An anticipatory approach to optimal experimental design for model discrimination

Abstract

The problem of model discrimination arises when several models are proposed to describe one and the same process. To identify the best model from the set of rival models, it may be necessary to collect new information about the process, and thus additional experiments have to be performed. This paper deals with the experimental design methodologies that are used to find the experimental conditions that allow to discriminate among rival models with the least experimental effort. For this, the expected experimental results should be predicted differently by the rival models, and the uncertainty on the measurements and on the model predictions should not be too large. These aspects were included in the approach developed by Buzzi-Ferraris and co-workers [G. Buzzi-Ferraris, P. Forzatti, G. Emig, H. Hofmann, Sequential experimental design procedure for model discrimination in the case of multiple responses. Chemical Engineering Science (1) (1984) 81–85], but in their approach the uncertainties are estimated from the information content of the already performed experiments. This work presents a modification of the Buzzi-Ferraris approach in which the expected information content of the newly designed experiment is considered, even before the experiment is performed (anticipatory design). In this way, a better estimate of the uncertainties is achieved, and an experiment with an increased discriminatory potential is obtained. The approaches were illustrated and compared by applying them to a case study in which two rival models are proposed to describe the in vitro kinetics of an enzyme.