Self-regenerating photocatalytic water treatment systems: integrating cognitive AI control, bio-inspired self-healing, and thermoelectric energy harvesting

Abstract

Conventional photocatalytic water treatment systems face persistent limitations in terms of catalyst deactivation, energy consumption, and adaptability to variable influent quality. This review examines the integration of three emerging technological domains, machine learning driven process optimization, bio-inspired self-healing material strategies, and thermoelectric energy recovery, as a framework for developing more autonomous and resource-efficient photocatalytic treatment systems. A systematic literature search was conducted in Web of Science, Scopus, IEEE Xplore, and PubMed (January 2001 to June 2024), following PRISMA guidelines. After multi-step screening, 104 peer-reviewed studies were included in the quantitative synthesis. Reviewed studies reported degradation efficiencies above 85% for model recalcitrant pollutants under optimized conditions. ML approaches, particularly artificial neural networks and ensemble methods, improved predictive accuracy of degradation kinetics by 20–35% relative to empirical models. Self-healing polymeric matrices retained catalytic activity exceeding 80% after repeated use cycles. Thermoelectric modules integrated in solar-driven reactors generated partial energy offsets under laboratory conditions, though full energy self-sufficiency has not yet been demonstrated at the pilot scale. Critical challenges remain, including long-term material stability under real wastewater conditions, scalability of digital twin architectures, and rigorous techno-economic validation. A research roadmap to address these barriers is proposed, with an emphasis on pilot-scale demonstration and standardized performance benchmarking.