Mechanism-Enabled Population Balance Modeling of Particle Formation en Route to Particle Average Size and Size Distribution Understanding and Control

2019 ◽  
Vol 141 (40) ◽  
pp. 15827-15839 ◽  
Author(s):  
Derek R. Handwerk ◽  
Patrick D. Shipman ◽  
Christopher B. Whitehead ◽  
Saim Özkar ◽  
Richard G. Finke
Keyword(s):  
Size Distribution ◽  
Population Balance ◽  
Particle Formation ◽  
Population Balance Modeling ◽  
Average Size ◽  
And Control

Population Balance Modeling of Turbulent Mixing for Miscible Fluids

2005 ◽  
Vol 127 (3) ◽  
pp. 564-571 ◽  
Author(s):  
Giridhar Madras ◽  
Benjamin J. McCoy
Keyword(s):  
Size Distribution ◽  
Turbulent Mixing ◽  
Volume Ratio ◽  
Population Balance ◽  
Interfacial Area ◽  
Fundamental Operation ◽  
Population Balance Modeling ◽  
State Dependent ◽  
Negative Moment ◽  
Dependent Parameter

Blending one fluid into another by turbulent mixing is a fundamental operation in fluids engineering. Here we propose that population balance modeling of fragmentation-coalescence simulates the size distribution of dispersed fluid elements in turbulent mixing. The interfacial area between dispersed and bulk fluids controls the transfer of a scalar molecular property, for example, mass or heat, from the dispersed fluid elements. This interfacial area/volume ratio is proportional to a negative moment of the time-dependent size distribution. The mass transfer coefficient, in the form of a Damkohler number, is the single geometry- and state-dependent parameter that allows comparison with experimental data. The model results, easily realized by simple computations, are evaluated for batch and flow vessels.

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.

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