Abstract
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.