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Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach

Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated...

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Main Authors: Chun T.S., Malek M.A., Ismail A.R.
Other Authors: 56338030500
Format: Article
Published: Royal Society of Chemistry 2023
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spelling my.uniten.dspace-220542023-05-16T10:47:00Z Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach Chun T.S. Malek M.A. Ismail A.R. 56338030500 55636320055 36995749000 Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality - namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS) - was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97th month of operation. The COD and TSS effluent removals were simulated at the 85th and the 121st months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique. This journal is © the Partner Organisations 2014. Final 2023-05-16T02:47:00Z 2023-05-16T02:47:00Z 2014 Article 10.1039/c4em00282b 2-s2.0-84906555471 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906555471&doi=10.1039%2fc4em00282b&partnerID=40&md5=f8874645976ce415d8bd65e98bd42bba https://irepository.uniten.edu.my/handle/123456789/22054 16 9 2208 2214 Royal Society of Chemistry Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality - namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS) - was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97th month of operation. The COD and TSS effluent removals were simulated at the 85th and the 121st months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique. This journal is © the Partner Organisations 2014.
author2 56338030500
author_facet 56338030500
Chun T.S.
Malek M.A.
Ismail A.R.
format Article
author Chun T.S.
Malek M.A.
Ismail A.R.
spellingShingle Chun T.S.
Malek M.A.
Ismail A.R.
Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach
author_sort Chun T.S.
title Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach
title_short Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach
title_full Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach
title_fullStr Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach
title_full_unstemmed Prediction analysis of effluent removal in a septic sludge treatment plant: A biomimetics engineering approach
title_sort prediction analysis of effluent removal in a septic sludge treatment plant: a biomimetics engineering approach
publisher Royal Society of Chemistry
publishDate 2023
_version_ 1806425629212540928
score 13.223943

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