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
Prediction of specific gravity of Afghan coal based on conventional coal properties by stepwise regression and random forest
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-2265-6321
2019 (English)In: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, ISSN 1556-7036, E-ISSN 1556-7230Article in journal (Refereed) Epub ahead of print
Abstract [en]

Coal can be considered as the main fuel for electricity generation in Afghanistan. However, there is a quite limited data available about the overall quality, distribution, and character of coals in Afghanistan. Specific gravity (S.G) of coal as a key factor can be used for the estimation of potential tonnage production and be a fundamental parameter for the selection of coal washery process method. However, there is no investigation which comprehensively explores relationships between S.G and coal properties. In this investigation, the potential of S.G prediction based on conventional properties for Afghan coal samples was explored by stepwise regression and random forest. Pearson correlation (r) and variable importance measurement (VIM) of random forest (RF) were applied to select the most effective variables among conventional parameters for the S.G prediction. Results of VIM indicated that ash and carbon content of coal samples had the highest importance for the S.G prediction. Stepwise regression and RF models were developed based on these two coal variables. Testing the generated models indicated that S.G of Afghan coals can quite accurately predict by these models (R2 > 0.90). Modeling outcomes showed that the highest S.G (S.G > 2) for Afghan coal occurred when ash was higher than 40% and carbon was lower than 30%.

Place, publisher, year, edition, pages
Taylor & Francis, 2019.
Keywords [en]
Proximate, ultimate, ash, carbon, coal production, electricity, specific gravity
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-76104DOI: 10.1080/15567036.2019.1670288ISI: 000487244600001OAI: oai:DiVA.org:ltu-76104DiVA, id: diva2:1354020
Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-10-10

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
By organisation
Minerals and Metallurgical Engineering
In the same journal
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
Metallurgy and Metallic Materials

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1 hits
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