An industrial implementation of a generic NMPC controller with application to a batch process

Pluymers, Bert; Ludlage, Jobert; Ariaans, L.J.J.M.; Brempt, Wim Van

doi:10.3182/20080706-5-kr-1001.00168

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…NMPC both from tracking and economic optimization perspective has seen great advances. After initial implementations in the 2000s, e.g., 66, 67, focus has moved to reducing computational cost to ensure real‐time capability, investigation of uncertainty, proof of stability, and targeted formulation to ensure stability.…”

Section: Methods For Optimal Dynamic Process Operationmentioning

confidence: 99%

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Esche

Repke

2020

Chemie Ingenieur Technik

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Participating in electricity markets through demand response causes new requirements for optimizing process control of chemical plants. The last ten years have brought great advances in the formulation and solution of economic nonlinear model predictive control and state estimation to support operation of processes under dynamic constraints. However, gaps remain regarding the availabilities of suitable plant models capable of describing processes active in demand response as well as of robust schemes for state estimation and economic nonlinear model predictive control in commercial tools.

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Section: Methods For Optimal Dynamic Process Operationmentioning

confidence: 99%

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Esche

Repke

2020

Chemie Ingenieur Technik

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“…Although the setup used might seem somewhat outdated from an academic point of view, in practice these types of algorithms have advantages that makes them amenable for industrial use (Foss and Schei, 2007;Pluymers et al, 2008). Compared to using finite differences, one can argue that using analytical methods for sensitivity integration as explored herein, has extra potential in cases where the models come from advanced modeling tools with capability of producing symbolic Jacobians (Imsland et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

On Gradient Computation in Single-shooting Nonlinear Model Predictive Control

Ringset

Imsland

Foss³

2010

IFAC Proceedings Volumes

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“…[2] or [3]). Although some companies develop industrial NMPC implementations for some processes (see [4], [5], [6]) its practical use is still in its early stages and not a widespread reality.…”

Section: Introductionmentioning

confidence: 99%

An environment for the efficient testing and implementation of robust NMPC

Lucia

Tătulea-Codrean

Schoppmeyer

et al. 2014

2014 IEEE Conference on Control Applications (CCA)

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In the last years many research studies have presented simulation or experimental results using Nonlinear Model Predictive Control (NMPC). The computation times needed for the solution of the resulting nonlinear optimization problems are in many cases no longer an obstacle due to the advances in algorithms and computational power. However, NMPC is not yet an industrial reality as its linear counterpart is. Two reasons for this are the lack of good tool support for the development of NMPC solutions and the fact that it is difficult to ensure the robustness of NMPC to plant-model mismatch. In this paper, we address both these issues. The main contribution is the development of an environment for the efficient implementation and testing of NMPC solutions, offering flexibility to test different algorithms and formulations without the need to re-encode the model or the algorithm. In addition, we present and discuss the approach of multi-stage robust NMPC to systematically deal with the robustness issue. The benefits of our approach are illustrated by experimental results on a laboratory plant.

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An industrial implementation of a generic NMPC controller with application to a batch process

Cited by 4 publications

References 13 publications

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

On Gradient Computation in Single-shooting Nonlinear Model Predictive Control

An environment for the efficient testing and implementation of robust NMPC

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