Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

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dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridGolgiyaz, Sedat/0000-0003-0305-9713
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.authorwosidOnat, Cem/W-7629-2018
dc.authorwosidGolgiyaz, Sedat/GSI-4458-2022
dc.contributor.authorGolgiyaz, Sedat
dc.contributor.authorTalu, Muhammed Fatih
dc.contributor.authorDas, Mahmut
dc.contributor.authorOnat, Cem
dc.date.accessioned2024-08-04T20:50:23Z
dc.date.available2024-08-04T20:50:23Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIt is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (lambda) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, lambda data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature-lambda. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [117M121]en_US
dc.description.sponsorshipThis work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK, Project number: 117M121) and MIMSAN AS?.en_US
dc.identifier.doi10.1016/j.aej.2021.06.022
dc.identifier.endpage1089en_US
dc.identifier.issn1110-0168
dc.identifier.issn2090-2670
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85108987791en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1079en_US
dc.identifier.urihttps://doi.org/10.1016/j.aej.2021.06.022
dc.identifier.urihttps://hdl.handle.net/11616/100017
dc.identifier.volume61en_US
dc.identifier.wosWOS:000744579100009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofAlexandria Engineering Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectExcess air coefficient estimationen_US
dc.subjectFlame imageen_US
dc.subjectGauss modelen_US
dc.subjectFlame stabilityen_US
dc.subjectArtificial neural network regression modelen_US
dc.titleEstimation of excess air coefficient on coal combustion processes via gauss model and artificial neural networken_US
dc.typeArticleen_US

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