Modeling of transient flow through a viscoelastic preparative chromatography packing

Hekmat, Dariusch; Kühn, Michael; Meinhardt, Verena; Weuster‐Botz, Dirk

doi:10.1002/btpr.1768

Cited by 10 publications

(10 citation statements)

References 35 publications

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“…The compression is defined as the ratio of change in section height and initial section height according to

λ_{S e c} = \frac{h_{0, S e c} - h_{S e c}}{h_{0, S e c}},

where h 0,Sec is the initial section height. Assuming that the solid matrix of the chromatographic media is incompressible, compression is a result of reduction of the interparticle and the intraparticle void volume . The packed bed porosity ɛ and the compression factor λ are then correlated by

ɛ = \frac{ɛ_{0} - λ}{1 - λ},

where ɛ 0 is the gravity settled bed porosity …”

Section: Methodsmentioning

confidence: 99%

“…In this work, a so‐called soft‐particle DEM model was utilized, where the particles are allowed to overlap slightly. The repulsive normal force F n can be derived from the spatial overlap δ n and the normal relative velocity Δ u n , by a simple Kelvin‐Voigt spring‐dashpot model with spring and damper in parallel

{boldF}_{n} = - k_{n} {boldδ}_{n} + c_{n} {Δ boldu}_{n} .

…”

Section: Methodsmentioning

confidence: 99%

“…The mathematical models of all publications mentioned above are based on the assumption of purely elastic deformation behavior of the chromatographic packing according to Biot's theory of three‐dimensional consolidation . Hekmat et al reported a modeling approach of transient flow through a chromatography packing considering the viscoelasticity of the resins for the first time . In this work, the observed compression–relaxation hysteresis of the packing during flow compression was described by a simple one‐dimensional model.…”

Section: Introductionmentioning

confidence: 97%

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Simulation of the dynamic packing behavior of preparative chromatography columns via discrete particle modeling

Dorn

Hekmat

2015

Biotechnology Progress

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Preparative packed-bed chromatography using polymer-based, compressible, porous resins is a powerful method for purification of macromolecular bioproducts. During operation, a complex, hysteretic, thus, history-dependent packed bed behavior is often observed but theoretical understanding of the causes is limited. Therefore, a rigorous modeling approach of the chromatography column on the particle scale has been made which takes into account interparticle micromechanics and fluid-particle interactions for the first time. A three-dimensional deterministic model was created by applying Computational Fluid Dynamics (CFD) coupled with the Discrete Element Method (DEM). The column packing behavior during either flow or mechanical compression was investigated in-silico and in laboratory experiments. A pronounced axial compression-relaxation profile was identified that differed for both compression strategies. Void spaces were clearly visible in the packed bed after compression. It was assumed that the observed bed inhomogeneity was because of a force-chain network at the particle scale. The simulation satisfactorily reproduced the measured behavior regarding packing compression as well as pressure-flow dependency. Furthermore, the particle Young's modulus and particle-wall friction as well as interparticle friction were identified as crucial parameters affecting packing dynamics. It was concluded that compaction of the chromatographic bed is rather because of particle rearrangement than particle deformation. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 32:363-371, 2016.

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“…The compression is defined as the ratio of change in section height and initial section height according to

λ_{S e c} = \frac{h_{0, S e c} - h_{S e c}}{h_{0, S e c}},

ɛ = \frac{ɛ_{0} - λ}{1 - λ},

where ɛ 0 is the gravity settled bed porosity …”

Section: Methodsmentioning

confidence: 99%

{boldF}_{n} = - k_{n} {boldδ}_{n} + c_{n} {Δ boldu}_{n} .

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Simulation of the dynamic packing behavior of preparative chromatography columns via discrete particle modeling

Dorn

Hekmat

2015

Biotechnology Progress

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“…Additionally, structural mechanics models have been applied to calculate critical velocity values by investigating the stress state in rigid and compressible chromatography resins (Chase and Willis, 1992;Chen et al, 2005;Cherrak et al, 2001;Colby et al, 1996;Hekmat et al, 2013;Keener et al, 2004;McCue et al, 2007;Östergren, 1999). The majority of these models were developed in a two-dimensional coordinate reference system.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a structural mechanics model to predict the effect of inserts in the bed support of chromatographic columns

Gerontas¹,

Lan²,

Micheletti³

et al. 2015

Chemical Engineering Science

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Inserts are placed within chromatography columns to enhance wall support. The impact of column inserts is captured by structural mechanics models.Simulations are shown to be in good agreement with experimental data. The model helps selecting dimensions of inserts to increase column linear velocity. a b s t r a c tCell culture titres are expected to increase still further over the forthcoming years. This imposes challenges for downstream processing including the potential need to use larger volumes of chromatography resins. Such a move could create throughput bottlenecks because of the compressible nature of many commercially available resins, which makes the operation of columns with diameters beyond 2 m infeasible due to resin collapse. The use of cylindrical inserts of negligible thickness has been proposed in the literature as a way to enhance the level of wall support, allowing higher superficial flow velocities to be applied and hence larger diameter columns to be used. In this study, a structural mechanics model has been developed to evaluate the effect of inserts on the column pressure drop and flow characteristics. Simulations were shown to be in good agreement with published experimental data. The model was then used to predict the effect of insert number, diameter, height and roughness on critical velocity of manufacturing scale columns.

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“…Later, several correlations between the pressure drop and the measurable parameters were proposed to provide scaling rules for a packed column with compressible media. [8][9][10][11][12][13][14][15] Colby et al 11 used a modified version of the volume-averaged continuum theory to predict the pressure drop across a compressible particle bed of Sepharose gels, which is the first systematic approach for an accurate prediction of the pressure drop across a compressible gel particle bed. Tung et al 13 further studied the hydrodynamic behavior of fluid flow through a particle bed of soft Sepharose gel particles and investigated the effect of particle deformation on the porosity and the flow resistance.…”

Section: Introductionmentioning

confidence: 99%

Fluid flow through compressible soft particle beds

Tung

Chen

et al. 2016

AIChE Journal

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The fluid flow through a soft particle bed was studied theoretically and experimentally in this report. Several correction factors were introduced to modify the Ergun equation and account for the deformed shape and reduced volume of compressed particles in a compressible soft particle bed. To acquire the correction factors, both uni-compression tests and computational fluid dynamics simulation methods were used. The pressure drop estimated from the customized modeling equation was further compared with the experimental data obtained from a bed composed of calcium-alginate gel particles. In contrast to the conventional Ergun equation, the pressure drop estimated from the customized modeling equation was highly consistent with the experimental data. The result suggests that the customized modeling equation is a convenient tool for accurately predicting the pressure drop across a compressible soft particle bed.

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Modeling of transient flow through a viscoelastic preparative chromatography packing

Cited by 10 publications

References 35 publications

Simulation of the dynamic packing behavior of preparative chromatography columns via discrete particle modeling

Simulation of the dynamic packing behavior of preparative chromatography columns via discrete particle modeling

Evaluation of a structural mechanics model to predict the effect of inserts in the bed support of chromatographic columns

Fluid flow through compressible soft particle beds

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