Design of Optimal Experiments for Parameter Estimation of Microalgae Growth Models

Rafael Muñoz-Tamayo 1 Pierre Martinon 2, 3 Gaël Bougaran 4 Francis Mairet 1 Olivier Bernard 1
1 BIOCORE - Biological control of artificial ecosystems
LOV - Laboratoire d'océanographie de Villefranche, CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique
3 Commands - Control, Optimization, Models, Methods and Applications for Nonlinear Dynamical Systems
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France, UMA - Unité de Mathématiques Appliquées
Abstract : 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 that allows 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 solved as an optimal control problem. E-optimal experiments were obtained by using two discretization approaches namely sequential and simultaneous. The results showed that an adequate parameterization of the experimental inputs provided optimal solutions very close to those provided by the simultaneous discretization. Simulation results showed the relevance of determining optimal experimental inputs for achieving an accurate parameter estimation.
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Submitted on : Monday, September 2, 2013 - 2:12:53 PM
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Rafael Muñoz-Tamayo, Pierre Martinon, Gaël Bougaran, Francis Mairet, Olivier Bernard. Design of Optimal Experiments for Parameter Estimation of Microalgae Growth Models. Computer Applied to Biotechnology, Dec 2013, Mumbai, India. ⟨hal-00856749⟩



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