Knowledge discovery and performance-energy optimization in heterogeneous catalytic ozonation via adaptive multi-task learning

Heterogeneous catalytic ozonation (HCO) is a promising technology with significant potential for water treatment, but its application is limited by the optimization of performance and energy consumption. This study proposed an innovative adaptive multi-task learning (MTL) framework for performance optimization (PO), energy consumption (EC), and performance-energy balance (PEB). The PEB-MTL constructed with implicit functions and Pareto optimization achieved a multi-task balance between pseudo-first-order rate constant (k) and Electrical Energy per Order (EE/O), and could simultaneously optimize performance and energy consumption through adaptive task weighting and random perturbation-enhanced PSO algorithm. Feature importance analysis revealed that O₃ dosage is the most critical factor. Catalyst composition plays a decisive role for the degradation performance of O₃-resistant pollutants. Moreover, it exhibited a stronger impact on energy consumption than on degradation performance. Among molecular descriptors, E_gap and ionization potential were foremost for PO and EC, respectively. Reverse experiments demonstrated prediction errors below 10 %. This work provides a robust framework for HCO optimization, advancing sustainable water treatment technologies.