A high-dimensional, stochastic model for twin-screw granulation Part 2: Numerical methodology
Chemical Engineering Science
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McGuire, A., Mosbach, S., Lee, K., Reynolds, G., & Kraft, M. (2018). A high-dimensional, stochastic model for twin-screw granulation Part 2: Numerical methodology. Chemical Engineering Science, 188 18-33. https://doi.org/10.1016/j.ces.2018.04.077
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.
granulation, twin-screw, stochastic particle model, population balance model, stochastic weighted algorithm
The authors would like to thank AstraZeneca for funding this work. This work was partially funded by EPSRC Grant 1486478 and the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
External DOI: https://doi.org/10.1016/j.ces.2018.04.077
This record's URL: https://www.repository.cam.ac.uk/handle/1810/280605
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: http://creativecommons.org/licenses/by-nc-nd/4.0/
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