Neural network-based ammonium aeration control of wastewater treatment plant

Due to the expensive operation and stringent effluent requirements of wastewater treatment plants, the wastewater treatment operator has been forced to find an alternative to improve the current control strategy, particularly for those using conventional activated sludge systems. The goal of this re...

Full description

Saved in:

Bibliographic Details
Main Author:	Husin, Maimun Huja
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/102545/1/MaimunHujaHusinPSKE2022.pdf.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.102545
record_format	uketd_dc
spelling	my-utm-ep.1025452023-09-03T06:42:10Z Neural network-based ammonium aeration control of wastewater treatment plant 2022 Husin, Maimun Huja TK Electrical engineering. Electronics Nuclear engineering Due to the expensive operation and stringent effluent requirements of wastewater treatment plants, the wastewater treatment operator has been forced to find an alternative to improve the current control strategy, particularly for those using conventional activated sludge systems. The goal of this research is to design a controller capable of reducing aeration energy while improving effluent quality. The objectives are met through a technique known as ammonium-based aeration control (ABAC). In this study, neural network (NN) – ABAC was designed and proposed for the Benchmark Simulation Model No. 1. The simulation results were compared to those of the proportional-integral (PI) controller and PI ABAC control configurations. During the NN training, a dropout layer was added to improve NN generalization. The simulation results show that the dropout layer successfully reduced the complexity of the NN while maintaining a good mean squared error and regression value. When compared to PI, the proposed NN – ABAC is more effective in terms of energy efficiency by lowering aeration energy by up to 23.86%, improving effluent quality by up to 1.94%, and lowering the total overall cost index by up to 4.61%. The findings suggest that the NN – ABAC has the potential to improve the performance of the activated sludge system. 2022 Thesis http://eprints.utm.my/id/eprint/102545/ http://eprints.utm.my/id/eprint/102545/1/MaimunHujaHusinPSKE2022.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149223 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Husin, Maimun Huja Neural network-based ammonium aeration control of wastewater treatment plant
description	Due to the expensive operation and stringent effluent requirements of wastewater treatment plants, the wastewater treatment operator has been forced to find an alternative to improve the current control strategy, particularly for those using conventional activated sludge systems. The goal of this research is to design a controller capable of reducing aeration energy while improving effluent quality. The objectives are met through a technique known as ammonium-based aeration control (ABAC). In this study, neural network (NN) – ABAC was designed and proposed for the Benchmark Simulation Model No. 1. The simulation results were compared to those of the proportional-integral (PI) controller and PI ABAC control configurations. During the NN training, a dropout layer was added to improve NN generalization. The simulation results show that the dropout layer successfully reduced the complexity of the NN while maintaining a good mean squared error and regression value. When compared to PI, the proposed NN – ABAC is more effective in terms of energy efficiency by lowering aeration energy by up to 23.86%, improving effluent quality by up to 1.94%, and lowering the total overall cost index by up to 4.61%. The findings suggest that the NN – ABAC has the potential to improve the performance of the activated sludge system.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Husin, Maimun Huja
author_facet	Husin, Maimun Huja
author_sort	Husin, Maimun Huja
title	Neural network-based ammonium aeration control of wastewater treatment plant
title_short	Neural network-based ammonium aeration control of wastewater treatment plant
title_full	Neural network-based ammonium aeration control of wastewater treatment plant
title_fullStr	Neural network-based ammonium aeration control of wastewater treatment plant
title_full_unstemmed	Neural network-based ammonium aeration control of wastewater treatment plant
title_sort	neural network-based ammonium aeration control of wastewater treatment plant
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/102545/1/MaimunHujaHusinPSKE2022.pdf.pdf
_version_	1776100944503635968

Neural network-based ammonium aeration control of wastewater treatment plant

Similar Items