Conversion of methane into hydrogen and C2 hydrocarbons in a dielectric barrier discharge reactor

Summary form only given. Methane is a major component of natural gas and biogas. The IPCC (Intergovernmental Panel on Climate Change) reports that, over a 20-year time frame, methane has a global warming potential of 86 compared to CO₂. The conversion of abundant methane with and without using oxidants into hydrogen and value-added chemicals such as higher hydrocarbons has exhibited promising potential and attracted great interest. Although direct activation of methane with the aid of oxidants is thermodynamically more favorable compared to non-oxidative conversion of methane, a particular advantage for the later route is the production of CO_x free, hydrogen-rich gases, which is of great interest to the development of highly efficient and cost-effective fuel cells. The conversion of methane into C2 hydrocarbons and H₂ has been performed in a coaxial dielectric barrier discharge (DBD) reactor at low temperatures. The effect of discharge power, gas flow rate and frequency on the reaction performance of the plasma methane conversion has been 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 results and simulated ones 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 ANN model can accurately simulate - nd predict the complex plasma chemical reaction.