Modelling and optimization of homogenous Photo-Fenton degradation of rhodamine B by response surface methodology and artificial neural network

The predictive ability of Response Surface Methodology (RSM) And Artificial Neural Network (ANN) in the modelling of photo-Fenton degradation of Rhodamine B (Rh-B) was investigated in the present study. The dye degradation was studied with respect to four factors viz., initial concentration of dye, concentration of H₂O₂ and Fe²⁺ ions and process time. Central Composite Design (CCD) was used to evaluate the effect of four factors and a second order regression model was obtained. The optimum degradation of 99.84% Rh-B was obtained when 159 ppm dye, 239 ppm H₂O₂, 46 ppm Fe²⁺ were treated for 27 min. The independent variables were fed as inputs to ANN with the percentage dye degradation as outputs. For the optimum percentage dye degradation, a three-layered feed-forward network was trained by Levenberg-Marquardt (LM) algorithm and the optimized topology of 4:10:1 (input neurons: hidden neurons: output neurons) was developed. A high regression coefficient (R² = 0.9861) suggested that the developed ANN model was more accurate and predicted in a better way than the regression model given by RSM (R² = 0.9112).