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Estimation of gross calorific value based on coal analysis using regression and artificial neural networks
Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
Department of Mining Engineering, Science and Research Branch,Islamic Azad University.ORCID iD: 0000-0002-2265-6321
2009 (English)In: International Journal of Coal Geology, ISSN 0166-5162, Vol. 79, no 1-2, p. 49-54Article in journal (Refereed) Published
Abstract [en]

Relationships of ultimate and proximate analysis of 4540 US coal samples from 25 states with gross calorific value (GCV) have been investigated by regression and artificial neural networks (ANNs) methods. Three set of inputs: (a) volatile matter, ash and moisture (b) C, H, N, O, S and ash (c) C, H exclusive of moisture, N, O exclusive of moisture, S, moisture and ash were used for the prediction of GCV by regression and ANNs. The multivariable regression studies have shown that the model (c) is the most suitable estimator of GCV. Running of the best arranged ANNs structures for the models (a) to (c) and assessment of errors have shown that the ANNs are not better or much different from regression, as a common and understood technique, in the prediction of uncomplicated relationships between proximate and ultimate analysis and coal GCV.

Place, publisher, year, edition, pages
Elsevier, 2009. Vol. 79, no 1-2, p. 49-54
Keywords [en]
Coal, Proximate analysis, Ultimate analysis, Regression, Artificial neural networks
National Category
Mineral and Mine Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-72296DOI: 10.1016/j.coal.2009.04.002ISI: 000267645500005Scopus ID: 2-s2.0-67349254686OAI: oai:DiVA.org:ltu-72296DiVA, id: diva2:1272083
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2019-02-21

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Chelgani, Saeed Chehreh

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
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