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Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network
Surface Science Western, University of Western Ontario, Canada.ORCID iD: 0000-0002-2265-6321
Center for Applied Energy Research, University of Kentucky, USA.
Surface Science Western, University of Western Ontario, Canada.
2011 (English)In: Fuel Processing Technology, ISSN 0378-3820, Vol. 92, no 3, p. 349-355Article in journal (Refereed) Published
Abstract [en]

The effects of proximate, ultimate and elemental analysis for a wide range of American coal samples on Free-swelling Index (FSI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that variables of ultimate analysis are better predictors than those from proximate analysis. The non linear multivariable regression, correlation coefficients (R2) from ultimate analysis inputs was 0.71, and for proximate analysis input variables was 0.49. With the same input sets, feed-forward artificial neural network (FANN) procedures improved accuracy of predicted FSI with R2 = 0.89, and 0.94 for proximate and ultimate analyses, respectively. The ANN based prediction method, as a first report, shows FSI is a predictable variable, and ANN can be further employed as a reliable and accurate method in the free-swelling index prediction.

Place, publisher, year, edition, pages
Elsevier, 2011. Vol. 92, no 3, p. 349-355
Keywords [en]
Free-swelling index (FSI), Proximate and ultimate analysis, Regression, Artificial neural network
National Category
Mineral and Mine Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-72286DOI: 10.1016/j.fuproc.2010.09.027ISI: 000287428900009Scopus ID: 2-s2.0-78751643285OAI: oai:DiVA.org:ltu-72286DiVA, id: diva2:1272070
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
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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