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

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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).

Original languageEnglish
Pages (from-to)543-554
Number of pages12
JournalInternational Journal of Environmental Research
Volume10
Issue number4
Publication statusPublished - 01-09-2016

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photodegradation
artificial neural network
dye
degradation
modeling
topology
response surface methodology
ion

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)

Cite this

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title = "Modelling and optimization of homogenous Photo-Fenton degradation of rhodamine B by response surface methodology and artificial neural network",
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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AU - Speck, F.

AU - Raja, S.

AU - Ramesh, V.

AU - Thivaharan, V.

PY - 2016/9/1

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N2 - 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).

AB - 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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