Uncertainty-Aware Decision Support for Photo-Fenton Hydrogen and Remediation Systems: Measurement and Comparator Design

Abstract

Operating a dual-function Photo-Fenton system requires decisions under competing objectives, variable water matrices, and incomplete knowledge of catalyst condition. This review examines how uncertainty-aware AI can support those decisions without presenting a point prediction as certainty. Gaussian-process surrogates, Bayesian state estimation, and ensemble calibration are discussed for combining hydrogen yield, degradation kinetics, spectral response, pH, irradiation, and contaminant composition. Measurement design is linked to decision value: the next sample should reduce uncertainty relevant to catalyst selection, operating set points, maintenance, or safe release. Comparator design includes mechanism-only models, fixed operating rules, and learned policies evaluated under the same chemical and sensor shifts. The synthesis distinguishes aleatory variation from epistemic gaps and recommends abstention when the target matrix lies outside the evidence domain. It also records model, catalyst-batch, and calibration provenance for later review. No new yield or removal result is claimed. The article contributes an AI decision framework in which uncertainty changes the action taken, rather than appearing only as an interval appended to an otherwise automatic recommendation.