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Title: Optimal experiment design for nonlinear dynamic (bio)chemical systems using sequential semidefinite programming
Authors: Telen, Dries ×
Logist, Filip
Quirynen, Rien
Houska, Boris
Diehl, Moritz
Van Impe, Jan #
Issue Date: May-2014
Publisher: American Institute of Chemical Engineers
Series Title: AIChE Journal vol:60 issue:5 pages:1728-1739
Abstract: In this paper optimal experiment design for parameter estimation in nonlinear dynamic (bio)chemical processes is studied. To reduce the uncertainty in an experiment a suitable measure of the Fisher information matrix or variance-covariance matrix has to be optimized. In this work, novel optimization algorithms based on sequential semidefinite programming are proposed. The sequential semidefinite programming approach has specific advantages over sequential quadratic programming in the context of optimal experiment design. First of all, it guarantees on a matrix level a decrease of the uncertainty in the parameter estimation procedure by introducing a linear matrix inequality. Second, it allows an easy formulation of E-optimal designs in a direct optimal control optimization scheme. Finally, a third advantage of semidefinite programming is that problems involving the inverse of a matrix can be easily reformulated. The proposed techniques are illustrated in the design of experiments for a fed-batch bioreactor and a microbial kinetics case study. © 2014 American Institute of Chemical Engineers AIChE J, 2014
ISSN: 0001-1541
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Bio- & Chemical Systems Technology, Reactor Engineering and Safety Section
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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