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

• Main Authors: Mohammad Reza Ehsani, Hamed Bateni, Ghazal Razi Parchikolaei
• Format: Article
• Language: English
• Published: Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR 2013-09-01
• Series: Iranian Journal of Chemistry & Chemical Engineering
• Subjects: oxidative coupling of methane (ocm); mn/na2wo4 /sio2 catalyst; principal components; artificial neural network (ann)
• Online Access: http://www.ijcce.ac.ir/article_5836_3c2c5a8829e8dfe422250e3990ca0b39.pdf
• ISSN: 1021-9986; 1021-9986

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.