Optimal experimental design and artificial neural networks applied to the photochemically enhanced Fenton reaction

Among advanced oxidation processes (AOPs), the photochemically enhanced Fen ton reaction may be considered as one of the most efficient for the degrada tion of contaminants in industrial wastewater. This process involves a seri es of complex reactions, Therefore, an empirical model based on-artificial neural networks has been developed for fitting the experimental data obtain ed in a laboratory batch reactor for the degradation of 2,4-dimethylaniline (2,4-xylidine), chosen as a model pollutant. The model describes the evolu tion of the pollutant concentration during irradiation time as a function o f the process conditions. It has been used for simulating the behavior of t he reaction system in sensitivity studies aimed at optimizing the amounts o f reactants employed in the process, an iron (III) salt and hydrogen peroxi de, as well as the temperature. The results show that the process is most s ensitive to the concentration of iron(III) salt and temperature, whereas th e concentration of hydrogen peroxide has a minor effect.