Machine learning for CO2 conversion driven by dielectric barrier discharge plasma and Cs2TeCl6 photocatalysts

Abstract

Although the combination of halide perovskite photocatalysts and plasma ensures the effective conversion of CO₂, there is still much room to improve its conversion ratio and energy efficiency. The traditional experimental trial-and-error method is extremely demanding for researchers in each experimental operation and result analysis, while the experiments greatly consume time and raw materials and require complex equipment. In this paper, for the first time, we modeled the process of CO₂ conversion synergistically driven by dielectric barrier discharge (DBD) plasma and a Cs₂TeCl₆ photocatalyst via machine learning. K-fold cross-validation combined with the coefficient of determination (R²) was used to evaluate the regression algorithms, and the BPANN with the best performance was selected to establish a model for predicting the CO₂ conversion ratio and energy efficiency. In order to make the predictions more accurate, genetic algorithms, particle swarm optimization and Bayesian optimization were applied to improve the hyperparameters of the neural network, and the GA-BPANN model achieved an R² of 0.9713 and 0.9622 on the training and testing sets, respectively, while its practical application was also demonstrated. In addition, the effect of each process parameter on conversion efficiency was quantified by the Spearman correlation coefficients, which could provide insights into the roles of different process parameters in CO₂ conversion. This work provides a new approach for boosting CO₂ conversion, which could facilitate future experimental design and process optimization to promote carbon dioxide utilization.