Advanced score system and automated search strategies for parameter estimation in mechanistic chromatography modeling

Journal of Chromatography A, vol. 1661, pp. 462693

Abstract

Least squares estimation of unknown parameters from measurement data is a well-established standard method in chromatography modeling but can suffer from critical disadvantages. The description of real-world systems is generally prone to unaccounted mechanisms, such as dispersion in external holdup volumes, and systematic measurement errors, such as caused by pump delays. In this scenario, matching the shape between simulated and measured chromatograms has been found to be more important than the exact peak positions. We have therefore developed a new score system that separately accounts for the shape, position and height of individual peaks. A genetic algorithm is used for optimizing these multiple objectives. Even for non-conflicting objectives, this approach shows superior convergence in comparison to single-objective gradient search, while conflicting objectives indicate incomplete models or inconsistent data. In the latter case, Pareto optima provide important information for understanding the system and improving experiments. The proposed method is demonstrated with synthetic and experimental case studies of increasing complexity. All software is freely available as open source code (https://github.com/modsim/CADET-Match).

Authors 6

1. William Heymann Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute of Geo- and Biosciences 1 (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str. 1, 52428 Jülich, Germany; RWTH Aachen University, 52062 Aachen, Germany

   Institute of Geo- and Biosciences 1 (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str. 1, 52428 Jülich, Germany

   RWTH Aachen University, 52062 Aachen, Germany

2. Juliane Glaser

   Amgen (Germany)

   Affiliation as printed

   Digital Integration and Predictive Technologies (DIPT), Amgen Research Munich, Staffelseestr. 2, 81477 München, Germany

3. Fabrice Schlegel

   Amgen (United States)

   Affiliation as printed

   Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142, United States

4. Will Johnson

   Amgen (United States)

   Affiliation as printed

   Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142, United States

5. Pablo A. Rolandi

   Amgen (United States)

   Affiliation as printed

   Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142, United States

6. Eric von Lieres corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Geo- and Biosciences 1 (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str. 1, 52428 Jülich, Germany. Electronic address: e.von.lieres@fz-juelich.de

   Institute of Geo- and Biosciences 1 (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str. 1, 52428 Jülich, Germany