Articles & Issues
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
-
Received May 3, 2011
Accepted September 24, 2011
-
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © KIChE. All rights reserved.
All issues
Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm
Department of Chemical Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
ehsanimr@cc.iut.ac.ir
Korean Journal of Chemical Engineering, July 2012, 29(7), 855-861(7), 10.1007/s11814-011-0250-y
Download PDF
Abstract
The effect of some operating conditions such as temperature, gas hourly space velocity (GHSV), CH4/O2 ratio and diluents gas (mol% N2) on ethylene production by oxidative coupling of methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na2WO4/SiO2 catalyst. Based on the properties of neural networks, an artificial neural network was used for model developing from experimental data. To prevent network complexity and effective data input to the network, principal component analysis method was used and the number of output parameters was reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and those aspects of catalytic performance including conversion of methane, C2 products selectivity, C2 yielding and C2H4/C2H6 ratio. Levenberg-Marquardt method is presented to train the network. For the first output, an optimum network with 4-9-1 topology and for the second output, an optimum network with 4-6-1 topology was prepared. After simulating the process as well as using ANNs, the operating conditions were optimized and a genetic algorithm based on maximum yield of C2 was used. The average error in comparing the experimental and simulated values for methane conversion, C2 products selectivity, yield of C2 and C2H4/C2H6 ratio, was estimated as 2.73%, 10.66%, 5.48% and 10.28%, respectively.
Keywords
References
Lunsford JH, Catal. Today, 63(2-4), 165 (2000)
Chua YT, Mohamed AR, Bhatia S, Appl. Catal. A: Gen., 343(1-2), 142 (2008)
Malekzadeh A, Dalai AK, Khodadadi A, Mortazavi Y, Catal.Commun., 9, 960 (2008)
Nouralishahi A, Pahlavanzadeh H, Daryan JT, Fuel Process. Technol., 89(7), 667 (2008)
Malekzadeh A, Khodadadi A, Abedini M, Amini M, Bahramian A, Dalali AK, Catal. Commun., 2, 241 (2001)
Mahmoodi S, Ehsani MR, Ghoreishi SM, J. Ind. Eng. Chem., 16(6), 923 (2010)
Himmelblau DM, Korean J. Chem. Eng., 17(4), 373 (2000)
Medina EA, Paredes JIP, Math. Comput. Model., 49, 207 (2009)
Michalopoulos J, Papadokonstadakis S, Arampatzis G, Lygeros A, Trans. IChemE., 79, 137 (2001)
Blasco JA, Fueyo N, Larroya JC, Dopazo C, Chen YJ, Comput. Chem. Eng., 23(9), 1127 (1999)
Priddy KL, Keller PE, Artificial neural networks: An introduction, The Soc. of Photo-Opt. Instrum. Eng. (SPIE) Publication, Washington (2005)
Lahiri SK, Ghanta KC, Chem. Ind. Chem. Eng. Q., 15(2), 103 (2009)
Barshan E, Ghodsi A, Azimifar Z, Jahromi MZ, Pattern Recognit., 44, 1357 (2011)
Jolliffe IT, Principal component analysis, 2nd Ed., Springer-Verlag, New York (2002)
Lhabitant FS, Hedge funds: Quantitative insights, John Wily and Sons Ltd., Chichester (2004)
Deep K, Das KN, Appl. Math. Comput., 203, 86 (2008)
McCall J, J. Comput. Appl. Math., 184, 205 (2005)
Javadi AA, Farmani R, Tan TP, Adv. Eng. Inf., 19, 255 (2005)
Wu CC, Hsu PH, Chen JC, Wang NS, Comput. Oper. Res., 38, 1025 (2011)
Corriveau G, Guilbault R, Tahan A, Adv. Eng. Softw., 41, 422 (2010)
Huang CJ, Chen YJ, Wu CF, Huang YA, Appl. Soft Comput., 9, 824 (2009)
Cheng J, J. Const. Steel Res., 66, 1011 (2010)
Jozwiak L, Postula A, J. Syst. Archit., 48, 99 (2002)
Chua YT, Mohamed AR, Bhatia S, Appl. Catal. A: Gen., 343(1-2), 142 (2008)
Malekzadeh A, Dalai AK, Khodadadi A, Mortazavi Y, Catal.Commun., 9, 960 (2008)
Nouralishahi A, Pahlavanzadeh H, Daryan JT, Fuel Process. Technol., 89(7), 667 (2008)
Malekzadeh A, Khodadadi A, Abedini M, Amini M, Bahramian A, Dalali AK, Catal. Commun., 2, 241 (2001)
Mahmoodi S, Ehsani MR, Ghoreishi SM, J. Ind. Eng. Chem., 16(6), 923 (2010)
Himmelblau DM, Korean J. Chem. Eng., 17(4), 373 (2000)
Medina EA, Paredes JIP, Math. Comput. Model., 49, 207 (2009)
Michalopoulos J, Papadokonstadakis S, Arampatzis G, Lygeros A, Trans. IChemE., 79, 137 (2001)
Blasco JA, Fueyo N, Larroya JC, Dopazo C, Chen YJ, Comput. Chem. Eng., 23(9), 1127 (1999)
Priddy KL, Keller PE, Artificial neural networks: An introduction, The Soc. of Photo-Opt. Instrum. Eng. (SPIE) Publication, Washington (2005)
Lahiri SK, Ghanta KC, Chem. Ind. Chem. Eng. Q., 15(2), 103 (2009)
Barshan E, Ghodsi A, Azimifar Z, Jahromi MZ, Pattern Recognit., 44, 1357 (2011)
Jolliffe IT, Principal component analysis, 2nd Ed., Springer-Verlag, New York (2002)
Lhabitant FS, Hedge funds: Quantitative insights, John Wily and Sons Ltd., Chichester (2004)
Deep K, Das KN, Appl. Math. Comput., 203, 86 (2008)
McCall J, J. Comput. Appl. Math., 184, 205 (2005)
Javadi AA, Farmani R, Tan TP, Adv. Eng. Inf., 19, 255 (2005)
Wu CC, Hsu PH, Chen JC, Wang NS, Comput. Oper. Res., 38, 1025 (2011)
Corriveau G, Guilbault R, Tahan A, Adv. Eng. Softw., 41, 422 (2010)
Huang CJ, Chen YJ, Wu CF, Huang YA, Appl. Soft Comput., 9, 824 (2009)
Cheng J, J. Const. Steel Res., 66, 1011 (2010)
Jozwiak L, Postula A, J. Syst. Archit., 48, 99 (2002)
