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On the use of nonlinear model predictive control without parameter adaptation for batch processes

Jean-Christophe Binette and Bala Srinivasan

Article (2016)

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Cite this document: Binette, J.-C. & Srinivasan, B. (2016). On the use of nonlinear model predictive control without parameter adaptation for batch processes. Processes, 4(3). doi:10.3390/pr4030027
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Abstract

Optimization techniques are typically used to improve economic performance of batch processes, while meeting product and environmental specifications and safety constraints. Offline methods suffer from the parameters of the model being inaccurate, while re-identification of the parameters may not be possible due to the absence of persistency of excitation. Thus, a practical solution is the Nonlinear Model Predictive Control (NMPC) without parameter adaptation, where the measured states serve as new initial conditions for the re-optimization problem with a diminishing horizon. In such schemes, it is clear that the optimum cannot be reached due to plant-model mismatch. However, this paper goes one step further in showing that such re-optimization could in certain cases, especially with an economic cost, lead to results worse than the offline optimal input. On the other hand, in absence of process noise, for small parametric variations, if the cost function corresponds to tracking a feasible trajectory, re-optimization always improves performance. This shows inherent robustness associated with the tracking cost. A batch reactor example presents and analyzes the different cases. Re-optimizing led to worse results in some cases with an economical cost function, while no such problem occurred while working with a tracking cost.

Uncontrolled Keywords

process optimization; batch processes; process control; constrained optimization; sensitivity; real-time optimization

Open Access document in PolyPublie
Subjects: 1800 Génie chimique > 1800 Génie chimique
1800 Génie chimique > 1801 Principes des réactions et conception de réacteurs
2950 Mathématiques appliquées > 2960 Modélisation mathématique
Department: Département de génie chimique
Research Center: Non applicable
Funders: CRSNG / NSERC
Date Deposited: 10 Mar 2020 15:00
Last Modified: 25 Mar 2020 14:42
PolyPublie URL: https://publications.polymtl.ca/3604/
Document issued by the official publisher
Journal Title: Processes (vol. 4, no. 3)
Publisher: MDPI
Official URL: https://doi.org/10.3390/pr4030027

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