PREDICTION OF GROSS CALORIFIC VALUE OF COAL FROM PROXIMATEAND ULTIMATE ANALYSIS VARIABLES USING SUPPORT VECTORMACHINES WITH FEATURE SELECTION

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dc.contributor.authorAçıkkar, Mustafa
dc.date.accessioned2025-01-06T17:23:07Z
dc.date.available2025-01-06T17:23:07Z
dc.date.issued2020
dc.departmentAdana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi
dc.description.abstractThe gross calorific value (GCV) is an essential thermal property of coal which indicates the amount of heat energy that couldbe released by burning a specific quantity. The primary objective of the presented study is to develop new GCV prediction models using support vector machines (SVMs) combined with feature selection algorithm. For this purpose, the feature selector RReliefF is applied to the dataset consisting of proximate and ultimate analysis variables to determine the importance of each predictor of GCV. In this way, seven different hybrid input sets (data models) were constructed. The prediction performance of models was computed by using the square of multiple correlation coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Considering all the results obtained from this study, the predictor variables moisture (M) and ash (A) obtained from the proximate analysis and carbon (C), hydrogen (H) and sulfur (S) obtained from the ultimate analysis were found to be the most relevant variables in predicting GCV of coal, while the predictor variables volatile matter (VM) from the proximate analysis and nitrogen (N) from the ultimate analysis did not have a positive effect on the prediction accuracy. The SVM-based model using the predictor variables M, A, C, H, and S yielded the highest R2and the lowest RMSE and MAPE with 0.998, 0.22 MJ/kg, and 0.66%, respectively. For comparison purposes, multilayer perceptron and radial basis function network were also used to predict GCV.
dc.identifier.doi10.28948/ngumuh.585596
dc.identifier.endpage1141
dc.identifier.issn2564-6605
dc.identifier.issue2
dc.identifier.startpage1129
dc.identifier.trdizinid417821
dc.identifier.urihttps://doi.org/10.28948/ngumuh.585596
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/417821
dc.identifier.urihttps://hdl.handle.net/20.500.14669/636
dc.identifier.volume9
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofNiğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectMaden İşletme ve Cevher Hazırlama
dc.subjectJeokimya ve Jeofizik
dc.titlePREDICTION OF GROSS CALORIFIC VALUE OF COAL FROM PROXIMATEAND ULTIMATE ANALYSIS VARIABLES USING SUPPORT VECTORMACHINES WITH FEATURE SELECTION
dc.typeArticle

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