Figure 7: Upscaling to batch or inline modes
Let us consider that through certain mechanistic approaches, one establishes that the droplet size distribution of the emulsion is determined by the tip speed. This becomes one of the bases for the scale-up, and hence one needs to maintain the tip speed at both scale-1 and scale-2. Geometries of the rotor-stators help determine the speed at scale-2 based on the speed at scale-1.
Another item of interest would be to find out how long to mix at scale 2. This can be calculated by hypothesizing matching of the total time in high shear zone. Geometries of the rotor-stator, the active volume in the holding tank VL, Active1,2, help determine the duration of mixing at the plant scale.
Duration of mixing at plant scale t2=g(VH1 VH2 VL, Active1,2 )
Here VH,1 , VH,2 are the volumes of the high shear zones in the lab and plant mixers respectively, while t1 is the duration of mixing in the lab scale.
Additional level of improvements to such mechanistic approaches can be obtained through the use of computational fluid dynamics-based simulations, say to identify the active volume in the holding tank.
Where appropriate, when enough data is available and with appropriate sanity checks, fit for purpose data-based models can be employed to help with the needs.
Rotor- Stator based high shear mixers provide localized high intensity mixing and can help with needs in dispersing of liquid and solids.
Typical challenges of a practitioner include selection of right equipment and process conditions to disperse effectively and maintain the performance as the process translates across scales.
Understanding of underlying details of how the applied shear impacts the process liquids, and attention to details with the lab scale experimentation helps in defining appropriate scaling up strategies.
The science of mixing is advanced enough in terms of providing predictability for such operations for informed decision making by the practitioner. Predictive modelling based on mechanistic approaches provides quantitative guidance and reduce unnecessary trials and errors. As needed appropriate data driven capabilities can be leveraged.
In our first-hand experience, leveraging above approaches helped our clients improve reproducibility in obtaining a consistent dispersion/nanosuspensions with the right product performance and translate it across scales.
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