Modeling the kinetics of a photochemical water treatment process by means of artificial neural networks

Abstract

We have investigated the kinetics of the degradation of 2,4-dimethyl aniline (2,4-xylidine), chosen as a model pollutant, by the photochemically enhanced Fenton reaction. This process, which may be efficiently applied to the treatment of industrial waste waters, involves a series of complex reactions leading eventually to the mineralization of the organic pollutant. A model based on artificial neural networks has been developed for fitting the experimental data obtained in a laboratory batch reactor. The model can describe the evolution of the pollutant concentration during irradiation time under various conditions. It has been used for simulating the behavior of the reaction system in sensitivity studies aimed at optimizing the amounts of reactants employed in the process - an iron(II) salt and hydrogen peroxide. The results show that the process is much more sensitive to the iron(II) salt concentration than to the hydrogen peroxide concentration, a favorable condition in terms of economic feasibility.