DETERMINATION OF COMBUSTION DEGREE OF SOME COAL SAMPLES FROM THE SHORT AND SULPHUR ANALSIS RESULTS BY USING ARTIFICIAL NEURAL NETWORKS

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dc.contributor.authorOzsen, Seral
dc.contributor.authorOzsen, Hakan
dc.contributor.authorSensogut, Cem
dc.date.accessioned2020-03-26T18:14:06Z
dc.date.available2020-03-26T18:14:06Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description11th International Multidisciplinary Scientific GeoConference -- JUN 20-25, 2011 -- Albena, BULGARIAen_US
dc.description.abstractCoal is the most consumed fossil fuel in the world. Determination of the thermal properties of coal is a very important matter and it is not straightforward because of the heterogeneous structure of the coal. The short and elementary analysis results of coals with different carbonization degrees are different. The mineral composition of a coal also affects the thermal behavior. To detect thermal properties of coals, thermal analysis devices are generally used in many widespread methods. The most widely used methods in thermal analysis of coals are Differential Thermal Analysis (DTA) and Thermogravimetry (TG). In this study however, a different analysis method to determine combustion degree of coals was applied. By utilizing from some properties of coals obtained by short analysis and sulphur analysis, an Artificial Neural Network (ANN) was trained to predict the combustion degrees of coals. For this application 84 coal samples were prepared from 28 different locations in TURKEY. Among these, 67 samples were used in training ANN and the remaining 17 were used in test procedure. For the test samples, the trained ANN was used to predict the combustion degrees of them by presenting 8 different properties obtained from short and Sulphur analysis results. Then the mean squared error (mse) was calculated between the real combustion degrees which were also determined from the TG method and predicted combustion degrees of ANN. The test mse was found to be 2.9x10(-4). This result means that the trained ANN could predict combustion degree of a coal sample with a mean error of 2.9x10(-4). When the time and effort spend on determining thermal property of a coal sample with a classical method is considered, this gives another alternative to the experimenter for determining combustion degree of that sample in more short and effortless manner.en_US
dc.description.sponsorshipMinist Env & Water, Bulgarian Acad Sci, Acad Sci Czech Republ, Acad Sci IR Iran, Latvian Acad Sci, Polish Acad Sci, Russian Acad Sci, Serbian Acad Sci & Arts, Slovak Acad Sci, Natl Acad Sci Ukraine, Bulgarian Ind Assoc, Bulgarian Acad Sci, Albena Wellness Destinaten_US
dc.description.sponsorshipScientific Research Project of Seleuk University [11701197]en_US
dc.description.sponsorshipThis study has been supported by Scientific Research Project of Seleuk University (Project No. 11701197)en_US
dc.identifier.endpage+en_US
dc.identifier.issn1314-2704en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage759en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26305
dc.identifier.wosWOS:000307366000101en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherINT SCIENTIFIC CONFERENCE SGEMen_US
dc.relation.ispartof11TH INTERNATIONAL MULTIDISCIPLINARY SCIENTIFIC GEOCONFERENCE (SGEM 2011), VOL Ien_US
dc.relation.ispartofseriesInternational Multidisciplinary Scientific GeoConference-SGEM
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectTGen_US
dc.subjectDTAen_US
dc.subjectcoalen_US
dc.subjectcombustion propertiesen_US
dc.subjectartificial neural networks (ANN)en_US
dc.titleDETERMINATION OF COMBUSTION DEGREE OF SOME COAL SAMPLES FROM THE SHORT AND SULPHUR ANALSIS RESULTS BY USING ARTIFICIAL NEURAL NETWORKSen_US
dc.typeConference Objecten_US

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