Photocatalytic ozonation of ciprofloxacin from aqueous solution using TiO₂/MMT nanocomposite: Nonlinear modeling and optimization of the process via artificial neural network integrated genetic algorithm

The degradation of ciprofloxacin (CIP) via photocatalytic ozonation process was investigated. TiO₂ nanoparticles as an impressive photocatalyst were immobilized on the surface of montmorillonite (MMT). The main aim of the present work was to model and develop a relationship among the CIP degradation efficiency DE (%) and the key parameters affecting the complex photocatalytic ozonation process. The second object was to investigate the mutual effects of operating parameters on CIP DE (%) and express and optimum conditions that maximize CIP DE (%). To achieve these goals a nonlinear modeling and optimization of the process through artificial neural network (ANN) integrated genetic algorithm (GA) was successfully developed. The mutual effects of the input parameters including initial pH, ozone flow rate, initial CIP concentration, catalyst dosage and reaction time on CIP DE (%) were studied using the surface plots predicted with the nonlinear ANN model regarding the synergistic mechanism. GA was used as a nonlinear optimization method to determine the optimum conditions of input parameters. The good agreement between the experimental and GA predicted DE (%) under the optimum conditions confirmed that ANN integrated GA system is able to successfully model and optimize the photocatalytic ozonation performance.