Artificial neural network regression model to predict flue gas temperature and emissions with the spectral norm of flame image

Basit Öğe Kaydı
dc.authoridGolgiyaz, Sedat/0000-0003-0305-9713
dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridonat, cem/0000-0002-4295-4860
dc.authorwosidOnat, Cem/W-7629-2018
dc.authorwosidGolgiyaz, Sedat/GSI-4458-2022
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.contributor.authorGolgiyaz, Sedat
dc.contributor.authorTalu, Muhammed Fatih
dc.contributor.authorOnat, Cem
dc.date.accessioned2024-08-04T20:46:05Z
dc.date.available2024-08-04T20:46:05Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper presents an experimental study on flue gas temperature (FGT) and emissions estimation in home-type nut coal-fired burner. The proposed method does not require prior knowledge of Charge-Coupled Device (CCD) camera features. Therefore, it can be applied easily without costly and complex adaptation requirement to control the combustion process. In the proposed system, the flame image was taken with a CCD camera. At the same time, reference temperature and emissions were taken with flue gas analyzer. Combustion characteristics were extracted by image processing techniques from each two-color channels of the flame image. When the features were obtained, instead of converting the flame image to grayscale and obtaining the general features, local feature extraction was preferred from each of the two-color channels that express the combustion process better. For this process, the image was divided into local windows and individual features for each two-color channel was extracted. The optimum number of windows was decided by experimental investigation. The features were obtained by using the spectral norm of the region of interest. The obtaining image features were used to train the Artificial Neural Network (ANN) regression model which predicted the FGT and emissions. Estimation accuracy (correlation coefficient (R)) of developed FGT prediction model is 0.99. The emission prediction models estimate SO2, O-2, NOx, CO2 andCO emissions with R = 0.97, R = 0.96, R = 0.77, R = 0.96, and R = 0.87 accuracies, respectively. The experimental results show that the FGT and emissions can be estimated by the flame image.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [117M121]; MIMSAN ASen_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.fuel.2019.115827
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85069870727en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.fuel.2019.115827
dc.identifier.urihttps://hdl.handle.net/11616/98887
dc.identifier.volume255en_US
dc.identifier.wosWOS:000479141700009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofFuelen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFlame imageen_US
dc.subjectCoal-fired boileren_US
dc.subjectFlue gas temperature estimationen_US
dc.subjectEmission estimationen_US
dc.subjectArtificial neural network regression modelen_US
dc.titleArtificial neural network regression model to predict flue gas temperature and emissions with the spectral norm of flame imageen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu