Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen: http://dx.doi.org/10.25673/78159
Langanzeige der Metadaten
DC ElementWertSprache
dc.contributor.authorSingh, Abhinandan Kumar-
dc.contributor.authorTsotsas, Evangelos-
dc.date.accessioned2022-03-22T12:57:46Z-
dc.date.available2022-03-22T12:57:46Z-
dc.date.issued2021-
dc.date.submitted2021-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/80113-
dc.identifier.urihttp://dx.doi.org/10.25673/78159-
dc.description.abstractAgglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters.eng
dc.description.sponsorshipOVGU-Publikationsfonds 2021-
dc.language.isoeng-
dc.relation.ispartofhttp://www.mdpi.com/journal/energies-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectAgglomerationeng
dc.subjectMorphologyeng
dc.subjectMonte Carloeng
dc.subjectTunable aggregation modeleng
dc.subjectPolydisperse primary particleseng
dc.subject.ddc610.72-
dc.titleA fast and improved tunable aggregation model for stochastic simulation of spray fluidized bed agglomerationeng
dc.typeArticle-
dc.identifier.urnurn:nbn:de:gbv:ma9:1-1981185920-801135-
local.versionTypepublishedVersion-
local.bibliographicCitation.journaltitleEnergies-
local.bibliographicCitation.volume14-
local.bibliographicCitation.issue21-
local.bibliographicCitation.pagestart1-
local.bibliographicCitation.pageend18-
local.bibliographicCitation.publishernameMDPI-
local.bibliographicCitation.publisherplaceBasel-
local.bibliographicCitation.doi10.3390/en14217221-
local.openaccesstrue-
dc.identifier.ppn1776326024-
local.bibliographicCitation.year2021-
cbs.sru.importDate2022-03-22T12:53:29Z-
local.bibliographicCitationEnthalten in Energies - Basel : MDPI, 2008-
local.accessrights.dnbfree-
Enthalten in den Sammlungen:Fakultät für Verfahrens- und Systemtechnik (OA)

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
Singh et al._A fast and improved_2021.pdfZweitveröffentlichung1.93 MBAdobe PDFMiniaturbild
Öffnen/Anzeigen