Nonoxidative Conversion of Methane in a Dielectric Barrier Discharge Reactor: Prediction of Reaction Performance Based on Neural Network Model

Abstract

The nonoxidative conversion of CH4 into H2 and higher hydrocarbons has been performed in a coaxial dielectric barrier discharge reactor at atmospheric pressure and low temperatures. The effect of discharge power, gas flow rate, and excitation frequency on the reaction performance of the plasma methane conversion is investigated. A three-layer back-propagation artificial neural network (ANN) model has been developed and trained to simulate and predict the complex plasma chemical reaction in terms of the conversion of CH4, the selectivity and yield of gas products, and the energy efficiency of the plasma process. A good agreement between the experimental and simulated results is achieved. The ANN model shows that the maximum CH4 conversion of 36% can be obtained at a discharge power of 75 W with a high selectivity of C2H6 (42.4%). In this study, the discharge power is found to be the most influential parameter with a relative weight of 45–52% for the plasma nonoxidative coupling of methane, while the excitation frequency of the plasma system is the least important parameter affecting the plasma process. The results successfully demonstrate that the well-trained ANN model can accurately simulate and predict a complex plasma chemical reaction.