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

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

In the present study, synthesis gas was produced from dry reforming of methane over ceria supportedcobalt catalyst in a fixed bed stainless steel reactor. Artificial neural network (ANN) and Box Behnkendesign (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 predictedoutputs from the ANN model and the experimental data. Optimum reactant feed ratio of 0.60 and CH4partial pressure of 46.85 kPa were obtained at 728 8C with corresponding conversions of 74.84% and76.49% for CH4 and CO2, respectively.

In the present study, synthesis gas was produced from dry reforming of methane over ceria supportedcobalt catalyst in a fixed bed stainless steel reactor. Artificial neural network (ANN) and Box Behnkendesign (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 predictedoutputs from the ANN model and the experimental data. Optimum reactant feed ratio of 0.60 and CH4partial pressure of 46.85 kPa were obtained at 728 8C with corresponding conversions of 74.84% and76.49% for CH4 and CO2, respectively.