Uncertainty-Aware Decision Support for Photo-Fenton Hydrogen and Remediation Systems: Validity Domains and Revalidation Triggers

Abstract

A Photo-Fenton decision model may be well calibrated for one catalyst, contaminant mixture, irradiation profile, and reactor, yet fail quietly when any of those conditions change. This review defines a multidimensional validity domain for AI-assisted hydrogen and remediation decisions. Streaming residuals, conformal or ensemble uncertainty, and change-point detection are used to identify departures in spectra, degradation kinetics, gas evolution, pH, temperature, and matrix composition. The response is tied to the type of evidence loss: sensor recalibration for bounded measurement drift, new experiments for unfamiliar chemistry, shadow testing after model updates, and abstention when safety or treatment adequacy cannot be supported. The synthesis separates uncertainty caused by natural variability from uncertainty caused by missing knowledge and records catalyst, data, and software lineage for every revalidation event. It does not present a new reactor trial. The article's contribution is an AI assurance design in which validation has an explicit expiration condition and uncertainty triggers concrete measurement, review, or control actions rather than being reported without operational consequence.