Abstract
The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV.
| Original language | English |
|---|---|
| Pages (from-to) | 9-19 |
| Number of pages | 11 |
| Journal | International Journal of Coal Preparation and Utilization |
| Volume | 31 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2011 |
Keywords
- ANFIS
- Gross calorific value
- Mineral matter
- Petrography
- Regression
ASJC Scopus subject areas
- General Chemical Engineering
- Fuel Technology
- Geotechnical Engineering and Engineering Geology
- Energy Engineering and Power Technology
- Mechanical Engineering
Fingerprint
Dive into the research topics of 'Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver