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On the Use of Online Reparametrization in Automated Platforms for Kinetic Model Identification
Author(s) -
Quaglio Marco,
Waldron Conor,
Pankajakshan Arun,
Cao Enhong,
Gavriilidis Asterios,
Fraga Eric S.,
Galvanin Federico
Publication year - 2019
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.201800095
Subject(s) - robustness (evolution) , identification (biology) , estimation theory , computer science , parameter identification problem , system identification , online model , optimization problem , model parameter , mathematical optimization , work (physics) , algorithm , data modeling , mathematics , engineering , chemistry , statistics , mechanical engineering , biochemistry , botany , database , gene , biology
Abstract Parameter estimation algorithms integrated in automated platforms for kinetic model identification are required to solve two optimization problems: i) a parameter estimation problem given the available samples; ii) a model‐based design of experiments problem to select the conditions for collecting future samples. These problems may be ill‐posed, leading to numerical failures when optimization routines are applied. In this work, an approach of online reparametrization is introduced to enhance the robustness of model identification algorithms towards ill‐posed parameter estimation problems.