Prediction and optimization of electrochemical oxidation efficiency using machine learning: Insights from carbon-based anodes for water treatment

Abstract

Electrochemical oxidation (EO) has emerged as a promising and environmentally sustainable strategy for the treatment of contaminated water, enabling the degradation of refractory pollutants through the in-situ generation of reactive oxidative species. Carbon-based anodes-such as graphite plates, carbon felt, and carbon fibers-are increasingly adopted in EO systems due to their cost-effectiveness, electrochemical stability, and environmental compatibility. However, despite their widespread application, a systematic understanding based on data-driven modeling of how different types of unmodified carbon-based anodes influence pollutant removal under varying operational conditions remains lacking. In this study, we applied nine machine learning (ML) algorithms to evaluate the effects of both material and operational parameters on EO performance using carbon-based anodes. A comprehensive dataset of over 1400 experimental entries was analyzed, encompassing variables such as anode type, current density, pH, electrolyte concentration, pollutant characteristics, and reaction time. Among all models tested, Light Gradient Boosting Machine (LightGBM) exhibited the highest predictive accuracy (R2 = 0.926; RMSE = 8.846). SHAP-based feature interpretation revealed that operational factors-particularly reaction time, pollutant type, and current density-had the greatest influence on removal efficiency. In contrast, the specific type of unmodified carbon-based anode had minimal impact under the conditions studied, likely due to the inherent similarity in their electrochemical behavior. This trend may yield different results in systems employing surface-modified electrodes or direct electron transfer mechanisms. These findings emphasize that optimized operational conditions play a more decisive role than material selection in enhancing EO performance. This study presents a data-driven framework to guide EO system optimization, reduce experimental trial-and-error, and support the development of scalable, high-efficiency water and wastewater treatment technologies.