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Open AccessFeature PaperArticle

Robust Model Selection: Flatness-Based Optimal Experimental Design for a Biocatalytic Reaction

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Institute of Energy and Process Systems Engineering, Technische Universität Braunschweig, Franz-Liszt-Straße 35, 38106 Braunschweig, Germany
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Center of Pharmaceutical Engineering, Technische Universität Braunschweig, Franz-Liszt-Straße 35a, 38106 Braunschweig, Germany
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Author to whom correspondence should be addressed.
Processes 2020, 8(2), 190; https://doi.org/10.3390/pr8020190
Received: 29 November 2019 / Revised: 15 January 2020 / Accepted: 27 January 2020 / Published: 5 February 2020
(This article belongs to the Special Issue Advanced Methods in Process and Systems Engineering)
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.
Keywords: model selection; model-based design of experiments; differential flatness; parameter uncertainty; point estimate method; nonlinear programming model selection; model-based design of experiments; differential flatness; parameter uncertainty; point estimate method; nonlinear programming
MDPI and ACS Style

Schulze, M.; Schenkendorf, R. Robust Model Selection: Flatness-Based Optimal Experimental Design for a Biocatalytic Reaction. Processes 2020, 8, 190.

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