Darja Kavšek (Author), Adriána Bednárová (Author), Miša Biro (Author), Roman Kranvogl (Author), Darinka Brodnjak-Vončina (Author), Ernest Beinrohr (Author)

Abstract

Chemical composition of Slovenian coal has been characterised in terms of proximate and ultimate analyses and the relations among the chemical descriptors and the higher heating value (HHV) examined using correlation analysis and multivariate data analysis methods. The proximate analysis descriptors were used to predict HHV using multiple linear regression (MLR) and artificial neural network (ANN) methods. An attempt has been made to select the model with the optimal number of predictor variables. According to the adjusted multiple coefficient of determination in the MLR model, and alternatively, according to sensitivity analysis in ANN developing, two descriptors were evaluated by both methods as optimal predictors: fixed carbonand volatile matter. The performances of MLR and ANN when modelling HHV were comparable; the mean relative difference between the actual and calculated HHV values in the training data was 1.11% for MLR and 0.91% for ANN. The predictive ability of the models was evaluated by an external validation data set; the mean relative difference between the actual and predicted HHV values was 1.39% in MLR and 1.47% in ANN. Thus, the developed models could be appropriately used to calculate HHV.

Keywords

Slovenian Coal;higher heating value;HHV;regression;artificial neural network;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UM FKKT - Faculty of Chemistry and Chemical Engineering
UDC: 66:004.5
COBISS: 17017878 Link will open in a new window
ISSN: 1895-1066
Views: 28525
Downloads: 332
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Other data

Secondary language: Slovenian
Secondary keywords: slovenski premog;bruto kalorična vrednost;BKV;regresija;umetne nevronske mreže;
URN: URN:SI:UM:
Type (COBISS): Scientific work
Pages: str. 1481-1491
Volume: ǂVol. ǂ11
Issue: ǂno. ǂ9
Chronology: 2013
DOI: 10.2478/s11532-013-0280-x
ID: 9599411