Hybrid artificial neural network-genetic algorithm technique for modeling and optimization of plasma reactor

A hybrid artificial neural network-genetic algorithm (ANN-GA) numerical technique was successfully developed to model, to simulate, and to optimize a dielectric barrier discharge (DBD) plasma reactor without catalyst and heating. Effects of CH₄/CO₂ feed ratio, total feed flow rate, and discharge voltage on the performance of noncatalytic DBD plasma reactor were studied by an ANN-based simulation with a good fitting. From the multiobjectives optimization, the Pareto optimal solutions and corresponding optimal process parameter ranges resulted for the noncatalytic DBD plasma reactor owing to the optimization of three cases, i.e., CH₄ conversion and C₂₊ selectivity, CH₄ conversion and C₂₊ yield, and CH ₄ conversion and H₂ selectivity.