A high-dimensional, stochastic model for twin-screw granulation - Part 2: Numerical methodology
- 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.
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.
- This paper draws from the preprint: A high-dimensional, stochastic model for twin-screw granulation - Part 2: Numerical methodology.
- Access the article at the publisher: https://doi.org/10.1016/j.ces.2018.04.077
Keywords: Granulation, twin-screw, stochastic particle model, population balance model, stochastic weighted algorithm