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Incremental Identification of Hybrid Models of Dynamic Process Systems

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Abstract

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.

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References

  1. Agarwal, M.: Combining neural and conventional paradigms for Modeling, prediction and control. Int. J. Syst. Sci. 28, 65-81 (1997)

    Article  MATH  Google Scholar 

  2. Bard, Y.: Nonlinear Parameter Estimation. Academic Press, New York (1974)

    MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  4. Bonvin, D., Rippin, D.W.T.: Target factor analysis for the identification of stoichiometric models. Chem. Eng. Sci. 45, 3417-3426 (1990)

    Article  Google Scholar 

  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. Brendel, M., Marquardt, W.: Experimental design for the identification of hybrid reaction models from transient data. Chem. Eng. J 141, 264-277 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  8. Dulmage, A.L., Mendelsohn, N.S.: Two algorithms for bipartite graphs. SIAM Journal 11, 183-194 (1963)

    MATH  MathSciNet  Google Scholar 

  9. Fronment, G.F., Bischoff, K.B.: Chemical Reactor Analysis and Design. John Wiley and Sons, New York. (1990)

    Google Scholar 

  10. Hansen, P.C.: Rank-Deficient and Discrete III-posed Problems. SIAM, Philadelphia (1998)

    Google Scholar 

  11. Kahrs, O., Marquardt, W.: Incremental identification of hybrid process models. Comput. Chem. Eng. 32, 694-705 (2007)

    Article  Google Scholar 

  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. Kahrs, O.: Semi-Empirical Modeling of Process Systems. PhD Thesis, RWTH Aachen University, Germany (2009)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  17. Michalik, C., Chachuat, B., Marquardt, W.: Incremental Global Parameter Estimation in Dynamical Systems. Submitted (2009)

    Google Scholar 

  18. Olivera, R.: Combining first principles modeling and artificial neural network: a general framework. Comp. Chem. Eng. 28, 755-766 (2004)

    Article  Google Scholar 

  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. Psichogios, D.C., Ungar, L.H.: A hybrid neural network – first principles approach to process modeling. AIChE J. 38, 1499-1511 (1992)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  22. Tholodur, A., Ramirez, W.F.: Optimization of fed batch bioreactors using neural net parameter function models. Biotechnol. Prog. 12, 302-309 (1996)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Correspondence to Olaf Kahrs .

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Kahrs, O., Brendel, M., Michalik, C., Marquardt, W. (2009). Incremental Identification of Hybrid Models of Dynamic Process Systems. In: Hof, P., Scherer, C., Heuberger, P. (eds) Model-Based Control:. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0895-7_11

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  • DOI: https://doi.org/10.1007/978-1-4419-0895-7_11

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-0894-0

  • Online ISBN: 978-1-4419-0895-7

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