1 póster.-- 29th EFFoST International Conference, 10-12 November 2015, Athens, GreeceRigorous, physics based, modeling is at the core of computer aided food process engineering. Models often
require the values of some, typically unknown, parameters (thermo-physical properties, kinetic constants,
etc). Therefore, parameter estimation from experimental data is critical to achieve desired model predictive
properties. Unfortunately, it must be admitted that often experiment design and modeling are fully
separated tasks: experiments are not designed for the purpose of modeling and models are usually derived
without paying especial attention to available experimental data or experimentation capabilities. When, at
some point, the parameter estimation problem is put on the table, modelers use available experimental
data to ``manually'' tune the unknown parameters. This results in inaccurate parameter estimates, usually
experiment dependent, with the implications this has in model validation.
This work takes a new look into the parameter estimation problem in food process modeling. First the
common pitfalls in parameter estimation are described. Second we present the theoretical background and
the numerical techniques to define a parameter estimation protocol to iteratively improve model predictive
capabilities. This protocol includes: reduced order modeling, structural and practical identifiability analyses,
data fitting with global optimization methods and optimal experimental design.
And, to finish, we illustrate the performance of the proposed protocol with an example related to the
thermal processing of packaged foods. The model was experimentally validated in the IIM-CSIC pilot plantThe authors acknowledge financial support from the EU (Project SPECTRAFISH), Spanish
Ministry of Science and Innovation (Project ISFORQUALITY) and CSIC (Project CONTROLA)Peer reviewe
