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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
INSI Publications
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/17701/ http://irep.iium.edu.my/17701/1/AJBAS_6_62-69.pdf |
| _version_ | 1848778091457937408 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T14:56:22Z |
| format | Article |
| id | iium-17701 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T14:56:22Z |
| publishDate | 2012 |
| publisher | INSI Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 |
| 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 |
| title | 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_short | 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 |
| topic | TP155 Chemical engineering |
| 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 |