A high-dimensional, stochastic model for twin-screw granulation Part 2: Numerical methodology. (12th October 2018)

• Record Type: Journal Article
• Authors: McGuire, Andrew D.; Mosbach, Sebastian; Lee, Kok Foong; Reynolds, Gavin; Kraft, Markus
• Is Part Of: Chemical engineering science. Volume 188(2018)
• Page Start: 18
• Page End: 33
• Publication Date: 2018-10-12
• Subjects: Granulation -- Twin-screw -- Stochastic particle model -- Population balance model -- Stochastic weighted algorithm; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2018.04.077
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: Four-dimensional, stochastic population balance model for twin-screw granulation. Stochastic implementation of an immersion nucleation mechanism. Variable weighting algorithm for particle inception. Significant computational speed-up over traditional inception methods. 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 operating conditions are 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.