Physics Embedded Approximations of Aggregation and Breakage Kernels for Population Balances


Project Title
Physics Embedded Approximations of Aggregation and Breakage Kernels for Population Balances
 
Funding Code
Ref 3.4-1146221-IND-IP
 
 
Principal Investigator
 
Status
Abgeschlossen
 
Duration
01-07-2018
-
30-06-2021
 
Funding Organization
 
Project Abstract
Particulate processes are the processes in which particles change their physical properties due to several mechanisms happening due to the interactions of the continuous and dispersed phases. Particulate processes are widely used in various fields of engineering such as aerosol, polymerization, chemical engineering, mining engineering, and emulsion processes. The physical properties that change due to different particulate processes are size, mass, porosity, shape etc. A well-known application of processing of particulate materials is the spray fluidized bed granulator (SFBG). SFBG are widely used to produce granular products for solubility control, tablet production, and other special materials in various industries. It has several advantageous features like temperature homogeneity, excellent heat and mass transfer properties, possibility of continuous operation and production of homogeneous granules.

The objective of this research is twofold: first is to simulate SFBG process using DEM-CFD approach, second to identify influential parameters and approximate them in the form of aggregation and breakage kernels of the PBE. More specifically, DEM-CFD simulations (substitute for lab scale experiments) will only facilitate to design the mathematical form of the kernels as a function of different micro-properties so that the PBE framework can be used to predict particle size distribution (primary interest for the final product) at low cost. The main problem of the modeling stands for the identification of important parameters that can reasonably be correlated. We wish to find a new kernel structure that makes full use of scaling and splitting opportunities, reducing the problem of identification to some time-independent factor that is either constant or can be reasonably correlated with operating parameters.
 

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