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).
Speck, F. , Raja, S. , Ramesh, V. and 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
MLA
Speck, F. , , Raja, S. , , Ramesh, V. , and 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, 10, 4, 2016, 543-554. doi: 10.22059/ijer.2016.59683
HARVARD
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
CHICAGO
F. Speck , S. Raja , V. Ramesh and 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
VANCOUVER
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
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