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The benefit of model-based process simulation and optimization to enhance and improve process development depends on the reliability of the underlying model. Its selection and the values of the model parameters must be validated with experimental results. This work demonstrates that a model-based design of experiments integrated in a steady-state process flowsheet simulator supports model discrimination and parameter estimation and therefore increases the reliability of models and their parametrization. Realistic use cases including simulated vapor–liquid equilibria experiments for 2 binary systems (methanol + water for estimating parameters of an NRTL model and methanol + n-butane for selection of a PC SAFT association scheme) besides simulated plant experiments of a cumene plant for the estimation of kinetic parameters are chosen for an illustrative demonstration. Furthermore, details about the practical implementation and the workflow are given.
Asprion et al. (Mon,) studied this question.