Advancing sustainable decomposition of biomass tar model compound: Machine learning, kinetic modeling, and experimental investigation in a non-thermal plasma dielectric barrier discharge reactor

Abstract

This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors.