Using the reactive/transport dispersive models to simulate a monolithic anion exchanger: Experimental parameter determination, simultaneous model evaluation, and validation.
Electrophoresis
; 2024 Jun 08.
Article
em En
| MEDLINE
| ID: mdl-38850174
ABSTRACT
Selecting an adequate model to represent the mass transfer mechanisms occurring in a chromatographic process is generally complicated, which is one of the reasons why monolithic chromatography is scarcely simulated. In this study, the chromatographic separation of model proteins bovine serum albumin (BSA), ß-lactoglobulin-A, and ß-lactoglobulin-B on an anion exchange monolith was simulated based on experimental parameter determination, simultaneous model testing, and validation under three statistical criteria retention time, dispersion accuracies, and Pearson correlation coefficient. Experimental characterization of morphologic, physicochemical, and kinetic parameters was performed through volume balances, pressure drop analysis, breakthrough curve analysis, and batch adsorptions. Free Gibbs energy indicated a spontaneous adsorption process for proteins and counterions. Dimensionless numbers were estimated based on height equivalent to a theoretical plate analysis, finding that pore diffusion controlled ß-lactoglobulin separation, whereas adsorption/desorption kinetics was the dominant mechanism for BSA. The elution profiles were modeled using the transport dispersive model and the reactive dispersive model coupled with steric mass action (SMA) isotherms because these models allowed to consider most of the mass transport mechanisms that have been described. RDM-SMA presented the most accurate simulations at pH 6.0 and at low (250 mM) and high (400 mM) NaCl concentrations. This simulation will be used as reference to forecast the purification of these proteins from bovine whey waste and to extrapolate this methodology to other monolith-based separations using these three statistical criteria that have not been used previously for this purpose.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article