AI-Driven Biohydrogen Production from Agri-Food Residues: Process Integration, Modeling Architectures, and Industrial Perspectives

Agri-food residues offer renewable feedstocks for hydrogen production, but compositional variability, conversion constraints, and inconsistent data reporting hinder reliable modelling and scale-up. This review examines artificial intelligence (AI) across dark fermentation, photo-fermentation, microbial electrolysis, gasification, and pyrolysis–reforming. Its distinctive contribution is a residue-centred comparison linking process constraints to AI tasks, validation requirements, and industrial decisions, rather than considering prediction accuracy alone. Supervised, unsupervised, optimisation-assisted, and hybrid approaches are assessed using feedstock relevance, data provenance, physical consistency, and transferability. Selected studies provide quantitative evidence of improved prediction, process optimisation, and computational efficiency; these outcomes are distinguished from experimentally verified hydrogen gains. Despite promising results, laboratory validation and simulation performance do not establish cross-feedstock robustness or industrial readiness. Mechanistic integration can help represent acid accumulation, nitrogen-dependent inhibition, hydrogen partial pressure, and thermochemical energy constraints, provided parameters and uncertainty are validated. Future progress requires harmonised datasets, independent validation, and integration with techno-economic and life-cycle assessment. Digital twins remain a development opportunity whose operational benefits require prospective demonstration.