In the Quality by Design (QbD) framework, there is a significant emphasis on the robust characterization of manufacturing processes and prospectively identifying the engineering design space that ensures product quality. Although the QbD approach is expected to bring about significant long-term benefits, there are concerns regarding the increased amount of characterization work that will be required upfront, which can be very resource intensive. In cases where the number of input variables is large, performing a design of experiments (DOE) with statistical significance may be impractical.
Computational fluid dynamics (CFD) provides a first principles approach to gain insight into the hydrodynamics and mass transfer. The scope of traditional CFD methods can be extended to explore the engineering design space of the mixing process by coupling to statistical tools. In this talk, the authors summarize the results of a multivariate study of a prototypical fermentation process. And while a direct link to product quality cannot be provided by CFD alone, the authors hope to illustrate how CFD coupled with statistical methods can lower the risk of this unit operation having an adverse impact on product quality.
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