Feedback Control for Optimal Process Operation

Engell, Sebastian

doi:10.3182/20060402-4-br-2902.00013

Cited by 91 publications

(132 citation statements)

References 98 publications

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“…The task of the DO layer is to regulate the offset of outputs and set-points. Both SSTC and DO are executed at the same frequency [5].…”

Section: Two-layer Model Predictive Controlmentioning

confidence: 99%

“…2. This structure has the following merits: (i) a faster change of the set-points after the occurrence of disturbances is realized; (ii) large set-point changes that may drive the linear controllers unstable are avoided [5].…”

Section: Two-layer Model Predictive Controlmentioning

confidence: 99%

“…In order to maintain the process operation as close as possible to the economic optimum, Marlin et al created a direct link between regulatory control and economic optimization of the plant based on hierarchical structure, and gave set-points of the lower-level control loop adopting real-time optimization (RTO) [2]. In order to narrow the gap between the low frequency nonlinear steady-state optimization performed on the RTO layer and the relatively fast linear MPC layer, the so-called LP-MPC and QP-MPC (both have two-stage MPC structures) are frequently used in industry [3][4][5], which are cascaded structures of a steady-state optimizer and the typical MPC controller. In these structures, set-points of MPC controller are upgraded by the steady-state optimization in real time.…”

Section: Introductionmentioning

confidence: 99%

“…The controller with such cascaded structure is called Two-layer Model Predictive Control (TMPC) which differs from traditional MPC [5]. The upper-layer steady-state optimization of TMPC is linear programming (or quadratic programming) problem.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional set-point tracking is necessary for the lower control loop, but the economic cost cannot be optimized. Engell obtained optimal process operation through optimization control directly based on layered structure in 2007 [5]. Since then, TMPC was successfully applied to debutanizer [7], toluene distillation system [8], propylene separator [9], and Bayer polymerization process [10].…”

Section: Introductionmentioning

confidence: 99%

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A steady-state target calculation method based on “point” model for integrating processes

et al. 2015

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“…The task of the DO layer is to regulate the offset of outputs and set-points. Both SSTC and DO are executed at the same frequency [5].…”

Section: Two-layer Model Predictive Controlmentioning

confidence: 99%

Section: Two-layer Model Predictive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A steady-state target calculation method based on “point” model for integrating processes

et al. 2015

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Phase behavior analysis for industrial polymerization reactors

2010

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The production of polymer is a very important sector in the chemical processing industry as demand for polymeric materials increases, and these polymerization processes exhibit nonlinear dynamics posing difficulties on process operation and control. It is desirable to understand the relationship between open-loop controllability and process conditions. The phase behavior that is an open-loop indicator of the controllability for the methyl methacrylate and propylene polymerization reactor over the entire feasible operating region is determined, and the influences of design/operating parameters and model uncertainties on this inherent characteristic at the design stage are analyzed. Based on zero dynamics and singularity theory, directly in the nonlinear setting, a methodology for the preliminary analysis of the phase behavior over the whole feasible operating region is presented and applied into the above two reactors.The above research results demonstrate that to modify certain parameters in certain direction would improve the controllability and are expected to provide helpful guidelines for improving plant and control system design. This is desirable because it allows identification of the cause of the limitation to give an indication where to modify the design.

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Economic model predictive control of nonlinear process systems using Lyapunov techniques

2011

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In this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed-loop performance with respect to general economic considerations for a broad class of nonlinear process systems. Specifically, in the proposed designs, the economic MPC optimizes a cost function, which is related directly to desired economic considerations and is not necessarily dependent on a steady-state-unlike conventional MPC designs. First, we consider nonlinear systems with synchronous measurement sampling and uncertain variables. The proposed economic MPC is designed via Lyapunovbased techniques and has two different operation modes. The first operation mode corresponds to the period in which the cost function should be optimized (e.g., normal production period); and in this operation mode, the MPC maintains the closed-loop system state within a predefined stability region and optimizes the cost function to its maximum extent. The second operation mode corresponds to operation in which the system is driven by the economic MPC to an appropriate steady-state. In this operation mode, suitable Lyapunov-based constraints are incorporated in the economic MPC design to guarantee that the closed-loop system state is always bounded in the predefined stability region and is ultimately bounded in a small region containing the origin. Subsequently, we extend the results to nonlinear systems subject to asynchronous and delayed measurements and uncertain variables. Under the assumptions that there exist an upper bound on the interval between two consecutive asynchronous measurements and an upper bound on the maximum measurement delay, an economic MPC design which takes explicitly into account asynchronous and delayed measurements and enforces closed-loop stability is proposed. All the proposed economic MPC designs are illustrated through a chemical process example and their performance and robustness are evaluated through simulations.

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Feedback Control for Optimal Process Operation

Cited by 91 publications

References 98 publications

A steady-state target calculation method based on “point” model for integrating processes

A steady-state target calculation method based on “point” model for integrating processes

Phase behavior analysis for industrial polymerization reactors

Economic model predictive control of nonlinear process systems using Lyapunov techniques

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