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dc.contributor.authorJimeno, G
dc.contributor.authorLee, Yeaw Chu
dc.contributor.authorNi, X-W
dc.date.accessioned2021-05-07T09:28:09Z
dc.date.available2021-05-07T09:28:09Z
dc.date.issued2018-12
dc.identifier.issn0255-2701
dc.identifier.issn1873-3204
dc.identifier.urihttp://hdl.handle.net/10026.1/17100
dc.description.abstract

While continuous oscillatory baffled reactors (COBR) have been proven a viable alternative to traditional batch reactors for organic synthesis and crystallization, research into the estimation of power density for this type of device has largely been stagnated for the past 25 years. This work reports, for the first time, detailed analysis and examination of the applicability, capability and deficiencies of two existing models using CFD methodology. The “quasi-steady” model (QSM) over-estimates power dissipation rates due to the inaccurate formulation of two of its geometric parameters for modern COBRs. By using a revised power law dependency on the number-of-baffles term (nx) and an appropriate orifice discharge coefficient (CD), we demonstrate that the updated QSM can not only be used for a much wider application range than previously outlined, but also for both batch and continuous operations. The “eddy enhancement” model (EEM) generally provides better predictions of power density for the conditions tested; however, its accuracy can substantially be enhanced by applying the aforementioned power law dependency on n and an empirical correlation proposed in this work to estimate EEM's “mixing length”. After full validation, both models give very similar power density estimations and can be used interchangeably with high confidence.

dc.format.extent153-162
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectPower density
dc.subjectOscillatory baffled reactors
dc.subjectPressure drop propagation
dc.subjectComputational fluid dynamics (CFD)
dc.titleOn the evaluation of power density models for oscillatory baffled reactors using CFD
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000453338400016&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume134
plymouth.publication-statusPublished
plymouth.journalChemical Engineering and Processing - Process Intensification
dc.identifier.doi10.1016/j.cep.2018.11.002
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2018-11-02
dc.rights.embargodate2021-5-15
dc.identifier.eissn1873-3204
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.cep.2018.11.002
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-12
rioxxterms.typeJournal Article/Review


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