Modeling of aggregation kernel using Monte Carlo simulations of spray fluidized bed agglomeration

Abstract

The present work attempts to consider the microscopic mechanisms of spray fluidized bed agglomeration while modeling the macroscopic kinetics of the process. A microscale approach, constant volume Monte-Carlo simulation, is used to analyze the effects of micro-processes on the aggregation behavior and identify the influencing parameters. The identified variables, namely the number of wet particles, the total number of particles, and the number of droplets are modeled and combined in the form of an aggregation kernel. The proposed kernel is then used in a one-dimensional population balance equation for predicting the particle number density distribution. The only fitting parameters remaining in the population balance system are the collision frequency per particle and a success fraction accounting for the dissipation of kinetic energy. Predictions of the population balance model are compared with the results of Monte-Carlo simulations for a variation of significant operating parameters and found to be in good agreement. V C 2014 American Institute of Chemical Engineers AIChE J, 60: [855][856][857][858][859][860][861][862][863][864][865][866][867][868] 2014

Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process

“…The powders can be mixed, granulated, and dried in the same equipment, which minimizes the equipment costs, loss of product transfers, and possibility of the cross-contamination [3]. The rationale of spray fluidized-bed granulation can be briefly described as follows: (1) Particles circulate within granulator by pumping hot air from the bottom of granulator and meanwhile binder liquid is sprayed on the fluidized particles in the forms of small droplets [38]. (2) As a result of collisions and coalescence between the surface-wetted particles, liquid bridges are formed and aggregation of particles occurs leading to the growth of granules [1,39].…”

“…Detailed derivation process and setting of model parameters are given in [37,38], and they will not be covered here. The developed mechanism model is capable of not only linking APS of particles with the operating variable (binder spray rate), but also reflecting the relationship between APS and material attributes, such as viscosity of binder and particle density.…”

“…where ( , ) is the particle number density function in terms of particle diameter, , and is the aggregation model [37,38].…”

“…( , , ) can generally be partitioned into size dependent and size independent parts [3,37,38,40,41],…”

“…Second, such a model can be used as a simulator of a real SFBGP to generate the required process data used for simulation and analysis. In this work, a widely accepted mechanistic model for SFBGP [37,38] is introduced to simulate the actual SFBGP and produce the required process data.…”