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

Abstract

Artificial neural networks have been used for modeling the TiO2 photocatalytic degradation of 2,4-dihydroxybenzoic acid, chosen as a model water contaminant, 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 uniform array). Contrary to a classical treatment of the data, based on apparent rate constants modeled by a quadratic polynomial function, neural network analysis of the same experimental data does not require the use of any kinetic or phenomenological equations and allows the simulation and the prediction of the pollutant degradation as a function of irradiation time, as well as prediction of reaction rates, under varying conditions within the experimental region. © EDP Sciences, Wiley-VCH.