Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression

Liu, Y; Pan, Y; Huang, D; Wang, Q

Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression

Liu, Y Pan, Y Huang, D Wang, Q

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Control Engineering Practice, 2017, 62, pp. 46-54
Issue Date:: 2017-05-01

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Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Pan, Y
dc.contributor.author	Huang, D
dc.contributor.author	Wang, Q https://orcid.org/0000-0002-5744-2331
dc.date.accessioned	2022-08-19T05:39:34Z
dc.date.available	2022-08-19T05:39:34Z
dc.date.issued	2017-05-01
dc.identifier.citation	Control Engineering Practice, 2017, 62, pp. 46-54
dc.identifier.issn	0967-0661
dc.identifier.issn	1873-6939
dc.identifier.uri	http://hdl.handle.net/10453/160521
dc.description.abstract	The activated sludge process (ASP) is widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, the occurrence of filamentous sludge bulking often compromises the stable operation of the ASP. For timely diagnosis of filamentous sludge bulking for an activated sludge process in advance, this study proposed a Multi-Output Gaussian Processes Regression (MGPR) model for multi-step prediction and presented the Vector auto-regression (VAR) to learn the MGPR modelling deviation. The resulting models and associated uncertainty levels are used to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), such that the evolution of SVI can be predicted for both one-step and multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prognosis of filamentous bulking sludge with real-time SVI prediction were tested through a simulation study. The results demonstrated that the proposed methodology was capable of predicting future SVI with good accuracy, thereby providing sufficient time for filamentous sludge bulking.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Control Engineering Practice
dc.relation.isbasedon	10.1016/j.conengprac.2017.02.003
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression
dc.type	Journal Article
utslib.citation.volume	62
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-19T05:39:33Z
pubs.publication-status	Published
pubs.volume	62

Abstract:

The activated sludge process (ASP) is widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, the occurrence of filamentous sludge bulking often compromises the stable operation of the ASP. For timely diagnosis of filamentous sludge bulking for an activated sludge process in advance, this study proposed a Multi-Output Gaussian Processes Regression (MGPR) model for multi-step prediction and presented the Vector auto-regression (VAR) to learn the MGPR modelling deviation. The resulting models and associated uncertainty levels are used to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), such that the evolution of SVI can be predicted for both one-step and multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prognosis of filamentous bulking sludge with real-time SVI prediction were tested through a simulation study. The results demonstrated that the proposed methodology was capable of predicting future SVI with good accuracy, thereby providing sufficient time for filamentous sludge bulking.

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http://hdl.handle.net/10453/160521