Studies of relationships between Free Swelling Index (FSI) and coal quality by regression and Adaptive Neuro Fuzzy Inference System

Tayebi Khorami, M., Chelgani, S. Chehreh, Hower, James C. and Jorjani, E. (2011) Studies of relationships between Free Swelling Index (FSI) and coal quality by regression and Adaptive Neuro Fuzzy Inference System. International Journal of Coal Geology, 85 1: 65-71. doi:10.1016/j.coal.2010.09.011


Author Tayebi Khorami, M.
Chelgani, S. Chehreh
Hower, James C.
Jorjani, E.
Title Studies of relationships between Free Swelling Index (FSI) and coal quality by regression and Adaptive Neuro Fuzzy Inference System
Journal name International Journal of Coal Geology   Check publisher's open access policy
ISSN 0166-5162
1872-7840
Publication date 2011-01-01
Year available 2011
Sub-type Article (original research)
DOI 10.1016/j.coal.2010.09.011
Open Access Status Not Open Access
Volume 85
Issue 1
Start page 65
End page 71
Total pages 7
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Formatted abstract
The results of proximate, ultimate, and petrographic analysis for a wide range of Kentucky coal samples were used to predict Free Swelling Index (FSI) using multivariable regression and Adaptive Neuro Fuzzy Inference System (ANFIS). Three different input sets: (a) moisture, ash, and volatile matter; (b) carbon, hydrogen, nitrogen, oxygen, sulfur, and mineral matter; and (c) group-maceral analysis, mineral matter, moisture, sulfur, and Rmax were applied for both methods. Non-linear regression achieved the correlation coefficients (R2) of 0.38, 0.49, and 0.70 for input sets (a), (b), and (c), respectively. By using the same input sets, ANFIS predicted FSI with higher R2 of 0.46, 0.82 and 0.95, respectively. Results show that input set (c) is the best predictor of FSI in both prediction methods, and ANFIS significantly can be used to predict FSI when regression results do not have appropriate accuracy.
Keyword Energy & Fuels
Geosciences, Multidisciplinary
Energy & Fuels
Geology
ENERGY & FUELS
GEOSCIENCES, MULTIDISCIPLINARY
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Sustainable Minerals Institute Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 20 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Fri, 02 May 2014, 02:07:43 EST by Maedeh Tayebi-Khorami on behalf of Julius Kruttschnitt Mineral Research Centre