The increasing presence of emerging pollutants (EPs) in water poses significant environmental and health risks, necessitating effective treatment solutions. Originating from industrial, agricultural, and domestic sources, these contaminants threaten ecological and public health, underscoring the urgent need for innovative and efficient treatment methods. TiO2-based semiconductor photocatalysts have emerged as a promising approach for the degradation of EPs, leveraging their unique band structures and heterojunction schemes. However, few studies have examined the synergistic effects of operating conditions on these contaminants, representing a key knowledge gap in the field. This review addresses this gap by exploring recent trends in TiO2-driven heterogeneous photocatalysis for water and wastewater treatment, with an emphasis on photoreactor setups and configurations. Challenges in scaling up these photoreactors are also discussed. Furthermore, Machine Learning (ML) models play a crucial role in developing predictive frameworks for complex processes, highlighting intricate temporal dynamics essential for understanding EPs behavior. This capability integrates seamlessly with Computational Fluid Dynamics (CFD) modeling, which is also addressed in this review. Together, these approaches illustrate how CFD can simulate the degradation of EPs by effectively coupling chemical kinetics, radiative transfer, and hydrodynamics in both suspended and immobilized photocatalysts. By elucidating the synergy between ML and CFD models, this study offers new insights into overcoming traditional limitations in photocatalytic process design and optimizing operating conditions. Finally, this review presents recommendations for future directions and insights on optimizing and modeling photocatalytic processes.
Keywords: CFD modeling; Degradation; Emerging pollutants; Machine learning; Photocatalytic reactor designs; TiO(2)-based photocatalysis.
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