Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process

Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of reaction conditions, and identification of mechanism. In this study, a task decomposition strategy was adopted by combining a catalyst and reaction condition optimization machine learning (CRCO-ML) model and a mechanism identification machine learning (MI-ML) model to address these issues. The Categorical Boosting (CatBoost) model was identified as the best-performing model for the dataset (1024 sets and 7122 data points) in this study, achieving an R² of 0.92 and an RMSE of 1.28. Catalyst composition, PAA dosage, and catalyst dosage were identified as the three most important features through SHAP analysis in the CRCO-ML model. The HCO₃^- is considered the most influential water matrix affecting the k value. The errors between all reverse experiment results and the predictions of the CRCO-ML and MI-ML models were <10 % and 15 %, respectively. This interdisciplinary work provides novel insights into the design and application of the heterogeneous activation of PAA process, significantly contributing to the rapid development of this technology.