Prediction and interpretation of photocatalytic NO removal on g-C3N4-based catalysts using machine learning

Abstract

Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement. However, great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems. Herein, a dataset of g-C3N4-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning (ML) model was proposed to predict the NO removal rate. The result shows that the Gradient Boosting Decision Tree (GBDT) demonstrated the greatest prediction accuracy with R2 of 0.999 and 0.907 on the training and test data, respectively. The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate, in the order of importance, were catalyst characteristics > reaction process > preparation conditions. Moreover, the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features (e.g., doping ratio, flow rate, and pore volume) to the model output outcomes. This ML approach presents a pure data-driven, interpretable framework, which provides new insights into the influence of catalyst characteristics, reaction process, and preparation conditions on NO removal.