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Publication Detail
An online reparametrisation approach for robust parameter estimation in automated model identification platforms
© 2019 Elsevier Ltd Automated model identification platforms were recently employed to identify parametric models online in the course of unmanned experimental campaigns. The algorithms controlling these platforms include two computational elements: i) a tool for parameter estimation; ii) a tool for model-based experimental design. Both tools require the solution of complex optimisation problems and their effective outcome relies on their respective objective functions being well-conditioned. Ill-conditioned objective functions may arise when the model is characterised by a weak parametrisation, i.e. the model parameters are practically non-identifiable and/or extremely correlated. In this work, a robust reparametrisation technique is proposed and tested both in-silico and in an automated model identification platform. The benefit of reparametrisation is demonstrated on a case study for the identification of a kinetic model of catalytic esterification of benzoic acid with ethanol in a flow microreactor.
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