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Processes 2018, 6(12), 255;

An Analysis of Uncertainty Propagation Methods Applied to Breakage Population Balance

Chemical and BioProcess Technology and Control, Department of Chemical Engineering, KU Leuven, Gebroeders de Smetstraat 1, 9000 Ghent, Belgium
Crystalization Technology Unit, Janssen Pharmaceutica NV, Turnhoutseweeg 30, 2340 Beerse, Belgium
Author to whom correspondence should be addressed.
Received: 29 October 2018 / Revised: 21 November 2018 / Accepted: 6 December 2018 / Published: 8 December 2018
(This article belongs to the Special Issue Recent Advances in Population Balance Modeling)
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In data-driven empirical or hybrid modeling, the experimental data influences the model parameters and thus also the model predictions. The experimental data has some variability due to measurement noise and due to the intrinsic stochastic nature of certain pharmaceutical processes such as aggregation or breakage. To use predictive models, it is imperative that the accuracy of the predictions is known. To this extent, various uncertainty propagation techniques applied to a predictive breakage population balance model are studied. Three uncertainty propagation techniques are studied: linearization, sigma point, and polynomial chaos. These are compared to the uncertainty obtained from Monte Carlo simulations. Linearization performs the worst in the given scenario, while sigma point and polynomial chaos methods have similar performance in terms of accuracy. View Full-Text
Keywords: quality by design; uncertainty; population balance quality by design; uncertainty; population balance

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Bhonsale, S.; Telen, D.; Stokbroekx, B.; Van Impe, J. An Analysis of Uncertainty Propagation Methods Applied to Breakage Population Balance. Processes 2018, 6, 255.

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