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Experimental evaluation of an approach to online redesign of experiments for parameter determination
Author(s) -
Barz Tilman,
López Cárdenas Diana C.,
ArellanoGarcia Harvey,
Wozny Günter
Publication year - 2013
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.13957
Subject(s) - nonlinear system , identifiability , solver , estimation theory , computer science , nonlinear programming , mathematical optimization , feature (linguistics) , algorithm , mathematics , machine learning , linguistics , philosophy , physics , quantum mechanics
The online redesign of experiments for parameter determination of nonlinear dynamic systems has been studied recently by different research groups. In this article, this technique is assessed in a real case study for the first time. The presented algorithm adopts well‐known concepts from model‐based control. Compared to previous studies, special attention is given to the efficient treatment of the underlying nonlinear and possibly ill‐conditioned parameter estimation and experiment design problems. These problems are solved with single shooting and gradient‐based nonlinear programming (NLP) solvers. We use an initial value solver, which generates first‐ and second‐order sensitivities to compute exact derivatives of the problem functions. As a special feature, we propose the integration of a local parameter identifiability analysis and a corresponding algorithm that generates well‐conditioned problems. The practical applicability is demonstrated by experimental application to a chromatography column system where A, D, and E optimal experiments are performed. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1981–1995, 2013