Process optimization and kinetics study for photocatalytic ciprofloxacin degradation using TiO2 nanoparticle: A comparative study of Artificial Neural Network and Surface Response Methodology

The photocatalytic degradation of ciprofloxacin was investigated by developing a predictive mathematical model using response surface methodology and an artificial neural network. The four independent variables involve solution pH, reaction time, catalyst dose, and initial antibiotic concentration considered as factors in central composite design to observe the response in the form of antibiotic degradation. Accordingly, at an optimum antibiotic concentration of 5.02 mg/L, catalyst dose of 44.51 mg/L, solution pH of 5.04, and reaction time of 75.80 min, the photocatalysis method achieved a ciprofloxacin degradation of 88.30%. The experimental outputs were very much consistent along with the predicted output of experiments through response surface methodology (R² = 0.9969) and artificial neural network (R² = 0.975). The adsorption isotherm and kinetic study reveal that Langmuir isotherm and pseudo-second-order kinetic models respectively were best fitted for degradation of ciprofloxacin through photocatalysis. The finding provides a novel method for evaluating the photocatalysis process for the optimization of ciprofloxacin antibiotic removal from pharmaceutical waste using experiments and computer simulation tools.