Model based robustness analysis of an ion-exchange chromatography step

Process development, optimization and robustness analysis for chromatographic separation are often entirely based on experimental work and generic knowledge. This paper describes a model-based approach that can be used to gain process knowledge and assist in the robustness analysis of an ion-exchang...

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Bibliographic Details
Published in:	Journal of Chromatography A 2007-01, Vol.1138 (1), p.109-119
Main Authors:	Jakobsson, Niklas, Degerman, Marcus, Stenborg, Emelie, Nilsson, Bernt
Format:	Article
Language:	eng
Subjects:	Analytical chemistry Chemical Engineering Chemistry Chromatographic methods and physical methods associated with chromatography chromatography Chromatography, Ion Exchange - methods Computer Simulation Engineering and Technology Exact sciences and technology Hydrogen-Ion Concentration ion-exchange Ion-exchange chromatography Kemiteknik Modeling Models, Theoretical Other chromatographic methods Preparative chromatography process-scale chromatogaphy Process-scale chromatography Robustness analysis Simulation Teknik Validation
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Summary:	Process development, optimization and robustness analysis for chromatographic separation are often entirely based on experimental work and generic knowledge. This paper describes a model-based approach that can be used to gain process knowledge and assist in the robustness analysis of an ion-exchange chromatography step using a model-based approach. A kinetic dispersive model, where the steric mass action model accounts for the adsorption is used to describe column performance. Model calibration is based solely on gradient elution experiments at different gradients, flow rates, pH and column loads. The position and shape of the peaks provide enough information to calibrate the model and thus single-component experiments can be avoided. The model is calibrated to the experiments and the confidence intervals for the estimated parameters are used to account for the model error throughout the analysis. The model is used to predict the result of a robustness analysis conducted as a factorial experiment and to design a robust pooling approach. The confidence intervals are used in a “worst case” approach where the parameters for the components are set at the edge of their confidence intervals to create a worst case for the removal of impurities at each point in the factorial experiment. The pooling limit was changed to ensure product quality at every point in the factorial analysis. The predicted purities and yields were compared to the experimental results to ensure that the prediction intervals cover the experimental results.
ISSN:	0021-9673