Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box–Behnken design

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 46.85kPa were obtained at 728°C with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively.