2001
DOI: 10.1002/aic.690470224
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Modeling mass transfer and hydrodynamics in fluidized‐bed adsorption of proteins

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Cited by 44 publications
(42 citation statements)
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“…The values of D p and k f for the three studied proteins varied from 1.24 × 10 −11 to 1.94 × 10 −11 m 2 /s and 11.28 × 10 −6 to 8.05 × 10 −6 m/s, respectively. These results are in the range of D p and k f values for macroporous particles reported in the literature [39,20,11,40]. According to Guiochon et al [41], film thickness and therefore mass transfer coefficient are determined by hydrodynamic conditions and depend on the liquid flow around the particle.…”
Section: Kinetic Of Adsorptionsupporting
confidence: 65%
“…The values of D p and k f for the three studied proteins varied from 1.24 × 10 −11 to 1.94 × 10 −11 m 2 /s and 11.28 × 10 −6 to 8.05 × 10 −6 m/s, respectively. These results are in the range of D p and k f values for macroporous particles reported in the literature [39,20,11,40]. According to Guiochon et al [41], film thickness and therefore mass transfer coefficient are determined by hydrodynamic conditions and depend on the liquid flow around the particle.…”
Section: Kinetic Of Adsorptionsupporting
confidence: 65%
“…Therefore, it will be more significant to research multi-component proteins competitive adsorption in an EBA process. Many authors have investigated single-component protein ion exchange rates in a fixed bed and in an EBA process (Alan and Carta, 2000;Bruce and Chase, 2001;Chen et al, 2003, Dziennik et al, 2005Kaczmarski et al, 2001;Li et al, 2004;Tong et al, 2003;Wright and Glasser, 2001), and some authors also have considered the case of multi-component proteins competitive adsorption in a fixed bed (Hubbuch et al, 2003;Lewus and Carta, 1999;Martin et al, 2005;Weinmrenner and Etzel, 1994), but few studies have been reported about protein competitive adsorption and desorption in an EBA process.…”
Section: Introductionmentioning
confidence: 98%
“…The flow structures in the lab-based fluidized beds were complex [12,13] and accurate quantification of these flow structures needed to be obtained in a wide range of time and expenses scales by experimental methods [14][15][16][17][18]. In the last few years, computational fluid dynamics (CFD) has been developed to understand such intricacies because it is more flexible than experimental studies and less expensive, and it allowed process engineers to predict, manipulate, and realize the desired fluid dynamics in process equipment [19,20], which was complementary to experimental methods with the onset of better computational and experimental facilities [14,[21][22][23][24][25][26][27][28][29][30][31][32][33].…”
Section: Introductionmentioning
confidence: 99%