Artificial neural network guided optimization of limiting factors for enhancing photocatalytic treatment of textile wastewater using UV/TiO₂ and kinetic studies

Abstract

Wastewater effluents discharged from textile industries are characterized by excessive chemical and biochemical oxygen demands with significant amounts of harmful synthetic dyes that spoil the healthy environment. The present investigation focused on process optimization to develop an effective strategy for degrading and decolorizing wastewater from textile industries through an advanced photocatalysis oxidation process. The synergistic effect of Titanium dioxide (TiO2) nanoparticles as a photocatalyst with ultraviolet irradiation was applied as a novel technique to detoxify wastewater effluents from textile industries. In addition, the process was modeled and optimized using response surface methodology (RSM) and artificial neural networks (ANN). The kinetics of the photocatalytic degradation of wastewater effluents from textile industries were analyzed. The optimal condition predicted by the RSM was similar to the experimental outcomes, further, the predicted values from the ANN model had confirmed the prediction. The most significant limiting parameters, such as catalyst dosage, pH, and irradiation time, were optimized systematically as TiO2 dosage of 429.31mg/l, pH of 8.89, and irradiation time of 5h, respectively. Under these optimal conditions, COD reduction and decolorization of 90 and 88%, respectively, were achieved.