Wang, L.G., Pradhan, S.U., Wassgren, C. et al. (3 more authors) (2020) A breakage kernel for use in population balance modelling of twin screw granulation. Powder Technology, 363. pp. 525-540. ISSN 0032-5910
Abstract
This paper presents a novel breakage kernel for use in population balance modelling for twin screw granulation (TSG) using mechanistic understanding in different screw elements. Breakage-isolated experiments are conducted using conveying and distributive mixing elements for a range of model formulations of widely different yield stresses. The breakage kernel, i.e. the selection and breakage functions, are mathematically formed based on the identification of the dominant breakage mechanisms of chipping and fragmentation in the conveying and distributive mixing elements, respectively, and the unique geometries of the two screw elements. A parametric study for the proposed breakage kernel is performed to identify the influential parameters on the breakage kernel. This is the first breakage model specifically developed for a TSG and incorporates a mechanistic understanding of several key parameters, particularly the role of screw geometry. The breakage model is well suited to population balance modelling framework for model-driven design of twin screw granulation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier. This is an author produced version of a paper subsequently published in Powder Technology. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Breakage kernel; Population balance model; Critical breakage size; Screw geometry; Twin screw granulation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Nov 2022 12:42 |
Last Modified: | 25 Nov 2022 12:42 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.powtec.2020.01.024 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193745 |