(672c) Equipment Optimization for Cryogenic Particle Formation Correlating Experimental and Model Results | AIChE

(672c) Equipment Optimization for Cryogenic Particle Formation Correlating Experimental and Model Results


Stamato, H. - Presenter, Bristol-Myers Squibb
Yazdanpanah, N., Procegence
Lopez, J., Procegence
Carson, R., MannKind Corporation
Bay, E., MannKind Corporation
Amoro, E., MannKind Corporation
Antunovich, J., MannKind Corporation
Cryogenic particle formation is applicable across industries and is often a first step to further drying at low pressure and temperature for a stable solid form of otherwise vulnerable materials (1,2,3,4). This essential process step is governed by complex phenomena where, in some cases, the actual process performance is still influenced by the machinist’s skill. In this abstract the effective use of an in-silico model delivered in the short term to inform and expedite physical models and experimentation is described.

Rapid particle formation at cryogenic conditions includes complex phenomena. The complexity is much more severe when the consistent particle properties (such as size, homogeneity, microstructure) are required for drug delivery and medical applications, and in addition, when the solution has nonnewtonian behavior, droplet generation and particle formation is fast, thus multiscale, multiphysics models are involved. The equipment, process, and product design for these cases can be extremely costly, time consuming, and inefficient, due to the complexities, fast transport phenomena, and nonlinearities.

The trial and error experimental efforts have been carried out conventionally for many cases and products. However, this approach is not comprehensive and less affordable in the long term. The value of in-silico modeling and first-principle analysis is well established and grows with every advance in software and computing platforms (5,6).

In this work the effective use of a high-fidelity simulation delivered in a short time to inform physical modifications and testing is described. The nozzles of a cryogenic particle formation machine were modified both in-silico and validated experimentally to understand and improve droplet formation prior to freezing. The process is used in production of powders for inhalation (7).

In this presentation, the simulations, underlying physics, experimental validation, model implementation, and industrial scale application will be discussed. The parametric study for different variables, sensitivity analysis, computation fluid dynamic (CFD) for Plateau–Rayleigh instability, and pressure wave resonance in droplet generation will be discussed in detail along with lab measurement and experimental data validation.


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