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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Main Authors:	Jami, Mohammed Saedi, Husain, Iman A. F., Kabbashi, Nassereldeen Ahmed, Abdullah, Norhafiza
Format:	Article
Language:	English
Published:	INSI Publications 2012
Subjects:	TP155 Chemical engineering
Online Access:	http://irep.iium.edu.my/17701/ http://irep.iium.edu.my/17701/ http://irep.iium.edu.my/17701/1/AJBAS_6_62-69.pdf

id	iium-17701
recordtype	eprints
spelling	iium-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 PeerReviewed 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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
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/ http://irep.iium.edu.my/17701/ http://irep.iium.edu.my/17701/1/AJBAS_6_62-69.pdf
first_indexed	2023-09-18T20:26:41Z
last_indexed	2023-09-18T20:26:41Z
_version_	1777408463406628864

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

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