FN Archimer Export Format
PT J
TI Getting the most out of it: Optimal experiments for parameter estimation of microalgae growth models
BT 
AF MUNOZ-TAMAYO, Rafael
   MARTINON, Pierre
   BOUGARAN, Gael
   MAIRET, Francis
   BERNARD, Olivier
AS 1:1;2:2;3:3;4:1;5:1,4;
FF 1:;2:;3:PDG-RBE-BRM-PBA;4:;5:;
C1 BIOCORE INRIA, F-06902 Sophia Antipolis, France.
   Ecole Polytech, CMAP, Commands INRIA Saclay, F-91128 Palaiseau, France.
   IFREMER, Lab Physiol & Biotechnol Algues, F-44311 Nantes 3, France.
   LOV UPMC CNRS, UMR 7093, Stn Zool, F-06234 Villefranche Sur Mer, France.
C2 INRIA, FRANCE
   ECOLE POLYTECH, FRANCE
   IFREMER, FRANCE
   UNIV PARIS 06, FRANCE
SI AUTRE
   NANTES
SE AUTRE
   PDG-RBE-BRM-PBA
IN WOS Ifremer jusqu'en 2018
   copubli-france
   copubli-univ-france
IF 2.653
TC 22
UR https://archimer.ifremer.fr/doc/00191/30252/28684.pdf
LA English
DT Article
DE ;Biofuel;Biological processes;Modelling;Parameter identification;Optimal experiment design
AB Mathematical models are expected to play a pivotal role for driving microalgal production towards a profitable process of renewable energy generation. To render models of microalgae growth useful tools for prediction and process optimization, reliable parameters need to be provided. This reliability implies a careful design of experiments that can be exploited for parameter estimation. In this paper, we provide guidelines for the design of experiments with high informative content based on optimal experiment techniques to attain an accurate parameter estimation. We study a real experimental device devoted to evaluate the effect of temperature and light on microalgae growth. On the basis of a mathematical model of the experimental system, the optimal experiment design problem was formulated and solved with both static (constant light and temperature) and dynamic (time varying light and temperature) approaches. Simulation results indicated that the optimal experiment design allows for a more accurate parameter estimation than that provided by the existing experimental protocol. For its efficacy in terms of the maximum likelihood properties and its practical aspects of implementation, the dynamic approach is recommended over the static approach.
PY 2014
PD JUL
SO Journal Of Process Control
SN 0959-1524
PU Elsevier Sci Ltd
VL 24
IS 6
UT 000338806000027
BP 991
EP 1001
DI 10.1016/j.jprocont.2014.04.021
ID 30252
ER

EF