Modeling of Oxidative Coupling of Methane over Mn/Na2WO4/SiO2 Catalyst Using Artificial Neural Network

Document Type : Research Article

Authors

Department of Chemical Engineering, Isfahan University of Technology, P.O. Box 84156-83111 Isfahan, I.R. IRAN

Abstract

In this article, the effect of 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 development from experimental data. In order to prevent network complexity and effective data input to network, principal component analysis method was used and the numbers of output parameters were reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and aspects of catalytic performance, which include conversion of methane, C2+ products selectivity, yield of C2+ and C2H4/C2H6 ratio. Levenberg– Marquardt method is presented to train the network. For first output, optimum network with 4-9-1 topology and for second output, optimum network with 4-6-1 topology was prepared.

Keywords

Main Subjects

References

[1] Farsi A., Moradi A., Ghader S., Shadravan V., Manan Z.A., Kinetics Investigation of Direct Natural Gas Conversion by Oxidative Coupling of Methane, J. Nat. Gas Sci. and Eng., 2, p. 270 (2010).
[2] Zheng W., Cheng D., Zhu N., Chen F., Zhan X., Studies on the Structure and Catalytic Performance of S and P Promoted Na-W-Mn-Zr/SiO2 Catalyst for Oxidative Coupling of Methane, J. Nat. Gas Chem., 19, p. 15 (2010).
[3] Prodip K. Kundu, Yan Zhang, Ajay K. Ray, Modeling and Simulation of Simulated Countercurrent Moving Bed Chromatographic Reactor for Oxidative Coupling of Methane, Chem. Eng. Sci., 64, p. 5143 (2009).
[4] Talebizadeh A., Mortazavi Y., Khodadadi A.A., Comparative Study of the Two-Zone Fluidized-Bed Reactor and the Fluidized-Bed Reactor for Oxidative Coupling of Methane over Mn/Na2WO4/SiO2 Catalyst, Fuel Process. Technol., 90, p. 1319 (2009).
[5] Nouralishahi A., Pahlavanzadeh H., Daryan J.T., Determination of Optimal Temperature Profile in an OCM Plug Flow Reactor for the Maximizing of Ethylene Production, Fuel Process. Technol., 89, p. 667 (2008).
[6] Malekzadeh A., Khodadadi A., Abedini M., Amini M., Bahramian A., Dalali A.K., Correlation of Electrical Properties and Performance of OCM MOx/Na2WO4/SiO2 Catalysts, Catal. Commun., 2, p. 241 (2001).
[7] Mahmoodi S., Ehsani M.R., Ghoreishi S.M., Effect of Promoter in the Oxidative Cupling of Methane over Synthesized Mn/SiO2 Nanocatalysts via Incipient Wetness Impregnation, J. Ind. Eng. Chem., 16, p. 923 (2010).
[8] Gardner M.W., Dorling S.R., Artificial Neural Network (the Multilayer Perceptron) - A Review of Applications in the Atmospheric Sciences, Atmos. Environ., 32, p. 2627 (1998).
[9] Himmelblau D.M., Applications of Artificial Neural Networks in Chemical Engineering, Korean J. Chem. Eng., 17 (4), p. 373 (2000).
[10] Medina E.A., Paredes J.I.P., Artificial Neural Network Modeling Techniques Applied to the Hydrodesulfurization Process, Math. Comput. Modell., 49, p. 207 (2009).
[11] Mousavi M., Avami A., Modeling and Simulation of Water Softening by Nanofiltration Using Artificial Neural Network, Iran. J. Chem. Chem. Eng., 25 (4), p. 37 (2006).
[12] Blasco J.A., Fueyo N., Larroya J.C., Dopazo C., Chen Y.J., A Single-Step Time-Integrator of a Methane-Air Chemical System Using Artificial Neural Networks, Comput. Chem. Eng., 23, p. 1127 (1999).
[13] Michalopoulos J., Papadokonstadakis S., Arampatzis G., Lygeros A., Modelling of an Industrial Fluid Catalytic Cracking Unit Using Neural Networks, Trans I Chem E, 79, p. 137 (2001).
[14] Istadi I., Amin N.A.S., Modelling and Optimization of Catalytic-Dielectric Barrier Discharge Plasma Reactor for Methane and Carbon Dioxide Conversion Using Nybrid Artificial Neural Network-Genetic Algorithm Technique, Chem. Eng. Sci., 62, p. 6568 (2007).
[15] Priddy K.L., Keller P.E., "Artificial Neural Networks: An Introduction", The Society of Photo-Optical Instrumentation Engineers (SPIE), Washington (2005).
[16] Lahiri S.K., Ghanta K.C., Artificial Neural Network Model with the Parameter Tuning Assisted by a Differential Evolution Technique: the Study of the Hold Up of the Slurry Flow in a Pipeline, Chem. Ind. Chem. Eng. Q., 15 (2), p. 103 (2009).
[17] Barshan E., Ghodsi A., Azimifar Z., Jahromi M.Z., Supervised Principal Component Analysis: Visualization, Classification and Regression on Subspaces and Submanifolds, Pattern Recognit., 44, p. 1357 (2011).
[18] Jolliffe I.T., "Principal Component Analysis", 2nd ed, Springer-Verlage, New York (2002).
[19] Lhabitant F.S., "Hedge Funds: Quantitative Insights", John Wily & Sons Ltd, Chichester, UK (2004).
Iranian Journal of Chemistry and Chemical Engineering (IJCCE)
Volume 32, Issue 3 - Serial Number 67
September and October 2013
Pages 107-114

Files

Share

How to cite

Statistics