Incremental Identification of Hybrid Models of Dynamic Process Systems

  • Olaf Kahrs
  • Marc Brendel
  • Claas Michalik
  • Wolfgang Marquardt


This contribution presents the so called incremental approach to the general modeling task and shows various fields of application as well as conceptual extensions of the method. The incremental model identification procedure has been developed within a collaborative interdisciplinary research center (CRC) at RWTH Aachen. First, the so called MEXA process, which is at the core of the research at the CRC is presented. Next, the incrementalmodel identification approach (which is one crucial step within the MEXA process) is contrasted with the classical simultaneous approach. The application of the incremental approach is then shown for the special case of hybrid reaction kinetic models. In a next step, the basic idea of the incremental approach - the decomposition of the problem into simpler subproblems - is generalized to also account for (mechanistic and hybrid) algebraic and dynamic models (from arbitrary fields, e.g., not necessarily reaction kinetics). Finally, open questions within the incremental framework are discussed and the future research focus is given.


Hybrid Model Incremental Approach Hydrogel Bead Incremental Model Data Reconciliation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Agarwal, M.: Combining neural and conventional paradigms for Modeling, prediction and control. Int. J. Syst. Sci. 28, 65-81 (1997)MATHCrossRefGoogle Scholar
  2. [2]
    Bard, Y.: Nonlinear Parameter Estimation. Academic Press, New York (1974)MATHGoogle Scholar
  3. [3]
    Bardow, A., Marquardt, W.: Identification Methods for Reaction Kinetics and Transport. In: Floudas, C.A., Pardalos, P.M. (eds.), Encyclopedia of Optimization, 2nd ed., Springer US, 1549-1556 (2009)CrossRefGoogle Scholar
  4. [4]
    Bonvin, D., Rippin, D.W.T.: Target factor analysis for the identification of stoichiometric models. Chem. Eng. Sci. 45, 3417-3426 (1990)CrossRefGoogle Scholar
  5. [5]
    Brendel, M., Mhamdi, A., Bonvin, D., Marquardt, W.: An incremental approach for the identification of reaction kinetics. ADCHEM 2003, 177-182 (2003)Google Scholar
  6. [6]
    Brendel, M., Marquardt, W.: Experimental design for the identification of hybrid reaction models from transient data. Chem. Eng. J 141, 264-277 (2009)CrossRefGoogle Scholar
  7. [7]
    Chang, J.S., Hung, B.C.: Optimization of batch polymerization reactors using neural network rate function models. Ind. Eng. Chem. Res. 11, 2716-2727 (2002)CrossRefGoogle Scholar
  8. [8]
    Dulmage, A.L., Mendelsohn, N.S.: Two algorithms for bipartite graphs. SIAM Journal 11, 183-194 (1963)MATHMathSciNetGoogle Scholar
  9. [9]
    Fronment, G.F., Bischoff, K.B.: Chemical Reactor Analysis and Design. John Wiley and Sons, New York. (1990)Google Scholar
  10. [10]
    Hansen, P.C.: Rank-Deficient and Discrete III-posed Problems. SIAM, Philadelphia (1998)Google Scholar
  11. [11]
    Kahrs, O., Marquardt, W.: Incremental identification of hybrid process models. Comput. Chem. Eng. 32, 694-705 (2007)CrossRefGoogle Scholar
  12. [12]
    Kahrs, O., Marquardt, W.: The validity domain of hybrid models and its application in process optimization. Chem. Eng. Prog. 46, 1041-1242 (2007)Google Scholar
  13. [13]
    Kahrs, O.: Semi-Empirical Modeling of Process Systems. PhD Thesis, RWTH Aachen University, Germany (2009)Google Scholar
  14. [14]
    Van Lith, P.F., Betlem, B.H.L., Roffel, B.: A structured modeling approach for dynamic hybrid fuzzy first-principles models. J. Proc. Cont. 12, 605-615 (2002)CrossRefGoogle Scholar
  15. [15]
    Marquardt, W.: Towards a process modeling methodology. In: Berber, R. (ed) Methods of Model-based Control. NATO-Asi Series, Kluwer, The Netherlands, 3-41 (1995)Google Scholar
  16. [16]
    Marquardt, W.: Model-based Experimental Analysis of Kinetic Phenomena in Multi-phase Reactive Systems. Trans IChemE, Part A, Chemical Engineering Research and Design, 83, 561-573 (2005)CrossRefGoogle Scholar
  17. [17]
    Michalik, C., Chachuat, B., Marquardt, W.: Incremental Global Parameter Estimation in Dynamical Systems. Submitted (2009)Google Scholar
  18. [18]
    Olivera, R.: Combining first principles modeling and artificial neural network: a general framework. Comp. Chem. Eng. 28, 755-766 (2004)CrossRefGoogle Scholar
  19. [19]
    Pantelides, C.C., Urban, Z.E.: Process Modelling Technology: A Critical Review of Recent Developments. In: Floudas, C.A., Agarwal, R. (eds.) Proc. Int. Conf. on Foundations of Process Design, FOCAPD 2004, 69-83 (2004)Google Scholar
  20. [20]
    Psichogios, D.C., Ungar, L.H.: A hybrid neural network – first principles approach to process modeling. AIChE J. 38, 1499-1511 (1992)CrossRefGoogle Scholar
  21. [21]
    Ruppen, D., Bonvin, D., Rippin, D.W.T.: Implementation of adaptive optimal operation for a semi-batch reactor. Comp. Chem. Eng. 22, 185-199 (1997)CrossRefGoogle Scholar
  22. [22]
    Tholodur, A., Ramirez, W.F.: Optimization of fed batch bioreactors using neural net parameter function models. Biotechnol. Prog. 12, 302-309 (1996)CrossRefGoogle Scholar
  23. [23]
    Yeow, Y.L., Wickramasinghe, S.R., Han, B., Leong, Y.K.: A new method of processing the time-concentration data of reaction kinetics. Chem. Eng. Sci. 58, 3601-3610 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Olaf Kahrs
    • 1
    • 2
  • Marc Brendel
    • 1
    • 3
  • Claas Michalik
    • 1
  • Wolfgang Marquardt
    • 1
  1. 1.Aachener Verfahrenstechnik - Process Systems EngineeringRWTH Aachen UniversityGermany
  2. 2.BASF SELudwigshafenGermany
  3. 3.Evonik Degussa GmbHHanauGermany

Personalised recommendations