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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Optimal experimental design for nonlinear systems:
Application to microbial kinetics identification
- Van Impe\, JFM (Katholieke Universiteit Leuven)
DTSTART;TZID=Europe/London:20110718T140000
DTEND;TZID=Europe/London:20110718T150000
UID:TALK32070AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/32070
DESCRIPTION:Dynamic biochemical processes are omnipresent in i
ndustry\, e.g.\, brewing\, production of enzymes a
nd pharmaceuticals. However\, since accurate model
s are required for model based optimisation and me
asurements are often labour and cost intensive\, O
ptimal Experiment Design (OED) techniques for para
meter estimation are valuable tools to limit the e
xperimental burden while maximising the informatio
n content. To this end\, often scalar measures of
the Fisher information matrix (FIM) are exploited
in the objective function. In this contribution\,
we focus on the parameter estimation of nonlinear
microbial kinetics. More specifically\, the follow
ing issues are addressed: (1) Nonlinear kinetics.
Since microbial kinetics is most often nonlinear\,
the unknown parameters appear explicitly in the d
esign equations. Therefore\, selecting optimal ini
tialization values for these parameters as well as
setting up a convergent sequential design scheme
is of great importance. (2) Biological kinetics. S
ince we deal with models for microbial kinetics\,
the design of dynamic experiments is facing additi
onal constraints. For example\, upon applying a st
ep change in temperature\, an (unmodelled) lag ph
ase is induced in the microbial population's respo
nse. To avoid this\, additional constraints need t
o be formulated on the admissible gradients of the
input profiles thus safeguarding model validity u
nder dynamically changing environmental conditions
. (3) Not only do different scalar measures of the
FIM exist\, but they may also be competing. For i
nstance\, the E-criterion tries to minimise the la
rgest error\, while the modified E-criterion aims
at obtaining a similar accuracy for all parameters
. Given this competing nature\, a multi-objective
optimisation approach is adopted for tackling thes
e OED problems. The aim is to produce the set of o
ptimal solutions\, i.e.\, the so-called Pareto set
\, in order to illustrate the trade-offs to be mad
e. In addition\, combinations of parameter estimat
ion quality and productivity related objectives ar
e explored in order to allow an accurate estimatio
n during production runs\, and decrease down-time
and losses due to modelling efforts. To this end\,
ACADO Multi-Objective has been employed\, which i
s a flexible toolkit for solving dynamic optimisat
ion or optimal control problems with multiple and
conflicting objectives. The results obtained are i
llustrated with both simulation studies and experi
mental data collected in our lab. \n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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