Modeling an Adaptive Hybrid Soft Sensor with Co-training Learning toward Applications in Wastewater Treatment

Wu, J; Tang, L; Jin, S; Li, X; Liu, H; Li, D; Liu, Y; Wang, Q

Modeling an Adaptive Hybrid Soft Sensor with Co-training Learning toward Applications in Wastewater Treatment

Wu, J Tang, L Jin, S Li, X

Liu, H

Li, D Liu, Y Wang, Q

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Publisher:: AMER CHEMICAL SOC
Publication Type:: Journal Article
Citation:: Industrial and Engineering Chemistry Research, 2023, 62, (41), pp. 16841-16853
Issue Date:: 2023-10-18

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Field	Value	Language
dc.contributor.author	Wu, J
dc.contributor.author	Tang, L
dc.contributor.author	Jin, S
dc.contributor.author	Li, X https://orcid.org/0000-0003-1768-9556
dc.contributor.author	Liu, H https://orcid.org/0000-0003-4513-4988
dc.contributor.author	Li, D
dc.contributor.author	Liu, Y
dc.contributor.author	Wang, Q https://orcid.org/0000-0002-5744-2331
dc.date.accessioned	2024-04-18T04:58:28Z
dc.date.available	2024-04-18T04:58:28Z
dc.date.issued	2023-10-18
dc.identifier.citation	Industrial and Engineering Chemistry Research, 2023, 62, (41), pp. 16841-16853
dc.identifier.issn	0888-5885
dc.identifier.issn	1520-5045
dc.identifier.uri	http://hdl.handle.net/10453/178039
dc.description.abstract	In wastewater treatment processes, sampling delays, resulting from difficult-to-measure but important variables, usually shape wastewater treatment processes (WWTPs) as multirate systems. In addition, when soft sensor models are applied to the wastewater treatment process, the performance of the model prediction always degrades because of the time-varying characteristics, uncertainty in process variables, and inaccurate measurement instruments. In this paper, an adaptive hybrid soft sensor model with semisupervised learning is proposed to deal with multiple rate issues in the WWTPs. First, the method uses a semisupervised learning approach, called the co-training framework algorithm, to refine useful information hidden in partially sampling-rate-inconsistent data and then solves the multirate system problem with the optimized model. Second, two different types of regression models, Recursive Partial Least Squares and Recursive Extreme Learning Machine, are used to model and predict the hard-to-measure variables as well as to update the prediction model adequately and finally to solve the performance degradation of the soft sensor model. Additionally, we validate the proposed method with two cases, namely, a simulation data set and a full-scale WWTP data set. The experimental results show that the proposed method in this paper can achieve a shorter time consumption and better prediction accuracy than standard soft sensor models.
dc.language	English
dc.publisher	AMER CHEMICAL SOC
dc.relation	http://purl.org/au-research/grants/arc/FT200100264
dc.relation.ispartof	Industrial and Engineering Chemistry Research
dc.relation.isbasedon	10.1021/acs.iecr.3c03067
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	03 Chemical Sciences, 09 Engineering
dc.subject.classification	Chemical Engineering
dc.subject.classification	34 Chemical sciences
dc.subject.classification	40 Engineering
dc.title	Modeling an Adaptive Hybrid Soft Sensor with Co-training Learning toward Applications in Wastewater Treatment
dc.type	Journal Article
utslib.citation.volume	62
utslib.for	03 Chemical Sciences
utslib.for	09 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	*
dc.date.updated	2024-04-18T04:58:21Z
pubs.issue	41
pubs.publication-status	Published
pubs.volume	62
utslib.citation.issue	41

Abstract:

In wastewater treatment processes, sampling delays, resulting from difficult-to-measure but important variables, usually shape wastewater treatment processes (WWTPs) as multirate systems. In addition, when soft sensor models are applied to the wastewater treatment process, the performance of the model prediction always degrades because of the time-varying characteristics, uncertainty in process variables, and inaccurate measurement instruments. In this paper, an adaptive hybrid soft sensor model with semisupervised learning is proposed to deal with multiple rate issues in the WWTPs. First, the method uses a semisupervised learning approach, called the co-training framework algorithm, to refine useful information hidden in partially sampling-rate-inconsistent data and then solves the multirate system problem with the optimized model. Second, two different types of regression models, Recursive Partial Least Squares and Recursive Extreme Learning Machine, are used to model and predict the hard-to-measure variables as well as to update the prediction model adequately and finally to solve the performance degradation of the soft sensor model. Additionally, we validate the proposed method with two cases, namely, a simulation data set and a full-scale WWTP data set. The experimental results show that the proposed method in this paper can achieve a shorter time consumption and better prediction accuracy than standard soft sensor models.

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http://hdl.handle.net/10453/178039