Genetic algorithms and neural networks based optimization applied to the wastewater decolorization by photocatalytic reaction

This paper proposes a genetic algorithm and a neural network based procedure to estimate the optimal conditions for a dyestuff wastewater treatment process consisting of a heterogeneous photocatalytic oxidation. A simulated dyestuff effluent containing the azo dye Reactive Black 5 is decolorized by a photocatalytic reaction using TiO2 P-25 as catalyst in the presence of Fe+3 and H2O2. A simple feed forward neural network with one hidden layer was projected and used to predict the evolution in time of the decolorization of this type of wastewater. The neural model was included in the optimization procedure solved with a simple genetic algorithm. The goal of the optimization is to calculate the optimal reaction conditions (illumination time and amounts of reagents) which assure an imposed value for the transmittance.