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Improvement of artificial neural network model for the prediction of wastewater treatment plant performance

A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN us...

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Main Authors: Jami, Mohammed Saedi, Ahmed Kabashi, Nassereldeen, Husain, Iman A.F., Abdullah, Norhafiza
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
TD159 Municipal engineering
Online Access:http://irep.iium.edu.my/2997/1/FinalManuscript_16_05_11.pdf
http://irep.iium.edu.my/2997/
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spelling my.iium.irep.29972012-01-12T07:54:58Z http://irep.iium.edu.my/2997/ Improvement of artificial neural network model for the prediction of wastewater treatment plant performance Jami, Mohammed Saedi Ahmed Kabashi, Nassereldeen Husain, Iman A.F. Abdullah, Norhafiza TD159 Municipal engineering A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN using ANN toolbox in commercially available software, MATLAB. The data were obtained from one of Sewage Treatment Plant in Malaysia. The input parameters for the ANN were BOD, SS, and COD of the influent, while the output parameters were combination of the effluent characteristics. The networks for single input-single output were compared with those of single input-multiple output. The ANN was developed for raw and screened data and the results were compared for both networks. It was found that the use of data screening is essential to come up with a better ANNs model. From the regression analysis, networks with one hidden layer and 20 neurons were found to be the best one for single input-single output approach. While the best network for the multiple inputs-single output approach was with BOD as outputs and 30 neurons. The second approach which showed a lower RMSE and higher R values was selected. 2011-07-06 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/2997/1/FinalManuscript_16_05_11.pdf Jami, Mohammed Saedi and Ahmed Kabashi, Nassereldeen and Husain, Iman A.F. and Abdullah, Norhafiza (2011) Improvement of artificial neural network model for the prediction of wastewater treatment plant performance. In: The 3rd IASTED International Conference on Environmental Management and Engineering (EME), 4 – 6 July 2011, Calgary, Canada.
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TD159 Municipal engineering
spellingShingle TD159 Municipal engineering
Jami, Mohammed Saedi
Ahmed Kabashi, Nassereldeen
Husain, Iman A.F.
Abdullah, Norhafiza
Improvement of artificial neural network model for the prediction of wastewater treatment plant performance
description A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN using ANN toolbox in commercially available software, MATLAB. The data were obtained from one of Sewage Treatment Plant in Malaysia. The input parameters for the ANN were BOD, SS, and COD of the influent, while the output parameters were combination of the effluent characteristics. The networks for single input-single output were compared with those of single input-multiple output. The ANN was developed for raw and screened data and the results were compared for both networks. It was found that the use of data screening is essential to come up with a better ANNs model. From the regression analysis, networks with one hidden layer and 20 neurons were found to be the best one for single input-single output approach. While the best network for the multiple inputs-single output approach was with BOD as outputs and 30 neurons. The second approach which showed a lower RMSE and higher R values was selected.
format Conference or Workshop Item
author Jami, Mohammed Saedi
Ahmed Kabashi, Nassereldeen
Husain, Iman A.F.
Abdullah, Norhafiza
author_facet Jami, Mohammed Saedi
Ahmed Kabashi, Nassereldeen
Husain, Iman A.F.
Abdullah, Norhafiza
author_sort Jami, Mohammed Saedi
title Improvement of artificial neural network model for the prediction of wastewater treatment plant performance
title_short Improvement of artificial neural network model for the prediction of wastewater treatment plant performance
title_full Improvement of artificial neural network model for the prediction of wastewater treatment plant performance
title_fullStr Improvement of artificial neural network model for the prediction of wastewater treatment plant performance
title_full_unstemmed Improvement of artificial neural network model for the prediction of wastewater treatment plant performance
title_sort improvement of artificial neural network model for the prediction of wastewater treatment plant performance
publishDate 2011
url http://irep.iium.edu.my/2997/1/FinalManuscript_16_05_11.pdf
http://irep.iium.edu.my/2997/
_version_ 1643605061150965760
score 13.211869

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