A superstructure-based design of experiments framework for simultaneous domain-restricted model identification and parameter estimation

We present a novel design of experiments (DOE) approach to incorporate model identification into optimal experimental designs based on a postulated model superstructure and an associated relaxation strategy. We show that an adaptive online design of experiments allows for the accurate estimation of the parameters of a domain-restricted model, as well as the model structure and domain on which that model is valid. We further show that previous attempts at combining model identification and parameter estimation are a special case of this framework (when the objective function is formulated in terms of the trace of the Fisher information matrix), and thus the proposed formulation provides the option to use alternate or more complex objective functions. The efficacy of the proposed framework is shown through two case studies: a batch reactor with Arrhenius-type reactions and a carbon dioxide adsorption system.