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dc.contributor.authorLiu, Yiqi
dc.contributor.authorPan, Yongping
dc.contributor.authorHuang, Daoping
dc.contributor.authorWang, Qilin
dc.date.accessioned2017-05-29T12:35:11Z
dc.date.available2017-05-29T12:35:11Z
dc.date.issued2017
dc.identifier.issn0967-0661
dc.identifier.doi10.1016/j.conengprac.2017.02.003
dc.identifier.urihttp://hdl.handle.net/10072/337802
dc.description.abstractThe 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.description.peerreviewedYes
dc.languageEnglish
dc.publisherPergamon
dc.relation.ispartofpagefrom46
dc.relation.ispartofpageto54
dc.relation.ispartofjournalControl Engineering Practice
dc.relation.ispartofvolume62
dc.subject.fieldofresearchEnvironmental Engineering not elsewhere classified
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchMechanical Engineering
dc.subject.fieldofresearchcode090799
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0913
dc.titleFault prognosis of filamentous sludge bulking using an enhanced multi-output Gaussian processes regression
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Qilin


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