A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology
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 ca...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/86000 http://hdl.handle.net/10220/48319 |
| _version_ | 1811679532741033984 |
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| author | McGuire, Andrew D. Mosbach, Sebastian Lee, Kok Foong Reynolds, Gavin Kraft, Markus |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering McGuire, Andrew D. Mosbach, Sebastian Lee, Kok Foong Reynolds, Gavin Kraft, Markus |
| author_sort | McGuire, Andrew D. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:10:39Z |
| format | Journal Article |
| id | ntu-10356/86000 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:10:39Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/860002023-12-29T06:46:15Z A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology McGuire, Andrew D. Mosbach, Sebastian Lee, Kok Foong Reynolds, Gavin Kraft, Markus School of Chemical and Biomedical Engineering Granulation Twin-screw DRNTU::Engineering::Chemical engineering 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. Accepted version 2019-05-22T07:25:56Z 2019-12-06T16:14:07Z 2019-05-22T07:25:56Z 2019-12-06T16:14:07Z 2018 Journal Article McGuire, A. D., Mosbach, S., Lee, K. F., Reynolds, G., & Kraft, M. (2018). A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology. Chemical Engineering Science, 188, 18-33. doi:10.1016/j.ces.2018.04.077. 0009-2509 https://hdl.handle.net/10356/86000 http://hdl.handle.net/10220/48319 10.1016/j.ces.2018.04.077 en Chemical Engineering Science Chemical Engineering Science © 2018 Elsevier. All rights reserved. This paper was published in Chemical Engineering Science and is made available with permission of Elsevier. 50 p. application/pdf |
| spellingShingle | Granulation Twin-screw DRNTU::Engineering::Chemical engineering McGuire, Andrew D. Mosbach, Sebastian Lee, Kok Foong Reynolds, Gavin Kraft, Markus A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology |
| title | A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology |
| title_full | A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology |
| title_fullStr | A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology |
| title_full_unstemmed | A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology |
| title_short | A high-dimensional, stochastic model for twin-screw granulation part 2: numerical methodology |
| title_sort | high dimensional stochastic model for twin screw granulation part 2 numerical methodology |
| topic | Granulation Twin-screw DRNTU::Engineering::Chemical engineering |
| url | https://hdl.handle.net/10356/86000 http://hdl.handle.net/10220/48319 |
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