Electrochemical advanced oxidation processes (EAOPs) can degrade persistent contaminants in complex waters, yet deployment is often constrained by narrow operating windows, coupled reaction pathways, and hard trade-offs among removal, energy intensity, and oxychlorine byproducts (e.g., chlorate and perchlorate). Machine learning (ML) is increasingly used not as post-hoc curve fitting but as an engineering layer to build decision-grade surrogates, enable inverse design under explicit constraints, and support byproduct-aware optimization and control. This perspective critically synthesizes recent evidence primarily in EAOP contexts and structures the discussion around three practical questions: how surrogate models should be validated and inverted for multiobjective decisions; which descriptor blocks (operating, electrode, matrix, contaminant) are required for transfer across electrodes, waters, and reactor configurations; and how interpretability, uncertainty quantification, and physics-informed hybrids can reduce nonphysical recommendations while remaining credible to electrochemists and regulators. Common failure modes include small data sets, weak external validation, and unstable explanations. Priorities for progress include consistent reporting, curated/open data sets, descriptor-rich benchmarking, constraint-aware optimization (including byproduct limits), and deployment-oriented pathways from surrogates to monitoring, safe control, and digital-twin concepts linked to sustainability metrics.
Haitham Elnakar (Thu,) studied this question.