(570e) Applying the Method of Moments to Design Gradient Elution Chromatography

Besides the widespread application of chromatography as a powerful and flexible analytical technique, there is a large interest in the pharmaceutical industry and in biotechnology to isolate and purify value added products using preparative liquid chromatography. This led in the last decades to the development of advanced operating concepts, including regimes which exploit forced modulations of certain parameters during the chromatographic process. In contrast to classical isocratic elution the application of such gradient concepts offers significant potential to improve the separation performance. However, rational design and optimization of gradient chromatography requires knowledge regarding numerous thermodynamic and kinetic parameters [1-3].

Recently an analytical solution was derived to describe the effect of linear solvent gradients on the shape of elution profiles. This solution allows generating instructive equations for the first three moments of pulse responses [4]. Considering a case study, we will apply these moment expressions to estimate jointly column efficiencies and adsorption isotherm parameters as a function of the solvent composition. The method requires only a small number of chromatograms, which should be measured for different start and end times of linear gradients.

Finally, we will demonstrate the application of a parametrized column model to identify suitable gradient shapes.

[1] Guiochon G., Felinger A., Shirazi D.G., Katti A.M.,

Fundamentals of Preparative and Nonlinear Chromatography, 2^nd Edition,

Academic Press, New York, 2006

[2] Carta G., Jungbauer A.;

Protein Chromatography: Process Development and Scale-Up, 2^nd Edition,

Wiley-VCH, 2020

[3] Schmidt-Traub H., Schulte M., Seidel-Morgenstern A. (Eds.),

Preparative Chromatography, 3^rd Edition,

Wiley-VCH, Weinheim, 2020

[4] Qamar, S., Rehman, N., Carta, G., Seidel-Morgenstern, A.,

Analysis of gradient elution chromatography using the transport model,

Chemical Engineering Science, 2020, 225,115809

Besides the widespread application of chromatography as a powerful and flexible analytical technique, there is a large interest in the pharmaceutical industry and in biotechnology to isolate and purify value added products using preparative liquid chromatography. This led in the last decades to the development of advanced operating concepts, including regimes which exploit forced modulations of certain parameters during the chromatographic process. In contrast to classical isocratic elution the application of such gradient concepts offers significant potential to improve the separation performance. However, rational design and optimization of gradient chromatography requires knowledge regarding numerous thermodynamic and kinetic parameters [1-3].

Recently an analytical solution was derived to describe the effect of linear solvent gradients on the shape of elution profiles. This solution allows generating instructive equations for the first three moments of pulse responses [4]. Considering a case study, we will apply these moment expressions to estimate jointly column efficiencies and adsorption isotherm parameters as a function of the solvent composition. The method requires only a small number of chromatograms, which should be measured for different start and end times of linear gradients.

Finally, we will demonstrate the application of a parametrized column model to identify suitable gradient shapes.

[1] Guiochon G., Felinger A., Shirazi D.G., Katti A.M.,

Fundamentals of Preparative and Nonlinear Chromatography, 2^nd Edition,

Academic Press, New York, 2006

[2] Carta G., Jungbauer A.;

Protein Chromatography: Process Development and Scale-Up, 2^nd Edition,

Wiley-VCH, 2020

[3] Schmidt-Traub H., Schulte M., Seidel-Morgenstern A. (Eds.),

Preparative Chromatography, 3^rd Edition,

Wiley-VCH, Weinheim, 2020

[4] Qamar, S., Rehman, N., Carta, G., Seidel-Morgenstern, A.,

Analysis of gradient elution chromatography using the transport model,

Chemical Engineering Science, 2020, 225,115809