Analysis of municipal wastewater treatment plant performance using artificial neural network approach

Artificial neural network (ANN) was used in this research as a statistical modeling tool for predicting the performance of wastewater treatment plant. A two years data of the waste water treatment plants’ effluent and influent parameters was collected and applied in developing and training the ANN u...

Full description

Bibliographic Details
Main Authors:	Husain, Iman, Jami, Mohammed Saedi, Kabashi, Nassereldin, Abdullah, Nurhafizah
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	TD159 Municipal engineering
Online Access:	http://irep.iium.edu.my/3161/ http://irep.iium.edu.my/3161/1/icbioe_ANN_modified.pdf

id	iium-3161
recordtype	eprints
spelling	iium-31612012-02-15T02:48:24Z http://irep.iium.edu.my/3161/ Analysis of municipal wastewater treatment plant performance using artificial neural network approach Husain, Iman Jami, Mohammed Saedi Kabashi, Nassereldin Abdullah, Nurhafizah TD159 Municipal engineering Artificial neural network (ANN) was used in this research as a statistical modeling tool for predicting the performance of wastewater treatment plant. A two years data of the waste water treatment plants’ effluent and influent parameters was collected and applied in developing and training the ANN using the ANN toolbox in MATLAB. The data were obtained from Bandar Tun Razak Sewage Treatment Plant (BTR STP), that is managed by Indah Water Konsurtium (IWK), Malaysia's national sewerage company. The input and output parameters for the ANN were BOD, SS, and COD. It was found that the use of data screening is essential to come up with better ANNs model. Moreover, using multiple input-single output models was even a better model than single input-single output. The optimum number of hidden layer and neurons were determined which gave excellent results in predicting both the BOD and COD of the effluent which are required by the DOE. From the regression analysis, networks with one hidden layer and 20 nodes and BOD as input and COD as output were found to be the best one. The optimum number of hidden layers is 10 and the R value is improved by 30 %. The Mean Squared Error (MSE) is the lowest for the network. From the regression analysis, it is obvious that networks using screened data give better results in term of R values and MSE, and were selected for the subsequent modeling analysis in this study, that is prediction. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/3161/1/icbioe_ANN_modified.pdf Husain, Iman and Jami, Mohammed Saedi and Kabashi, Nassereldin and Abdullah, Nurhafizah (2011) Analysis of municipal wastewater treatment plant performance using artificial neural network approach. In: 2nd International Conference on Biotechnology Engineering (ICBioE 2011), (2011) (17-19 May), Kuala Lumpur, Malaysia .
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TD159 Municipal engineering
spellingShingle	TD159 Municipal engineering Husain, Iman Jami, Mohammed Saedi Kabashi, Nassereldin Abdullah, Nurhafizah Analysis of municipal wastewater treatment plant performance using artificial neural network approach
description	Artificial neural network (ANN) was used in this research as a statistical modeling tool for predicting the performance of wastewater treatment plant. A two years data of the waste water treatment plants’ effluent and influent parameters was collected and applied in developing and training the ANN using the ANN toolbox in MATLAB. The data were obtained from Bandar Tun Razak Sewage Treatment Plant (BTR STP), that is managed by Indah Water Konsurtium (IWK), Malaysia's national sewerage company. The input and output parameters for the ANN were BOD, SS, and COD. It was found that the use of data screening is essential to come up with better ANNs model. Moreover, using multiple input-single output models was even a better model than single input-single output. The optimum number of hidden layer and neurons were determined which gave excellent results in predicting both the BOD and COD of the effluent which are required by the DOE. From the regression analysis, networks with one hidden layer and 20 nodes and BOD as input and COD as output were found to be the best one. The optimum number of hidden layers is 10 and the R value is improved by 30 %. The Mean Squared Error (MSE) is the lowest for the network. From the regression analysis, it is obvious that networks using screened data give better results in term of R values and MSE, and were selected for the subsequent modeling analysis in this study, that is prediction.
format	Conference or Workshop Item
author	Husain, Iman Jami, Mohammed Saedi Kabashi, Nassereldin Abdullah, Nurhafizah
author_facet	Husain, Iman Jami, Mohammed Saedi Kabashi, Nassereldin Abdullah, Nurhafizah
author_sort	Husain, Iman
title	Analysis of municipal wastewater treatment plant performance using artificial neural network approach
title_short	Analysis of municipal wastewater treatment plant performance using artificial neural network approach
title_full	Analysis of municipal wastewater treatment plant performance using artificial neural network approach
title_fullStr	Analysis of municipal wastewater treatment plant performance using artificial neural network approach
title_full_unstemmed	Analysis of municipal wastewater treatment plant performance using artificial neural network approach
title_sort	analysis of municipal wastewater treatment plant performance using artificial neural network approach
publishDate	2011
url	http://irep.iium.edu.my/3161/ http://irep.iium.edu.my/3161/1/icbioe_ANN_modified.pdf
first_indexed	2023-09-18T20:10:52Z
last_indexed	2023-09-18T20:10:52Z
_version_	1777407468564905984

Analysis of municipal wastewater treatment plant performance using artificial neural network approach

Similar Items