Harnessing Machine Learning to Enhance UV-based Advanced Oxidation Processes for Sustainable Micropollutant Abatement

Abstract

Micropollutants represent growing risks to water quality and human health, necessitating solutions beyond conventional treatment. While ultraviolet-based advanced oxidation processes (UV-AOPs) effectively degrade those micropollutants, their implementation is complex because of difficulties in predicting treatment performance, deriving kinetic parameters, inferring reaction mechanisms, and optimizing energy-intensive operations. Recent advances in machine learning (ML) are providing novel, data-driven solutions to these long-standing challenges. This work provides an overview on the rapid progress in leveraging ML to model UV-AOPs, including forecasting micropollutant degradation efficacy, estimating bimolecular rate constants of radicals with micropollutants, mapping plausible transformation pathways of micropollutants in various UV-AOPs, and performing intelligent optimization toward operating parameters. We also discuss the challenges, research gaps, and future directions, involving embedding physicochemical principles into interpretable ML frameworks, integrating real-time control, advancing prediction of byproduct toxicity, and the development of LLM (large language model)-assisted knowledge infrastructure, ultimately enabling smarter, more efficient, and safer UV-AOP systems and fostering deeper integration of data science and water treatment engineering.