Evaluating the Trade-Offs in Optimal Experiment Design Using a Multi-Objective Optimisation Approach

Abstract Dynamic process models are widely used for operating, controlling and optimising important bioprocesses, e.g., pharmaceuticals, enzyme production and brewing. After selection of an appropriate process model structure, parameter estimates have to be obtained based on real-life experiments. To reduce the amount of labour and often cost intensive experiments Optimal Experiment Design (OED) is an indispensable tool. In Optimal Experiment Design for parameter estimation a scalar measure of the Fisher Information Matrix is used as an objective function. Over the years different criteria have been developed. These criteria may be competing as they each have a slightly different objective. For systematically evaluating the competing nature and to improve the parameter estimation procedure, a multi-objective optimisation approach is selected. To solve the multi-objective dynamic optimisation problems efficiently ACADO Multi-Objective ( www.acadotoolkit.org ) has been employed, which is a flexible toolkit for solving dynamic optimisation or optimal control problems with multiple and conflicting objectives.