Processes with Rotor Stator Mixers: Leveraging Engineering Approaches To Bring Predictability and Take Informed Process Decisions


Priyanka Dhar (pdhar@anagha.consulting), Anagha Consultants

https://www.prodtwin.com/

https://www.anagha.consulting/

Introduction

Rotor-stator mixers are high shear homogenizers, consisting of high-speed rotors surrounded by stationary stator, used in food, cosmetics, pharmaceuticals, and specialty inks. The distinguishing feature of rotor stator is that they provide required amount of energy, power, or shear which expediates the physical processes, for e.g., mixing, homogenization, dissolution, neutralization, mass transfer and chemical reactions. Other applications are to maintain specific product/drop size to meet the requirements.1,2 These mixers primarily operate in two modes: Batch and Inline (Figure 1).

Figure 1. Rotor stator operation modes

Typical issues and asks

In case of rotor stator mixers, the practitioner’s asks in equipment selection and identification of process conditions are typically determined by the following considerations.

  • Ability to disperse, degree of uniformity/homogeneity in the mix.

  • Morphology and rheology determining the quality of the product.

  • Run time, and shear/power needed to meet the product quality requirements.

  • Maintaining the product quality as the process is translated across scales with the introduction of complexities (like changes in geometries and designs of the mixing head, and vessels)

How can we make informed decision to meet the above needs?

The science over the years has advanced to bring predictability for informed decision making in process development. Detailed insights, like for example6-8, link how the process conditions, geometry/ configurations of the mixing head and vessel impact the global and local level power received by the process liquid given its properties. Let us briefly look at what such learnings mean for practitioners in their routine product/ process development efforts.

  • Changes in any of the following aspects changes the flow characteristics of the liquid and the fragmentation mechanisms experienced by the dispersed phase. (Figures, 2, 3 and 4)

a. Designs of either of the rotor, stator, or the vessel

b. The rotor speed

c. The batch size

Figure 2: Impact of rotor stator design (screen design holes) on flow characteristics


  • Details of the actual usage of the mixer in an experiment add to this complexity and getting reproducible dispersion. (Figure 4)

a. Location of the mixing head in the vessel

b. Thermal management (through a cooling bath etc) as depending on the system, one can expect significant heat to be generated.

Depending on the application, if ingredients are added while mixing (for example introduction of solid particles, or wetting agents), their rate and location of addition.

Figure 3: Impact of rotor stator design and fluid properties on flow characteristics


  • Understanding of the intrinsic material characteristics is essential to understand the impact of the above factors and define steps to mitigate risks on product quality (Figures 5 and 6). Process analytic technologies can be helpful in this regard (understand the underlying mechanisms of particle dispersion, i.e coalescence, aggregation, rupture, erosion).

a. Impact of the applied shear on the morphology (size distribution of the dispersed phase) and the viscosity characteristics

b. Impact of temperature on the above behaviour


Figure 4: Key considerations for reproducibility at various scales: (i) Details of the actual usage of the mixer


Figure 5: Key considerations for reproducibility at various scales: (ii) Intrinsic material characteristics


Figure 6: Studies to mitigate risks of product quality: Kinetics of breakup.

Attention to the above details right from initial stages of product development at the lab scale is critical. This can help save unnecessary experimentation, significant resources (people and equipment time), and cycles of learning during the tech transfer stage. While in some situations, getting to this level of attention may mean ‘additional’ experimentation than a ‘traditional’ approach, we have seen clients seeing reduction in their overall quantity of experimentation as the quality of each of their experiment improved. Such data from the lab scale makes the journey to the plant easier.

Leveraging predictive capabilities for informed decision making

Detailed understanding, like the ones noted above, enable development of predictive capabilities to provide quantitative guidance for practitioner’s routine needs. Let us consider how one can leverage such capabilities using the example case of needing to scale-up from lab to plant scale for a liquid-liquid dispersion. Typically, in such a case, a batch mixer is used in the lab while an inline mixer is used in the plant as shown in Figure 6, however the geometries may be different.

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.

RPM2=f(RPM1)

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.

Key take aways

  • 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.

References

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