Skip to main content
SpringerLink
Log in

Mining for Mining — Application of Data Mining Tools for Coal Postprocessing Modelling

  • Chapter
New Challenges for Intelligent Information and Database Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 351))

  • 840 Accesses

Abstract

This article deals with the problem of coal postprocessing modelling. The sediment filter centrifuge is the part of the postprocessing that is being modelled with several data mining techniques. In this paper the results of parametrical and nonparametrical models applications are described.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aleksa, H.: Condition and development trends of coal concentrates dewatering technology (in Polish). Mining Review (9), 44–50 (2005)

    Google Scholar 

  2. Aleksa, H., Dyduch, F., Piech, E.: Possibility of low ash coal suspends dewatering intensification with the usage of the sedimental filter centrifuge (in Polish). Mining Review (7-8), 41–45 (1994)

    Google Scholar 

  3. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proc. of the 5th Annu. Workshop on Comput. Learn. Theory, pp. 144–152 (1992)

    Google Scholar 

  4. Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A.J., Vapnik, V.N.: Support vector regression machines. In: Adv. in Neural Inf. Process. Syst. IX, pp. 155–161 (1997)

    Google Scholar 

  5. Dyduch, F., Aleksa, H.: Impact of hydraulic load of centrifuge and its technical parameters onto size of sorted grain. Prace Naukowe GIG, Mining and Environment 1, 43–51 (2008)

    Google Scholar 

  6. Epanechnikov, V.A.: Nonparametric Estimation of a Multivariate Probability Density. Theory of Probab. and its Appl. 14, 153–158 (1969)

    Article  Google Scholar 

  7. Fan, J., Gijbels, I.: Variable Bandwidth and Local Linear Regression Smoothers. Ann. of Stat. 20, 2008–2036 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gasser, T., Muller, H.G.: Estimating Regression Function and Their Derivatives by the Kernel Method. Scand. J. of Stat. 11, 171–185 (1984)

    MathSciNet  MATH  Google Scholar 

  9. Nadaraya, E.A.: On estimating regression. Theory of Probab. and its Appl. 9, 141–142 (1964)

    Article  Google Scholar 

  10. Powell, M.D.: Radial Basis Function Approximation to Polynomials. In: Numerical Analysis Proceedings, pp. 223–241 (1987)

    Google Scholar 

  11. Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Statistics and Computing 14(3), 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  12. Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, Boca Raton (1995)

    MATH  Google Scholar 

  13. Watson, G.S.: Smooth Regression Analysis. Sankhya - The Indian J. of Stat. 26, 359–372 (1964)

    MATH  Google Scholar 

  14. Wen, W.W.: An integrated fine coal preparation technology: the GranuFlow Process. Int. J. Miner. Process. 38, 254–265 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Central Mining Institute, Plac Gwarkow 1, 40-166, Katowice, Poland

    Marcin Michalak, Sebastian Iwaszenko & Krzysztof Wierzchowski

  2. Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Marcin Michalak

Authors
  1. Marcin Michalak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastian Iwaszenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krzysztof Wierzchowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Informatics , Wrocław University of Technology, Wybrzeże Wyspiańnskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Institute of Informatics , Wroclaw University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Bogdan Trawiński

  3. Department of Computer Engineering , Yeungnam University, Dae-Dong, 712-749, Gyeungsan, Korea

    Jason J. Jung

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Michalak, M., Iwaszenko, S., Wierzchowski, K. (2011). Mining for Mining — Application of Data Mining Tools for Coal Postprocessing Modelling. In: Nguyen, N.T., Trawiński, B., Jung, J.J. (eds) New Challenges for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19953-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19953-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19952-3

  • Online ISBN: 978-3-642-19953-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation