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

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.

References:

1. D Fissore Freeze-drying in the coffee industry, 2015 https://www.newfoodmagazine.com/article/16968/freeze-drying-in-the-coffee-industry/ Accessed March 19, 2022
2. TL Rogers, KP Johnston, RO Williams III, Solution-Based Particle Formation of Pharmaceutical Powders by Supercritical or Compressed Fluid CO2 and Cryogenic Spray Freezing Technologies, Drug Development and Industrial Pharmacy 27(10) 2001, pp 1003-1015.
3. H Shiffter, J Condliffe, S Conhoff, Spray-freeze-drying of nanosuspensions: the manufacture of insulin particles for needle-free ballistic powder delivery, Journal of the Royal Society Interface 2010 Aug 6; 7 Suppl 4 S483-500
4. Droplet Stream Freeze-Drying for the Production of Protein Formulations: From Simulation to Production S Wanning, M Jager, SN Eggerstedt, R Suverkrup, A Lamprecht, M Dietzel, M Kuschel, A Darvan, M Sommerfeld, in Process Spray U Fritsching, (ed) Springer 2 August 2016, pp.341-381
5. E C Joseph, S Conway, C Dekate, Economic Model For a Return on Investment Analysis of United States Government High Performance Computing (HPC) Research and Development (R & D) Investment, Technical Report for U. S. Department of Energy Office of Science and Technical Information September 2013 https://www.osti.gov/biblio/1156830 accessed March 19, 2022
6. J Kostal A Voutchkova-Kostal Going All In: A strategic Investment in In Silico Toxicology, Chem Res. Toxicol. 2020, 33(4), 880-888
7. Amoro, K Vanackere, MA White Apparatus and method for cryogranulating a pharmaceutical composition U.S. Patent 8950320B2