Cited 22 time in
High shear seeded granulation: Its preparation mechanism, formulation, process, evaluation, and mathematical simulation
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Maharjan, Ravi | - |
| dc.contributor.author | Jeong, Seong Hoon | - |
| dc.date.accessioned | 2024-08-08T07:02:15Z | - |
| dc.date.available | 2024-08-08T07:02:15Z | - |
| dc.date.issued | 2020-04-15 | - |
| dc.identifier.issn | 0032-5910 | - |
| dc.identifier.issn | 1873-328X | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/19454 | - |
| dc.description.abstract | Seeded granulation is one of the recently adopted wet granulation techniques. The seeded granules consist of high-density core nuclei and low-density particles layered on the outside. It depends on various process and formulation factors. The mechanism of granule formation will be clarified and the methods to predict the suitable conditions for the optimum granules. Moreover, measurement tools and models need to be explored to understand the granulation process. Computational modeling will be introduced such as discrete element method (DEM) to evaluate granules, and computational fluid dynamics (CFD) to study binder fluid interactions. Both can be coupled and CFD-DEM-PBM framework can relate the effects of particle-fluid interactions. The state-of-the-art simulation methods may bridge the gap between micro and meso scales of seeded granulation process. Measurement techniques are also discussed to obtain reliable experimental data and this review assesses the prospective of on-line soft sensing measurement and application to continuous granulation. (C) 2020 Elsevier B.V. All rights reserved. | - |
| dc.format.extent | 22 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | ELSEVIER | - |
| dc.title | High shear seeded granulation: Its preparation mechanism, formulation, process, evaluation, and mathematical simulation | - |
| dc.type | Article | - |
| dc.publisher.location | 네델란드 | - |
| dc.identifier.doi | 10.1016/j.powtec.2020.03.020 | - |
| dc.identifier.scopusid | 2-s2.0-85081691155 | - |
| dc.identifier.wosid | 000528488000065 | - |
| dc.identifier.bibliographicCitation | POWDER TECHNOLOGY, v.366, pp 667 - 688 | - |
| dc.citation.title | POWDER TECHNOLOGY | - |
| dc.citation.volume | 366 | - |
| dc.citation.startPage | 667 | - |
| dc.citation.endPage | 688 | - |
| dc.type.docType | Review | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
| dc.subject.keywordPlus | POPULATION BALANCE MODEL | - |
| dc.subject.keywordPlus | TWIN-SCREW GRANULATION | - |
| dc.subject.keywordPlus | DISCRETE PARTICLE SIMULATION | - |
| dc.subject.keywordPlus | FLUIDIZED-BED GRANULATION | - |
| dc.subject.keywordPlus | PHARMACEUTICAL WET GRANULATION | - |
| dc.subject.keywordPlus | BEAM REFLECTANCE MEASUREMENT | - |
| dc.subject.keywordPlus | RESIDENCE TIME DISTRIBUTION | - |
| dc.subject.keywordPlus | NEAR-INFRARED SPECTROSCOPY | - |
| dc.subject.keywordPlus | CELLULAR-AUTOMATON MODEL | - |
| dc.subject.keywordPlus | END-POINT DETERMINATION | - |
| dc.subject.keywordAuthor | Seeded granulation | - |
| dc.subject.keywordAuthor | Granulation parameter | - |
| dc.subject.keywordAuthor | Simulation | - |
| dc.subject.keywordAuthor | Discrete element method (DEM) | - |
| dc.subject.keywordAuthor | Computational fluid dynamics (CFD) | - |
| dc.subject.keywordAuthor | Population balance model (PBM) | - |
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