Stochastic back-off-based robust process design for continuous crystallization of ibuprofen. Xie, X. & Schenkendorf, R. Computers and Chemical Engineering, 124:80-92, Elsevier Ltd, 2019.
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Robust model-based process design in continuous pharmaceutical manufacturing aims to implement quality by design principles under uncertainty. Notably, various studies have discussed the back-off concept to solve the underlying robust optimization problem; however, for the concept to have practical value, its efficiency and convergence must be improved. In this work, we introduce a novel, highly efficient stochastic back-off strategy. Instead of using statistical moments of limited validity, we incorporate the full statistical information of the constraints to solve the robust process design problem. To ensure manageable computational costs, we make use of polynomial chaos expansion for uncertainty quantification and propagation. The proposed concept is demonstrated with the design of a tubular crystallizer for ibuprofen crystallization. The results show that the novel stochastic back-off strategy is considerably faster compared with the standard back-off concept and provides more reliable quality by design results in general.

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