Prediction of gross calorific value of coal based on proximate analysis using multiple linear regression and artificial neural networks

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dc.authoridAcikkar, Mustafa/0000-0001-8888-4987
dc.authoridSivrikaya, Osman/0000-0001-8146-5747
dc.contributor.authorAcikkar, Mustafa
dc.contributor.authorSivrikaya, Osman
dc.date.accessioned2025-01-06T17:37:00Z
dc.date.available2025-01-06T17:37:00Z
dc.date.issued2018
dc.description.abstractGross calorific value (GCV) of coal was predicted by using as-received basis proximate analysis data. Two main objectives of the study were to develop prediction models for GCV using proximate analysis variables and to reveal the distinct predictors of GCV. Multiple linear regression (MLR) and artifcial neural network (ANN) (multilayer perceptron MLP, general regression neural network GRNN, and radial basis function neural network RBFNN) methods were applied to the developed 11 models created by different combinations of the predictor variables. By conducting 10fold cross-validation, the prediction accuracy of the models has been tested by using R-2, RMSE, MAE, and MAPE. In this study, for the first time in the literature, for a single dataset, maximum number of coal samples were utilized and GRNN and RBFNN methods were used in GCV prediction based on proximate analysis. The results showed that moisture and ash are the most discriminative predictors of GCV and the developed RBFNN-based models produce high performance for GCV prediction. Additionally, performances of the regression methods, from the best to the worst, were RBFNN, GRNN, MLP, and MLR.
dc.identifier.doi10.3906/elk-1802-50
dc.identifier.endpage2552
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85054538749
dc.identifier.scopusqualityQ2
dc.identifier.startpage2541
dc.identifier.urihttps://doi.org/10.3906/elk-1802-50
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2074
dc.identifier.volume26
dc.identifier.wosWOS:000448109200031
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectCoal gross calorific value
dc.subjectregression
dc.subjectmultiple linear regression
dc.subjectmultilayer perceptron
dc.subjectgeneral regression neural network
dc.subjectradial basis function neural network
dc.titlePrediction of gross calorific value of coal based on proximate analysis using multiple linear regression and artificial neural networks
dc.typeArticle

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