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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 (English)In: International Journal of Coal Geology, ISSN 0166-5162, Vol. 83, no 1, p. 31-34Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2010. Vol. 83, no 1, p. 31-34
Keywords [en]
Vitrinite maximum reflectance, Gross calorific value, Regression, Artificial neural network
National Category
Mineral and Mine Engineering
Identifiers
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
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2019-02-25

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Chelgani, Saeed Chehreh

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