Dynamic real-time optimization of distributed MPC systems using rigorous closed-loop prediction

Li, Hao; Swartz, Christopher L.E.

doi:10.1016/j.compchemeng.2018.08.028

Cited by 27 publications

(17 citation statements)

References 32 publications

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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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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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“…This framework furthermore allows for explicit consideration of the tracking behavior of a subordinated model predictive controller (MPC) in scheduling of ASUs . In related works, Jamaludin and Swartz and Li and Swartz further propose to introduce the optimality conditions of subordinated MPC into the formulation of the optimal operation. However, this latter approach has not yet been applied to ASUs or other large‐scale distillation systems.…”

Section: Introductionmentioning

confidence: 99%

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

et al. 2019

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The availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.

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“…We use the terms of scheduling and control as defined, for example, by Reference , where control takes into account operational decisions on a time scale of minutes to hours and scheduling is used for operational decisions on a time scale of several hour up to days. The integration of scheduling and process control plays a crucial rule for the introduction of sustainable processes and can be achieved either by accounting for the closed‐loop system dynamics in the scheduling layer, for example, or by applying economic model predictive control (eNMPC), also termed bottom‐up strategy, for example, by Reference , where economic objectives are directly considered in the supervisory control layer, for example …”

Section: Introductionmentioning

confidence: 99%

“…To achieve feasible setpoints, the process dynamics are considered in the scheduling layer by using a model which represents the closed‐loop behavior of the process. This closed‐loop model is obtained either by embedding the necessary optimality conditions of the process in closed‐loop with a tracking controller or by using a data‐driven model to approximate closed‐loop behavior . The controller level is not altered by the top‐down approaches.…”

Section: Introductionmentioning

confidence: 99%

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A flexible air separation process: 2. Optimal operation using economic model predictive control

et al. 2019

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The penetration of renewable electricity promises an economic advantage for flexible operation of energy-intense processes. One way to achieve flexible operation is economic model predictive control (eNMPC), where an economic dynamic optimization problem is directly solved at controller level taking into account a process model and operational constraints. We apply eNMPC in silico to an air separation process with an integrated liquefier and liquid-assist operation. We use a mechanistic dynamic model as both controller model and plant surrogate. We conduct a closed-loop case study over a time horizon of 2 days with historical electricity prices and input disturbances. We solve the dynamic optimization problems in DyOS. Compared to the optimal steady-state operation, the eNMPC operating strategy gives a significant improvement of 14%. We further show that the eNMPC enables economic improvements similar to an idealized quasistationary scheduling. While the eNMPC provides control profiles qualitatively similar to those obtained from deterministic global optimization of quasistationary scheduling, the eNMPC satisfies the product purity constraints all the time whereas the quasistationary scheduling sometimes fails to do so. The eNMPC applies local optimization methods and achieves profiles similar to the scheduling solved using deterministic global optimization methods over the complete closed-loop simulation time horizon. K E Y W O R D S air separation unit, demand side management, direct single-shooting, economic model predictive control, integrated liquefication cycle 1 | INTRODUCTION The enhanced use of renewables, for example, onshore wind and photovoltaic, for electricity production results in fluctuating electricity prices. 1,2 Exploiting these fluctuations promises an economic advantage of flexibly operating electricity-intensive processes. There are two requirements: (a) a "flexible process", that is, a process with a wide operation range and without big losses of efficiency and (b) "flexible process operation", that is, exploiting the process flexibility in the operation. Thus, there is both economical and ecological incentive to take advantage of flexible process load adjustment as a reaction to changing market conditions. This is known as demandside management (DSM). 3 DSM has been applied to various process systems in the last decades, for example, by Ghobeity and Mitsos 4 and Brée et al. 5 Different concepts, recent advances and challenges of industrial DSM have been summarized by Zhang and Grossmann 6 and the rising need for flexible operation in context of the integration of renewable

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Dynamic real-time optimization of distributed MPC systems using rigorous closed-loop prediction

Cited by 27 publications

References 32 publications

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

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

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

A flexible air separation process: 2. Optimal operation using economic model predictive control

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