Table of Contents
Journal of Fuels
Volume 2014, Article ID 392698, 11 pages
http://dx.doi.org/10.1155/2014/392698
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.

Linked References

  1. B. G. Miller, Coal Energy Systems, Elsevier, 2005.
  2. American Society for Testing and Materials Standards, Standard Test Method for Fusibility of Coal and Coke Ash, D1857-03, ASTM, 2003.
  3. B. Caylor, “Coal’s role in the national energy plan,” Energia, vol. 13, no. 3, article 5, 2001. View at Google Scholar
  4. E. C. Winegartner and B. T. Rhodes, “An empirical study of the relation of chemical properties to ash fusion temperatures,” Journal of Engineering for Gas Turbines and Power, vol. 97, no. 3, pp. 395–406, 1975. View at Google Scholar · View at Scopus
  5. F. E. Huggins, D. A. Kosmack, and G. P. Huffman, “Correlation between ash-fusion temperatures and ternary equilibrium phase diagrams,” Fuel, vol. 60, no. 7, pp. 577–584, 1981. View at Google Scholar · View at Scopus
  6. R. W. Bryers and T. E. Taylor, “An examination of the relationship between ash chemistry and ash fusion temperatures in various coal size and gravity fractions using polynomial regression analysis,” ASME Paper 75-WA/CD-3, 1975. View at Google Scholar
  7. V. R. Gray, “Prediction of ash fusion temperature from ash composition for some New Zealand coals,” Fuel, vol. 66, no. 9, pp. 1230–1239, 1987. View at Google Scholar · View at Scopus
  8. M. Seggiani, “Empirical correlations of the ash fusion temperatures and temperature of critical viscosity for coal and biomass ashes,” Fuel, vol. 78, no. 9, pp. 1121–1125, 1999. View at Publisher · View at Google Scholar · View at Scopus
  9. S. A. Lolja, H. Haxhi, R. Dhimitri, S. Drushku, and A. Malja, “Correlation between ash fusion temperatures and chemical composition in Albanian coal ashes,” Fuel, vol. 81, no. 17, pp. 2257–2261, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. E. Jak, “Prediction of coal ash fusion temperatures with the F*A*C*T thermodynamic computer package,” Fuel, vol. 81, no. 13, pp. 1655–1668, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Özbayoğlu and M. E. Özbayoğlu, “A new approach for the prediction of ash fusion temperatures: a case study using Turkish lignites,” Fuel, vol. 85, no. 4, pp. 545–552, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. P. Liu, M. G. Wu, and J. X. Qian, “Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network,” Thermochimica Acta, vol. 454, no. 1, pp. 64–68, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. SPSS, “Version 13,” SPSS Inc., Help Files, 2004.
  14. R. Babuska, Fuzzy Modeling for Control, Kluwer Academic, Boston, Mass, USA, 1998.
  15. J. Jantzen, “Neurofuzzy modelling,” Tech. Rep. 98-H-874, Department of Automation, Technical University of Denmark, 1998. View at Google Scholar
  16. 2009, http://en.wikipedia.org/wiki/Neuro-fuzzy.
  17. J.-S. R. Jang and C.-T. Sun, “Neuro-fuzzy modeling and control,” Proceedings of the IEEE, vol. 83, no. 3, pp. 378–406, 1995. View at Publisher · View at Google Scholar · View at Scopus