Deep insights into the integration of Artificial Neural Networks (ANNs) for predicting the photocatalytic activities of metal-based catalysts in water pollutant reduction

This review investigates the integration of Artificial Neural Networks (ANNs) in predicting and optimizing the photocatalytic activities of metal oxide-based catalysts for water pollutant reduction. It begins by addressing the critical issue of water pollution and the vital role of photocatalysis in wastewater treatment. The photocatalysis fundamentals are thoroughly examined, including degradation mechanisms and key factors influencing efficiency. The discussion emphasizes metal oxide photocatalysts, exploring their properties, structural characteristics, and challenges such as limited light absorption and carrier recombination. Recent advancements in morphology control, doping, and semiconductor heterojunctions are presented as strategies to enhance photocatalytic performance. A significant focus is given to the integration of ANNs, highlighting their architecture, training methods, and successful applications in optimizing photocatalytic processes. Moreover, the review identifies key challenges in ANN integration, such as the availability of comprehensive datasets, model generalizability, and the complex interplay of photocatalytic parameters. These challenges underscore the need for further research to enhance ANN-driven predictions. Case studies on photocatalysts like TiO₂, ZnO, and SnO₂ illustrate the practical benefits and emerging trends in ANN-based optimization. This interdisciplinary approach merges computational intelligence with photocatalytic science, offering innovative pathways for efficient, cost-effective water treatment and advancing the design of next-generation photocatalysts