A basic ANN system for prediction of excess air coefficient on coal burners equipped with a CCD camera

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dc.authorscopusid22136251700
dc.authorscopusid57195249134
dc.contributor.authorOnat C.
dc.contributor.authorDaskin M.
dc.date.accessioned2024-08-04T20:03:39Z
dc.date.available2024-08-04T20:03:39Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractExcess air coefficient (?) is the most important parameter characterizing the combustion efficiency. Conventional measurement of ? is practiced by way of the flue analyze device with high market priced. Estimating of the ? from flame images is crucial in perspective of the combustion control because of decreasing structural dead time of the combustion process. Beside, estimation systems can be used continuously in a closed loop control system, unlike conventional analyzers. This paper represents a basic ? prediction system with a neural network for small scale nut coal burner equipped with a CCD camera. The proposed estimation system has two inputs. First input is stack gas temperature simply measuring from the flue. To choose the second input, eleven different matrix parameters have been evaluated together with flue gas temperature values and performed by matrix-based multiple linear regression analysis. As a result of these analyses, it has been seen that the trace of image matrix obtained from the flame image provides higher accuracy than the other matrix parameters. This instantaneous trace value of image source matrix is then filtered from high frequency dynamics by means of a low pass filter. Experimental data of the inputs and ? are synchronously matched by a neural network. Trained algorithm has reached R=0.984 in terms of accuracy. It is seen from the result that proposed estimating system using flame image with assistance of the stack gas temperature can be preferred in combustion control systems. © 2019 by authors.en_US
dc.description.sponsorship117M121; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAKen_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.doi10.13189/ms.2019.070101
dc.identifier.endpage9en_US
dc.identifier.issn2332-2071
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85066795722en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.13189/ms.2019.070101
dc.identifier.urihttps://hdl.handle.net/11616/91994
dc.identifier.volume7en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHorizon Research Publishingen_US
dc.relation.ispartofMathematics and Statisticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCombustionen_US
dc.subjectCombustion controlen_US
dc.subjectExcess air coefficienten_US
dc.subjectImage processingen_US
dc.subjectMulti linear regressionen_US
dc.subjectNeural networken_US
dc.titleA basic ANN system for prediction of excess air coefficient on coal burners equipped with a CCD cameraen_US
dc.typeArticleen_US

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