화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Journal of Industrial and Engineering Chemistry, Vol.32, 246-258, December, 2015
DOI10.1016/j.jiec.2015.08.021  Export Citation
Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box.Behnken design
Bamidele V. Ayodele, and Chin Kui Cheng†
  • Faculty of Chemical & Natural Resources Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang Kuantan, Pahang, Malaysia
E-mail:
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 ℃ with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively.
Keywords:Artificial neural network;Box Behnken design;Ceria;Cobalt;Methane dry reforming;Syngas
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