*math.log(theta1/phi1))+l1-((phi1/x1)*((x1*l1)+(x2*l2)));\n",
"ln_gaamma2_c = math.log(phi2/x2)+((z/2)*q2*math.log(theta2/phi2))+l2-((phi2/x2)*((x1*l1)+(x2*l2)));\n",
"ln_gaamma1_r = q1*(1-math.log((theta1*tau_11)+(theta2*tau_21))-(((theta1*tau_11)/((theta1*tau_11)+(theta2*tau_21)))+((theta2*tau_12)/((theta1*tau_12)+(theta2*tau_22)))));\n",
"ln_gaamma2_r = q2*(1-math.log((theta1*tau_12)+(theta2*tau_22))-(((theta1*tau_21)/((theta1*tau_11)+(theta2*tau_21)))+((theta2*tau_22)/((theta1*tau_12)+(theta2*tau_22)))));\n",
"ln_gaamma1 = ln_gaamma1_c+ln_gaamma1_r\n",
"ln_gaamma2 = ln_gaamma2_c+ln_gaamma2_r\n",
"gaamma1 = math.exp(ln_gaamma1);\t\t\t \n",
"gaamma2 = math.exp(ln_gaamma2);\t\t\t \n",
"\n",
"# Results\n",
"print 'The activity coefficients for the system using the UNIQUAC equation are : gamma1 \\\n",
" = %f \\t gamma2 = %f \\t '%( gaamma1,gaamma2);\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The activity coefficients for the system using the UNIQUAC equation are : gamma1 = 1.060567 \t gamma2 = 3.679066 \t \n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 11.11 Page No : 405"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"# Variables\n",
"T = 307.\t\t\t #temperature of the mixture in K\n",
"x1 = 0.3\t\t\t #mole fraction of acetone in the liquid phase (no unit)\n",
"\n",
"nu_ki = [1.,1.,2.,3.]\n",
"R_k = [0.9011,1.6724,0.6744]\n",
"Q_k = [0.848,1.488,0.540];\t\t\t \n",
"a_19 = 476.40;\t\t\t #group interaction parameter for the system in K\n",
"a_91 = 26.760;\t\t\t #group interaction parameter for the system in K\n",
"z = 10. \t\t\t #co-ordination number usually taken as 10 (no unit)\n",
"\n",
"# Calculations\n",
"x2 = 1-x1;\t\t\t # Calculations of mole fraction of benzene in liquid phase (no unit)\n",
"r1 = (nu_ki[0]*R_k[0])+(nu_ki[1]*R_k[1])\n",
"r2 = (nu_ki[2]*R_k[0])+(nu_ki[3]*R_k[2])\n",
"phi1 = (x1*r1)/((x1*r1)+(x2*r2))\n",
"phi2 = (x2*r2)/((x2*r2)+(x1*r1))\n",
"q1 = (nu_ki[0]*Q_k[0])+(nu_ki[1]*Q_k[1])\t\t\t\n",
"q2 = (nu_ki[2]*Q_k[0])+(nu_ki[3]*Q_k[2])\n",
"theta1 = (x1*q1)/((x1*q1)+(x2*q2));\n",
"theta2 = (x2*q2)/((x1*q1)+(x2*q2));\n",
"l1 = ((z/2)*(r1-q1))-(r1-1);\t\t\n",
"l2 = ((z/2)*(r2-q2))-(r2-1);\t\t\n",
"ln_gaamma1_c = math.log(phi1/x1)+((z/2)*q1*math.log(theta1/phi1))+l1-((phi1/x1)*((x1*l1)+(x2*l2)));\n",
"ln_gaamma2_c = math.log(phi2/x2)+((z/2)*q2*math.log(theta2/phi2))+l2-((phi2/x2)*((x1*l1)+(x2*l2)));\n",
"a_11 = 0.\n",
"a_99 = 0.\n",
"psi_19 = math.exp(-(a_19)/(T));\t\t\t\n",
"psi_91 = math.exp(-(a_91)/(T));\t\t\t\n",
"psi_11 = 1.;\t\t\t \n",
"psi_99 = 1.;\t\t\t \n",
"x1_1 = nu_ki[0]/(nu_ki[0]+nu_ki[1]);\t\t\t\n",
"x1_18 = nu_ki[1]/(nu_ki[0]+nu_ki[1]);\t\t\n",
"theta1_1 = (Q_k[0]*x1_1)/((Q_k[0]*x1_1)+(Q_k[1]*x1_18))\n",
"theta1_18 = (Q_k[1]*x1_18)/((Q_k[1]*x1_18)+(Q_k[0]*x1_1))\n",
"ln_tau1_1 = Q_k[0]*(1-math.log((theta1_1*psi_11)+(theta1_18*psi_91))-(((theta1_1*psi_11)/((theta1_1*psi_11)+(theta1_18*psi_91)))+((theta1_18*psi_19)/((theta1_1*psi_19)+(theta1_18*psi_11)))));\n",
"ln_tau1_18 = Q_k[1]*(1-math.log((theta1_1*psi_19)+(theta1_18*psi_99))-(((theta1_1*psi_91)/((theta1_1*psi_99)+(theta1_18*psi_91)))+((theta1_18*psi_99)/((theta1_1*psi_19)+(theta1_18*psi_99)))));\n",
"x2_1 = nu_ki[2]/(nu_ki[2]+nu_ki[3]);\t\t\t \n",
"x2_2 = nu_ki[3]/(nu_ki[2]+nu_ki[3]);\t\t\t \n",
"ln_tau2_1 = 0;\n",
"ln_tau2_2 = 0;\n",
"x_1 = ((x1*nu_ki[0])+(x2*nu_ki[2]))/((((x1*nu_ki[0])+(x1*nu_ki[1])))+((x2*nu_ki[2])+(x2*nu_ki[3])));\n",
"x_2 = ((x2*nu_ki[3]))/((((x1*nu_ki[0])+(x1*nu_ki[1])))+((x2*nu_ki[2])+(x2*nu_ki[3])));\n",
"x_18 = ((x1*nu_ki[1]))/((((x1*nu_ki[0])+(x1*nu_ki[1])))+((x2*nu_ki[2])+(x2*nu_ki[3])));\n",
"theta_1 = (Q_k[0]*x_1)/((Q_k[0]*x_1)+(Q_k[1]*x_18)+(Q_k[2]*x_2));\t\t\t\n",
"theta_2 = (Q_k[2]*x_2)/((Q_k[0]*x_1)+(Q_k[1]*x_18)+(Q_k[2]*x_2));\t\t\t\n",
"theta_18 = (Q_k[1]*x_18)/((Q_k[0]*x_1)+(Q_k[1]*x_18)+(Q_k[2]*x_2));\t\t\t\n",
"ln_tau_1 = Q_k[0]*(1-math.log((theta_1*psi_11)+(theta_2*psi_11)+(theta_18*psi_91))-((((theta_1*psi_11)+(theta_2*psi_11))/((((theta_1*psi_11)+(theta_2*psi_11))+(theta_18*psi_91)))+((theta_18*psi_19)/((theta_1*psi_19)+(theta_2*psi_19)+(theta_18*psi_11))))));\n",
"ln_tau_2 = Q_k[2]*(1-math.log((theta_1*psi_11)+(theta_2*psi_11)+(theta_18*psi_91))-((((theta_1*psi_11)+(theta_2*psi_11))/((((theta_1*psi_11)+(theta_2*psi_11))+(theta_18*psi_91)))+((theta_18*psi_19)/((theta_1*psi_19)+(theta_2*psi_19)+(theta_18*psi_11))))));\n",
"ln_tau_18 = Q_k[1]*(1-math.log((theta_1*psi_19)+(theta_2*psi_19)+(theta_18*psi_99))-(((((theta_1+theta_2)*psi_91)/((theta_1*psi_11)+(theta_2*psi_11)+(theta_18*psi_91)))+((theta_18*psi_99)/((theta_1*psi_19)+(theta_2*psi_19)+(theta_18*psi_11))))));\n",
"ln_gaamma1_r = (nu_ki[0]*(ln_tau_1-ln_tau1_1))+(nu_ki[1]*(ln_tau_18-ln_tau1_18));\n",
"ln_gaamma2_r = (nu_ki[2]*(ln_tau_1-ln_tau2_1))+(nu_ki[3]*(ln_tau_2-ln_tau2_2));\n",
"ln_gaamma1 = ln_gaamma1_c+ln_gaamma1_r\n",
"ln_gaamma2 = ln_gaamma2_c+ln_gaamma2_r\n",
"gaamma1 = math.exp(ln_gaamma1);\t\t\t\n",
"gaamma2 = math.exp(ln_gaamma2);\t\t\t\n",
"\n",
"# Results\n",
"print 'The activity coefficients for the system using the UNIFAC method are\\\n",
" : gamma1 = %f \\t gamma2 = %f \\t '%( gaamma1,gaamma2);\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The activity coefficients for the system using the UNIFAC method are : gamma1 = 2.149891 \t gamma2 = 1.191192 \t \n"
]
}
],
"prompt_number": 4
}
],
"metadata": {}
}
]
}
|
