A two-loop optimization strategy for multi-objective optimal experimental design

Research output: Contribution to journalConference Contribution

Abstract

A new strategy of optimal experimental design (OED) is proposed for a kinetically controlled synthesis system by considering both observation design and input design. The observation design that combines sampling scheduling and measurement set selection is treated as a single optimization problem arranged in the inner loop, while the optimization of input intensity is calculated in the outer loop. This multi-objective dynamic optimization problem is solved via the integration of particle swarm algorithm (for the outer loop) and the interior-point method (for the inner loop). Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties. In addition, process optimization of the case study enzyme reaction system is investigated with the aim to obtain maximum production rate by taking into account of the experimental cost.
LanguageEnglish
Pages803-808
Number of pages6
JournalIFAC-PapersOnLine
Volume49
Issue number7
Early online date1 Jun 2016
DOIs
Publication statusPublished - 9 Aug 2016
EventThe 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS-CAB 2016) - Trondheim, Trondheim, Norway
Duration: 6 Jun 20168 Jun 2016

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Design of experiments
Parameter estimation
Enzymes
Scheduling
Sampling
Costs

Keywords

  • multi-objective optimal experimental design
  • process optimization
  • observation strategy
  • input design
  • parameter estimation
  • enzyme reaction system
  • kinetically controlled synthesis system

Cite this

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title = "A two-loop optimization strategy for multi-objective optimal experimental design",
abstract = "A new strategy of optimal experimental design (OED) is proposed for a kinetically controlled synthesis system by considering both observation design and input design. The observation design that combines sampling scheduling and measurement set selection is treated as a single optimization problem arranged in the inner loop, while the optimization of input intensity is calculated in the outer loop. This multi-objective dynamic optimization problem is solved via the integration of particle swarm algorithm (for the outer loop) and the interior-point method (for the inner loop). Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties. In addition, process optimization of the case study enzyme reaction system is investigated with the aim to obtain maximum production rate by taking into account of the experimental cost.",
keywords = "multi-objective optimal experimental design, process optimization, observation strategy, input design, parameter estimation, enzyme reaction system, kinetically controlled synthesis system",
author = "Hui Yu and Hong Yue and Peter Halling",
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A two-loop optimization strategy for multi-objective optimal experimental design. / Yu, Hui; Yue, Hong; Halling, Peter.

In: IFAC-PapersOnLine, Vol. 49, No. 7, 09.08.2016, p. 803-808.

Research output: Contribution to journalConference Contribution

TY - JOUR

T1 - A two-loop optimization strategy for multi-objective optimal experimental design

AU - Yu, Hui

AU - Yue, Hong

AU - Halling, Peter

PY - 2016/8/9

Y1 - 2016/8/9

N2 - A new strategy of optimal experimental design (OED) is proposed for a kinetically controlled synthesis system by considering both observation design and input design. The observation design that combines sampling scheduling and measurement set selection is treated as a single optimization problem arranged in the inner loop, while the optimization of input intensity is calculated in the outer loop. This multi-objective dynamic optimization problem is solved via the integration of particle swarm algorithm (for the outer loop) and the interior-point method (for the inner loop). Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties. In addition, process optimization of the case study enzyme reaction system is investigated with the aim to obtain maximum production rate by taking into account of the experimental cost.

AB - A new strategy of optimal experimental design (OED) is proposed for a kinetically controlled synthesis system by considering both observation design and input design. The observation design that combines sampling scheduling and measurement set selection is treated as a single optimization problem arranged in the inner loop, while the optimization of input intensity is calculated in the outer loop. This multi-objective dynamic optimization problem is solved via the integration of particle swarm algorithm (for the outer loop) and the interior-point method (for the inner loop). Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties. In addition, process optimization of the case study enzyme reaction system is investigated with the aim to obtain maximum production rate by taking into account of the experimental cost.

KW - multi-objective optimal experimental design

KW - process optimization

KW - observation strategy

KW - input design

KW - parameter estimation

KW - enzyme reaction system

KW - kinetically controlled synthesis system

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