Mathematical Model of Ecopyrogenesis Reactor with Fuzzy Parametrical Identification

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 342)


This paper presents the development of the mathematical model with fuzzy parametrical identification of the ecopyrogenesis (EPG) complex reactor as a temperature control object. The synthesis procedure of the fuzzy parametrical identification system of Mamdani type is presented. The analysis of computer simulation results in the form of static and dynamic characteristic graphs of the reactor as a temperature control object confirms the high adequacy of the developed model to the real processes. The developed mathematical model with fuzzy parametrical identification gives the opportunity to investigate the behavior of the temperature control object in steady and transient modes, in particular, to synthesize and adjust the temperature controller of the reactor temperature automatic control system (ACS).


Automatic Control System Pyrolysis Reactor Nonlinear Optimization Algorithm Parametrical Identification System Fuzzy Logic Inference 
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  1. 1.
    Markina, L.M.: Development of new energy-saving and environmental safety technology at the organic waste disposal by ecopyrogenesis. J. Collect. Works NUS 4(8) (2011) (in Ukrainian)Google Scholar
  2. 2.
    Štemberk, P., Lanska, N.: Heating system for curing concrete specimens under prescribed temperature. In: 13th Zittau Fuzzy Colloquium, Proceedings of East-West Fuzzy Colloquium, pp.82–88. Zittau, Hochschule Zittau, Goerlitz, Germany (2006)Google Scholar
  3. 3.
    Han, Z.X., Yan, C.H., Zhang, Z.: Study on robust control system of boiler steam temperature and analysis on its stability. J. Zhongguo Dianji Gongcheng Xuebao, Proc. Chin Soc. Electr. Eng. 30(8), 101–109 (2010)Google Scholar
  4. 4.
    Fiss, D., Wagenknecht, M., Hampel, R.: Modeling a boiling process under uncertainties. In: 19th Zittau Fuzzy Colloquium, Proceedings of East-West Fuzzy Colloquium, pp. 141–22. Zittau, Hochschule Zittau, Goerlitz, Germany (2012)Google Scholar
  5. 5.
    Chaikin, B.S., Mar’yanchik, G.E., Panov, E.M., Shaposhnikov, P.T., Vladimirov, V.A., Volovik, I.S., Makarevich, B.A.: State-of-the-art plants for drying and high-temperature heating of ladles. Int. J. Refract. Ind. Ceram. 47(5), 283–287 (2006)Google Scholar
  6. 6.
    Kondratenko, Y.P., Kozlov, O.V.: Fuzzy controllers in reactors control systems of multiloop pyrolysis plants. In: 19th Zittau Fuzzy Colloquium, Proceedings of East-West Fuzzy Colloquium, pp. 15–22. Zittau, Hochschule Zittau, Goerlitz, Germany (2012)Google Scholar
  7. 7.
    Rotach, V.Y.: Automatic control theory of heat and power processes. M. Energoatomizdat 296p, (1985) (in Russian)Google Scholar
  8. 8.
    Kondratenko, Y.P., Sydorenko, S., Kravchenko, D.: Fuzzy control systems of non-stationary plants with variable parameters. In: 12th Zittau East-West Fuzzy Colloquium, Conference Proceedings, Heft 84/2005, Nr.2090-2131, Wissenschaftliche Berichte, Institut fur Prozesstechnik, Prozessautomatisierung und Messtechnik, Zittau, pp.140–152 (2005)Google Scholar
  9. 9.
    Himmelblau, D.: Applied nonlinear programming (Trans. from English. a. Bihovckiy M.L. (ed.), M.: Mir), 534p., 1974 (in Russian)Google Scholar
  10. 10.
    Zadeh, L.A.: Information and control. Fuzzy Sets 8, 338–353 (1965)Google Scholar
  11. 11.
    Zimmermann, H.-J.: Fuzzy Set Theory—and Its Applications. Kluwer Academic Publishers, Boston (1992)Google Scholar
  12. 12.
    Zadeh, L.A.: The role of fuzzy logic in modeling, identification and control, modeling identification and control. Model. Identif. Control 15(3), 191–203 (1994)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modeling and Control. John Wiley, New York (1994)Google Scholar
  14. 14.
    Hampel, R., Wagenknecht, M., Chaker, N.: Fuzzy Control: Theory and Practice. Physika-Verlag, Heidelberg (2000)CrossRefMATHGoogle Scholar
  15. 15.
    Piegat, A.: Fuzzy Modeling and Control. Physica-Verlag, Heidelberg, New York (2001)CrossRefMATHGoogle Scholar
  16. 16.
    Jamshidi, M., Vadiee, N., Ross, T.J. (eds.): Fuzzy logic and control: software and hardware application. In: Jamshidi, M. (ed.) Prentice Hall Series on Environmental and Intelligent Manufacturing Systems, vol. 2. Prentice Hall, Englewood Cliffs (1993)Google Scholar
  17. 17.
    Kacprzyk, J., Yager, R.R., Zadrożny, S.: A fuzzy logic based approach to linguistic summaries of databases. Int. J. Appl. Math. Comput. Sci. 10(4), 813–834 (2000)MATHGoogle Scholar
  18. 18.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15(1) (1985)Google Scholar
  19. 19.
    Vachkov, G., Kiyota, Y., Komatsu, K.: Identification of dynamic cause-effect relations for systems performance evaluation. Appl. Sci. Soft Comput. Adv. Soft Comput. 24, 187–194 (2004)CrossRefGoogle Scholar
  20. 20.
    Yager, R.R., Filev, D.P.: Unified structure and parameter identification of fuzzy models. Sys. Man Cybern. 23(4) (1993)Google Scholar
  21. 21.
    Suna, Q., Li, R., Zhang, P.: Stable and optimal adaptive fuzzy control of complex systems using fuzzy dynamic model. J. Fuzzy Sets Syst. 133, 1–17 (2003)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Skrjanc, I.: Design of fuzzy model-based predictive control for a continuous stirred-tank reactor. In: 12th Zittau Fuzzy Colloquium, Proceedings of East-West Fuzzy Colloquium, pp.126–139. Zittau, Hochschule Zittau, Goerlitz, Germany (2005)Google Scholar
  23. 23.
    Kondratenko, Y.P., Al Zubi E.Y.M.: The optimisation approach for increasing efficiency of digital fuzzy controllers. In: Annals of DAAAM for 2009 & Proceeding of the 20th Int. DAAAM Symp. Intelligent Manufacturing and Automation, Published by DAAAM International, Vienna, Austria, pp. 1589–1591 (2009)Google Scholar
  24. 24.
    Hayajneh, M.T., Radaideh, S.M., Smadi, I.A.: Fuzzy logic controller for overhead cranes. Eng. Comput. 23(1), 84–98 (2006)Google Scholar
  25. 25.
    Kondratenko, Y.P., Kozlov, O.V., Klymenko, L.P., Kondratenko, G.V.: Synthesis and research of neuro-fuzzy model of ecopyrogenesis multi-circuit circulatory system. In: Advance Trends in Soft Computing, Studies in Fuzziness and Soft Computing, vol. 312, pp. 1–14. Springer-Verlag, Berlin (2014)Google Scholar
  26. 26.
    Kondratenko, Y.P., Kozlov, O.V.: Mathematic modeling of reactor’s temperature mode of multiloop pyrolysis plant. In: Modeling and Simulation in Engineering, Economics and Management, K.J. Engemann, A.M. Gil-Lafuente, J.L. Merigo (Eds.), International Conference MS 2012, New Rochelle, NY, USA (May 30–June 1, 2012), Proceedings. Lecture Notes in Business Information Processing, vol. 115, pp. 178–187 (2012)Google Scholar
  27. 27.
    Kondratenko, Y.P., Shishkin, A.S.: Nonlinear regression mathematical model of magnetic systems for signal registration slip. J. NTU KPI Ser. Instrum. 33, 127–134 (2007). (in Russian)Google Scholar

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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Intelligent Information SystemsPetro Mohyla Black Sea State UniversityMykolaivUkraine
  2. 2.Department of Computerized Control SystemsAdmiral Makarov National University of ShipbuildingMykolaivUkraine

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