Analytical and Preparative Chromatography | AIChE

Analytical and Preparative Chromatography

Type

Conference Presentation

Conference Type

AIChE Annual Meeting

Presentation Date

November 17, 2020

Duration

14 minutes

Skill Level

Intermediate

PDHs

0.20

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 years to remarkable progress in the areas of developing and commercializing highly selective and efficient stationary phases, understanding and quantifying the consequences of overloading chromatographic columns and exploiting more efficiently optimized driving forces for the separation using advanced operating regimes [1-3].

Focus of this lecture will be the application of forced modulations of certain process parameters during the process. In contrast to classical isocratic elution such gradient operation offers the potential to improve certain performance criteria. However, the design and optimization of gradient chromatography requires knowledge regarding numerous problem specific parameters.

In the main part of the lecture we will report recent progress regarding the derivation of instructive and simple to apply expressions of the moments of elution profiles under gradient conditions [4]. These expressions provide a useful tool for parameter estimation.

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

Fundamentals of Preparative and Nonlinear Chromatography, 2nd Edition,

Academic Press, New York, 2006

[2] Carta G., Jungbauer A.;

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

Wiley-VCH, 2020

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

Preparative Chromatography, 3rd 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

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