AI-Driven Membrane-Integrated Wastewater Biorefineries for Resource Recovery and Hydrogen Production: A Critical Review

Wastewater treatment plants (WWTPs) are being reconceived as biorefineries that recover water, nutrients and energy, and membrane processes are central to this transition. This critical review examines how artificial intelligence (AI), and in particular, its data-driven subset machine learning (ML), is applied across membrane-integrated wastewater biorefineries: aeration control, membrane bioreactor fouling prediction, membrane-based nutrient recovery (membrane contactors, forward osmosis, electrodialysis, membrane distillation), struvite crystallisation, biohydrogen production by dark fermentation and microbial electrolysis, thermochemical sludge conversion, and hydrogen purification by gas-separation membranes. Reported model accuracies are high (coefficients of determination, R2, above 0.9 for several resource-recovery tasks), but they measure model fit rather than reactor performance and were obtained mostly on small laboratory or literature-compiled datasets (municipal, industrial, mixed or synthetic wastewaters and other organic feedstocks) with heterogeneous validation schemes and are, therefore, not directly comparable. Documented full-scale evidence is concentrated in aeration control (19.8% lower total operating cost than fuzzy-logic (knowledge-based AI) control at one plant; 26–30% lower aeration energy in a vendor-reported case) and membrane fouling forecasting. Validation practice, cross-plant transferability, the gap between explainable-AI attribution and mechanistic understanding, and the scarcity of techno-economic evidence are identified as the main barriers. A research agenda based on hybrid mechanistic–ML models, federated learning and a proposed plant-wide “resource ledger” is outlined for pilot-scale validation.