Multimodal Measurement and Diagnosis for Photo-Fenton Hydrogen and Remediation Systems: Traceable Translation under Uncertainty

Abstract

Photo-Fenton systems couple hydrogen generation with pollutant degradation, so one performance signal can improve while another pathway deteriorates. This review develops a multimodal AI perspective that jointly considers spectra, gas-production rate, pollutant concentration, catalyst state, pH, temperature, irradiation, and water-matrix composition. Representation learning can align these asynchronous measurements, while physics-informed residuals and uncertainty-aware anomaly detection preserve discordant signals that may reveal catalyst deactivation, side reactions, or sensor error. The synthesis follows each diagnosis back to sample preparation, calibration, preprocessing, and source evidence, making translation across reactors and water matrices conditional rather than implicit. It proposes decision outputs that distinguish optimization, additional sampling, maintenance, and abstention instead of collapsing all evidence into a single score. Domain shift and missing modalities are treated as explicit reasons to reduce confidence. This critical review introduces no new photochemical experiment. Its contribution is a traceable AI diagnostic architecture that balances the dual objectives of hydrogen and remediation and carries uncertainty from heterogeneous measurements into accountable operational decisions.