Influence of particle diameter distribution on protein recovery in the expanded bed adsorption process

Impact of Two-Dimensional Particle Size Distribution on Estimation of Water Vapor Diffusivity in Micrometric Size Cellulose Particles

This work aims at assessing the impact of two-dimensional particle size distribution (2D-PSD) on the identification of water vapor diffusivity in micrometric size cellulose particles displaying a size aspect ratio lower than 2 and a cylindrical shape. First, different methodologies to obtain the two-dimensional (2D) particle size distribution (diameter versus length) were compared, based on image analysis. Then, experimental sorption kinetics were obtained by using a quartz crystal microbalance (QCM) coupled with a water vapor adsorption system. Diffusivity values were estimated when considering either the 2D-PSD or global descriptors, such as the mean or median diameter and length of particles. Results revealed that the use of an analytical approach when considering the 2D mean-PSD or the median-PSD was the most accurate way to get diffusivity values at the scale of particles in a polydisperse sample of cellulose particles. Following this approach, a water vapor apparent diffusivity of 3.1 × 10⁻¹² ± 2.3 × 10⁻¹² m²·s⁻¹ was found for the considered cellulose sample. Neglecting PSD in diffusivity estimation led to an underestimation of a factor of 2. This procedure could be extended for all the polydisperse samples in order to have an accurate estimation of water vapor diffusivity at the scale of single particles.

Evaluation and characterization of axial distribution in expanded bed. I. Bead size, bead density and local bed voidage

Expanded bed adsorption (EBA) is an innovative chromatography technology that allows the adsorption of target proteins directly from unclarified feedstock, and the most important property of an expanded bed is the perfectly classified fluidization of resin beads in the column. Due to the variation of both size and density of bulk resin beads, the axial distributions of bead size, bead density and bed voidage are the inherent characteristics of an expanded bed. However, the understanding on these properties is quite limited. In this study, raw beads (3% crosslinked agarose containing tungsten carbide) and 2cm-diameter nozzle column were used as the model system and mean bead size, bead density and local bed voidage along the bed height were measured systematically with the in-bed sampling method for two settled bed heights (11.5 and 23.1cm) and different expansion factors (1.4-2.6). With the increase of bed height, mean bead size and wet density of the beads decreased from 140 to 90μm and from 4 to 2g/ml, respectively. The local bed voidage increased from 0.6 to 0.9 with the increasing bed height. The relative bed height and relative bed voidage were introduced to describe the general rule of axial distribution. Some empirical equations were used to correlate the mean bead size, bead density and local bed voidage along the bed height with the standard deviations of 10.6%, 6.1% and 5.5, respectively. In addition, a general equation was proposed to predict the axial distributions of bead size, bead density and local bed voidage in the column with standard deviations less than 10% for most of the experimental data, which would be useful for the characterization of resin beads distribution in an expanded bed under varying operation conditions.

Modeling of ion exchange expanded-bed chromatography for the purification of C-phycocyanin

This work is focused on the experimental evaluation and mathematical modeling of ion exchange expanded-bed chromatography for the purification of C-phycocyanin from crude fermentative broth containing Spirulina platensis cells. Experiments were carried out in different expansion degree to evaluate the process performance. The experimental breakthrough curves were used to estimate the mass transfer and kinetics parameters of the proposed model, using the Particle Swarm Optimization algorithm (PSO). The proposed model satisfactorily fitted the experimental data. The results from the model application pointed out that the increase in the initial bed height does not influence the process efficiency, however enables the operation of expanded-bed column at high volumetric flow rates, improving the productivity. It was also shown that the use of mathematical modeling was a good and promising tool for the optimization of chromatographic processes.

Approaches to high-performance preparative chromatography of proteins

Preparative liquid chromatography is widely used for the purification of chemical and biological substances. Different from high-performance liquid chromatography for the analysis of many different components at minimized sample loading, high-performance preparative chromatography is of much larger scale and should be of high resolution and high capacity at high operation speed and low to moderate pressure drop. There are various approaches to this end. For biochemical engineers, the traditional way is to model and optimize a purification process to make it exert its maximum capability. For high-performance separations, however, we need to improve chromatographic technology itself. We herein discuss four approaches in this review, mainly based on the recent studies in our group. The first is the development of high-performance matrices, because packing material is the central component of chromatography. Progress in the fabrication of superporous materials in both beaded and monolithic forms are reviewed. The second topic is the discovery and design of affinity ligands for proteins. In most chromatographic methods, proteins are separated based on their interactions with the ligands attached to the surface of porous media. A target-specific ligand can offer selective purification of desired proteins. Third, electrochromatography is discussed. An electric field applied to a chromatographic column can induce additional separation mechanisms besides chromatography, and result in electrokinetic transport of protein molecules and/or the fluid inside pores, thus leading to high-performance separations. Finally, expanded-bed adsorption is described for process integration to reduce separation steps and process time.

Comparison of general rate model with a new model—artificial neural network model in describing chromatographic kinetics of solanesol adsorption in packed column by macroporous resins

Herein, two models, the general rate model taking into account convection, axial dispersion, external and intra-particle mass transfer resistances and particle size distribution (PSD) and the artificial neural network model (ANN) were developed to describe solanesol adsorption process in packed column using macroporous resins. First, Static equilibrium experiments and kinetic experiments in packed column were carried out respectively to obtain experimental data. By fitting static experimental data, Langmuir isotherm and Freundlich isotherm were estimated, and the former one was used in simulation coupled with general rate model considering better correlative coefficients. The simulated results showed that theoretical predictions of general rate model with PSD were well consistent with experimental data. Then, a new model, the ANN model, was developed to describe present adsorption process in packed column. The encouraging simulated results showed that ANN model could describe present system even better than general rate model. At last, by using the predictive ability of ANN model, the influence of each experimental parameter was investigated. Predicted results showed that with the increases of particle porosity and the ratio of bed height to inner column diameter (ROHD), the breakthrough time was delayed. On the contrary, an increase in feed concentration, flow rate, mean particle diameter and bed porosity decreased the breakthrough time.

Predictive modeling of protein adsorption along the bed height by taking into account the axial nonuniform liquid dispersion and particle classification in expanded beds

Expanded bed adsorption (EBA) is a special chromatography technique with perfect classification of adsorbent particles in the column, thus the performance of protein adsorption in expanded beds is particular, obviously nonuniform and complex along the column. Detailed description of the complex adsorption kinetics of proteins in expanded bed is essential for better analyzing of adsorptive mechanisms, the design of chromatographic processes and the optimization of operation parameters of EBA processes. In this work, a theoretical model for the prediction of protein adsorption kinetics in expanded beds was developed by taking into account the classified distribution of adsorbent particles along the bed height, the nonuniform behaviors of axial liquid dispersion, the axial variation of local bed voidage as well as the axial changes of target component mass transfer. The model was solved using the implicit finite difference scheme combining with the orthogonal collocation method, and then applied to predict the breakthrough behaviors of bovine serum albumin (BSA) on Streamline DEAE and lysozyme on Streamline SP along the bed height in expanded beds under various conditions. In addition, the experiments of front adsorption of BSA on Streamline DEAE at different axial column positions were carried out to reveal the adsorption kinetics of BSA along the bed height in a 20 mm I.D. expanded bed, and the influences of liquid velocity and feed concentration on the breakthrough behaviors were also analyzed. The breakthrough behaviors predicted by the present model were compared with the experimental data obtained in this work and in the literature published. The agreement between the prediction and the experimental breakthrough curves is satisfied.