While model-based optimal experimental design (OED) strategies aiming at maximizing the parameter precision are regularly applied in industry and academia, only a little attention has been payed to OED techniques for model discrimination in practical applications. A broader use of these techniques is mainly hindered by two drawbacks: (i) The use of such techniques is desirable in an early model identification phase, where only a little knowledge on the process is available. The known methods, however, rely on good estimates of the parameters of all candidate model structures. (ii) The available methods are tailored to few (ideally two) model candidates and do not work well if numerous candidate structures are taken into account. In this work we propose a novel design criterion for model-based OED for model discrimination in the case of multiple model candidates. The resulting OED method is thus well-suited for designing experiments in an early stage of the model identification process to efficiently reduce the number of model candidates, thereby reducing the overall cost for model identification.

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