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Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods
Surface Science Western, Research Park, University of Western Ontario, Canada.ORCID iD: 0000-0002-2265-6321
Biomedical Engineering Graduate Program, University of Western Ontario, Canada.
2013 (English)In: Fuel Processing Technology, ISSN 0378-3820, Vol. 110, p. 79-85Article in journal (Refereed) Published
Abstract [en]

This study investigates the effects of proximate, ultimate and elemental analysis for Afghan coal samples on Hardgrove grindability index (HGI), Gross calorific value (GCV), and Ash fusion temperatures (AFTs) by using multivariable regression (MR) and Adaptive neuro-fuzzy inference system (ANFIS) to increase information about the properties of the Afghan coal. Statistical modeling (MR, and ANFIS) indicated that coal parameters (HGI, GCV, AFTs) can be predicted with high accuracy, where GCV, AFTs, and HGI were estimated by R2 = 0.99, 0.95, and 0.94, respectively. The small difference between the estimated parameters and their actual values shows that these accurate results can be also applied to estimate coal properties in other coal resources of Afghanistan.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 110, p. 79-85
Keywords [en]
Afghanistan, Hardgrove grindingability index, Gross calorific value, Ash fusion temperature, Regression, ANFIS
National Category
Mineral and Mine Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-72278DOI: 10.1016/j.fuproc.2012.11.005ISI: 000316586800011Scopus ID: 2-s2.0-84871108270OAI: oai:DiVA.org:ltu-72278DiVA, id: diva2:1272080
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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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf