Speck, F., Raja, S., Ramesh, V., Thivaharan, V. (2016). Modelling and Optimization of Homogenous Photo-Fenton Degradation of Rhodamine B by Response Surface Methodology and Artificial Neural Network. International Journal of Environmental Research, 10(4), 543-554. doi: 10.22059/ijer.2016.59683
F. Speck; S. Raja; V. Ramesh; V. Thivaharan. "Modelling and Optimization of Homogenous Photo-Fenton Degradation of Rhodamine B by Response Surface Methodology and Artificial Neural Network". International Journal of Environmental Research, 10, 4, 2016, 543-554. doi: 10.22059/ijer.2016.59683
Speck, F., Raja, S., Ramesh, V., Thivaharan, V. (2016). 'Modelling and Optimization of Homogenous Photo-Fenton Degradation of Rhodamine B by Response Surface Methodology and Artificial Neural Network', International Journal of Environmental Research, 10(4), pp. 543-554. doi: 10.22059/ijer.2016.59683
Speck, F., Raja, S., Ramesh, V., Thivaharan, V. Modelling and Optimization of Homogenous Photo-Fenton Degradation of Rhodamine B by Response Surface Methodology and Artificial Neural Network. International Journal of Environmental Research, 2016; 10(4): 543-554. 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
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).
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