Erratum to “Dynamic optimization of batch processes II. Role of measurements in handling uncertainty”

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

doi:10.1016/s0098-1354(03)00005-x

Cited by 105 publications

(214 citation statements)

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“…For the above static optimization problem, the constraint and the sensitivity parts of the NCO are given by (Srinivasan et al, 2003):…”

Section: Measurement-based Run-to-run Optimizationmentioning

confidence: 99%

“…One possibility of reducing this conservatism consists of using measurements to improve the performance of the real process. This can be accomplished via model refinement and re-optimization (explicit optimization) (Eaton et al, 1990) or by adapting the inputs directly (implicit optimization) (Srinivasan et al, 2003). This paper considers the use of measurements for optimization via the tracking of the necessary conditions of optimality (NCO) (Srinivasan et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…This can be accomplished via model refinement and re-optimization (explicit optimization) (Eaton et al, 1990) or by adapting the inputs directly (implicit optimization) (Srinivasan et al, 2003). This paper considers the use of measurements for optimization via the tracking of the necessary conditions of optimality (NCO) (Srinivasan et al, 2003). NCO tracking attempts to meet the NCO by adjusting the parameters of a solution model that is typically generated from numerical optimization of a nominal (tendency) model.…”

Section: Introductionmentioning

confidence: 99%

“…The resulting free variables are then linked to the various elements of the NCO, and appropriate adaptation laws are provided. So far, NCO tracking has been investigated via simulation of academic-size problems involving relatively low-order single-input systems (François et al, 2002, Srinivasan et al, 2003. However, the scheme should also be applicable to more complex systems since the ease with which it is implemented does not depend on the complexity of the system (in terms of the number of states or equations), but rather on the possibility of approximating the optimal solution with a solution model.…”

Section: Introductionmentioning

confidence: 99%

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Measurement-based run-to-run optimization of a batch reaction-distillation system

Marchetti

Srinivasan

Bonvin

et al. 2005

Computer Aided Chemical Engineering

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Measurement-based optimization schemes have been developed to deal with uncertainty and process variations. One of the methods therein, labeled NCO tracking, relies on appropriate parameterization of the input profiles and adjusts the corresponding input parameters using measurements so as to satisfy the necessary conditions of optimality (NCO). The applicability of NCO-tracking schemes has been demonstrated on several academic-size examples. The goal of this paper is to show that it can be applied with similar ease to more complex real-life systems. Run-to-run optimization of a batch reaction-separation system with propylene glycol is used for illustration.

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“…For the above static optimization problem, the constraint and the sensitivity parts of the NCO are given by (Srinivasan et al, 2003):…”

Section: Measurement-based Run-to-run Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measurement-based run-to-run optimization of a batch reaction-distillation system

Marchetti

Srinivasan

Bonvin

et al. 2005

Computer Aided Chemical Engineering

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“…The standard approach in the presence of uncertainties is to implement a conservative open-loop strategy that leads to feasible but sub-optimal operation. However, the measurements available during the batch or at batch end could be used to improve the performance by reducing this conservatism (Srinivasan et al, 2003a). Depending on how these measurements are used, two methodologies can be distinguished:…”

Section: Introductionmentioning

confidence: 99%

Combined On-Line and Run-to-Run Optimization of Batch Processes with Terminal Constraints

Welz

Srinivasan

Bonvin

2004

IFAC Proceedings Volumes

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This paper describes the optimization of batch processes in the presence of uncertainty and constraints. The optimal solution consists of keeping certain path and terminal constraints active and driving the sensitivities to zero. The case where the terminal constraints have a larger bearing on the cost than the sensitivities is considered, for which a two-time-scale methodology is proposed. The problem of meeting the active terminal constraints is addressed on-line using trajectory tracking, whilst pushing the sensitivities to zero is implemented on a run-to-run basis. The paper also discusses the run-to-run improvement of trajectory tracking via iterative learning control. The proposed methodology is illustrated in simulation on a batch distillation system.

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Evaluation of input parameterization for batch process optimization

Welz¹,

Srinivasan²,

Marchetti³

et al. 2006

AIChE Journal

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For the optimization of dynamic systems, it is customary to use measurements to combat the effect of uncertainty. In this context, an approach that consists of tracking the necessary conditions of optimality is gaining in popularity. The approach relies strongly on the ability to formulate an appropriate solution model, i.e. an approximate parameterization of the optimal inputs with a precise link to the necessary conditions of optimality. Hence, the need to be able to assess the capability of a solution model to optimize an uncertain process. This paper introduces a loss function that can be used to verify the conjecture that the solution model derived from a simplified process model can be applied to a more rigorous process model with negligible loss in performance. This conjecture is tested in simulation via the dynamic optimization of a batch distillation column.

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Erratum to “Dynamic optimization of batch processes II. Role of measurements in handling uncertainty”

Cited by 105 publications

References 0 publications

Measurement-based run-to-run optimization of a batch reaction-distillation system

Measurement-based run-to-run optimization of a batch reaction-distillation system

Combined On-Line and Run-to-Run Optimization of Batch Processes with Terminal Constraints

Evaluation of input parameterization for batch process optimization

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