Cross-Scale Modelling and Validation for Photo-Fenton Hydrogen and Remediation Systems: A Mechanism-to-Decision Synthesis

Abstract

Photo-Fenton performance arises from interactions among catalytic sites, radical chemistry, pollutant pathways, reactor transport, irradiation, and plant operation. This review organizes those levels into a cross-scale AI modelling strategy. Physics-informed surrogates encode reaction and mass-transfer constraints, while graph and sequence models capture interactions among catalyst properties, water chemistry, spectra, hydrogen evolution, and degradation trajectories. Validation proceeds from reaction-level plausibility to reactor prediction and finally to decision utility under changed matrices and operating regimes. The synthesis warns against inferring scale agreement from correlated trends and requires uncertainty propagation across every interface. A provenance graph links source studies, catalyst batches, preprocessing, parameter estimates, model versions, and control recommendations, enabling reviewers to locate the evidence behind a prediction. The article reports no new experiment or universal performance ranking. Its contribution is a mechanism-to-decision blueprint showing how AI can integrate heterogeneous photochemical evidence while retaining physical constraints, cross-scale uncertainty, and human accountability for optimization, remediation, and release decisions.