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Towards Nonlinear Model-Based Predictive Optimal Control of Large-Scale Process Models with Application to Air Separation Plants

In: Online Optimization of Large Scale Systems

Author

Listed:
  • Thomas Kronseder

    (Technische Universität München, Lehrstuhl für Höhere Mathematik und Numerische Mathematik)

  • Oskar von Stryk

    (Technische Universität Darmstadt, Fachgebiet Simulation und Systemoptimierung)

  • Roland Bulirsch

    (Technische Universität München, Lehrstuhl für Höhere Mathematik und Numerische Mathematik)

  • Andreas Kröner

    (Linde AG, Process Engineering and Contracting Division)

Abstract

We propose a concept for model predictive control of large-scale dynamical systems. This concept has been designed for the optimal control of chemical engineering processes, in particular for cryogenic air separation plants which are modelled by large systems of coupled differential and algebraic equations of (differential) index two with state dependent discontinuities. Our concept considers different time scales for various tasks which are prescribed by the real-time nature of the process of interest. In this paper (items refer to Figure 4) the components (a)-(d) and (f) of the general concept are considered in detail. Until now there has been a lack of a clear concept for real-time optimality. Therefore, we conclude by discussing some fundamental issues of the notion of real-time optimality.

Suggested Citation

  • Thomas Kronseder & Oskar von Stryk & Roland Bulirsch & Andreas Kröner, 2001. "Towards Nonlinear Model-Based Predictive Optimal Control of Large-Scale Process Models with Application to Air Separation Plants," Springer Books, in: Martin Grötschel & Sven O. Krumke & Jörg Rambau (ed.), Online Optimization of Large Scale Systems, pages 385-410, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-04331-8_21
    DOI: 10.1007/978-3-662-04331-8_21
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