Modelling and Optimization of Homogenous Photo-Fenton Degradation of Rhodamine B by Response Surface Methodology and Artificial Neural Network

Speck, F.; Raja, S.; Ramesh, V.; Thivaharan, V.

doi:10.22059/ijer.2016.59683

Modelling and Optimization of Homogenous Photo-Fenton Degradation of Rhodamine B by Response Surface Methodology and Artificial Neural Network

Document Type : Original Research Paper

Authors

F. Speck

S. Raja

V. Ramesh

V. Thivaharan

Department of Biotechnology, Manipal Institute of Technology, Manipal, Karnataka, 576104, India

10.22059/ijer.2016.59683

Abstract

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).

Keywords

Photo-Fenton process

Rhodamine B degradation

Response Surface Methodology

Artificial Neural Network