Unburnt carbon estimation through flame image and gauss process regression

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dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridDAŞKIN, Mahmut/0000-0001-7777-1821
dc.authoridGolgiyaz, Sedat/0000-0003-0305-9713
dc.authoridCELLEK, MEHMET SALIH/0000-0001-5802-0715
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.authorwosidDAŞKIN, Mahmut/AAT-4529-2021
dc.authorwosidDemir, Usame/AAB-7728-2022
dc.authorwosidOnat, Cem/W-7629-2018
dc.authorwosidGolgiyaz, Sedat/GSI-4458-2022
dc.contributor.authorGolgiyaz, Sedat
dc.contributor.authorDemir, Usame
dc.contributor.authorCellek, Mehmet Salih
dc.contributor.authorDaskin, Mahmut
dc.contributor.authorTalu, M. Fatih
dc.contributor.authorOnat, Cem
dc.date.accessioned2024-08-04T20:53:31Z
dc.date.available2024-08-04T20:53:31Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe presence of unburned carbon in coal-burning systems undoubtedly causes a loss in the amount of energy that can be obtained from the system, and also reveals an inadequacy in terms of the usability of the ashes. The expensiveness of existing unburned carbon prediction methods is one of the reasons why these technologies cannot be used. This situation requires working on alternative non-combustible carbon technologies. In this paper, a new approach is presented for estimating unburned carbon in a small-scale coal burner system using the Gaussian regression model and CCD camera-acquired flame image. The proposed approach evaluates brightness, fluctuation amplitude, area, and radiation signal properties of the flame image. The proposed non-combustible carbon estimation technique does not require prior knowledge of CCD camera features. In the feature acquisition phase, results were obtained for each natural component of the flame image in RGB colour space separately, in pairs, all together and for three artificial colour channels (grey image). With the proposed method, the unburned carbon estimation was obtained with an accuracy of R = 0.9664 when all colour channels of the RGB image were used together. This result shows that unburned carbon can be estimated from the instantaneous flame images obtained with the CCD camera.en_US
dc.description.sponsorshipUlusal Metroloji Enstitusu, Turkiye Bilimsel ve Teknolojik Arastirma Kurumu [117M121]; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu [117M121]en_US
dc.description.sponsorshipThe work was supported by the Ulusal Metroloji Enstitusu, Turkiye Bilimsel ve Teknolojik Arastirma Kurumu [117M121]; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu [117M121].en_US
dc.identifier.doi10.1080/2374068X.2023.2184040
dc.identifier.endpage922en_US
dc.identifier.issn2374-068X
dc.identifier.issn2374-0698
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85149683018en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage903en_US
dc.identifier.urihttps://doi.org/10.1080/2374068X.2023.2184040
dc.identifier.urihttps://hdl.handle.net/11616/101210
dc.identifier.volume10en_US
dc.identifier.wosWOS:000943352700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofAdvances in Materials and Processing Technologiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUnburnt carbon predictionen_US
dc.subjectLBP texture analysis methoden_US
dc.subjectRotation invariant uniform LBPen_US
dc.subjectFlame image momentsen_US
dc.subjectCombustion optimizationen_US
dc.subjectGauss process regression modelen_US
dc.titleUnburnt carbon estimation through flame image and gauss process regressionen_US
dc.typeArticleen_US

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