Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network

In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H2O2:Fe2+ ratio, Fe2+ concentration, pH and process reaction time were studied closely. The results showed that the highest and l...

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Main Authors: Roudi, A. M., Chelliapan, S., Mohtar, W. H. M. W., Kamyab, H.
Format: Article
Language:English
Published: MDPI AG 2018
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Online Access:http://eprints.utm.my/id/eprint/79747/1/AnitaMaslahatiRoudi2018_PredictionandOptimizationoftheFenton.pdf
http://eprints.utm.my/id/eprint/79747/
http://dx.doi.org/10.3390/w10050595
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spelling my.utm.797472019-01-28T06:50:10Z http://eprints.utm.my/id/eprint/79747/ Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network Roudi, A. M. Chelliapan, S. Mohtar, W. H. M. W. Kamyab, H. TA Engineering (General). Civil engineering (General) In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H2O2:Fe2+ ratio, Fe2+ concentration, pH and process reaction time were studied closely. The results showed that the highest and lowest predicted chemical oxygen demand (COD) removal efficiency were 78.9% and 9.3%, respectively. The overall prediction error using the developed ANN model was within -0.625%. The derived model was adequate in predicting responses (R2 = 0.9896 and prediction R2 = 0.6954). The initial pH, H2O2:Fe2+ ratio and Fe2+ concentrations had positive effects, whereas coagulation pH had no direct effect on COD removal. Optimized conditions under specified constraints were obtained at pH = 3, Fe2+ concentration = 781.25 mg/L, reaction time = 28.04 min and H2O2:Fe2+ ratio = 2. Under these optimized conditions, 100% COD removal was predicted. To confirm the accuracy of the predicted model and the reliability of the optimum combination, one additional experiment was carried out under optimum conditions. The experimental values were found to agree well with those predicted, with a mean COD removal efficiency of 97.83%. MDPI AG 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79747/1/AnitaMaslahatiRoudi2018_PredictionandOptimizationoftheFenton.pdf Roudi, A. M. and Chelliapan, S. and Mohtar, W. H. M. W. and Kamyab, H. (2018) Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network. Water (Switzerland), 10 (5). ISSN 2073-4441 http://dx.doi.org/10.3390/w10050595 DOI:10.3390/w10050595
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Roudi, A. M.
Chelliapan, S.
Mohtar, W. H. M. W.
Kamyab, H.
Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network
description In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H2O2:Fe2+ ratio, Fe2+ concentration, pH and process reaction time were studied closely. The results showed that the highest and lowest predicted chemical oxygen demand (COD) removal efficiency were 78.9% and 9.3%, respectively. The overall prediction error using the developed ANN model was within -0.625%. The derived model was adequate in predicting responses (R2 = 0.9896 and prediction R2 = 0.6954). The initial pH, H2O2:Fe2+ ratio and Fe2+ concentrations had positive effects, whereas coagulation pH had no direct effect on COD removal. Optimized conditions under specified constraints were obtained at pH = 3, Fe2+ concentration = 781.25 mg/L, reaction time = 28.04 min and H2O2:Fe2+ ratio = 2. Under these optimized conditions, 100% COD removal was predicted. To confirm the accuracy of the predicted model and the reliability of the optimum combination, one additional experiment was carried out under optimum conditions. The experimental values were found to agree well with those predicted, with a mean COD removal efficiency of 97.83%.
format Article
author Roudi, A. M.
Chelliapan, S.
Mohtar, W. H. M. W.
Kamyab, H.
author_facet Roudi, A. M.
Chelliapan, S.
Mohtar, W. H. M. W.
Kamyab, H.
author_sort Roudi, A. M.
title Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network
title_short Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network
title_full Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network
title_fullStr Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network
title_full_unstemmed Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network
title_sort prediction and optimization of the fenton process for the treatment of landfill leachate using an artificial neural network
publisher MDPI AG
publishDate 2018
url http://eprints.utm.my/id/eprint/79747/1/AnitaMaslahatiRoudi2018_PredictionandOptimizationoftheFenton.pdf
http://eprints.utm.my/id/eprint/79747/
http://dx.doi.org/10.3390/w10050595
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