PREDICTION OF ASH FUSION TEMPERATURES (AFTs) USING ASH CHEMICAL
COMPOSITION OF US COAL SAMPLES BY MEANS OF
MATHEMATICAL METHODS
Mahdi Samanipour 1, Seyed Hamid Hosseini 2
1- PhD Student, Islamic Azad University, Science and Research branch, Faculty of Engineering ,
Mineral processing Engineering department, Tehran, Iran, m.samanipour@gmail.com
2- Assistant Professor, Islamic Azad University, South Tehran branch, Faculty of Engineering ,
Mining Engineering department, Tehran, Iran, hoseini@azad.ac.ir
ABSTRACT
In this study, the relationship between coal ash analysis and ash fusion temperatures (AFTs) of 855 US coal
samples from 3 different states were evaluated using mathematical methods. The results of all three ash
fusion temperatures (initial deformation temperature (IDT), softening temperature (ST), and fluid
temperature (FT)) obtained by univariate regression showed that the amount of CaO, MnO, Na2O, Fe2O3,
SO3, Base and base to acid ratio have had a negative effect. On the other hand, the amount of SiO 2, Al2O3,
K2O, TiO2, Acid and silica ratio have had a positive effect during the prediction. The maximum correlation
coefficient of 0.90 was obtained using univariate regression for acid with a cubic equation.
Different combinations of independent variables were examined to predict melting points. According to
optimum results, the correlation coefficients (R2) of the prediction using nonlinear multivariate analysis were
enhanced to 0.92, 0.94, and 0.95 for IDT, ST and FT, respectively. The correlation coefficient was increased
by processing of IDT to ST and ST to FT. The inputs were also analyzed using a Fuzzy-Neural network to
improve the results. The results showed the correlation coefficient (R 2) of the prediction was enhanced to
0.95, 0.96, and 0.97 for IDT, ST, and FT, respectively.
The prediction precision that was achieved using a Fuzzy-Neural network exceeded that obtained by nonlinear regression for prediction of AFT. The results demonstrated that mathematical models approximated
the measured values with high accuracy and the correlation coefficients of these magnitudes have not been
reported in previously published works.
KEYWORDS
Prediction, Ash fusion temperatures, Mathematical model, Fuzzy-Neural network, Univariate regression