Table of Contents
Journal of Fuels
Volume 2014 (2014), Article ID 392698, 11 pages
Research Article

Multivariable Regression and Adaptive Neurofuzzy Inference System Predictions of Ash Fusion Temperatures Using Ash Chemical Composition of US Coals

1Department of Mining Engineering, Tehran Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
2Young Researchers and Elites Club, Tehran Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran

Received 12 September 2013; Accepted 16 April 2014; Published 22 May 2014

Academic Editor: Kaustubha Mohanty

Copyright © 2014 Shahab Karimi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this study, the effects of ratios of dolomite, base/acid, silica, SiO2/Al2O3, and Fe2O3/CaO, base and acid oxides, and 11 oxides (SiO2, Al2O3, CaO, MgO, MnO, Na2O, K2O, Fe2O3, TiO2, P2O5, and SO3) on ash fusion temperatures for 1040 US coal samples from 12 states were evaluated using regression and adaptive neurofuzzy inference system (ANFIS) methods. Different combinations of independent variables were examined to predict ash fusion temperatures in the multivariable procedure. The combination of the “11 oxides + (Base/Acid) + Silica ratio” was the best predictor. Correlation coefficients ( ) of 0.891, 0.917, and 0.94 were achieved using nonlinear equations for the prediction of initial deformation temperature (IDT), softening temperature (ST), and fluid temperature (FT), respectively. The mentioned “best predictor” was used as input to the ANFIS system as well, and the correlation coefficients ( ) of the prediction were enhanced to 0.97, 0.98, and 0.99 for IDT, ST, and FT, respectively. The prediction precision that was achieved in this work exceeded that reported in previously published works.