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

Povzetek

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.

Ključne besede

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

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UM FKKT - Fakulteta za kemijo in kemijsko tehnologijo
UDK: 66:004.5
COBISS: 17017878 Povezava se bo odprla v novem oknu
ISSN: 1895-1066
Št. ogledov: 28525
Št. prenosov: 332
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: slovenski premog;bruto kalorična vrednost;BKV;regresija;umetne nevronske mreže;
URN: URN:SI:UM:
Vrsta dela (COBISS): Znanstveno delo
Strani: str. 1481-1491
Letnik: ǂVol. ǂ11
Zvezek: ǂno. ǂ9
Čas izdaje: 2013
DOI: 10.2478/s11532-013-0280-x
ID: 9599411