Measurement-based optimization of batch processes: Meeting terminal constraints on-line via trajectory following

Welz, C.; Srinivasan, Balasubramaniam; Bonvin, Dominique

doi:10.1016/j.jprocont.2007.10.005

Cited by 16 publications

(16 citation statements)

References 10 publications

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“…In other words, the measurements are used to adapt the inputs along constraint-seeking arcs, whereas the inputs along sensitivityseeking arcs are calculated via the fixed model. The rationale behind this is that meeting path and terminal constraints in dynamic optimization often proves to be more important than meeting the sensitivity part of the NCO; this is confirmed by the variational analysis of the NCO in the presence of uncertainty [17]. Clearly, the solution obtained through the proposed scheme may be suboptimal, e.g., when the optimal inputs contain sensitivity-seeking arcs.…”

Section: Feasibility Aspectsmentioning

confidence: 82%

Batch process optimization via run-to-run constraints adaptation

Marchetti

Chachuat

Bonvin

2007

2007 European Control Conference (ECC)

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Abstract-In the batch process industry, the available models carry a large amount of uncertainty and can seldom be used to directly optimize real processes. Several measurement-based optimization methods have been proposed to deal with model mismatch and process disturbances. Constraints often play a dominant role in the dynamic optimization of batch processes. In their presence, the optimal input profiles are characterized by a set of arcs, switching times and active path and terminal constraints. This paper presents a novel method tailored to those problems where the potential of optimization arises mainly from the correct set of path and terminal constraints being active. The input profiles are computed between successive runs by dynamic optimization of a fixed nominal model, and the constraints in the optimization problem are adapted using measured information from previous batches. Note that, unlike many existing optimization schemes, the measurements are not used to update the process model. Moreover, the proposed approach has the potential to uncover the optimal input structure. This is demonstrated on a simple semi-batch reactor example.

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Section: Feasibility Aspectsmentioning

confidence: 82%

Batch process optimization via run-to-run constraints adaptation

Marchetti

Chachuat

Bonvin

2007

2007 European Control Conference (ECC)

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“…While RTO methods have primarily been developed for the more widespread continuous processes, there is also a significant interest in applying RTO to transient processes, and the process engineering literature is rich with applications to batch and semi-batch chemical processes for fine chemicals (Ruppen et al, 1998;Filippi-Bossy et al, 1989;Ubrich et al, 1999), polymerization (Kadam et al, 2007;François et al, 2004;Zafiriou and Zhu, 1990;Clarke-Pringle and Mac Gregor, 1998), distillation (Welz et al, 2008), crystallization (Fiordalis and Georgakis, 2013), and bio-processes (Visser et al, 2000;Bodizs et al, 2007). A review of RTO for batch processes is given by Bonvin et al (2002).…”

Section: Ma For Run-to-run Optimization Of Dynamic Processesmentioning

confidence: 99%

A Directional Modifier-Adaptation Algorithm for Real-Time Optimization

Costello

François

Bonvin

2016

Journal of Process Control

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The steady advances of computational methods make model-based optimization an increasingly attractive method for process improvement. Unfortunately, the available models are often inaccurate. The traditional remedy is to update the model parameters, but this generally leads to a difficult parameter estimation problem that must be solved on-line. In addition, the resulting model may poorly represent the plant when there is structural mismatch between the two. The iterative optimization method called Modifier Adaptation overcomes these obstacles by directly incorporating plant measurements into the optimization framework, principally in the form of constraint values and gradients. However, the experimental cost (i.e. the number of experiments required) to estimate these gradients increases linearly with the number of process inputs, which tends to make the method intractable for processes with many inputs. This paper presents a new algorithm, called Directional Modifier Adaptation, that overcomes this limitation by only estimating the plant gradients in certain privileged directions. It is proven that plant optimality with respect to these privileged directions can be guaranteed upon convergence. A novel, statistically optimal, gradient estimation technique is developed. The algorithm is illustrated through the simulation of a realistic airborne wind-energy system, a promising renewable energy technology that harnesses wind energy using large kites. It is shown that Directional Modifier Adaptation can optimize in real time the path followed by the dynamically flying kite.

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“…The batch end constraint defines the ultimate goal of the batch process; sometimes, the characteristic of batch process makes optimization unfeasible. This problem is studied in Welz et al (2008) 26 ,…”

Section: Trajectory Design As An Optimization Problemmentioning

confidence: 99%

“…Several authors have worked on dynamic optimization for batch process control with nonlinear predictive controllers [12][13][14][15]19,22,26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 8 productivity has been considered and that state controllability was neglected 27 . This fact produces systems that are prone to disturbances which can affect the product quality and productivity.…”

Section: Optimization and Control In Batch Processesmentioning

confidence: 99%

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Reference Trajectory Design Using State Controllability for Batch Processes

Gómez-Pérez

Gómez

Álvarez

2015

Ind. Eng. Chem. Res.

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address: Carrera 80 #65-223, office: M3-100, Medellín, Colombia; zip code: 050041. Abstract:A proposal for the design of a reference trajectory for batch processes was developed, using set theory state controllability concepts. This proposal introduces the Available Control Action Set, which is an indicator of the state controllability and contains the largest amount of possible control actions that reach the desired final state. The volume of Available Control Action Sets can be maximized to improve the safety of batch processes while maintaining consistently high product quality. Thus a closed loop system has sufficient energy to reject disturbances, ensuring end-of-batch product quality. Case studies are presented to demonstrate the applicability of the approach to batch and semi-batch processes and to illustrate how the proposed method for the design of a reference trajectory for batch processes increases safe and robust operation.

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Measurement-based optimization of batch processes: Meeting terminal constraints on-line via trajectory following

Cited by 16 publications

References 10 publications

Batch process optimization via run-to-run constraints adaptation

Batch process optimization via run-to-run constraints adaptation

A Directional Modifier-Adaptation Algorithm for Real-Time Optimization

Reference Trajectory Design Using State Controllability for Batch Processes

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