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

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.Web of Science1615art. no. 583

Role of experimental, modeling, and simulation studies of plasma in sustainable green energy

This comprehensive review paper offers a multifaceted examination of non-thermal plasma applications in addressing the complex challenge of tar removal within biomass-oriented tech nologies. It begins with a concise introduction to the research background, setting the context for our exploration. The research framework is then unveiled, providing a structured foundation for understanding the intricate dynamics of plasma–tar interactions. As we delve deeper into the sub ject, we elucidate the reactivity of tar compounds and the transformation of alkali metals through plasma-based methodologies, essential factors in enhancing product gas quality. Through an array of empirical studies, we investigated the nuanced interactions between plasma and diverse ma terials, yielding crucial insights into plasma kinetics, modeling techniques, and the optimization of plasma reactors and processes. Our critical review also underscores the indispensable role of kinetic modeling and simulation in advancing sustainable green energy technologies. By harnessing these analytical tools, researchers can elevate system efficiency, reduce emissions, and diversify the spectrum of available renewable energy sources. Furthermore, we delve into the intricate realm of modeling plasma behavior and its intricate interplay with various constituents, illuminating a path toward innovative plasma-driven solutions. This comprehensive review highlights the significance of holistic research efforts that encompass empirical investigations and intricate theoretical modeling, collectively advancing the frontiers of plasma-based technologies within the dynamic landscape of sustainable energy. The insights gained from this review contribute to the overall understanding of plasma technologies and their role in achieving a greener energy landscape.Web of Science1519art. no. 1419