Constrained model-based design of experiments for the identification of approximated models

Abstract

The identification of an approximated model, once an opportune mathematical structure is selected, requires both a precise estimation of its parameters and the determination of the range of conditions in which the model provides accurate predictions, i.e., the domain of model reliability. A variety of model-based design of experiments (MBDoE) techniques are available in the literature for designing highly informative trials for the precise estimation of model parameters. Available MBDoE methods assume that the model structure is exact in the formulation of experimental design metrics. Hence, in the presence of an approximated model, the employment of conventional MBDoE approaches may lead to the collection and fitting of data at conditions where the model performance is very poor, thus leading to the degradation of the fitting performance and a loss of model predictive power. In this work, an iterative framework for the identification of approximated models is proposed in which the MBDoE step is constrained to the domain of model reliability. The method is tested on a simulated case study on the identification of an approximated kinetic model of catalytic ethanol dehydrogenation.