Filter-based Constraints to Easier Plant Feasibility in Modifier Adaptation Schemes

Papasavvas, Aris; François, Grégory

doi:10.1016/b978-0-12-818634-3.50212-5

Cited by 2 publications

(9 citation statements)

References 8 publications

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“…end At first sight, KMAy and MAy look very similar, but the addition of constraints clearly a↵ects the properties of the model-based optimization problem, with a risk of loosing the property to converge to the plant optimum of MAy. Indeed, it is shown hereafter that this property is not lost if the additional filter-based constraints satisfy certain conditions [37,38].…”

Section: Compute U K+1 By Solving Problem (32)mentioning

confidence: 97%

“…KMAy instead ensures modified model-based feasibility at the operating points that will be applied to the plant. Yet, modified model-based feasibility is not plant feasibility, but whenever the model is capable of fairly predicting the constraints, KMAy is su cient to reduce significantly the risk of plant constraint violations as it avoids implementing a filtered input whereby the model feasibility has not been checked [38]. Hereafter, su cient conditions for feasibility of a RTO iterate obtained with MAy or with KMAy are compared.…”

Section: Modified Model-based Feasibilitymentioning

confidence: 99%

“…The authors have recently proposed to implement filtering in the model-based optimization problem by means of filter-based additional constraints, rather than performing optimization and filtering sequentially (KMAy- [37]). It has been shown that it relaxes the model adequacy criterion [37] and makes the iterates safer [38]. This article motivates, introduces, develops further and extends KMAy by providing: (i) an automatic method to select the filter gain, (ii) a method to enforce model adequacy that is less detrimental to the accuracy of the model than its replacement by a convex approximation of the cost and constraints [10], and (iii) arguments in favor of input filtering This article is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

“…This article motivates, introduces, develops further and extends KMAy by providing: (i) an automatic method to select the filter gain, (ii) a method to enforce model adequacy that is less detrimental to the accuracy of the model than its replacement by a convex approximation of the cost and constraints [10], and (iii) arguments in favor of input filtering This article is organized as follows. After a statement of the problem and a review of MAy, KMAy is introduced by means of the extension of two recent conference articles [37,38], [37] being the conference article that motivated the invitation to contribute to this special issue that is reviewed and extended in Section 3. Then, in Section 4, KMAy is developed further, with the introduction of a new adaptive filter gain selection method (labeled "ad-KMAy"), which ensures that the largest applicable gain is implemented that guarantees model adequacy.…”

Section: Introductionmentioning

confidence: 99%

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Output modifier adaptation with filter-based constraints

Papasavvas

François

2020

Journal of Process Control

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Modifier adaptation (MA) and output modifier adaptation (MAy) are iterative model-based real-time optimization (RTO) algorithms that have the proven ability to drive plants to their optimal operating condition upon convergence despite disturbances and modeling uncertainty, provided the model at hand satisfies model adequacy conditions. But there is no guarantee that constraints are satisfied before convergence. In this article, an improvement of the formulation of MA and MAy is proposed that is proven to bring significant improvements w.r.t. these two limitations-model adequacy and feasibility of iterates. While standard MA or MAy suggests to perform optimization and filtering sequentially, it is proposed to integrate the input filtering stage in the modified model-based optimization problem by means of additional filter-based constraints. The corresponding approach, labeled "KMAy", is (i) proven to preserve constraint qualification despite additional constraints, (ii) proven to preserve the property of MA methods to converge to the true plant optimal inputs, (iii) proven to significantly relax the model adequacy condition-leading it to be independent of the constraints of the optimization problem, (iv) shown to increase the chances of converging from the safe side of the plant constraints and (v) shown to support the choice of input filtering, instead of output or modifier filtering, if the input filter is appropriately chosen. A method for the automatic selection of the largest filter gain with the five aforementioned assets, while minimizing the filter-induced conservatism, is also proposed. The performances of KMAy with and without adaptive gain are successfully illustrated by means of the optimization of a benchmark simulated chemical reactor.

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Section: Compute U K+1 By Solving Problem (32)mentioning

confidence: 97%

Section: Modified Model-based Feasibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Output modifier adaptation with filter-based constraints

Papasavvas

François

2020

Journal of Process Control

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“…with feasible plant iterates, convergence to the plant optimum have to been looked for. It has been proposed either to add penalty terms to the cost and constraints functions of the model 18 , to limit the step size between iterates 19 , or to implement exponential filtering 20 .…”

Section: Introductionmentioning

confidence: 99%

Internal Modifier Adaptation for the Optimization of Large-Scale Plants with Inaccurate Models

Papasavvas

François

2019

Ind. Eng. Chem. Res.

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Modifier adaptation (MA) methods are iterative model-based real-time optimization (RTO) methods with the proven ability to reach, upon converge, the unknown optimal steady-state operating conditions of a plant despite plant-model mismatch and disturbances. So far, MA has been applied to small-scale but never-to the best of the authors' knowledge-to large-scale systems, the optimization of which being, in practice, a very difficult engineering problem. While standard MA uses plant measurements of the cost and constraints only, in this article, a new MA approach is proposed, namely Internal Modifier Adaptation (IMA), which allows the use of all available plant measurements leading to corrections at the level of the inner structure of the model. This article also provides a mathematical proof that IMA preserves the property of MA methods to reach the optimal inputs of the plant upon convergence. The application and the benefits of the proposed method are illustrated through two large-scale simulated case studies: (i) a steel-making plant, and (ii) the Tennessee Eastman challenge problem.

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Filter-based Constraints to Easier Plant Feasibility in Modifier Adaptation Schemes

Cited by 2 publications

References 8 publications

Output modifier adaptation with filter-based constraints

Output modifier adaptation with filter-based constraints

Internal Modifier Adaptation for the Optimization of Large-Scale Plants with Inaccurate Models

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