Modelling the degradation of Sunset Yellow FCF azo dye by Fe2O3/ Bentonite catalyst using artificial neural networks

In this paper, the precipitation method has been used to stabilize Fe2O3 particles on Bentonite zeolite (BEN). Fe2O3/BEN catalysts have been characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Brunauer- Emmett-Teller (BET) surface area analysis. Artificial neural network (ANN) was used for modelling the photocatalytic degradation of Sunset Yellow FCF (SYF) azo dye in aqueous solution under irradiation in the batch photoreactor. The parameters including pH, catalyst amount, dye concentration and H2O2 concentration were applied as input; the output of the network was degradation percentage. Modelling the results the photocatalytic degradation of dye using a feed forward, back propagation three-layer network, topology (4:7:1) with four neurons in the input layer, seven neurons in the hidden layer and one neuron in the output layer were used. Comparison between data obtained from ANN and experimental data indicated that the proposed ANN model provides reasonable predictive performance. The optimum conditions were as follows: pH= 4, catalyst amount=60 mg/L, dye concentration =50 ppm and H2O2 concentration =32 ppm. The chemical oxygen demand (COD) analysis of the dye under optimum conditions showed 91% reduction in 80 min period.