Modeling and Optimization of Photocatalytic Degradation of Methylene Blue via TiO2-CuO/HAp Catalyst: The Use of Response Surface Methodology and Artificial Neural Network

This study is focused on the evaluation of the photocatalytic activity of TiO2-CuO/HAp catalyst as prepared by sol-gel method and characterized using FT-IR, XRD, SEM-EDX for the degradation of Methylene Blue (MB) from its aqueous solution under sunlight. The effects of MB concentration, contact time, and catalyst dosage on the degradation of MB were studied using the central composite design (CCD) method. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling techniques were also applied to model the process and examine their corresponding predictive and performance capabilities of the response (degradation efficiency).

The RSM optimized conditions show that TiO2-CuO/HAp achieved 99.62% MB degradation in the designed photocatalytic system that was set under sunlight at 20 mg/L methylene blue concentration, 0.15 g TiO2-CuO/HAp dosage and 2.5hours (150mins) irradiation time. On the other hand, optimization with ANN study revealed that the predicated model was perfectly fitted with the experimental data. The process was also modeled using the adsorption isotherms and kinetic models. The degradation of MB was best described well by the Pseudo-Second-Order model with (R2=0.995) and the equilibrium data for the photodegradation process fits well with the Langmuir isotherm model (R2=0.996). The results of our study will help communicate the most effective and economical options for the removal of dyes in wastewater.