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
|
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Chapter 7 : Chemical Kinetics"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"example 7.3 page number 305"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math \n",
"\n",
"r = 3**2*3; #according to the rate reaction\n",
"print \"reaction reate will be increased by with 3 times increase in pressure = %f times\"%(r)\n",
"\n",
"r = 3**2*3; #according to the rate reaction\n",
"print \"reaction reate will be increased by with 3 times decrease in volume = %f times\"%(r)\n",
"\n",
"r = 3**2; #according to the rate reaction\n",
"print \"reaction reate will be increased by with 3 times increase in conc of NO = %f times\"%(r)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"reaction reate will be increased by with 3 times increase in pressure = 27.000000 times\n",
"reaction reate will be increased by with 3 times decrease in volume = 27.000000 times\n",
"reaction reate will be increased by with 3 times increase in conc of NO = 9.000000 times\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"example 7.10 page number 316\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"from scipy.optimize import fsolve \n",
"import math \n",
"moles_A = 3.;\n",
"moles_B = 5.;\n",
"K = 1.;\n",
"\n",
"def F(x):\n",
" return 15.-8*x;\n",
"\n",
"\n",
"x = 10.;\n",
"y = fsolve(F,x)\n",
"\n",
"print \"amount of A transformed = %f percent\"%(y*100/3)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"amount of A transformed = 62.500000 percent\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"example 7.11 page number 316\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"from scipy.optimize import fsolve \n",
"import math \n",
"Cp = 0.02;\n",
"Cq = 0.02;\n",
"K = 4*10**-2;\n",
"Cb = 0.05;\n",
"Cb_i = Cb+Cp;\n",
"a = (Cp*Cq)/(K*Cb);\n",
"\n",
"def F(x):\n",
" return x-0.02-a;\n",
"\n",
"x = 10.;\n",
"y = fsolve(F,x)\n",
"\n",
"print \"conc of A= %f mol/l\"%(y)\n",
"print \"conc of B= %f mol/l\"%(Cb_i)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"conc of A= 0.220000 mol/l\n",
"conc of B= 0.070000 mol/l\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"example 7.12 page number 316\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math \n",
"\n",
"Ce_N2 = 3.; #equilibrium conc of N2\n",
"Ce_H2 = 9.; #equilibrium conc of H2\n",
"Ce_NH3 = 4.; #equilibrium conc oh NH3\n",
"\n",
"C_N2 = Ce_N2 + 0.5*Ce_NH3;\n",
"C_H2 = Ce_H2 + 1.5*Ce_NH3;\n",
"\n",
"print \"concentration of N2 = %f mol/l \\nconcentration of H2 = %f mol/l\"%(C_N2,C_H2)\n",
"\n",
"n_H2 = 3.; #stotiometric coefficient\n",
"n_N2 = 1.; #stotiometric coefficient\n",
"n_NH3= 2.; #stotiometric coefficient\n",
"delta_n = n_H2+n_N2-n_NH3;\n",
"if delta_n > 0:\n",
" print \"delta_n =%f since delta_n is greater than 0,equilibrium will shift to right with increase in volume\"%(delta_n)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"concentration of N2 = 5.000000 mol/l \n",
"concentration of H2 = 15.000000 mol/l\n",
"delta_n =2.000000 since delta_n is greater than 0,equilibrium will shift to right with increase in volume\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"example 7.13 page number 317\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"from scipy.optimize import fsolve \n",
"import math \n",
"moles_A = 0.02;\n",
"K = 1.;\n",
"\n",
"moles_B = 0.02;\n",
"def F(x):\n",
" return moles_A*moles_B-(moles_A+moles_B)*x;\n",
"\n",
"x = 10.;\n",
"y = fsolve(F,x)\n",
"print \"amount of A transformed = %f percent\"%(y*100/0.02)\n",
"\n",
"moles_B = 0.1;\n",
"y = fsolve(F,x)\n",
"print \"amount of A transformed = %f percent\"%(y*100/0.02)\n",
"\n",
"moles_B = 0.2;\n",
"y = fsolve(F,x)\n",
"print \"amount of A transformed = %.0f percent\"%(y*100/0.02)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"amount of A transformed = 50.000000 percent\n",
"amount of A transformed = 83.333333 percent\n",
"amount of A transformed = 91 percent\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"example 7.15 page no : 319\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math \n",
"from numpy import *\n",
"from matplotlib.pyplot import *\n",
"\n",
"%matplotlib inline\n",
"t = array([0,5,10,15,20,25])\n",
"C_A = array([25,18.2,13.2,9.6,7,5.1])\n",
"\n",
"s = 0;\n",
"k = zeros(6)\n",
"\n",
"for i in range(1,6):\n",
" k[i] = (1./t[i])*math.log(25./C_A[i])\n",
" #print (k[i],\"k values for various conc.\")\n",
" s = s+k[i]\n",
"\n",
"print \"average value of k = %f\"%(s/5)\n",
"print (\"ra =- 0.06367*CA\",\"since its a first order reaction,\")\n",
"\n",
"subplot(221) \n",
"plot(t,C_A)\n",
"\n",
"xlabel(\"time\")\n",
"ylabel(\"concentration\")\n",
"suptitle(\"integral method\")\n",
"\n",
"ra = array([1.16,0.83,0.60,0.43])\n",
"C_A = array([18.2,13.2,9.6,7])\n",
"\n",
"subplot(222) \n",
"plot(C_A,ra)\n",
"plot(C_A,ra,'ro')\n",
"xlabel(\"Concentration\")\n",
"ylabel(\"-ra\")\n",
"suptitle(\"differential method\")\n",
"xlim(1,20)\n",
"ylim(.1,2)\n",
"print \"rate from differential method = -0.064*CA\"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"average value of k = 0.063680\n",
"('ra =- 0.06367*CA', 'since its a first order reaction,')\n",
"rate from differential method = -0.064*CA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['draw_if_interactive', 'new_figure_manager']\n",
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAACnCAYAAAD32+C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XdYFNf6B/Dv0CFgUCkqqOhakLIUsStgAdRYuBoFI4lK\nkmtuVCxJrt7cJGKKJqZQjCWJJQU1tovXoEGNEVQSExWNJZpEApG1InYQpby/P+bHXqq7LLs7W97P\n8/DIDrNz3vE5s+/OOXPOEYiIwBhjzCxZSB0AY4wx6XASYIwxM8ZJgDHGzBgnAcYYM2OcBBhjzIxx\nEmCMMTPGSYDpVXh4OEaPHg0ACAsLw/vvvw8AWLZsGWxsbODg4IBbt24hJCQEtra26NOnj85iuXDh\nAiZNmqR8nZubC09Pz0e+59ChQ7Czs9N6LCkpKfj000+Vr7t06YJXXnlF4+M19/3MfFhJHQAzL4Ig\nKH/Pzs5W/r5y5Uo899xzWLFiBQDg2LFjqKyshIWFet9THj58CBsbmybFcuHCBaSnpytfBwcHQ6FQ\nNOkY2pKeng4nJyf8/e9/B1D7/0kTgiA0+xjMPPCdANO50NBQWFtbw9nZGfn5+coP9upvq1OnTsW5\nc+fw2WefoVOnTmjbti0AwNHREXPnzsXJkyfh7u4Oe3t72NvbIyUlBYB4V9G5c2c4OzvD29sbp0+f\nhpubW4P7devWDa1atYKVlRVGjRoFAHjqqafw4MEDODg4oE+fPsjJyVF+yz906BAef/xxODg4wM7O\nTnmsxiQnJ8PZ2RkeHh6wtrZG37598cILL8DJyQk2NjbYu3cvADR4Ljk5OTh48CB27doFBwcHZSLc\nu3cvHn/8cVhZWWHGjBkAgIqKCsjlctja2sLW1laZNCoqKuDt7Q0bGxu4uLjg9u3b4HGgTC3EmA59\n8sknZGdnRyUlJVRUVETW1tY0ZswYIiLq0qULvfLKK/V+JyKqWTXbtm1LK1asICKiH374gaysrIiI\nKCwsjBwcHOj+/fsq92vRogWVl5fT77//ToIg0N27d+nQoUNka2urLOfgwYPK1zdv3qSSkhIiItqz\nZw/Z2dnV26empKQkEgSBzp07R3fv3iULCwsaMmQIERGNGzeO/P39HxljeHi48v+FiEgmk1GHDh2I\niOibb74hS0tLIiJ68cUXqXXr1kREdP78ebKwsKAjR47QzJkzycXFhYiITp8+TYIg1Pr/ZKwxfCfA\ndGr79u3o06cPHBwc4OLiArlc3ui+1Mg318uXL+Oll16Cg4MDhg4dCiKCQqGAIAjo27ev8tu7qv2s\nrKzQtWtXWFtb4+zZs4/8plxUVARvb2/Y2dlh7NixKCsrU3muLVu2RPfu3eHo6AgnJyfExMQAAPr3\n74+ioqJHxggAVVVVymMJgoAxY8YAAEaNGoXKykoAYhNadHQ0AEAmk8HLywsbN25EVlYWxo4dCwDw\n9fVV3k0xpgr3CTCdsrCwqPVh+6gP3ke5ceNGgx2yDg4Oau1Xs79AEARUVFQ8srwpU6agbdu2uHDh\nAh4+fAhbW1uVMVpZ/e9yEgQBjz32GADA0tKy1gd8YzHW1dA+giDUOpaq7YypwncCTKeio6Px888/\n4969e7h+/TpOnTrV5GO0a9cOTz/9tPJ1zc5cTfar5uzsrPyGXdeDBw/Qpk0bAEBCQkJTQ25UYzHa\n29ujtLRU5fsHDx6MjIwMVFVVIS8vDwUFBZg8eTLCw8ORkZEBADh79iwuX76stZiZaeMkwHTqueee\nQ+/evdGyZUt06dIFHh4eje7b2NMs3333nfLRTDs7O7z66qvKv9V8ekjd/ar5+fnBzc1N+ShqzSdq\n3n33XWRmZsLBwQEnTpxQGeejnsap+bfGYnzxxRdx6NChWh3DDR0vOTkZbdq0gb29PXx8fPD8888j\nODgYycnJaNWqFWxsbBAaGgpXV9cGY2GsLoE0vT9njDFm9PhOgDHGzBgnAcYYM2OcBBhjzIxxEmCM\nMTPGSYAxxswYJwHGGDNjnAQYY8yMcRJgjDEzxkmAMcbMmM6SQGFhIUJDQ+Hv74/u3btj6dKlAIDE\nxER4enoiKCgIQUFByMzM1FUIjGlNY/W5roSEBPj6+iI4OBjHjx/Xc5SMNZ3Opo24evUqioqK4Ofn\nh3v37iE4OBhbtmzB9u3b4eTkhHnz5umiWMZ0orH6HBAQoNxn27Zt+Oqrr7B9+3YcP34c06ZNqzfv\nEGOGRmd3Au7u7vDz8wMgrhAll8tx8eJFAJpPJ8yYVBqqz5cuXaq1z65du5QzhAYFBaGiokKy5SoZ\nU5de+gQKCgpw5MgRDBo0CACwfPly9OjRA3Fxcbhx44Y+QmBMa6rr88CBA2ttVygUaN++vfK1p6cn\nJwFm+HS9dNndu3cpJCSE0tPTiYioqKiIqqqqqKqqit544w2aPHly/eXOLGQEgH/4R2c/MplMK/W5\npsjISDp8+LDydVRUVK3XREQBAQGSnzv/mO6PJvVap0ng4cOHFBkZSR999FGDf7948SJ169atflAA\nuboSHT+uy+gatnDhQv0XKmG5UpYt5TkDTa/6qupzfHw8bdmyRfna19eXFApFs8vVJin/z9XB8TWP\nJvVLZ81BRIRnn30WPj4+mDt3rnL7tWvXlL9v27YNvr6+Db5/5Upg1Cjgr790FSFj6musPtc0cuRI\nrF+/HgCQm5sLS0vLRy6iw5gh0Nkawzk5OUhLS4NcLkdQUBAAYPHixdiwYQNOnjyJhw8fomPHjliz\nZk2D7x8/HlAogBEjgJwcoGVLXUXKmGqN1ecLFy4AAKZPn47x48dj//798PX1ha2tLdatWydlyIyp\nRWdJYODAgQ0ufD1ixAi1jzF7NnDhAhAdDezeDaixNnezhYeH674QAypXyrKlPOemaqw+1/Xxxx/r\nIRrNGfr/Ocenfwa5vKQgCMrHSKuqgNhYQBCAjRuBBpaKZazJatYxcyiXmQdN6pfBf6RaWABffglc\nugT8859SR8MYY6bF4JMAIDYD/fe/wM6dQGqq1NEwxpjp0FmfgLa1agV8+y0wcCDg6QmMGyd1RIwx\nZvyMJgkAgJcX8M03QFQU4O4ODBggdUSMMWbcjKI5qKagIOCrr8RHSH/7TepoGGPMuBldEgDEO4El\nS8QxBFeuSB0NY4wZL6NqDqpp2jSgsFAcVZyVBTg6Sh0RY4wZH4MfJ/AoRMDzz4uPj+7YAVgZbUpj\n+sbjBJgpMslxAo8iCOIcQ0TAP/4h/ssYY0x9Rp0EAMDaGtiyBcjNBd5+W+poGGPMuJhEA4qjoziQ\nrF8/oH17YOpUqSNijDHjYBJJAADatBEHk4WHA+3aAZGRUkfEGGOGz+ibg2ry9ga2bgXi4gBe35sx\nxlRT6+mg/Px8KBQKkLgSGQRBQGhoqO6CauYTFNu2idNQ5+QAHTtqMTBmMvjpIGaKNKlfKpuD5syZ\ng/T0dPj6+sLS0lK5XZdJoLl4QRrGGFOPyjuBzp074+zZs7C1tdVXTFr7tjRvHnDsmP4WpGHGg+8E\nmCnSyTgBHx8fVFZWNjmYwsJChIaGwt/fH927d8fSpUsBADdu3EBERATkcjmioqJw69atJh9bXR98\nIE40N2WKuDgNY4yx2lTeCYwbNw6//PILhg4dqrwbEAQBqSom9r969SqKiorg5+eHe/fuITg4GFu2\nbMHq1ashk8kwZ84cJCcnIz8/HykpKbWD0uK3pbIyICIC6NsXeP99rRySmYCm1rH4+Hjs3LkTbm5u\nOHXqVL2/Z2VlYezYsejcuTMAYPz48XjttdeaXS5jTaGTPoExY8ZgzJgxEAQBAJQdw6q4u7vD3d0d\nAODo6Ai5XI6LFy9i165d+PnnnwEAcXFx6Nu3b70koE3VC9IMGCCOIUhI0FlRzIRNmzYNs2bNwjPP\nPNPoPmFhYdixY4ceo2Ks+VQmgalTp6KsrAynT5+GIAjw8/Nrcv9AQUEBjhw5grVr16KoqAitW7cG\nALi4uODatWuaRd4EvCANa65BgwahoKDgkfvwN3xmjFQmgd27d2Pq1Kno2rUrAOCPP/7AF198gUg1\nR2Pdu3cPTz75JFJSUtCiRQu1A0tMTFT+Hh4ejvDwcLXf2xBekMa8ZWVlISsrS2fHFwQBP/74I/z9\n/eHm5oaPPvoIAQEBOiuPMW1R2Sfg7++P9PR0dOnSBQCQl5eH6OjoBttF6yovL8eoUaMwfPhwzJ07\nFwAgk8nw008/wcXFBUVFRejXrx/Onz9fOygdtpvu3i12FGdnA92766QIZgQ0qWMFBQUYPXp0g3X/\n3r17sLKygp2dHfbs2YPp06cjPz+/wXIXLlyofK2NLzjMfNX9crNo0aIm12uVScDX1xdnzpxRua0u\nIsKUKVPQunVrJCUlKbfPmjVL2TGclJSE/Pz8ep3Muu48W7cOeOst4IcfxOkmmPnRdhKoq3v37sjO\nzkabOhWMO4aZLumkY1gul2P69OmYNGkSiAibNm2CXC5XeeCcnBykpaVBLpcjKCgIALBkyRIsWrQI\nMTExWLt2Ldq0aYPNmzc3KWBtmDYNuHCBF6Rh2nP9+nW4uLgAAI4dO4aSkhK4ublJHBVjqqm8E7h/\n/z6SkpKQk5MDQOwgmzNnDux0OPpKH9+WiIDnnhOXp/zvf3lBGnPT1Do2adIkZGdn4/r163B3d8ei\nRYtQXl4OAJg+fTqWLVuGTz/9FABgY2ODpKSkBkfV850A0yVN6pdRryzWXOXlwJgx4qOjn3wiLlLD\nzAOPGGamSKtJYMKECdiyZQv8/PzqjQsQBAEnT57UPFJVQenxQrl3DwgLA/72N6CBsT3MRHESYKZI\nq0ng0qVLaNeuHf766696BxUEAR11OD2nvi+UK1fEBWkWLuQFacwFJwFmirQ6d1C7du0AACtWrICX\nl1etnxUrVjQvUgNTvSDNggVAerrU0TDGmP6onEBuz5499bZ98803OglGSt7e4hKVs2YB773Hi9Yz\nxsxDo8/ErFy5EitWrEBeXh78/f2V20tLSxEYGKiX4PStZ0/gp5+AsWOBX38VO4t5CmrGmClrtE/g\n9u3buHnzJhYsWID33ntP2c5kb2+vnBhOZ0FJ3G5aWir2DSgUYvOQjk+XSYD7BJgp0tkjokSEy5cv\no6KiQrmtQ4cOTY9Q3aAM4EKpqgLefBP4/HNxHAFPA2NaOAkwU6STRWW2bNmCzp07o2vXrggLC4OX\nlxdGjBihcZDGwsICSEwU+wciIsREwBhjpkZlEnj99ddx5MgRdOvWDfn5+cjKykLfvn31EZtBiIkR\nO4xnzADefZc7jBljpkVlEnjsscfg4uKC8vJyEBFCQ0Nx9OhRfcRmMHr1EjuMt24VZyAtK5M6IsYY\n0w6VM+a0aNECpaWl6N+/PyZNmgQ3NzdYW1vrIzaD4uEBHDggdhgPGcIdxowx7Tuwcyf2pKbC6sED\nVNjaIjIhAaFPPKHTMlV2DJeUlMDOzg7l5eX48ssvUVZWhsmTJytXB9NJUAbcecYdxqaBO4aZoTmw\ncyd2z56Nd/LylNv+LZMhKiVF7USg9aeDKisrERkZiX379jXpoM1lDBfKpk3iwLLPPhPHFTDjwkmA\nGZrXoqLwdgODc1+PisJbmZlqHUPr6wlYWlrCysoKd+/ehZOTU5MObOpiYoDOncWJ586eBebP51lI\nGWOaqagAbioeNPg3Sx13QqrsE7C1tYWPjw8iIyPh4OAAQMw2dVcDM0fVHcbVI4w//ZRHGDPG1Hf7\nNrBmDZCaCnS9ZdvgPpU6/lBR+XTQ+PHj8dZbbyE0NBQhISHo2bMnevbsqdbB4+Pj4e7uXmvaicTE\nRHh6eiIoKAhBQUHIVPM2x1BVdxiXlYkdxlevSh0Re5Ts7GzlynjW1tawsLBAixYtJI6KmZu8PGD2\nbKBTJ+DIEbF5+fX1Cfi3TFZrv1dlMkTMmqXTWFTeCdy8eRNz5syptS05OVmtg0+bNg2zZs3CM888\no9wmCALmzZuHefPmNTFUw+XgAHz9tdhh3KcPdxgbshkzZiA9PR3dunVDWVkZNmzYoHK9bMa0gUj8\nwpicDBw8KK5s+Msv4qJWIrHz9/Vly2BZVoZKOzsMnzVL508HgVQIDAyst83Pz0/V25Ty8/Nr7Z+Y\nmEgffPDBI9+jRlgG6+uviVxdibZvlzoS1pDq+lyzjgUHB+utfGOu20wzDx4QffEFUVAQUbduRCtW\nEN27p5uyNKlfjd4JbNy4ERs2bEB+fj5Gjx6t3F5aWgpnZ+dmJZ7ly5dj9erV6NmzJ1JTU9GqVatm\nHc+QcIexYXN0dFSuDTx//ny4u7ujtLRU5fvi4+Oxc+dOuLm54dSpUw3uk5CQgH379sHW1hZr1qxB\nUFCQVmNnxqWoSJyJeMUKwNcXeOstYMQIcUoaQ9JoEujfvz/atm2LoqIivPzyy7VmEW1O5Z4xYwbe\neOMNAGL/QEJCAtLS0urtl5iYqPw9PDwc4eHhGpepb9xhbHiysrKQlZWFnj17YtGiRQDEp98UCgV2\n7Nih8v0NNW3WtG3bNly4cAFnzpzB8ePHMW3aNJw4cUKr58CMw+nTQEqKOMPA+PHA7t1AjW5Rw6P9\nG5La6jYH1XTx4kXq1q1bve16CEsvSkqIJkwg6teP6MoVqaNhFRUVFBcXR0Sa1bFH1eX4+HjaunWr\n8rWvry8VFhbW289U6jarrbKSaNcuoogIojZtiBYtIrp6Vf9xaFK/VN6YbNiwAV5eXnB0dISTkxOc\nnJya9TTFtWvXlL9v27YNvr6+Gh/L0FV3GEdGih3Gv/widUTmzdLSEoWFhbWmRNcWhUKB9v/r4YOn\npycUCoXWy2GGpbQUWLVKbO7517+AyZOBggLgjTcANzepo1OPyqeDFixYgN27d6NHjx5NPvikSZOQ\nnZ2N69evo3379li0aBH279+PkydP4uHDh+jYsSPWrFmjUeDGonpK6h49gGHDxBHG0dFSR2W+2rdv\nj379+gEAPvzwQwD/e2KtuajOSE2hkc4gY27qZKKLF4GPPwZWrwb69wdWrgTCwvTf/1fdzNkcKpOA\nl5eXRgkAEDuX64qPj9foWMauZofxuXPcYSwVmUwGmUyGo0eP4vfff0e7du20clxPT08UFhaiT58+\nAMQ7A09Pzwb3rZkEmHE5ckR8xPPbb4G4OODHH4EuXaSLp+6XiOr+rqZQOYHc7Nmzce3aNYwZMwY2\nNjbimwQB48aNa3JhagdlwvOrXLwodhj7+HCHsZQEQUBgYCCOHz+u9nsKCgowevToBp8O2rZtG9LS\n0pCeno7c3FxMmzYNvzTQ/mfKddtUVVSIY3+SkoDCQiAhAXj2WaCZD0nqhNbnDgLEtYZtbW2xp87E\nRrpMAqaMp6Q2Tg01bVY/ajp9+nSMHz8e+/fvh6+vL2xtbbFu3TqJI2bNVXNKBw8PYO5csSnXSuWn\npnFRa41hfTOHb0s8JbW0BEHA8uXL8eKLL+q9XFOv28YuL0/84P/qK2D4cGDOHKB3b6mjUo9O1hg+\nc+YMBg4cCG9vbwDAr7/+qlG7E6ut5hrGw4YB//mP1BGZH30nAGa4iIDsbPGbfp8+gL09cPIksGGD\n8SQATam8E+jTpw9SU1Pxwgsv4Pjx4yAi+Pn56XS+FXP7tnTkCBAbCwQGiu2OHTpIHZHp4/UEzFPd\nlbvCX0jApbtPIDkZKCkRv/U/8wzw2GNSR6oZnfQJlJWVKZ94qC7E0tKy6dGxRvXqBZw5AyxdCgQH\nA/PmAS+9BNg2PLMsY0wDDa3c9dS+PBT6AW8vfgLDhxvelA76oPKUW7VqhfPnzytfZ2Rk6HRpSXNl\nZycOMDlyRJxywt9fHG7OGNOOPamptRIAAGyozEN4m2UYOdI8EwCgxp3AqlWrMGXKFJw7dw4dOnSA\nq6srNm3apI/YzFKnTmJHcUYG8OKL3ETEWHOVlwPbtwOnDkuzcpehU5n7unfvjpycHBQWFuLYsWM4\nduwYukg5OsJMjBolNhEFBIhNRIsXAw8arsOMsQYUFQHvvCMO0kxNBVy9pFm5y9CpTALz58/HnTt3\n4OLiAldXV9y+fRuvvvqqPmIze9xExFjTHT0KTJkCdOsG/Pkn8M034iIuzyyWZuUuQ6fy6aDAwMB6\nU+IGBQU1aaRlk4PiJygalJEhLknHTUTNx08HmZaHD8Wpm5ctAy5dEptSn3sOqNt9eWDnTuytsXJX\nhD5W7tIjTeqXyiTQo0cPnDx5EtbW1gCAhw8fQi6X49y5c5pHqioovlAaVVYmPkWUmspPETUHJwHT\ncOWKuHDLJ58A3t7ArFnA6NGmN6pXXToZLBYbG4vBgwdjzZo1WL16NYYMGYJJkyZpHCRrHm4iYuaO\nCDh8WJy2uUcP4PJlYM8e4PvvxQkazTUBaEqtaSPS09Px3XffQRAEREREYOzYsboNir8tqY2biDTD\ndwLG58EDYNMmscmnuBiYMQOIjwdatpQ6MsOhk+YgKfCF0jTVTUQpKWLzEDcRqcZJwHhcvCjO1//Z\nZ+LTcrNmASNHAjxmtT6dNAdpe2Uxpn01m4gOH+YmImb8iMQneiZOFOvzrVvi3D579oht/pwAtEfl\nnUCHDh00XlksPj4eO3fuhJubm3IO9hs3biAmJgZXr15F27ZtsWnTJjjXmZibvy01DzcRqcZ3AtKr\nO49PZEICeg15Ahs2iKt2lZQAM2eK067z9071aFS/VC1CPGjQoCYvXFztwIEDlJubW2tx7pkzZ1JS\nUhIRESUlJVFCQkK996kRFlPh/n1xsetWrYjeeYeorEzqiAyLVHWM67YoOyODXpXJiMQv/UQATXeW\nUTunDBo5kujbb8XF21nTaFK/dL6yWN3VmGQyGX7++We0bt0a169fR9++fWvNTaRxNmMN+vNPcWbE\nc+fEDrWoKKkjMgx8JyCt16Ki8HadhaoA4KVBUfjwQKYEEZkGo1hZrKioSDkBnYuLC65du6bRcZh6\nOncGduzguYiY4SgrA4oKGp4DxcnCvOfxkYLKJPD555/rIYz6ai7GXXcxZdZ0o0aJi9csXQoEBZnf\nU0RZWVnIyspq1jEyMzPxyiuvoLKyElOmTMH8+fPrlTF27Fh07twZADB+/Hi89tprzSrTlFy+LD7l\n88knQHAlz+NjMFS1F+Xn59Pw4cPJycmJnJycaOTIkZSfn692e1N+fn6tPoHOnTtTUVERERFdu3aN\nZDJZvfeoERZrhrw8otGjibp2JcrMlDoaaTS1jpWVlZGXlxcpFAoqLy+nkJAQys3NrbXP/v37afTo\n0Vot1xQcPUoUF0fk7Ez0j38QnT3bcJ/Av2Qyys7IkDpco6ZJ/VL5iGhcXBwmTZqE4uJiFBcXIzY2\nFnFxcRonnZEjRyItLQ0AkJaWhpEjR2p8LKaZ6iaijz4Sm4gGDRLnXamokDoyw/XTTz/B19cXHh4e\nsLKyQkxMDHbu3FlvP+L2fgBiXdq6FRg4UBzFK5eL/VMrVojTO4Q+8QSiUlLwelQUEsPC8HpUFIan\npJjUPD5GQ1WWkMvl9bb5+/urlWFiY2Opbdu2ZG1tTZ6enrR27VoqLi6mYcOGkb+/P0VERNDNmzfr\nvU+NsJiWPHxItGkTUf/+RB06EL33HlFxsdRR6V5T69j69evphRdeUL7euHEjTZ8+vdY+WVlZ1Lp1\na/Lz86MhQ4bQiRMnml2usblxg2jpUrEuDRhAtGULUXm51FGZD03ql8o+gcceewwbN27ExIkTAQCb\nN2+Gk5OTWglm48aNDW7fu3evmimK6Zq1tTggZ+JEcQre1FRAJgNiYoCEBMDHR+oIDYMgCCr36dmz\nJxQKBezs7LBnzx5ER0cjPz+/3n6m2N/1229i3dm4EXjiCWDbNiAkROqoTJ82+rpUpo3z589TREQE\nOTo6kpOTE0VFRdH58+c1ylLqUiMspkOXLxMtXEjk7k4UEUGUkWF6z2w3tY4dOHCAnnjiCeXrpUuX\n0ttvv/3I93Tr1o0uX77crHINRXZGBv07MpIWhoXRvyMjKTsjg6qqiHbvJhoxgsjNjej114kuXZI6\nUvOmSf1S+Y64uDi6deuW8vXNmzfpmWeeaXJBTQrKSC8UU1NWRvTFF0TBwURduhClphLduSN1VNrR\n1Dp2//596tixIykUCnr48CGFhITQsWPHau1T/cADEdHRo0fJw8ODKutkT2Os2w114s5wlVEPzwyS\ny4nWrhUHJzLp6SQJBAYGqrVNm4zxQjFlVVVEBw8SPfmkOAJ5zhwiHd8M6pwmdWzXrl3k6+tLPXr0\noMWLFxMR0apVq2jVqlVERJSamkp+fn7k5+dHwcHBlJ2drZVypfbvyMhaCaD658VeUVRVJXV0rCZN\n6pfKEcM+Pj44fPiwctK427dvo2/fvjh79mzz2qEegUdVGq4LF8QnPNasAfr1E+coGjIEUKPJ3KDw\niGH1JYaHIzE7u/72sDAkNrc9mmmVTkYMz549GyEhIYiJiQERYfPmzXjppZc0DpIZtw4dgHffFWct\nTUsTO48tLMR/4+IAe3upI2TaVtHIiEIe2GUa1FpPIDc3F/v27YMgCBg6dCiCgoJ0G5QRflsyV0TA\nvn3iWgaHD4vrus6YAXh6Sh3Zo/GdgPoO7NyJ3bNn4528POW2V2Uyfq7fAPGiMkxS58+Lk9R99RUQ\nESE2FfXrZ5hNRZwEmsbUF2g3FZwEmEG4cwdYt05MCC1bislg4kTg/yehNQicBJgp4iTADEplJbBr\nl9hU9OuvwAsvANOnA+7uUkfGSYCZJp0sL8mYpiwtxaUAv/sO2LtXXCvW21tcKer4camjY4wBfCfA\n9Ky4GFi9Gli+HGjXDhg/XpxgrEsX/cbBdwLMFHFzEDMaFRXiU0Xp6cB//wu4uIjJ4G9/Exe+0XVn\nMicBZoo4CTCjVFUlPl6ani7+VFYC0dFiQhgwQGxW0jZOAswUcRJgRo8IOHXqfwnh0iVgzBgxIQwb\npr2V0DgJMFPESYCZnD//BLZvFxPCqVNAVBQwbhwwYgTw/zOZaISTADNFnASYSbt6VVwRLT0dOHRI\nXBHtb38T7xTc3Jp2LE4CzBRxEmBm484dcQxCejqwe7e4fGF1x7KXl+r3cxJgpsioxgl4eXlBLpcj\nKCgIvXuASap3AAAK10lEQVT3liqMepq9So+RlStl2c0pt0ULIDYW2LQJuHIFmD8fOHMG6NULCAoC\n3nxTbD7iz9vapKxn6uD49E+yJCAIArKysnD8+HH8/PPPUoVRjzF+IBpr2doq185OXNJw9Wrg8mUg\nORm4cQMYNQro2hV45RXghx/Ep5DMnaF/iHF8+ifpiGG+LWbaZmUFhIWJiaCgANi8WUwSf/874OEh\nTl2xZ4/UUTJmOCS9E4iIiIBcLsfHH38sVRjMhAkCEBwMvPUWcPo0cOAA0LkzUGOdd8ZYk9ci05Kr\nV68SEdG1a9coODiY9u7dq/ybTCYjAPzDPzr7kclkktT7sLAwyc+df0z3JyAgoMl10iCeDlqyZAkA\n4F//+pfEkTDGmHmRpDmotLQUpaWlAICSkhJkZmbC19dXilAYY8ysqVxjWBeuXr2K6OhoCIKA0tJS\nxMbGYsyYMVKEwhhjZs0gmoMYY4xJw+AWlcnMzIS/vz98fHzw3nvv6bVsfQ1gi4+Ph7u7O/z9/ZXb\nbty4oXxaKioqCrdu3dJLuYmJifD09ERQUBCCgoKQmZmp9XIBoLCwEKGhofD390f37t2xdOlSALo/\n78bK1dd5GxJDG6Ap1XXQnPgMqd5o7ZrS8sMPzVJWVkZeXl6kUCiovLycQkJCKDc3V2/le3l5UXFx\nsc7LOXDgAOXm5pKfn59y28yZMykpKYmIiJKSkighIUEv5SYmJtKHH36o9bLqunLlCp06dYqIiO7e\nvUtdu3alEydO6Py8GytXX+dtSPRVv9Ul1XWgLimvF3Vo65oyqDuBn376Cb6+vvDw8ICVlRViYmKw\nc+dOvcZAemgdGzRoEFq2bFlr265du/D0008DAOLi4nRy3g2VC+jnnN3d3eHn5wcAcHR0hFwux8WL\nF3V+3o2VC+jnvA2NIZ2zVNeBuqS8XtShrWvKoJKAQqFA+/btla89PT2hUCj0Vr6UA9iKiorQunVr\nAICLiwuuXbumt7KXL1+OHj16IC4uDjdu3NB5eQUFBThy5AgGDhyo1/OuLnfQoEEA9H/eUjOGAZpS\nXgfqMsR605xryqCSgKDrNQVVOHz4MHJzc7Fv3z6sW7cO3333naTx6MOMGTOQl5eHX3/9FTKZDAkJ\nCTot7969e3jyySeRkpKCFs1ZEECDcidMmICUlBQ4OTnp/bwNgTnWb20zxHrT3GvKoJKAp6cnCgsL\nla8LCwtr3Rnomtv/T0rv6uqKJ598EkeOHNFb2a6urrh+/ToA8duQW1MnyNeQi4sLBEGAIAiYPn26\nTs+5vLwc48ePx+TJkxEdHQ1AP+ddXe5TTz2lLFef520opKzf6pLqOlCXodUbbVxTBpUEevXqhdOn\nT+PixYsoLy/H5s2bMWLECL2ULfUAtpEjRyItLQ0AkJaWhpEjR+ql3Jq3itu2bdPZORMRnn32Wfj4\n+GDu3LnK7bo+78bK1dd5Gwqp67e6pLoO1GVI9UZr15TOuq41tGvXLvL19aUePXrQ4sWL9Vbun3/+\nSXK5nAICAqhr1670+uuv66ys2NhYatu2LVlbW5OnpyetXbuWiouLadiwYeTv708RERF08+ZNnZe7\nZs0aiouLI7lcTt7e3hQVFUUKhULr5RIRHTx4kARBoICAAAoMDKTAwED69ttvdX7eDZW7a9cuvZ23\nodBn/VaXVNeBpvHp83pRh7auKR4sxhhjZsygmoMYY4zpFycBxhgzY5wEGGPMjHESYIwxM8ZJgDHG\nzBgnAcYYM2OcBAzY7du3sXLlSgDA5cuXMWHCBIkjYubkypUriI2NhZ+fH+RyOYYNG4bffvtNsniS\nk5Nx//79Jr/viy++wOXLl5Wvn3/+eZw9e1aboRk1HidgwAoKCjB69GicOnVK6lCYmamsrETPnj3x\n8ssvIy4uDgBw8uRJ3LlzBwMHDpQkpk6dOuHo0aPKydFqqqqqgoVFw99pBw8ejA8++AA9e/bUdYhG\nie8EDNiCBQuQl5eHoKAgTJw4Ubm4xeeff47o6GiMGDECnTp1wscff4wPPvgAISEhCA4OVs4b8ttv\nv2Hw4MEICAhAnz59cObMGSlPhxmRPXv2wM3NTZkAAEAul2PAgAGYNWsWfHx84OPjgy+//BIAkJWV\nhfDwcMTGxqJbt26YMGGCcsrlnJwchISEIDAwEL169UJJSQkqKiowc+ZMBAQEoEePHkhNTX3kcVJT\nU3Hp0iUMHjwYQ4cOBSBOn/zyyy8jJCQEhw8fxqJFi9C7d294e3tj6tSpqKqqwtatW3H06FFMnjwZ\nwcHBKCsrQ3h4OI4dOwYAWLdunfJc5syZozxXR0dHvPbaa8rFY2reSZgcnY9tZhorKChQLmhR8/d1\n69ZRly5d6P79+1RUVEQtWrSg1atXExHR3Llz6f333yciov79+9Mff/xBRESHDx+mAQMGSHAWzBi9\n++67tGDBgnrb169fT1FRUUREVFxcTO3atSOFQkH79++nxx9/nK5cuUJVVVXUr18/ysrKorKyMvLw\n8KATJ04QEVFpaSlVVFRQSkoKvf3220QkLiYVHBxMv//+e6PHIaq/KI4gCPSf//xH+fr27dvK359+\n+mnaunUrERGFh4fTsWPHlH+rfv3XX3+Rh4cH3bx5kyorK2nYsGH09ddfK4/97bffEhHRP//5T1q4\ncGGz/08NlSQLzTP1UI2WOqrTajd48GDY2dnBzs4Ozs7Oykmi/P39ceLECRQXFyM3N7dWP4Im7anM\nPDU2rXtOTg5iY2MBAK1atcLQoUPx448/wtXVFb1794a7uzsAIDAwEBcuXICDgwO8vLwQEBAAALC3\ntwcg3mn88ccf2Lp1KwDgzp07+PPPP2FnZ1fvODVnFq7J0tJSOXMmAGRkZODDDz9ERUUFiouL4e3t\nrfxb3euHiHD48GEMGzYMzs7OAIBJkybh4MGDiImJgY2NDYYPHw4A6NmzJ3bv3t2E/z3jwknASNna\n2ip/t7CwUL62sLBAVVUViAiurq44fvy4VCEyI+bv74/k5OQG/1b3A7U6YdSsk5aWlqiqqnrkGiGr\nVq3C4MGDa23Lyspq8DgNsbOzUx7/3r17mDNnDk6ePIk2bdpg0aJFqKioqBdj3bjrftGq3s/a2lq5\nvfqaMlXcJ2DA7O3tldP/qqu6Uru4uMDV1RUZGRnK7dwnwNQVGRmJK1euYP369cptp06dQtu2bbFl\nyxYQEW7cuIHvv/8e/fr1a3DJRUEQIJfLUVBQgBMnTgAQp7GurKxEVFQUPvnkE+WHa35+vso7VXt7\ne5SUlDT4t4qKClhYWMDZ2Rn379/Hli1bHvk+QRDQr18/fP/997h16xaqqqqwefNmhIaGqvcfZEL4\nTsCAubu7IzAwED4+PvDz81N+S6le1KJa3d+rX2/atAnPP/88Xn31VVRWVmLixIkGOYc8MzyWlpbI\nzMzEnDlzsGTJElhaWqJNmzZYtmwZrl69Ch8fHwiCgCVLlqBdu3b4448/Gvy2bWNjg02bNiE+Ph5V\nVVWws7PD999/jxkzZqCgoAC+vr6wsbFBy5YtsWPHjnp1u6Znn30WgwcPRseOHbFv375a+zk7O2Pa\ntGnw9vZGx44d0adPH+Xfnn76aUybNg0tWrTADz/8oNzu6emJN998E/369QMAREVFKZtPG7umTBE/\nIsoYY2aMm4MYY8yMcRJgjDEzxkmAMcbMGCcBxhgzY5wEGGPMjHESYIwxM8ZJgDHGzNj/ATOW5vyu\n6kMaAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x2b3a490>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"example 7.16 page no : 322\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math \n",
"E = 75200. #in J/mol\n",
"E1 = 50100. #in J/mol\n",
"R = 8.314 #in J/mol K\n",
"T = 298. #in K\n",
"\n",
"ratio = math.exp((E1-E)/(R*T));\n",
"rate_increase = ratio**-1\n",
"\n",
"print \"increase in rate of reaction =\",rate_increase,\"times\"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"increase in rate of reaction = 25106.6042072 times\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
|
