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

Document Type : Original Research Paper

Authors

1 Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran Department of Electrical Engineering, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran

2 Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran.

3 Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran

10.22034/jna.2021.685871

Abstract

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 modeling 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 was applied as input; the output of the network was degradation
percentage. Modeling the results of the photocatalytic degradation of dye using a
feed-forward, backpropagation 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 was 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 a 91% reduction in 80 min period.

Keywords