A feedback-based implementation scheme for batch process optimization

Visser, Ewart J. de; Srinivasan, Bala; Паланки, Сринивас; Bonvin, Dominique

doi:10.1016/s0959-1524(00)00015-9

Cited by 69 publications

(47 citation statements)

References 52 publications

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“…Now, it is possible to determine λ(t) using (18), (21), and (22). However, the objective is to find the optimal control input; therefore, (22) is differentiated once more which yieldṡ…”

Section: Appendix Imentioning

confidence: 99%

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Profit Maximization of a Power Plant

Kragelund

Leth

Wiśniewski

et al. 2012

European Journal of Control

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Abstract-This paper addresses the problem of profit maximization of a power plant by utilizing three different fuel systems in an optimal manner. Pontryagin's maximum principle is used to derive properties of the optimal control strategy. These properties give rise to a switching function. Subsequently, certain heuristics are introduced and used in combination with discrete optimization to obtain an initial trajectory of the switching function. An iterative procedure is proposed, which uses the initial trajectory for the computation of the optimal control strategy. The control strategy derived is a combination of a state feedback and time-varying feedforward term. Its performance is tested against input noise.

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“…Now, it is possible to determine λ(t) using (18), (21), and (22). However, the objective is to find the optimal control input; therefore, (22) is differentiated once more which yieldṡ…”

Section: Appendix Imentioning

confidence: 99%

“…In this work, we propose an approach that results in a combined feedback and feed-forward solution, which yields a profit 30% greater than using an input signal obtained by discrete optimization. Feedback schemes for solving singular control problems have been proposed previously [22].…”

Section: Introductionmentioning

confidence: 99%

Profit Maximization of a Power Plant

Kragelund

Leth

Wiśniewski

et al. 2012

European Journal of Control

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show abstract

“…8. Iterations vs. reduction parameter r for the fed-batch penicillin fermentation process in [55] and the Van der Pol oscillator in [14,23], and for the fed-batch penicillin fermentation process T 0 = {0, 2, 4, 6, . .…”

Section: Discussionmentioning

confidence: 99%

“…The lower-level program is solved by integration over a fine grid. The problems selected are the fed-batch penicillin fermentation process in [55] and the Van der Pol (VDP) oscillator in [14,23]. The formulations for both problems are reported in Appendix A.…”

Section: Numerical Case Studiesmentioning

confidence: 99%

Local optimization of dynamic programs with guaranteed satisfaction of path constraints

Faust

Chachuat

et al. 2015

Automatica

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An algorithm is proposed for locating a feasible point satisfying the KKT conditions to a specified tolerance of feasible inequalitypath-constrained dynamic programs (PCDP) within a finite number of iterations. The algorithm is based on iteratively approximating the PCDP by restricting the right-hand side of the path constraints and enforcing the path constraints at finitely many time points. The main contribution of this article is an adaptation of the semi-infinite program (SIP) algorithm proposed in [Mitsos, Optimization, 2011, 60(10-11):1291-1308] to PCDP. It is proved that the algorithm terminates finitely with a guaranteed feasible point which satisfies the first-order KKT conditions of the PCDP to a specified tolerance. The main assumptions are: i) availability of a nonlinear program (NLP) local solver that generates a KKT point of the constructed approximation to PCDP at each iteration if this problem is indeed feasible; ii) existence of a Slater point of the PCDP that also satisfies the first-order KKT conditions of the PCDP to a specified tolerance; iii) all KKT multipliers are nonnegative and uniformly bounded with respect to all iterations. The performance of the algorithm is analyzed through two numerical case studies.

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“…The run-to-run optimization methodology proposed here is based on the concept of invariants (Visser et al, 2000;Bonvin et al, 2001). The idea is to identify those characteristics of the optimal solution that are invariant with respect to uncertainty and provide them as references to a feedback control scheme.…”

Section: Introductionmentioning

confidence: 99%

Run-to-run optimization via control of generalized constraints

Srinivasan

Primus

Bonvin

et al. 2001

Control Engineering Practice

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Run-to-run optimization methodologies exploit the repetitive nature of batch processes to determine the optimal operating policy in the presence of uncertainty. In this paper, a parsimonious parameterization of the inputs is used and the decision variables of the parameterization are updated on a run-to-run basis using a feedback control scheme which tracks signals that are invariant under uncertainty. In this run-to-run framework, terminal constraints of the optimization problem and cost sensitivities constitute the invariant signals. The methodology is adapted to improve the cost function from batch to batch without constraint violation.

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A feedback-based implementation scheme for batch process optimization

Cited by 69 publications

References 52 publications

Profit Maximization of a Power Plant

Profit Maximization of a Power Plant

Local optimization of dynamic programs with guaranteed satisfaction of path constraints

Run-to-run optimization via control of generalized constraints

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