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dc.contributor.authorLiu, Yiqi
dc.contributor.authorGuo, Jianhua
dc.contributor.authorWang, Qilin
dc.contributor.authorHuang, Daoping
dc.date.accessioned2017-06-13T02:41:33Z
dc.date.available2017-06-13T02:41:33Z
dc.date.issued2016
dc.identifier.issn2045-2322
dc.identifier.doi10.1038/srep31303
dc.identifier.urihttp://hdl.handle.net/10072/339747
dc.description.abstractActivated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherNature Macmillan
dc.relation.ispartofpagefrom31303-1
dc.relation.ispartofpageto31303-11
dc.relation.ispartofjournalScientific Reports
dc.relation.ispartofvolume6
dc.subject.fieldofresearchPhysical Sciences not elsewhere classified
dc.subject.fieldofresearchcode029999
dc.titlePrediction of filamentous sludge bulking using a state-based Gaussian processes regression model
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionPublished
gro.rights.copyright© The Author(s) 2016. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
gro.hasfulltextFull Text
gro.griffith.authorWang, Qilin


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