Data-Driven Recursive Kinetic Modeling for Fenton Reaction

The Fenton reaction is a widely used advanced oxidation process for water purification, valued for its simplicity and effectiveness in degrading refractory organic pollutants. However, accurately modeling its degradation kinetics remains challenging due to the complex reaction mechanism and strong sensitivity to multiple operational variables. Here, we develop a data-driven recursive kinetic model capable of predicting kinetic profiles from pollutant type, initial pollutant concentration, and Fenton reagent dosages. The model captures reaction kinetics by leveraging recursive relationships between reactant or product concentrations at different times, which is learned through a machine learning algorithm, rather than traditional concentration-time equations. Moreover, we integrate a multiple estimation strategy into the model for performance enhancement. This model demonstrates superior performance in terms of accuracy, few-shot learning capability, robustness, interpretability, and application scope on an experimental dataset from Fenton reactions of 12 phenolic compounds. Moreover, MERML enabled data-driven kinetic analysis, including reaction condition optimization, analysis of rate-influencing variables, and assisted mechanistic interpretation. This work provides a novel tool for pollutant degradation modeling, treatment optimization, and mechanistic interpretation in environmental reaction systems.