Modeling the Removal of Phenol Dyes Using a Photocatalytic Reactor with SnO2/Fe3O4 Nanoparticles by Intelligent System

Abstract

The objective of this study was to model the extent of improvement in the degradability of phenol dyes by SnO2/Fe3O4 nanoparticles using a photocatalytic reactor. The effect of operative parameters including catalyst concentration, initial dye concentration, stirring intensity, and UV radiation intensity on the photocatalytic batch reactor during removal of phenol red was investigated. Fractional factorial design and response surface methodology were used to design the experiment layout. The SnO2/Fe3O4 nanoparticles were synthesized using the core-shell method. The results of x-ray diffraction and transmission electron microscopy showed the successful synthesis of these nanoparticles. The ability of back-propagation neural network (BPNN), radial basis function neural network (RBFNN), and adaptive-network-based fuzzy inference system (ANFIS) in predicting the performance of photocatalytic reactor was also investigated. It was found that BPNN has better ability in predicting dye removal (%) than RBFNN, ANFIS, and the regression model. The best architecture of BPNN was a network consisting of three hidden layers with 20-30-20 neurons. The BPNN predicted the output values with a high determination coefficient (R 2) value of 0.0.9718, while the predicted R 2 of the regression model was 0.8978, and the predicted R 2 values of RBFNN and ANFIS were 0.8011 and 0.9023, respectively.