Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS

S. Chehreh Chelgani, Brian Hart, William C. Grady, James C. Hower

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

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 languageEnglish
Pages (from-to)9-19
Number of pages11
JournalInternational Journal of Coal Preparation and Utilization
Volume31
Issue number1
DOIs
StatePublished - 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