Abstract
Membrane chromatography (MC) is increasingly being used as a purification platform for large biomolecules due to higher operational flow rates. The zonal rate model (ZRM) has previously been applied to accurately characterize the hydrodynamic behavior in commercial MC capsules at different configurations and scales. Explorations of capsule size, geometry and operating conditions using the model and experiment were used to identify possible causes of inhomogeneous flow and their contributions to band broadening. In the present study, the hydrodynamics within membrane chromatography capsules are more rigorously investigated by computational fluid dynamics (CFD). The CFD models are defined according to precisely measured capsule geometries in order to avoid the estimation of geometry related model parameters. In addition to validating the assumptions and hypotheses regarding non-ideal flow mechanisms encoded in the ZRM, we show that CFD simulations can be used to mechanistically understand and predict non-binding breakthrough curves without need for estimation of any parameters. When applied to a small-scale axial flow MC capsules, CFD simulations identify non-ideal flows in the distribution (hold-up) volumes upstream and downstream of the membrane stack as the major source of band broadening. For the large-scale radial flow capsule, the CFD model quantitatively predicts breakthrough data using binding parameters independently determined using the small-scale axial flow capsule, identifying structural irregularities within the membrane pleats as an important source of band broadening. The modeling and parameter determination scheme described here therefore facilitates a holistic mechanistic-based method for model based scale-up, obviating the need of performing expensive large-scale experiments under binding conditions. As the CFD model described provides a rich mechanistic analysis of membrane chromatography systems and the ability to explore operational space, but requires detailed knowledge of internal capsule geometries and has much greater computational requirements, it is complementary to the previously described strengths and uses of the ZRM for process analysis and design.
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