Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS

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dc.authorid0000-0003-4351-2244
dc.authorid0000-0003-4842-2635
dc.authorid0000-0001-7947-5508
dc.authorscopusid36241785000
dc.authorscopusid11539603200
dc.authorscopusid54783608800
dc.authorwosidUzun, Erdinç/AAG-5529-2019
dc.authorwosidTufekci, Pinar/ABA-5121-2020
dc.contributor.authorKaya, Heysem
dc.contributor.authorTüfekçi, Pınar
dc.contributor.authorUzun, Erdinc
dc.date.accessioned2022-05-11T14:15:56Z
dc.date.available2022-05-11T14:15:56Z
dc.date.issued2019
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractPredictive emission monitoring systems (PEMS) are important tools for validation and backing up of costly continuous emission monitoring systems used in gas-turbine-based power plants. Their implementation relies on the availability of appropriate and ecologically valid data. In this paper, we introduce a novel PEMS dataset collected over five years from a gas turbine for the predictive modeling of the CO and NOx emissions. We analyze the data using a recent machine learning paradigm, and present useful insights about emission predictions. Furthermore, we present a benchmark experimental procedure for comparability of future works on the data.
dc.identifier.doi10.3906/elk-1807-87
dc.identifier.endpage4796
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85076639070
dc.identifier.scopusqualityQ3
dc.identifier.startpage4783
dc.identifier.urihttps://doi.org/10.3906/elk-1807-87
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6124
dc.identifier.volume27
dc.identifier.wosWOS:000506165400054
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKaya, Heysem
dc.institutionauthorTüfekçi, Pınar
dc.institutionauthorUzun, Erdinc
dc.language.isoen
dc.publisherTubitak Scientific & Technical Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectPredictive emission monitoring systems
dc.subjectCO
dc.subjectNOx
dc.subjectexhaust emission prediction
dc.subjectgas turbines
dc.subjectextreme learning machine
dc.subjectdatabase
dc.subjectExtreme Learning-Machine
dc.subjectModel
dc.titlePredicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS
dc.typeArticle

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