Population Balance Modeling To Predict Particle Size Distribution upon Scale-Up of a Combined Antisolvent and Cooling Crystallization of an Active Pharmaceutical Ingredient

2019 ◽  
Vol 23 (12) ◽  
pp. 2666-2677 ◽  
Author(s):  
Tamar Rosenbaum ◽  
Li Tan ◽  
Michael Dummeldinger ◽  
Niall Mitchell ◽  
Joshua Engstrom
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Scale Up ◽  
Population Balance ◽  
Active Pharmaceutical Ingredient ◽  
Population Balance Modeling ◽  
Cooling Crystallization

scholarly journals Advantages of Utilizing Population Balance Modeling of Crystallization Processes for Particle Size Distribution Prediction of an Active Pharmaceutical Ingredient

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 355
Author(s):  
Tamar Rosenbaum ◽  
Li Tan ◽  
Joshua Engstrom
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Process Parameters ◽  
Crystallization Process ◽  
Population Balance ◽  
Active Pharmaceutical Ingredient ◽  
Population Balance Modeling ◽  
The Impact

Active pharmaceutical ingredient (API) particle size distribution is important for both downstream processing operations and in vivo performance. Crystallization process parameters and reactor configuration are important in controlling API particle size distribution (PSD). Given the large number of parameters and the scale-dependence of many parameters, it can be difficult to design a scalable crystallization process that delivers a target PSD. Population balance modeling is a useful tool for understanding crystallization kinetics, which are primarily scale-independent, predicting PSD, and studying the impact of process parameters on PSD. Although population balance modeling (PBM) does have certain limitations, such as scale dependency of secondary nucleation, and is currently limited in commercial software packages to one particle dimension, which has difficulty in predicting PSD for high aspect ratio morphologies, there is still much to be gained from applying PBM in API crystallization processes.

POPULATION BALANCE MODEL OF ICE CRYSTALS SIZE DISTRIBUTION DURING ICE SLURRY STORAGE

2014 ◽  
Vol 22 (02) ◽  
pp. 1440001 ◽  
Author(s):  
AIXIANG XU ◽  
ZHIQIANG LIU ◽  
TENGLEI ZHAO ◽  
XIAOXIAO WANG
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Crystal Size ◽  
Population Balance ◽  
Ice Crystals ◽  
Balance Model ◽  
Ice Slurry ◽  
Slurry Storage

Particle size distribution and number of ice crystals have a great influence on the flow and heat transfer performance of ice slurry. A population balance model (PBM) containing population and mass balances has been built to simulate numerically the development of ice particle size distribution during adiabatic ice slurry storage. The model assumes a homogeneously mixed and long-term storage tank in which the effect of breakage and aggregation between ice crystals was considered. For solving the population balance equations (PBEs) in the PBM, a semi-discrete finite volume scheme was applied. Finally, the effect of breakage and aggregation on development of ice particle size distribution was analyzed respectively. The results show that both breakage and aggregation are the two important effects on the particle size distribution and evolution of ice particle during storage, but they have opposite effect on the development of ice crystal size. In storage, breakage and aggregation have almost equivalent effect in the initial phase, but aggregation has dominant effect at last. The PBM results are in good agreement with experimental results by Pronk et al. [Effect of long-term ice slurry storage on crystal size distribution, 5th Workshop on Ice Slurries of the IIR (2002), pp. 151–160]. Therefore, the PBM presented in this paper is able to predict the development of particle size distribution during ice slurry storage.

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