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

It is difficult to unveil the complicated interrelationships of wastewater parameters using linear models. 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 param...

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主要な著者: Jami, Mohammed Saedi, Husain, Iman A. F., Kabbashi, Nassereldeen Ahmed, Abdullah, Norhafiza
フォーマット: 論文
言語:English
出版事項: INSI Publications 2012
主題:
TP155 Chemical engineering
オンライン･アクセス:http://irep.iium.edu.my/17701/1/AJBAS_6_62-69.pdf
http://irep.iium.edu.my/17701/
http://www.insipub.com/ajbas/2012/January/62-69.pdf
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spelling my.iium.irep.177012012-06-15T01:37:53Z http://irep.iium.edu.my/17701/ Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance Jami, Mohammed Saedi Husain, Iman A. F. Kabbashi, Nassereldeen Ahmed Abdullah, Norhafiza TP155 Chemical engineering It is difficult to unveil the complicated interrelationships of wastewater parameters using linear models. 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 inputmultiple 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 inputssingle output approach was with BOD as outputs and 30 neurons. The multiple inputs- single output ANN models developed can be used in analyzing how wastewater parameters such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Suspended Solids (SS) are affecting each other. INSI Publications 2012-01 Article REM application/pdf en http://irep.iium.edu.my/17701/1/AJBAS_6_62-69.pdf Jami, Mohammed Saedi and Husain, Iman A. F. and Kabbashi, Nassereldeen Ahmed and Abdullah, Norhafiza (2012) Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance. Australian Journal of Basic and Applied Sciences, 6 (1). pp. 62-69. ISSN 1991-8178 http://www.insipub.com/ajbas/2012/January/62-69.pdf
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 TP155 Chemical engineering
spellingShingle TP155 Chemical engineering
Jami, Mohammed Saedi
Husain, Iman A. F.
Kabbashi, Nassereldeen Ahmed
Abdullah, Norhafiza
Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance
description It is difficult to unveil the complicated interrelationships of wastewater parameters using linear models. 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 inputmultiple 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 inputssingle output approach was with BOD as outputs and 30 neurons. The multiple inputs- single output ANN models developed can be used in analyzing how wastewater parameters such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Suspended Solids (SS) are affecting each other.
format Article
author Jami, Mohammed Saedi
Husain, Iman A. F.
Kabbashi, Nassereldeen Ahmed
Abdullah, Norhafiza
author_facet Jami, Mohammed Saedi
Husain, Iman A. F.
Kabbashi, Nassereldeen Ahmed
Abdullah, Norhafiza
author_sort Jami, Mohammed Saedi
title Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance
title_short Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance
title_full Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance
title_fullStr Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance
title_full_unstemmed Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance
title_sort multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance
publisher INSI Publications
publishDate 2012
url http://irep.iium.edu.my/17701/1/AJBAS_6_62-69.pdf
http://irep.iium.edu.my/17701/
http://www.insipub.com/ajbas/2012/January/62-69.pdf
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score 13.251814

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