Neural network modeling of the photocatalytic degradation of 2,4-dihydroxybenzoic acid in aqueous solution

Artificial neural networks have been used for modeling the TiO2 photocataly tic degradation of 2,4-dihydroxybenzoic acid, chosen as a model water conta minant, as a function of the concentrations of substrate and catalyst, The experimental design methodology was applied to the choice of an appropriate set of experiments well distributed in the experimental region (Doehlert u niform array). Contrary to a classical treatment of the data, based on appa rent rate constants modeled by a quadratic polynomial function, neural netw ork analysis of the same experimental data does nor require the use of any kinetic or phenomenological equations and allows the simulation and the pre diction of the pollutant degradation as a function of irradiation time, as well as prediction of reaction rates, under varying conditions within the e xperimental region.