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SUMMARY:Optimal experimental design for nonlinear systems: Application to 
 microbial kinetics identification - Van Impe\, JFM (Katholieke Universitei
 t Leuven)
DTSTART:20110718T130000Z
DTEND:20110718T140000Z
UID:TALK32070@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Dynamic biochemical processes are omnipresent in industry\, e.
 g.\, brewing\, production of enzymes and pharmaceuticals. However\, since 
 accurate models are required for model based optimisation and measurements
  are often labour and cost intensive\, Optimal Experiment Design (OED) tec
 hniques for parameter estimation are valuable tools to limit the experimen
 tal burden while maximising the information content. To this end\, often s
 calar measures of the Fisher information matrix (FIM) are exploited in the
  objective function. In this contribution\, we focus on the parameter esti
 mation of nonlinear microbial kinetics. More specifically\, the following 
 issues are addressed: (1) Nonlinear kinetics. Since microbial kinetics is 
 most often nonlinear\, the unknown parameters appear explicitly in the des
 ign equations. Therefore\, selecting optimal initialization values for the
 se parameters as well as setting up a convergent sequential design scheme 
 is of great importance. (2) Biological kinetics. Since we deal with models
  for microbial kinetics\, the design of dynamic experiments is facing addi
 tional constraints. For example\, upon applying a step change in temperatu
 re\,  an (unmodelled) lag phase is induced in the microbial population's r
 esponse. To avoid this\, additional constraints need to be formulated on t
 he admissible gradients of the input profiles thus safeguarding model vali
 dity under dynamically changing environmental conditions. (3) Not only do 
 different scalar measures of the FIM exist\, but they may also be competin
 g. For instance\, the E-criterion tries to minimise the largest error\, wh
 ile the modified E-criterion aims at obtaining a similar accuracy for all 
 parameters. Given this competing nature\, a multi-objective optimisation a
 pproach is adopted for tackling these OED problems. The aim is to produce 
 the set of optimal solutions\, i.e.\, the so-called Pareto set\, in order 
 to illustrate the trade-offs to be made. In addition\, combinations of par
 ameter estimation quality and productivity related objectives are explored
  in order to allow an accurate estimation during production runs\, and dec
 rease down-time and losses due to modelling efforts. To this end\, ACADO M
 ulti-Objective has been employed\, which is a flexible toolkit for solving
  dynamic optimisation or optimal control problems with multiple and confli
 cting objectives. The results obtained are illustrated with both simulatio
 n studies and experimental data collected in our lab. \n
LOCATION:Seminar Room 1\, Newton Institute
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