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

Abstract

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.

Improved packing of preparative biochromatography columns by mechanical vibration

“…It is well-known that such materials interact dissipatedly through inelastic collisions and a network of frictional force-chains which influence bed formation and long-term bed stability. 23,24 This was observed during the study of the influence of particle friction during the packing of two particle types with distinct and different levels of surface roughness. It was observed that smooth particles with lower friction slipped more readily during packing, resulting in a denser, albeit more inhomogeneous, microstructure.…”

“…The 3D reconstruction of real packings enables the geometric analysis of their structure 20,21 or their use in posterior computer simulations. 22 Additionally, granular simulations of in silico generated packings permit the study of events difficult to access in real packings, 23 thus enabling the study of timedependent processes such as structure evolution under operation or particle migration. Among several works following this approach, 6,13 the effect of the slurry concentration during packing on the bed structure and efficiency was studied.…”

“…27 Unless perturbed by an external force, the system remains static 24 and is unable to reach its most homogeneous structure. 15,23 To escape the meta-stable state, energy must be supplied to the system. External perturbations can unlock the packing and allow the structure to explore the phase space 24 and densify the packing as particles fill void spaces made accessible by bridge collapses.…”

The use of predictive models to develop chromatography-based purification processes

“…Such deformation can be expected because column packing typically involves linear flow rates of up to 7 m h −1 (Boi et al, 2020), which compress the resin particles beyond the point achieved by gravity settlement alone, typically by a factor of 1.15 for synthetic polymer resins (e.g., based on methacrylic polymers) that are considered to be semi-rigid (Baru 2003;Lee et al, 2015;Gebauer and Tschöp 2018). Although such packing stabilizes the chromatography bed (Dorn et al, 2017), it occurs predominantly at the inlet and outlet of the column (Dorn and Hekmat 2016;Dorn et al, 2017), thus distorting the flow regime Frontiers in Bioengineering and Biotechnology frontiersin.org in these regions. The analysis of fluid dynamics in packed beds can account for some degree of resin particle polydispersity (Shalliker et al, 2002;Püttmann et al, 2014) but the nonspherical shapes caused by dense packing are generally not considered (Bouhid de Aguiar et al, 2017).…”

Automatic 3D cluster modelling of COVID-19 through voxel-based redistribution

“…Therefore, the complexity of the particle morphology become one major restriction for the rapid modelling of an urgent problem. In the previous study, the spherical assumption was widely adopted to simulate the particle redistribution in general [ 10 ], and discussion has extended to other regular-shaped particles, including column [ 11 ], elliptic [ 12 ] and polygon [ 13 ]. MD solution also adopted the spherical assumption, but intermolecular bonding was introduced [ 14 ].…”