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

Document Type : Original Research Paper

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

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

International Journal of Environmental Research
Volume 10, Issue 4
Autumn 2016
Pages 543-554

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