A high-dimensional, stochastic model for twin-screw granulation Part 2: Numerical methodology

Abstract

In the second part of this study, we present the stochastic weighted particle population balance framework used to solve the twin-screw granulation model detailed in the first part of this study. Each stochastic jump process is presented in detail, including a new nucleation jump event capable of capturing the immersion nucleation processes in twin-screw granulation. A variable weighted inception algorithm is presented and demonstrated to reduce the computational cost of simulations by up to two orders of magnitude over traditional approaches. The relationship between the performance of the simulation algorithm and key numerical parameters within the nucleation jump process are explored and optimum are operating conditions identified. Finally, convergence studies on the complete simulation algorithm demonstrate that the algorithm is very robust against changes in the number of stochastic particles used, provided that the number of particles exceeds a minimum required for numerical stability.

Partial least squares regression to calculate population balance model parameters from material properties in continuous twin-screw wet granulation

“…Despite all these mechanistic modeling contributions [13,18,19,20,21,22,23,24,25] to simulate the TSWG process, some links among material properties, PPs, and screw configurations remain unestablished within those models for them to be more generic and applicable for new drug developments. PBMs need to be calibrated for each formulation, which requires extensive experimental efforts.…”

A Population Balance Methodology Incorporating Semi-Mechanistic Residence Time Metrics for Twin Screw Granulation

“…The conveying element zones were assumed to favor granule formation through aggregation and the kneading elements were assumed to be conducive towards breakage of granules. McGuire et al [8,9] also are one of the few researchers to have incorporate residence time distribution (RTD) into PBEs by assuming an average mean residence time (MRT) which translated into a constant axial velocity of the particles at all locations for all attributes.…”