Intelligent Two-Level Optimization and Model Predictive Control of Degrading Plants

  • Mincho Hadjiski
  • Alexandra Grancharova
  • Kosta Boshnakov
Part of the Studies in Computational Intelligence book series (SCI, volume 756)


The model predictive control methodology is very powerful for the design of hierarchical multilayer control systems, i.e. systems which can be used either to control plants characterized by significantly different dynamics or to use different models of the same plant with the aim to optimize a number of criteria. Here, a two-level structure for control and optimization of degradation type of plants is proposed, which uses plant models in the “fast” and in the “slow” time scales and incorporates case-based reasoning. In particular, the plantwide optimization of a Peirce-Smith converter is considered, which is a typical example of a plant with degrading performance. The number of blocked tuyeres is optimized in order to achieve an efficient long-term operation.


  1. 1.
    Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M.: Constrained model predictive control: Stability and optimality. Automatica 36, 789–814 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Eng. Pract. 11, 733–764 (2003)CrossRefGoogle Scholar
  3. 3.
    Scattolini, R.: Architectures for distributed and hierarchical model predictive control—A review. J. Process Control 19, 723–731 (2009)CrossRefGoogle Scholar
  4. 4.
    Backx, T., Bosgra, O., Marquardt, W.: Integration of model predictive control and optimization of processes. In: Proceedings of IFAC Symposium on Advanced Control of Chemical Processes, Pisa, Italy, pp. 249–260 (2000)Google Scholar
  5. 5.
    Kadam, J.V., Marquardt, W., Schlegel, M., Backx, T., Bosgra, O.H., Brouwer, P.J., Dünnebier, G., van Hessem, D., Tiagounov, A., de Wolf, S.: Towards integrated dynamic real-time optimization and control of industrial processes. In: Proceedings of the Foundations of Computer-Aided Process Operations (FOCAPO2003), pp. 593–596 (2003)Google Scholar
  6. 6.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. Proc. AICOM 7, 39–59 (1994)Google Scholar
  7. 7.
    Kaster, D., Medeiros, C., Rocha, H.: Supporting modeling and problem solving from precedent experiences: the role of workflows and case-based reasoning. Environ. Modell. Softw. 20, 689–704 (2005)CrossRefGoogle Scholar
  8. 8.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann Publishers, San Mateo, CA (1993)CrossRefzbMATHGoogle Scholar
  9. 9.
    Pal, S., Shin, S.: Foundation of Soft Case-Based Reasoning. Wiley (2004)Google Scholar
  10. 10.
    Recèo-Garcia, J.A., Diaz-Agudo, B., Sanches-Ruiz, A.A., Gonzales-Calero, P.A.: Lessons Learned in the Development of a CBR Framework, Expert Update, vol. 10, no. 1 (2010)Google Scholar
  11. 11.
    Case-Based Reasoning Framework jCOLIBRI.
  12. 12.
  13. 13.
    Component Architecture Technology for Developing CBR systems, CAT-CBR.
  14. 14.
    Zilles, L.: MyCBR Tutorial, myCBR Project (2009)Google Scholar
  15. 15.
  16. 16.
  17. 17.
    Sanchez-Ruiz-Granados, A.A., Recio-Garcia, J.A., Diaz-Agudo, B., Gonzalez-Calero, P.A.: Case Structures in jCOLIBRI. In: 24th SGAI International Conference on Inovative Technologies, UK (2005)Google Scholar
  18. 18.
    Mitra, R., Basak, J.: Methods of case adaptation: A survey. Int. J. Intell. Syst. 20(6), 627–645 (2005)CrossRefzbMATHGoogle Scholar
  19. 19.
    Richter, M.M., Weber, R.: Case-Based Reasoning. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Ligeza, A.: Logical Foundations for Rule-Based Systems. Series: Studies in Computational Intelligence, vol. 11. Springer, Berlin, Heidelberg (2006)zbMATHGoogle Scholar
  21. 21.
    Bard, J.F.: Practical Bilevel Optimization: Applications and Algorithms, Series: Nonconvex Optimization and Its Applications, vol. 30. Springer, US (1998)zbMATHGoogle Scholar
  22. 22.
    Colson, B., Marcotte, P., Savard, G.: An overview of bilevel optimization. Ann. Oper. Res. 153, 235–256 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Grancharova, A., Johansen, T.A.: Explicit Nonlinear Model Predictive Control: Theory and Applications, LNCIS, vol. 429. Springer, Berlin, Heidelberg (2012)zbMATHGoogle Scholar
  24. 24.
    Giselsson, P.: Improved fast dual gradient methods for embedded model predictive control. In: Proceedings of the 19th World Congress, Cape Town, South Africa, pp. 2303–2309 (2014)Google Scholar
  25. 25.
    Kufoalor, D.K.M., Aaker, V., Johansen, T.A., Imsland, L., Eikrem, G.O.: Automatically generated embedded model predictive control: Moving an industrial PC-based MPC to an embedded platform. Optim. Control Appl. Methods 36, 705–727 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Ng, K.W., Kapusta, J.P.T., Harris, R., Wraich, A.E., Parra, R.: Modeling Peirce-Smith converter operating costs. J. Miner. Met. Mater. Soc., pp. 52–57 (2005)Google Scholar
  27. 27.
    Davenport, W.G., King, M., Schlesinger, M., Biswas, A.K.: Extractive Metallurgy of Copper, 4th edn. Pergamon, Elsevier, Oxford, UK (2002)Google Scholar
  28. 28.
    Hadjiski, M., Boshnakov, K.: Extended supervisory control of Peirce-Smith converter. Comptes rendus de l’Academie bulgare des Sciences 67(5), 705–714 (2014)Google Scholar
  29. 29.
    Hadjiski, M., Boshnakov, K.: Nonlinear hybrid control of copper converter. Comptes rendus de l’Academie bulgare des Sciences 67(6), 855–862 (2014)Google Scholar
  30. 30.
    Goni, C., Barbes, M., Bazan, V., Brandalez, E., Parra, R., Gonzales, L.: The mechanism of thermal spalling in the wear of the Peirce-Smith copper converter. J. Ceram. Soc. Jpn. 114(8), 672–675 (2006)CrossRefGoogle Scholar
  31. 31.
    Oprea, G., Lo, W., Trozynski, T., Rigby, J.: Corrosion of refractories in PSC. In: Kapusta, J., Warner, T.: International Peirce-Smith Converting Centenial. Wiley, California, USA (2010)Google Scholar
  32. 32.
    Song, Y., Peng, X., Dong, W., Hu, Z.: Data driven optimal decision making modeling for copper-matte converting process. J. Comput. Inf. Syst. 7(3), 754–761 (2011)Google Scholar
  33. 33.
    Boshnakov, K., Ginchev, T., Petkov, V., Mihailov, E.: Strategy for predictive maintenance of Peirce-Smith converters. In: Proceedings of the Technical University of Sofia, vol. 62, Book 1, pp. 345–354 (2012) (in Bulgarian)Google Scholar
  34. 34.
    Jones, D.R.: The direct global optimization algorithm. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization, vol. 1, pp. 431–440. Kluwer, Dordrecht (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mincho Hadjiski
    • 1
  • Alexandra Grancharova
    • 2
  • Kosta Boshnakov
    • 2
  1. 1.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of Industrial AutomationUniversity of Chemical Technology and MetallurgySofiaBulgaria

Personalised recommendations