2018
DOI: 10.1002/cjce.23200
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A multi‐scenario nonlinear model predictive control approach for robust product transitions

Abstract: Dynamic product transitions are ubiquitous operations in the processing industry. When a first‐principles dynamic model is deployed for real system representation, the calculation of the dynamic optimal trajectory for product transition can be cast as an optimal control problem. A common practice in addressing the solution of optimal product transitions lies in the assumption of free of uncertainty first‐principle models. Ignoring the effect of model uncertainty on product transitions can result in unfeasible … Show more

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Cited by 3 publications
(4 citation statements)
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“…Therefore, in this paper the coaching capability of the GOESS is fully utilized to coordinate the local economic optimality and the fastest dynamic response. The GOESS can be obtained using Equation (6), upon which the closest local steady-states (CLSS) can be evaluated using Equation (18):…”
Section: Methods Descriptionmentioning
confidence: 99%
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“…Therefore, in this paper the coaching capability of the GOESS is fully utilized to coordinate the local economic optimality and the fastest dynamic response. The GOESS can be obtained using Equation (6), upon which the closest local steady-states (CLSS) can be evaluated using Equation (18):…”
Section: Methods Descriptionmentioning
confidence: 99%
“…Model predictive control (MPC) has been one of the most popular advanced process control (APC) strategies used in industrial fields. [1][2][3][4][5][6] Among them, two-layer model predictive control (TLMPC) architecture is extensively used in industrial control software, such as DMCplus and DMC3 of AspenTech. [7] Morshedi et al and Brosilow et al proposed a steadystate optimization (SSO) method based on linear programming, which established the basic architecture of TLMPC.…”
Section: Introductionmentioning
confidence: 99%
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“…In min–max approaches, a closed-loop min–max formulation calculates control inputs that minimize the worst-case cost with respect to the defined bounded set of parameters. , Tube-based MPC ensures that the closed-loop trajectories lie in a tube that satisfies the constraints using an ancillary controller . In the multistage NMPC, uncertainties are modeled using a scenario tree, and the future control inputs are optimized. , Recently, to address the concerns of prediction uncertainty, robust NMPC has focused on providing a mathematical framework for modeling uncertainty values by an uncertainty set and optimizing problems based on the worst scenario. ,, The basic ideas in this approach were introduced by Huang et al and Huang and Biegler, where different scenarios characterize the uncertainties, and the computed control action is feasible over the entire uncertainty region.…”
Section: Introductionmentioning
confidence: 99%