Advanced error modeling and Bayesian uncertainty quantification in mechanistic liquid chromatography modeling.

Heymann, William; Glaser, Juliane; Schlegel, Fabrice; Johnson, Will; Rolandi, Pablo; von Lieres, Eric

Heymann, William; Glaser, Juliane; Schlegel, Fabrice; Johnson, Will; Rolandi, Pablo; von Lieres, Eric.

Afiliación

Heymann W; Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str., Jülich 52428, Germany; RWTH Aachen University, Aachen 52062, Germany; Operations Digital Technology and Innovation Process Development (Ops DTI PD), Amgen Research Munich, Staffelseestr. 2, München 81477, Germa
Glaser J; Digital Integration and Predictive Technologies (DIPT), Amgen Research Munich, Staffelseestr. 2, München 81477, Germany.
Schlegel F; Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142.
Johnson W; Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142.
Rolandi P; Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142.
von Lieres E; Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str., Jülich 52428, Germany. Electronic address: e.von.lieres@fz-juelich.de.

J Chromatogr A ; 1708: 464329, 2023 Oct 11.

Article en En | MEDLINE | ID: mdl-37714013

Asunto(s)

Industrias; Incertidumbre; Teorema de Bayes; Cromatografía Liquida; Probabilidad

Palabras clave

Bayesian uncertainty quantification; CADET; Chromatography modeling; Error modeling

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Industrias Tipo de estudio: Prognostic_studies Idioma: En Revista: J Chromatogr A Año: 2023 Tipo del documento: Article

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