TY - JOUR
T1 - New Discrete Formulation for Reduced Population Balance Equation
T2 - An Illustration to Crystallization
AU - Singh, Mehakpreet
AU - Walker, Gavin
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/9
Y1 - 2022/9
N2 - In this paper, we focus on providing a discrete formulation for a reduced aggregation population balance equation. The new formulation is simpler, easier to code, and adaptable to any type of grid. The presented method is extended to address a mixed-suspension mixed-product removal (MSMPR) system where aggregation and nucleation are the primary mechanisms that affect particle characteristics (or distributions). The performance of the proposed formulation is checked and verified against the cell average technique using both gelling and non gelling kernels. The testing is carried out on two benchmarking applications, namely batch and MSMPR systems. The new technique is shown to be computationally less expensive (approximately 40%) and predict numerical results with higher precision even on a coarser grid. Even with a revised grid, the new approach tends to outperform the cell average technique while requiring less computational effort. Thus the new approach can be easily adapted to model the crystallization process arising in pharmaceutical sciences and chemical engineering.
AB - In this paper, we focus on providing a discrete formulation for a reduced aggregation population balance equation. The new formulation is simpler, easier to code, and adaptable to any type of grid. The presented method is extended to address a mixed-suspension mixed-product removal (MSMPR) system where aggregation and nucleation are the primary mechanisms that affect particle characteristics (or distributions). The performance of the proposed formulation is checked and verified against the cell average technique using both gelling and non gelling kernels. The testing is carried out on two benchmarking applications, namely batch and MSMPR systems. The new technique is shown to be computationally less expensive (approximately 40%) and predict numerical results with higher precision even on a coarser grid. Even with a revised grid, the new approach tends to outperform the cell average technique while requiring less computational effort. Thus the new approach can be easily adapted to model the crystallization process arising in pharmaceutical sciences and chemical engineering.
KW - Aggregation
KW - Cell average technique
KW - Finite volume scheme
KW - Integro-partial differential equation
KW - Reduced model
UR - http://www.scopus.com/inward/record.url?scp=85135850505&partnerID=8YFLogxK
U2 - 10.1007/s11095-022-03349-0
DO - 10.1007/s11095-022-03349-0
M3 - Article
C2 - 35945303
AN - SCOPUS:85135850505
SN - 0724-8741
VL - 39
SP - 2049
EP - 2063
JO - Pharmaceutical Research
JF - Pharmaceutical Research
IS - 9
ER -