# Heymann et al. (2023): Advanced error modeling and Bayesian uncertainty quantification in mechanistic liquid chromatography modeling

**URL:** <https://forum.cadet-web.de/t/heymann-et-al-2023-advanced-error-modeling-and-bayesian-uncertainty-quantification-in-mechanistic-liquid-chromatography-modeling/770>\
**Category:** CADET Literature Base\
**Tags:** application, technology\
**Created:** [September 13, 2023, 5:25pm UTC](https://forum.cadet-web.de/t/heymann-et-al-2023-advanced-error-modeling-and-bayesian-uncertainty-quantification-in-mechanistic-liquid-chromatography-modeling/770 "2023-09-13T17:25:55Z")\
**Posts on this page:** 1\
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**Author:** ![lieres](https://forum.cadet-web.de/user_avatar/forum.cadet-web.de/lieres/32/1238_2.png) [@lieres](https://forum.cadet-web.de/u/lieres)\
**Post date:** [September 13, 2023, 5:25pm UTC](https://forum.cadet-web.de/t/heymann-et-al-2023-advanced-error-modeling-and-bayesian-uncertainty-quantification-in-mechanistic-liquid-chromatography-modeling/770/1 "2023-09-13T17:25:55Z")

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Heymann, W.; Glaser, J.; Schlegel, F.; Johnson, W.; Rolandi, P.; von Lieres, E.: [Advanced error modeling and Bayesian uncertainty quantification in  
mechanistic liquid chromatography modeling](https://doi.org/10.1016/j.chroma.2023.464329), Journal of Chromatography A **1708** (2023): 464329.

Bill has analyzed root causes of non-Gaussian sources of uncertainty. A Bayesian approach allows to propagate the impacts of uncertainty across four levels of parameter estimation using chromatogram data under bypass, non-pore penetrating, pore-penetrating, and binding conditions. The method is demonstratet using synthetic and industrial process data.