Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
Modeling of gross calorific value based on coal properties by support vector regression method
University of Technology, Birjand, Iran.
University of Kentucky, Lexington, USA.
University of Michigan, Ann Arbor, USA.ORCID iD: 0000-0002-2265-6321
2017 (English)In: Modeling Earth Systems and Environment, ISSN 2363-6203, E-ISSN 2363-6211, Vol. 3, no 37Article in journal (Refereed) Published
Abstract [en]

Gross calorific value (GCV) is one the most important coal combustion parameters for power plants. Modeling of GCV based on coal properties could be a key for estimating the amount of coal consumption in the combustion system of various plants. In this study, support vector regression (SVR) as a powerful prediction method has been used to investigate relationships among coal sample properties with their GCVs for a wide range of records. Variable importance measurement by the SVR method throughout various coal analyses (proximate, ultimate, different sulfur types, and petrography) indicated that carbon, ash, moisture, and hydrogen contents are the most effective variables for the GCV prediction. Two models based on all variables and four the most effective ones are conducted. Outputs in the testing stage of both models verified that SVR can predict GCV quite satisfactorily where the correlations of determination (R2) for models was 0.99. Based on these results, development of a variable selection system among wide range of parameters, and also application of an accurate predictive model such as SVR, can potentially be further employed as a reliable tool for evaluation of complex relationships in earth and energy problems.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 3, no 37
Keywords [en]
Combustion, Support vector regression, Variable importance measurement, Proximate, Ultimate, Petrography
Identifiers
URN: urn:nbn:se:ltu:diva-72250DOI: 10.1007/s40808-017-0270-7OAI: oai:DiVA.org:ltu-72250DiVA, id: diva2:1281007
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-01-21Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Chelgani, Saeed Chehreh

Search in DiVA

By author/editor
Chelgani, Saeed Chehreh
In the same journal
Modeling Earth Systems and Environment

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
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