Dynamic real‐time optimization with closed‐loop prediction

Jamaludin, Mohammad Zamry; Swartz, Christopher L.E.

doi:10.1002/aic.15752

Cited by 31 publications

(24 citation statements)

References 48 publications

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“…In 69, this approach is applied on D‐RTO with a two‐layer architecture. The results of D‐RTO are realized by fast tracking MPC.…”

Section: Methods For Optimal Dynamic Process Operationmentioning

confidence: 99%

“…Apart from academic work on air separation units, chloralkali electrolysis, and hydrogen generation, research on NMPC applications has focused on systems with inherent dynamics. Many contributed on batch and semi-batch processes [82,92,95] with a strong emphasis on polymerization reactors [69,124]. Secondly, systems with cyclic steady state [116], e.g., for CO 2 separation [73], were investigated.…”

Section: Applications Of Nonlinear Mpcmentioning

confidence: 99%

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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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“…In 69, this approach is applied on D‐RTO with a two‐layer architecture. The results of D‐RTO are realized by fast tracking MPC.…”

Section: Methods For Optimal Dynamic Process Operationmentioning

confidence: 99%

Section: Applications Of Nonlinear Mpcmentioning

confidence: 99%

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

Esche

Repke

2020

Chemie Ingenieur Technik

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“…Ellis and Christofides (2014) [109] focused on selecting a suitable input configuration for such two-layered dynamic RTO structures such that the asymptotic stability is guaranteed. Jamaludin and Swartz (2017) [110] and Li and Swartz (2019) [111] employed a convex MPC problem in the lower level regulatory control, which enabled its exact substitution with KKT optimality conditions. Simkoff and Baldea (2019) [112] used the same substitution strategy on a production scheduling problem.…”

Section: Towards the Grand Unification Of Process Design Schedulingmentioning

confidence: 99%

Towards the Grand Unification of Process Design, Scheduling, and Control—Utopia or Reality?

2019

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As a founder of the Process Systems Engineering (PSE) discipline, Professor Roger W.H. Sargent had set ambitious goals for a systematic new generation of a process design paradigm based on optimization techniques with the consideration of future uncertainties and operational decisions. In this paper, we present a historical perspective on the milestones in model-based design optimization techniques and the developed tools to solve the resulting complex problems. We examine the progress spanning more than five decades, from the early flexibility analysis and optimal process design under uncertainty to more recent developments on the simultaneous consideration of process design, scheduling, and control. This formidable target towards the grand unification poses unique challenges due to multiple time scales and conflicting objectives. Here, we review the recent progress and propose future research directions.

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“…Recently, a closed‐loop DRTO (CL‐DRTO) framework was proposed in Jamaludin and Swartz 8 . In this study, the DRTO formulation accounts for the effect of the underlying controller on the predicted response of the plant.…”

Section: Introductionmentioning

confidence: 99%

Closed‐loop dynamic real‐time optimization with stabilizing model predictive control

2021

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Dynamic real‐time optimization (DRTO) is a supervisory strategy at the upper level of the industrial process automation architecture that computes economically optimal set‐point trajectories that are in turn passed on to the lower‐level model predictive control (MPC) for tracking. The economically optimal solution, in several process industries, could lead to operating the plant at or around an unstable steady state. The present article accounts for this by developing a closed‐loop DRTO (CL‐DRTO) formulation that enables handling unstable operating points via an underlying MPC with stability constraints. To this end, a stabilizing MPC that handles trajectory tracking for unstable systems is embedded within the upper‐level DRTO. The resulting CL‐DRTO problem is reformulated by applying a simultaneous solution approach. The economic benefits realized by the proposed strategy are illustrated through applications to both linearized and nonlinear dynamic models for single‐input single‐output and multi‐input multi‐output continuous stirred tank reactor case studies.

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Dynamic real‐time optimization with closed‐loop prediction

Cited by 31 publications

References 48 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

Towards the Grand Unification of Process Design, Scheduling, and Control—Utopia or Reality?

Closed‐loop dynamic real‐time optimization with stabilizing model predictive control

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