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Processes 2016, 4(3), 27; doi:10.3390/pr4030027

On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes

Département de Génie Chimique, École Polytechnique Montréal, C.P.6079 Succ., Centre-Ville Montréal, Montréal, QC H3C 3A7, Canada
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Academic Editor: Dominique Bonvin
Received: 12 May 2016 / Revised: 19 August 2016 / Accepted: 22 August 2016 / Published: 29 August 2016
(This article belongs to the Special Issue Real-Time Optimization)
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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. View Full-Text
Keywords: process optimization; batch processes; process control; constrained optimization; sensitivity; real-time optimization process optimization; batch processes; process control; constrained optimization; sensitivity; real-time optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Binette, J.-C.; Srinivasan, B. On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes. Processes 2016, 4, 27.

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