Advertisement

An Evolutionary Optimization Approach for Bulk Material Blending Systems

  • Michael P. Cipold
  • Pradyumn Kumar Shukla
  • Claus C. Bachmann
  • Kaibin Bao
  • Hartmut Schmeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)

Abstract

Bulk material blending systems still mostly implement static and non-reactive material blending methods like the well-known Chevron stacking. The optimization potential in the existing systems which can be made available using quality analyzing methods as online X-ray fluorescence measurement is inspected in detail in this paper using a multi-objective optimization approach based on steady state evolutionary algorithms. We propose various Baldwinian and Lamarckian repair algorithms, test them on real world problem data and deliver optimized solutions which outperform the standard techniques.

Keywords

Bulk Material Blending Multi-objective Evolutionary Algorithms Chevron Stacking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bond, J., Coursaux, R., Worthington, R.: Blending systems and control technologies for cement raw materials. IEEE Industry Applications Magazine, 49–59 (2000)Google Scholar
  2. 2.
    Laurila, M.J., Bachmann, C.C.: X-ray fluorescence measuring system and methods for trace elements. US Patent US 2004/0240606 (2004)Google Scholar
  3. 3.
    Kumral, M.: Bed blending design incorporating multiple regression modelling and genetic algorithms. International Journal of Surface Mining, Reclamation and Environment 17, 98–112 (2003)CrossRefGoogle Scholar
  4. 4.
    Pavloudakis, F., Agioutantis, Z.: Simulation of bulk solids blending in longitudinal stockpiles. Journal of the South African Institute of Mining and Metallurgy 106, 229–237 (2006)Google Scholar
  5. 5.
    Fischer, A., Shukla, P.K.: A Levenberg-Marquardt algorithm for unconstrained multicriteria optimization. Oper. Res. Lett. 36(5), 643–646 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley (2001)Google Scholar
  7. 7.
    Durillo, J.J., Nebro, A.J., Luna, F., Alba, E.: On the Effect of the Steady-State Selection Scheme in Multi-Objective Genetic Algorithms. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 183–197. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Deb, K., Gupta, S.: Understanding knee points in bicriteria problems and their implications as preferred solution principles. Engineering Optimization 43(11), 1175–1204 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fonseca, C.M., Guerreiro, A.P., López-Ibáñez, M., Paquete, L.: On the Computation of the Empirical Attainment Function. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 106–120. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Shukla, P.K., Hirsch, C., Schmeck, H.: A Framework for Incorporating Trade-Off Information Using Multi-Objective Evolutionary Algorithms. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI, Part II. LNCS, vol. 6239, pp. 131–140. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Shukla, P.K., Hirsch, C., Schmeck, H.: Towards a Deeper Understanding of Trade-offs Using Multi-objective Evolutionary Algorithms. In: Di Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., de Vega, F.F., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Langdon, W.B., Merelo-Guervós, J.J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Togelius, J., Urquhart, N., Uyar, A.Ş., Yannakakis, G.N. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 396–405. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michael P. Cipold
    • 1
    • 2
  • Pradyumn Kumar Shukla
    • 1
  • Claus C. Bachmann
    • 2
  • Kaibin Bao
    • 1
  • Hartmut Schmeck
    • 1
  1. 1.Institute AIFBKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.J&C Bachmann GmbHBad WildbadGermany

Personalised recommendations