Machine Learning-Enabled Photocatalytic Wastewater Treatment: Recent Advances in Catalyst Design, Performance Prediction, and Process Optimization

Abstract

Photocatalytic wastewater treatment is a promising technology for degrading persistent organic pollutants; however, its optimization remains challenging because photocatalytic performance depends on complex interactions among catalyst properties, operating conditions, and wastewater composition. Machine learning (ML) has emerged as a powerful tool for accelerating catalyst development, predicting photocatalytic performance, and optimizing process parameters. This review critically analyzes recent studies on ML-assisted photocatalytic wastewater treatment, covering supervised learning, ensemble learning, deep learning, and hybrid optimization approaches for predicting degradation efficiency, reaction kinetics, and catalyst performance. Rather than simply summarizing existing studies, the review compares the strengths, limitations, and applicability of different ML models while evaluating the influence of dataset quality, feature engineering, and validation strategies on predictive reliability. Emerging developments in explainable artificial intelligence, physics-informed machine learning, digital twins, and autonomous catalyst discovery are also discussed. Current challenges, including limited datasets, data heterogeneity, model overfitting, lack of standardized benchmarking, and poor transferability to real wastewater systems, are critically examined. Finally, future perspectives emphasizing standardized datasets, interpretable AI, rigorous model validation, and intelligent catalyst design are proposed. This review provides a practical roadmap for integrating artificial intelligence with photocatalysis to accelerate the development of reliable and sustainable wastewater treatment technologies.