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Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design

Ayodele, Bamidele V. and Cheng, C. K. (2015) Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design. Journal of Industrial and Engineering Chemistry, 32. pp. 246-258. ISSN 1226-086X

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Abstract

In the present study, synthesis gas was produced from dry reforming of methane over ceria supported cobalt catalyst in a fixed bed stainless steel reactor. Artificial neural network (ANN) and Box Behnken design (BBD) were employed to investigate the effects of reactant partial pressures, reactant feed ratios, reaction temperature and their optimum conditions. Good agreement was shown between the predicted outputs from the ANN model and the experimental data. Optimum reactant feed ratio of 0.60 and CH4 partial pressure of 46.85 kPa were obtained at 728 °C with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively.

Item Type: Article
Uncontrolled Keywords: Artificial neural network; Box Behnken design; Ceria; Cobalt; Methane dry reforming; Syngas
Subjects: T Technology > TP Chemical technology
Faculty/Division: Faculty of Chemical & Natural Resources Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 21 Dec 2015 07:59
Last Modified: 11 Jan 2018 02:10
URI: http://umpir.ump.edu.my/id/eprint/11581
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