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NLMPC: A Platform for Optimal Control of Feed- or Product-Flexible Manufacturing

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Assessment and Future Directions of Nonlinear Model Predictive Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 358))

Abstract

Nonlinear model predictive controllers (NLMPC) using fundamental dynamic models and online nonlinear optimization have been in service in ExxonMobil Chemical since 1994. The NLMPC algorithm used in this work employs a state space formulation, a finite prediction horizon, a performance specification in terms of desired closed loop response characteristics for the outputs, and costs on incremental manipulated variable action. The controller can utilize fundamental or empirical models. The simulation and optimization problems are solved simultaneously using sequential quadratic programming (SQP). In the paper, we present results illustrating regulatory and grade transition (servo) control by NLMPC on several industrial polymerization processes. The paper outlines the NLMPC technology employed, describes the current status in industry for extending linear model predictive control to nonlinear processes or applying NLMPC directly, and identifies several needs for improvements to components of NLMPC.

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Author information

Authors and Affiliations

  1. Core Engineering Process Control Department, ExxonMobil Chemical Company, 4500 Bayway Drive, Baytown, TX, 77522, USA

    R. Donald Bartusiak

Authors
  1. R. Donald Bartusiak
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Editor information

Editors and Affiliations

  1. Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70550, Stuttgart, Germany

    Rolf Findeisen  & Frank Allgöwer  & 

  2. Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Lorenz T. Biegler

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Bartusiak, R.D. (2007). NLMPC: A Platform for Optimal Control of Feed- or Product-Flexible Manufacturing. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_30

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  • DOI: https://doi.org/10.1007/978-3-540-72699-9_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72698-2

  • Online ISBN: 978-3-540-72699-9

  • eBook Packages: EngineeringEngineering (R0)

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