Hybrid Adsorption/UV-Driven Photocatalytic Oxidation for Fast Removal of Direct Red Dye from Industrial Wastewater: Experimental Work and Modeling

The photocatalytic decolorization of the organic dye Direct Red using a synthesized TiO2 nanocatalyst was investigated, modeled, and optimized employing response surface methodology (RSM) and artificial neural network (ANN). The effect of different variables including, oxidation time, solution pH, TiO2 dosage, and dye initial concentration was studied. Various dye removal efficiencies ranging from 68.4 to 99.1% were achieved under different parameter values with the potential to reach 100% efficiency under optimized conditions. The study of factors influencing the efficiency of the photocatalytic decolorization process revealed that TiO2 concentration had the most significant impact, followed by dye concentration and pH. The second-order model developed through RSM effectively simulated the impacts of the selected variables on the process (R2 = 95.94% and R_adj² = 91.19%). The kinetic study of the photocatalytic decolorization process demonstrated that it follows a pseudo-first-order model. The experimental results showed strong agreement with the ANN model, a high regression coefficient (R2) of 0.9979 and a low mean square error (MSE) of 0.000134 were achieved. This model, which incorporates interactions, offers a robust framework for predicting new developments in photocatalytic decolorization.