AI/ML-Enabled Advanced Oxidation for Real Wastewater Treatment: Mechanistic Evidence, Multi-Objective Optimization, and Scale-Up Roadmaps

Advanced oxidation processes (AOPs) are widely applied to degrade recalcitrant organic contaminants in municipal effluents, industrial wastewaters, and water-reuse streams. Their deployment, however, remains constrained by matrix scavenging, high energy or reagent demand, catalyst/electrode ageing, and the possible formation of toxic transformation products. Artificial intelligence and machine learning (AI/ML) have been proposed as tools for prediction, optimization, catalyst discovery, mechanism inference, and process control, but high accuracy on curated laboratory datasets is often confused with actionable knowledge for real treatment systems. This narrative review evaluates AI/ML-enabled AOPs through an evidence-to-deployment framework built on three principles: real wastewater is treated as the primary inference domain; mechanistic claims are graded according to convergent evidence; and AI/ML contributions are linked to explicit decisions rather than to model accuracy alone. We argue that progress depends less on black-box complexity than on standardized reporting, benchmark matrices, curated datasets, uncertainty-aware validation, and pilot-scale demonstrations that satisfy contaminant removal, energy efficiency, byproduct safety, and operational constraints simultaneously. A six-gate decision framework and a targeted research agenda are proposed to guide future studies toward deployment-grade evidence.