Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment.

Cheng, H; Wu, J; Huang, D; Liu, Y; Wang, Q

Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment.

Cheng, H Wu, J Huang, D Liu, Y Wang, Q

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: ISA Transactions, 2021, 117, pp. 210-220
Issue Date:: 2021-11

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Field	Value	Language
dc.contributor.author	Cheng, H
dc.contributor.author	Wu, J
dc.contributor.author	Huang, D
dc.contributor.author	Liu, Y
dc.contributor.author	Wang, Q https://orcid.org/0000-0002-5744-2331
dc.date.accessioned	2022-04-13T04:04:55Z
dc.date.available	2021-01-21
dc.date.available	2022-04-13T04:04:55Z
dc.date.issued	2021-11
dc.identifier.citation	ISA Transactions, 2021, 117, pp. 210-220
dc.identifier.issn	0019-0578
dc.identifier.issn	1879-2022
dc.identifier.uri	http://hdl.handle.net/10453/156194
dc.description.abstract	Quality-relevant process monitoring has attracted much attention for its ability to assist in maintaining efficient plant operation. However, when the process suffers from non-stationary and over-complex (with noise, multiplicative faults, etc.) characteristics, the traditional methods usually cannot be effectively applied. To this end, a novel method, termed as Robust adaptive boosted canonical correlation analysis (Rab-CCA), is proposed to monitor the wastewater treatment processes. First, a robust decomposition method is proposed to mitigate the defects of standard CCA by decomposing the corrupted matrix into a low-matrix and a sparse matrix. Second, to further improve the performance of the standard process monitoring method, a novel criterion function and control charts are reconstructed accordingly. Moreover, an adaptive statistical control limit is proposed that can adjust the thresholds according to the state of a system and can effectively reduce the missed alarms and false alarms simultaneously. The superiority of Rab-CCA is verified by Benchmark Simulation Model 1 (BSM1) and a real full-scale wastewater treatment plant (WWTP).
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/DP170102812
dc.relation	http://purl.org/au-research/grants/arc/DP200100933
dc.relation.ispartof	ISA Transactions
dc.relation.isbasedon	10.1016/j.isatra.2021.01.039
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering, 0910 Manufacturing Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.subject.mesh	Benchmarking
dc.subject.mesh	Canonical Correlation Analysis
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Water Purification
dc.subject.mesh	Benchmarking
dc.subject.mesh	Canonical Correlation Analysis
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Water Purification
dc.subject.mesh	Water Purification
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Benchmarking
dc.subject.mesh	Canonical Correlation Analysis
dc.title	Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment.
dc.type	Journal Article
utslib.citation.volume	117
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0910 Manufacturing 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	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-01-01T00:00:00+1000Z
dc.date.updated	2022-04-13T04:04:54Z
pubs.publication-status	Published
pubs.volume	117

Abstract:

Quality-relevant process monitoring has attracted much attention for its ability to assist in maintaining efficient plant operation. However, when the process suffers from non-stationary and over-complex (with noise, multiplicative faults, etc.) characteristics, the traditional methods usually cannot be effectively applied. To this end, a novel method, termed as Robust adaptive boosted canonical correlation analysis (Rab-CCA), is proposed to monitor the wastewater treatment processes. First, a robust decomposition method is proposed to mitigate the defects of standard CCA by decomposing the corrupted matrix into a low-matrix and a sparse matrix. Second, to further improve the performance of the standard process monitoring method, a novel criterion function and control charts are reconstructed accordingly. Moreover, an adaptive statistical control limit is proposed that can adjust the thresholds according to the state of a system and can effectively reduce the missed alarms and false alarms simultaneously. The superiority of Rab-CCA is verified by Benchmark Simulation Model 1 (BSM1) and a real full-scale wastewater treatment plant (WWTP).

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http://hdl.handle.net/10453/156194