A stochastic technique for multidimensional granulation modeling

Process Modeling in the Pharmaceutical Industry using the Discrete Element Method

“…The particle collision statistics compiled by the DEM simulation were used to develop a coalescence kernel. This kernel was then used with a Monte Carlo method to solve a multidimensional population balance 243. Good agreement with experiments was observed, but could be improved with better incorporation of material properties such as wet granule yield strength, Young's modulus, and asperity size.…”

Numerical study of a stochastic particle algorithm solving a multidimensional population balance model for high shear granulation

“…Moreover, stochastic algorithms have not only been applied to models 0021-9991/$ -see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.jcp.2010.06.021 for particulate processes such as crystallisation [17], nanoparticle synthesis [18][19][20][21][22] and granulation [23,24], but also to those for chemical reactions [25], liquid-liquid mixing [26,27] and droplet coalescence in clouds [28]. Several studies investigated the stochastic treatment of aggregation processes for one-and two-dimensional models [29][30][31][32][33].…”

Analysing the effect of screw configuration using a stochastic twin-screw granulation model

“…These examples have used a sectional solution approach [18,19,20] which allows the compartmental PBM to be approximated and solved as a system of ordinary differential equations.This numerical approach generally limits the particle representation to taken 45 3 on three dimensions at most. The Stochastic particle method [21,22,23, 24,25,26,27,28] is alternative approach that has been employed to solve PBMs for batch granulation systems [29, 30,31,32,33,34,35,36, 37], silica [38] and TiO 2 [39] nano-particle synthesis, soot formation [40,41], and more recently twin-screw granulation [42, 43]. Unlike sectional methods, stochastic particle 50 methods permit much more complex particle representations, which can then be leveraged within the process model description, whilst still yielding a numerical problem that can be solved with acceptable levels of computational effort.The main aims of this paper are:1.…”