Efficient and easily recyclable photocatalytic reduction of Se(IV) from wastewater using stable TiO2/BiOBr/cloth: Mechanism insight and machine learning modeling

Abstract

Photocatalytic technology is extensively employed for the reductive removal of water contaminants; however, it contends with low catalytic efficiency and challenges in catalyst recovery. In this study, we propose integrating experimental procedures with artificial intelligence modeling to enhance the purification of Se(IV)-contaminated wastewater. We present an efficient, easily recyclable, and cost-effective strategy for photocatalyst fabrication. Specifically, we develop a novel method for Se(IV) removal by immobilizing TiO2/BiOBr onto glass fiber cloth surfaces using chitosan for Se(IV) reduction in aqueous solutions. The TiO2/BiOBr/cloth (TB-4/cloth) catalyst achieves a remarkable 99.2 % Se(IV) removal within 2 h under visible light and maintains excellent Se(IV) reduction photocatalytic activity (86.4 %) even after eight cycles, remaining easily reusable. Additionally, we develop two machine learning models, namely artificial neural network (ANN) and long short-term memory (LSTM), to validate the anticipated experimental outcomes. Both models exhibit high accuracy and predictive capability (R2 > 0.99, RMSE < 0.03). This study introduces a novel approach that combines experimentation with artificial intelligence modeling, paving the way for future advancements in Se(IV)-contaminated wastewater purification methods.