Response Surface Methodology–Artificial Neural Network (RSM-ANN) Approach to Optimise Photocatalytic Degradation of Levofloxacin Using Graphene Oxide-Doped Titanium Dioxide (GO-TiO2)

Harnessing the synergistic potential of graphene oxide-doped titanium dioxide (GO-TiO2), this study pioneers an advanced photocatalytic approach by incorporating graphene oxide-doped titanium dioxide (GO-TiO2) as a catalyst to enhance the photocatalytic degradation of levofloxacin (LVX), with optimisation of parameters using response surface methodology (RSM) and artificial neural networks (ANNs). By adjusting key operational parameters such as catalyst dosage, LVX concentration, pH, and percentage dopant in TiO2, the study aimed to maximise degradation efficiency. The RSM statistical model highlighted optimal conditions, i.e., neutral pH, 0.1 g/g dopant, 1.1 g/L catalyst, and 25 ppm LVX concentration, achieving a degradation efficiency close to 80% (R2 = 0.88). An ANN model was also developed, offering a three-layer neural network that accurately predicts LVX degradation under varied conditions, with R2 reaching 0.97. Current modelling techniques frequently fail to strike a balance between practical insights for optimising photocatalytic degradation and predictive accuracy. By combining the parametric insights of RSM with the nonlinear predictive power of ANN, this study closes that gap and develops a sustainable, data-driven framework for effectively breaking down pharmaceutical pollutants and developing environmentally friendly wastewater treatment methods.