Staff View: Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process

Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process

Aeration control is a way to have a wastewater treatment plant (WWTP) that uses less energy and produces higher effluent quality to meet state and federal regulations. The goal of this research is to develop a neural network (NN) ammonia-based aeration control (ABAC) that focuses on reducing total n...

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Main Authors:	Husin, M. H., Rahmat, M. F., Wahab, N. A., Sabri, M. F. M.
Format:	Article
Language:	English
Published:	Exeley Inc. 2021
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/95549/1/MFRahmat2021_ImprovingTotalNitrogenRemoval.pdf http://eprints.utm.my/id/eprint/95549/ http://dx.doi.org/10.21307/ijssis-2021-016
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id	my.utm.95549
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spelling	my.utm.955492022-05-31T12:46:16Z http://eprints.utm.my/id/eprint/95549/ Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process Husin, M. H. Rahmat, M. F. Wahab, N. A. Sabri, M. F. M. TK Electrical engineering. Electronics Nuclear engineering Aeration control is a way to have a wastewater treatment plant (WWTP) that uses less energy and produces higher effluent quality to meet state and federal regulations. The goal of this research is to develop a neural network (NN) ammonia-based aeration control (ABAC) that focuses on reducing total nitrogen and ammonia concentration violations by regulating dissolved oxygen (DO) concentration based on the ammonia concentration in the final tank, rather than maintaining the DO concentration at a set elevated value, as most studies do. Simulation platform used in this study is Benchmark Simulation Model No. 1, and the NN ABAC is compared to the Proportional-Integral (PI) ABAC and PI controller. In comparison to the PI controller, the simulation results showed that the proposed controller has a significant improvement in reducing the AECI up to 23.86%, improving the EQCI up to 1.94%, and reducing the overall OCI up to 4.61%. The results of the study show that the NN ABAC can be utilized to improve the performance of a WWTP’s activated sludge system. Exeley Inc. 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95549/1/MFRahmat2021_ImprovingTotalNitrogenRemoval.pdf Husin, M. H. and Rahmat, M. F. and Wahab, N. A. and Sabri, M. F. M. (2021) Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process. International Journal on Smart Sensing and Intelligent Systems, 14 (1). pp. 1-16. ISSN 1178-5608 http://dx.doi.org/10.21307/ijssis-2021-016 DOI: 10.21307/ijssis-2021-016
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Husin, M. H. Rahmat, M. F. Wahab, N. A. Sabri, M. F. M. Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
description	Aeration control is a way to have a wastewater treatment plant (WWTP) that uses less energy and produces higher effluent quality to meet state and federal regulations. The goal of this research is to develop a neural network (NN) ammonia-based aeration control (ABAC) that focuses on reducing total nitrogen and ammonia concentration violations by regulating dissolved oxygen (DO) concentration based on the ammonia concentration in the final tank, rather than maintaining the DO concentration at a set elevated value, as most studies do. Simulation platform used in this study is Benchmark Simulation Model No. 1, and the NN ABAC is compared to the Proportional-Integral (PI) ABAC and PI controller. In comparison to the PI controller, the simulation results showed that the proposed controller has a significant improvement in reducing the AECI up to 23.86%, improving the EQCI up to 1.94%, and reducing the overall OCI up to 4.61%. The results of the study show that the NN ABAC can be utilized to improve the performance of a WWTP’s activated sludge system.
format	Article
author	Husin, M. H. Rahmat, M. F. Wahab, N. A. Sabri, M. F. M.
author_facet	Husin, M. H. Rahmat, M. F. Wahab, N. A. Sabri, M. F. M.
author_sort	Husin, M. H.
title	Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
title_short	Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
title_full	Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
title_fullStr	Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
title_full_unstemmed	Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
title_sort	improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process
publisher	Exeley Inc.
publishDate	2021
url	http://eprints.utm.my/id/eprint/95549/1/MFRahmat2021_ImprovingTotalNitrogenRemoval.pdf http://eprints.utm.my/id/eprint/95549/ http://dx.doi.org/10.21307/ijssis-2021-016
_version_	1735386817856798720
score	13.211869

Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process

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