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

We have investigated the kinetics of the degradation of 2,4-dimethyl anilin e (2,4-xylidine), chosen as a model pollutant, by the photochemically enhan ced Fenton reaction. This process, which may be efficiently applied to the treatment of industrial waste waters, involves a series of complex reaction s leading eventually to the mineralization of the organic pollutant. A mode l based on artificial neural networks has been developed for fitting the ex perimental data obtained in a laboratory batch reactor. The model can descr ibe the evolution of the pollutant concentration during irradiation time un der 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) s alt concentration than to the hydrogen peroxide concentration, a favorable condition in terms of economic feasibility. (C) 1999 Elsevier Science S.A. All rights reserved.