Modelling and optimization of catalytic–dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network—genetic algorithm technique

Abstract

A hybrid artificial neural network-genetic algorithm (ANN-GA) was developed to model, simulate and optimize the catalytic–dielectric barrier discharge plasma reactor. Effects of CH 4 / CO 2 feed ratio, total feed flow rate, discharge voltage and reactor wall temperature on the performance of the reactor was investigated by the ANN-based model simulation. Pareto optimal solutions and the corresponding optimal operating parameter range based on multi-objectives can be suggested for two cases, i.e., simultaneous maximization of CH 4 conversion and C 2 + selectivity (Case 1), and H 2 selectivity and H 2 / CO ratio (Case 2). It can be concluded that the hybrid catalytic–dielectric barrier discharge plasma reactor is potential for co-generation of synthesis gas and higher hydrocarbons from methane and carbon dioxide and performed better than the conventional fixed-bed reactor with respect to CH 4 conversion, C 2 + yield and H 2 selectivity.