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Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network
Surface Science Western, University of Western Ontario, Canada.ORCID-id: 0000-0002-2265-6321
Department of Mining Engineering, Science and Research Branch,Islamic Azad University, Iran.
Center for Applied Energy Research, University of Kentucky, USA.
2010 (engelsk)Inngår i: International Journal of Coal Geology, ISSN 0166-5162, Vol. 83, nr 1, s. 31-34Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Results from ultimate analysis, proximate and petrographic analyses of a wide range of Kentucky coal samples were used to predict coal rank parameters (vitrinite maximum reflectance (Rmax) and gross calorific value (GCV)) using multivariable regression and artificial neural network (ANN) methods. Volatile matter, carbon, total sulfur, hydrogen and oxygen were used to predict both Rmax and GCV by regression and ANN. Multivariable regression equations to predict Rmax and GCV showed R2 = 0.77 and 0.69, respectively. Results from the ANN method with a 2–5–4–2 arrangement that simultaneously predicts GCV and Rmax showed R2 values of 0.84 and 0.90, respectively, for an independent test data set. The artificial neural network method can be appropriately used to predict Rmax and GCV when regression results do not have high accuracy.

sted, utgiver, år, opplag, sider
2010. Vol. 83, nr 1, s. 31-34
Emneord [en]
Vitrinite maximum reflectance, Gross calorific value, Regression, Artificial neural network
HSV kategori
Identifikatorer
URN: urn:nbn:se:ltu:diva-72294DOI: 10.1016/j.coal.2010.03.004ISI: 000279489900004Scopus ID: 2-s2.0-77953620859OAI: oai:DiVA.org:ltu-72294DiVA, id: diva2:1272064
Tilgjengelig fra: 2018-12-18 Laget: 2018-12-18 Sist oppdatert: 2020-03-09bibliografisk kontrollert

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Chelgani, Saeed Chehreh

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