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Detection, quantification, and propagation of uncertainty in high-throughput experimentation by Monte Carlo methods

Osberghaus, Anna; Baumann, Pascal; Hepbildikler, Stefan; Nath, Susanne; Haindl, Markus; Lieres, Eric von; Hubbuch, Jürgen

The search for a favorable and robust operating point of a separation process represents a complex multi-factor optimization problem. This problem is typically tackled by design of experiments (DoE) in the factor space and empiric response surface modeling (RSM); however, separation optimizations based on mechanistic modeling are on the rise. In this paper, a DoE–RSM-approach and a mechanistic modeling approach are compared with respect to their performance and predictive power by means of a case study – the optimization of a multicomponent separation of proteins in an ion exchange chromatography step with a nonlinear gradient (ribonuclease A, cytochrome c and lysozyme on SP Sepharose FF). The results revealed that at least for complex problems with low robustness, the performance of the DoE-approach is significantly inferior to the performance of the mechanistic model. While some influential factors of the system could be detected with the DoE–RSM-approach, predictions concerning the peak resolutions were mostly inaccurate and the optimization failed. The predictions of the mechanistic model for separa ... mehr

Zugehörige Institution(en) am KIT Institut für Bio- und Lebensmitteltechnik (BLT)
Publikationstyp Zeitschriftenaufsatz
Jahr 2012
Sprache Englisch
Identifikator DOI: 10.1002/ceat.201100610
ISSN: 0930-7516
KITopen ID: 1000046691
Erschienen in Chemical Engineering and Technology
Band 35
Heft 8
Seiten 1456-1464
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