Artificial intelligence techniques

In this study, Fe₃O₄/TiO₂ nanocomposites were synthesized and optimized for enhanced photocatalytic degradation of organic pollutants. The synthesis involved a sol-gel process followed by calcination, forming a core-shell structure to improve charge separation and light absorption. Additionally, surface modifications were applied to enhance catalytic performance. The photocatalytic activity was evaluated under simulated sunlight, considering the effects of pH, initial pollutant concentration, adsorbent dosage, and reaction time on degradation efficiency. The results demonstrated that the Fe₃O₄/TiO₂ composite exhibited superior photocatalytic performance compared to pure TiO₂, achieving a high removal efficiency. Furthermore, the catalyst retained significant activity after multiple reuse cycles, indicating its stability and reusability. To optimize the process, an artificial intelligence (AI)-assisted model was developed to predict removal efficiency based on experimental data. The AI model exhibited strong predictive capabilities, providing a data-driven approach to optimize photocatalytic performance more efficiently than conventional experimental methods. This study highlights the potential of integrating AI with advanced photocatalysts for wastewater treatment and suggests a scalable strategy for industrial applications. The findings pave the way for future research on iron oxide-based composites for environmental remediation. • XRD confirmed Fe₃O₄@TiO₂ composite structure with peaks matching JCPDS reference data. • VSM analysis showed superparamagnetic behavior with reduced magnetization due to TiO₂ coating. • Fe₃O₄@TiO₂ demonstrated superior photocatalytic activity compared to pure TiO₂ under sunlight. • AI model predicted dye removal efficiency using variables like pH and ion concentration. • Composite synthesis is scalable, with potential for magnetic recovery in wastewater treatment systems.