Intelligent Supervisory Control of an Industrial Rotary Kiln

  • Esko Juuso
  • Mika Järvensivu
  • Olli Ahava
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 71)


The process industries face considerable control challenges, especially in terms of the consistent production of high quality products, more efficient use of energy and raw materials, and stable operation under different conditions. Flexibility and fast reactions to market situations and changing operating conditions are necessary. Interactions between control loops make multivariable systems non-linear. The important quality variables can be estimated only from other measured variables. The physical limitations of actuators must be taken into account. The different time delays depend greatly on operating conditions and can dramatically limit performance and even destabilise the closed loop system. Uncertainty is an unavoidable part of process control in real world applications. These demands cannot be met by traditional control techniques only, and several methodologies have therefore been developed to extend the applicability of control systems (Juuso 1999a).


Burnt Lime Closed Loop Mode Change Operating Condition Model Predictive Control Approach Lime Kiln 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bailey, R.M., Willison, T.R. (1986): Supervisory control for lime kilns slashes operating costs by up to 20 %. Pulp Paper 60 (2), 100–105Google Scholar
  2. Barreto, A.G. (1997): Lime kiln hybrid control system. In: Proc. IEEE Workshop on Dynamic Modeling Control Applications for Industry. NewYork, USA, pp. 44–50Google Scholar
  3. Brown, K., Rastogi, L. (1983): Mathematical modeling and computer control of lime kilns.In: Tappi Pulping Conference Proceedings. Tappi Press, Atlanta, pp. 585–592Google Scholar
  4. Charos, G.N., Taylor, R.A., Arkun, Y. (1991): Model predictive control of an industrial lime kiln. Tappi Journal 74 (2), 203–211Google Scholar
  5. Crowther, C., Blevins, T., Bums, D. (1987): A lime kiln control strategy to maximize efficiency and energy management. Appita Journal 40 (1), pp. 29–32Google Scholar
  6. Dekkiche, E.A. (1991): Advanced kiln control system. Zement-Kalk-Gips 44 (6), 286–290Google Scholar
  7. Elsilä M:, Leiviskä K., Nettamo K., Pulkkinen T. (1979): Computer control of causticization and lime kiln area is possible, Pulp Paper 53(12), 152–155,159Google Scholar
  8. Hall, M.B. (1993): Kiln stabilization and control - a COMDALEIC expert system approach. In: 35th IEEE Cement Industry Technical Conference Proceedings, Toronto, pp. 201 —218Google Scholar
  9. Haspel, D.W., Taylor, R., Brooks, J. (1991): A new perspective on the cement making process through LINKman. In: Proc. Cemtech Conference. Prague, Czechoslovakia, pp. 1. 3. 1–12Google Scholar
  10. S. Hagemoen, A. (1993): An expert system application for lime kiln automation, IEEE 6 pp. 91–97Google Scholar
  11. Juuso, E.K. (1999a): Fuzzy Control in Process Industry: The Linguistic Equation Approach. In: Verbruggen, H.B, Zimmermann, H.-J., Babuska, R. (eds.) Fuzzy Algorithms for Control, International Series in Intelligent Technologies. Kluwer, Boston, pp. 243–300CrossRefGoogle Scholar
  12. Juuso, E. K. (1999b): Intelligent Dynamic Simulation of a Lime Kiln with Linguistic Equations. In: ESM’99: Modelling and Simulation: A Tool for the Next Millennium, 13th European Simulation Multiconference Proceedings. SCS, Delft, The Netherlands, 1999. Volume 2, pp. 395–400Google Scholar
  13. Juuso, E., Ahola, T., Leiviskä, K. (1996): Fuzzy logic in lime kiln control. Proceedings of TOOLMET ‘86 Symposium — Tool Environments and Development Methods for Intelligent Systems. University of Oulu, Oulu, Finland, 111–119Google Scholar
  14. Juuso, E. K., Balsa, P., Valenzuela, L. (1998): Multilevel Linguistic Equation Controller Applied to a 1 MW Solar Power Plant. In: Proceedings of the 1998 American Control Conference — ACC’98, Philadelphia, PA, June 24–26, 1998, volume 6, pp. 3891–3895Google Scholar
  15. Järvensivu, M., Kivivasara, J. Saari, K. (1999a): A field survey of TRS emissions from a lime kiln. Pulp Pap. Canada 99 (11), 111–115Google Scholar
  16. Järvensivu, M., Jämsä-Jounela, S.L. (1999c): Intelligent control system for the lime kiln. Proc. 14th IFAC Word Conference, Beijing, China, 361–366Google Scholar
  17. Järvensivu M. and Seaworth B. (1998): Neural network model used for quality prediction and control. In: IFAC 1998 International Symposium on Artificial Intelligence in Real-Time Control (AIRTC’98). IFAC, Arizona, USAGoogle Scholar
  18. Järvensivu, M., Juuso, E., & Ahava, O. (2000): Intelligent supervisory-level control of industrial processes. Paperi ja Puu 82 (6), 386–391Google Scholar
  19. Mcllwain, J.A. (1992): Kiln control. Pulp and Paper Canada 93 (11), 34–37Google Scholar
  20. Nilson, L. (1997): Skoghall brings cement logic to causticizing. Pulp Pap. Eur. 2 (10), 24–27Google Scholar
  21. Penttinen, R. (1994): Fuzzy control of the lime kiln. Thesis for Master of Science (in Finnish). Control Engineering Laboratory, University of OuluGoogle Scholar
  22. Ruotsalainen, J. (1994): Control of the pulp mill chemical recovery circuit. Thesis for Licentiate in Technology. Control Engineering Laboratory, University of OuluGoogle Scholar
  23. Ostergaard, J.-J. (1993): Fuzzy control of cement kilns a retrospective summary. In: Zimmermann, H.-J. (ed.): Proceedings of EUFIT’93–First European Congress on Intelligent Techniques and Soft Computing. ELITE Foundation, Aachen, p. 552–562Google Scholar
  24. Ribeiro, B.M., Correia, A.D. (1995): Lime kiln simulation and control by neural networks. In: Neural Networks for Chemical Engineers. Elsevier Science Ltd., Amsterdam, 163–191Google Scholar
  25. Scheuer, A., & Principato, M. (1995): Experience with the PYROEXPERT kiln control system at the Leimen cement, ZKG International 48 (9), 464–471Google Scholar
  26. Sievola, H. (1999): Hot end temperature control of a lime kiln based on linguistic equations. Thesis for Master of Science (in Finnish). Control Engineering Laboratory, University of OuluGoogle Scholar
  27. Smith, D.B. & Aggarwal, P. (1998): Advanced lime kiln control: Operating results from a mill installation. In: Proc. Tappi Process Control, Electrical & Information Conference). Tappi Press, Atlanta, USA, pp. 347–353Google Scholar
  28. Smith, D.B., Edwards, L. (1991): Dynamic mathematical model of a rotary lime kiln. In: TAPPI Engineering Conference Proceedings. Tappi Press, Atlanta, USA, pp. 447–455Google Scholar
  29. Sutinen, R. (1981): Causticizing plant and lime kiln computer control. Pulp Pap. Canada 82 (8), 90–95Google Scholar
  30. Uronen, P., Aurasmaa, H. (1979): Modelling and simulation of causticization plant and lime kiln. Pulp Pap. Canada 80 (6), T162 - T165Google Scholar
  31. Uronen P., Leiviskä K., Aurasmaa H. (1975): Simulation study of the lime kiln, In: Hamza M. (ed.): Proceedings of Simulation’ 75, An International Symposium and Short Course, Zürich, p. 222–227Google Scholar
  32. Uronen, P. and Leiviskä, K. (1989): New topics in lime kiln control. Pulp and Paper Canada 90 (11), 113–117Google Scholar
  33. Valiquette, J. (1999): Practical aspects of model-predictive control implementation on an industrial lime kiln. Tappi J. 82 (5) 130–136Google Scholar
  34. Yang, B., Yi, L. & Shouring, Q. (1997): A rule based cement kiln control system using neural networks. In: Proc. On IEEE International Conference on Intelligent Processing, Beijing, China Systems, vol. 1, pp. 493–497Google Scholar
  35. Zanovello, R., Budman, H. (1999): Model predictive control with soft constraints with application to lime kiln control. Computers and Chemical Engineering 23 (6), 791–806CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Esko Juuso
    • 1
  • Mika Järvensivu
    • 2
  • Olli Ahava
    • 3
  1. 1.Control Engineering LaboratoryUniversity of OuluFinland
  2. 2.Pronyx Control SoftwareHelsinkiFinland
  3. 3.UPM-KymmenePietarsaariFinland

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