Exploring the CO2 conversion activated by the dielectric barrier discharge plasma assisted with photocatalyst via machine learning

Abstract

DBD plasma assisted with halide perovskite photocatalysts is very attractive for the conversion of CO2 into high value-added products under mild conditions. However, it is still challenging to further effectively improve the corresponding CO2 conversion efficiency due to the consumption of time, materials, equipments via traditional experimental approach. In this paper, we simulate the CO2 conversion process under the DBD plasma assisted with different photocatalysts by machine learning. The process conditions and material parameters were selected as the feature variables of the machine learning model, and all the features were screened by combining Pearson's correlation coefficient and MIR ranking. The regression algorithms were evaluated using K-fold cross-validation combined with the coefficient of determination (R2), while SVR and BPANN with the best prediction performance for CO2 conversion ratio and energy efficiency were selected to establish the prediction models. In order to increase the accuracy of predictions, the hyperparameters of these two base models were improved using genetic algorithm, particle swarm optimization and Bayesian optimization. The R2 of the GA-SVR-CO2 and Bayesian-BPANN-EE reached 0.8960 and 0.9679 for the testing sets, respectively, and their practical applications were successfully verified. In addition, the effects of feature parameters on the conversion efficiency were revealed by SHAP. SEI scatter plots and Spearman correlation coefficient have been used for correlation analysis to better explore the correlation between machine learning models and experiments. This work brings new insights into the contributions of multiple parameters in the plasma system assisted with photocatalyst for efficient conversion of CO2.