Korean Journal of Chemical Engineering

, Volume 29, Issue 7, pp 855–861 | Cite as

Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm

  • Mohammad Reza EhsaniEmail author
  • Hamed Bateni
  • Ghazal Razi Parchikolaei
Catalysis, Reaction Engineering


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.

Key words

Oxidative Coupling of Methane (OCM) Mn/Na2WO4/SiO2 Catalyst ANN Optimization Genetic Algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. H. Lunsford, Catal. Today, 63, 165 (2000).CrossRefGoogle Scholar
  2. 2.
    Y. T. Chua, A.R. Mohamed and S. Bhatia, Appl. Catal., A, 343, 142 (2008).CrossRefGoogle Scholar
  3. 3.
    A. Malekzadeh, A.K. Dalai, A. Khodadadi and Y. Mortazavi, Catal. Commun., 9, 960 (2008).CrossRefGoogle Scholar
  4. 4.
    A. Nouralishahi, H. Pahlavanzadeh and J. Towfighi Daryan, Fuel Process. Technol., 89, 667 (2008).CrossRefGoogle Scholar
  5. 5.
    A. Malekzadeh, A. Khodadadi, M. Abedini, M. Amini, A. Bahramian and A. K. Dalali, Catal. Commun., 2, 241 (2001).CrossRefGoogle Scholar
  6. 6.
    S. Mahmoodi, M. R. Ehsani and S. M. Ghoreishi, J. Ind. Eng. Chem., 16, 923 (2010).CrossRefGoogle Scholar
  7. 7.
    D. M. Himmelblau, Korean J. Chem. Eng., 17 (4), 373 (2000).CrossRefGoogle Scholar
  8. 8.
    E. A. Medina and J. I. P. Paredes, Math. Comput. Model., 49, 207 (2009).CrossRefGoogle Scholar
  9. 9.
    J. Michalopoulos, S. Papadokonstadakis, G. Arampatzis and A. Lygeros, Trans. IChemE, 79, 137 (2001).CrossRefGoogle Scholar
  10. 10.
    J. A. Blasco, N. Fueyo, J. C. Larroya, C. Dopazo and Y. J. Chen, Comput. Chem. Eng., 23, 1127 (1999).CrossRefGoogle Scholar
  11. 11.
    K. L. Priddy and P. E. Keller, Artificial neural networks: An introduction, The Soc. of Photo-Opt. Instrum. Eng. (SPIE) Publication, Washington (2005).Google Scholar
  12. 12.
    S. K. Lahiri and K. C. Ghanta, Chem. Ind. Chem. Eng. Q., 15 (2), 103 (2009).CrossRefGoogle Scholar
  13. 13.
    E. Barshan, A. Ghodsi, Z. Azimifar and M. Z. Jahromi, Pattern Recognit., 44, 1357 (2011).CrossRefGoogle Scholar
  14. 14.
    I. T. Jolliffe, Principal component analysis, 2nd Ed., Springer-Verlag, New York (2002).Google Scholar
  15. 15.
    F. S. Lhabitant, Hedge funds: Quantitative insights, John Wily and Sons Ltd., Chichester (2004).Google Scholar
  16. 16.
    K. Deep and K. N. Das, Appl. Math. Comput., 203, 86 (2008).CrossRefGoogle Scholar
  17. 17.
    J. McCall, J. Comput. Appl. Math., 184, 205 (2005).CrossRefGoogle Scholar
  18. 18.
    A.A. Javadi, R. Farmani and T. P. Tan, Adv. Eng. Inf., 19, 255 (2005).CrossRefGoogle Scholar
  19. 19.
    C.C. Wu, P. H. Hsu, J. C. Chen and N. S. Wang, Comput. Oper. Res., 38, 1025 (2011).CrossRefGoogle Scholar
  20. 20.
    G. Corriveau, R. Guilbault and A. Tahan, Adv. Eng. Softw., 41, 422 (2010).CrossRefGoogle Scholar
  21. 21.
    C. J. Huang, Y. J. Chen, C. F. Wu and Y.A. Huang, Appl. Soft Comput., 9, 824 (2009).CrossRefGoogle Scholar
  22. 22.
    J. Cheng, J. Const. Steel Res., 66, 1011 (2010).CrossRefGoogle Scholar
  23. 23.
    L. Jozwiak and A. Postuła, J. Syst. Archit, 48, 99 (2002).CrossRefGoogle Scholar

Copyright information

© Korean Institute of Chemical Engineers, Seoul, Korea 2012

Authors and Affiliations

  • Mohammad Reza Ehsani
    • 1
    Email author
  • Hamed Bateni
    • 1
  • Ghazal Razi Parchikolaei
    • 1
  1. 1.Department of Chemical EngineeringIsfahan University of TechnologyIsfahanIran

Personalised recommendations