## 9 Conclusions  
Visible-light-responsive photocatalysts hold significant promise for solar-driven water treatment due to their ability to harness a larger fraction of the solar spectrum. Materials such as doped TiO\({}_{2}\), g-C\({}_{3}\)N\({}_{4}\), BiVO\({}_{4}\), and heterojunction composites have demonstrated enhanced photocatalytic activity under visible irradiation. However, their real-world application requires further attention to challenges such as long-term material stability under environmental conditions, potential release of toxic metal ions (e.g., from Ag, Cu or Bi-based systems), high synthesis or modification costs, and reduced efficiency in complex natural water matrices. Therefore, ongoing research should focus not only on improving photocatalytic performance but also on enhancing material durability, environmental safety, and cost-effectiveness for large-scale implementation. In parallel, considerable progress has been made in optimizing photocatalytic reactors. Systems such as slurry reactors, immobilized configurations, annular designs, and flat plate and falling film reactors each offer distinct advantages, depending on the application scale, water composition and operational constraints. Emerging configurations like fluidized bed reactors and membrane photocatalytic reactors integrate multiple functionalities, enhanced mixing, reduced fouling, and simultaneous separation, pushing the boundaries of performance and applicability. To address the limitations of standalone photocatalysis, hybrid and synergistic systems have become increasingly prominent. The coupling of photocatalysis with membrane \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data-Driven Tool** & **Applications in Photocatalysis** & **Benefits** \\ \hline Predictive modeling & Forecasting photocatalyst performance based on electronic structure and composition. & Speeds up material screening and reduces trial-and-error experimentation. \\ \hline Machine learning (ML) & Identifying optimal synthesis routes and reaction conditions from large datasets. & Improves process efficiency and reproducibility through targeted parameter tuning. \\ \hline Artificial intelligence (AI) & Integrating multiple models for decision-making in catalyst selection and process design. & Enhances system adaptability and design precision with cross-domain intelligence. \\ \hline Computational chemistry and simulations & Simulating reaction mechanisms, surface interactions, and energy transfer dynamics. & Provides molecular-level insights that inform material modifications. \\ \hline Big data analytics & Analyzing complex experimental or real-time data to reveal trends and hidden variables. & Enables pattern recognition for predictive maintenance and long-term monitoring. \\ \hline Digital twins and virtual reactors & Replicating reactor behavior under varying conditions to test scenarios before deployment. & Supports safer, cost-effective scale-up with reduced experimental workload. \\ \hline Autonomous experimental platforms & Running self-optimizing experiments that adjust parameters in real time to improve efficiency. & Accelerates innovation cycles and fosters real-time process optimization. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Data-driven tools in photocatalytic process optimization. filtration enables catalyst recovery and enhances effluent quality. Adsorption and photo-catalysis combinations improve pollutant uptake and pre-concentration, facilitating rapid degradation. The integration of photocatalysis with biological treatment processes has shown potential to enhance overall treatment efficiency, particularly by transforming certain recalcitrant pollutants into more biodegradable intermediates. However, this effect is contaminant specific, and in some cases, photocatalysis may generate transformation products that remain toxic or resistant to biological degradation.