Considering the competitive and strongly regulated pharmaceutical industry, mathematical
modeling and process systems engineering might be useful tools for implementing quality by
design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However,
a crucial task in modeling (bio)pharmaceutical manufacturing processes is the reliable identification
of model candidates from a set of various model hypotheses. To identify the best experimental
design suitable for a reliable model selection and system identification is challenging for nonlinear
(bio)pharmaceutical process models in general. This paper is the first to exploit differential flatness
for model selection problems under uncertainty, and thus translates the model selection problem
to advanced concepts of systems theory and controllability aspects, respectively. Here, the optimal
controls for improved model selection trajectories are expressed analytically with low computational
costs. We further demonstrate the impact of parameter uncertainties on the differential flatness-based
method and provide an effective robustification strategy with the point estimate method for
uncertainty quantification. In a simulation study, we consider a biocatalytic reaction step simulating
the carboligation of aldehydes, where we successfully derive optimal controls for improved model
selection trajectories under uncertainty