Real-time monitoring of gradient chromatography using dual Kalman-filters
Zandler-Andersson, Gusten LU ; Espinoza, Daniel LU ; Andersson, Niklas LU
and Nilsson, Bernt
LU
(2024)
In Journal of Chromatography A
1731.
- Abstract
- Real-time state estimation in chromatography is a useful tool to improve monitoring of biopharmaceutical downstream processes, combining mechanistic model predictions with real-time data acquisition to obtain an estimation that surpasses that of either approach individually. One common technique for real-time state estimation is Kalman filtering. However, non-linear adsorption isotherms pose a significant challenge to Kalman filters, which are dependent on fast algorithm execution to function. In this work, we apply Kalman filtering of non-constant elution conditions using a non-linear adsorption isotherm using a novel approach where dual Kalman filters are used to estimate the states of the adsorption modifier, salt, and the components to... (More)
- Real-time state estimation in chromatography is a useful tool to improve monitoring of biopharmaceutical downstream processes, combining mechanistic model predictions with real-time data acquisition to obtain an estimation that surpasses that of either approach individually. One common technique for real-time state estimation is Kalman filtering. However, non-linear adsorption isotherms pose a significant challenge to Kalman filters, which are dependent on fast algorithm execution to function. In this work, we apply Kalman filtering of non-constant elution conditions using a non-linear adsorption isotherm using a novel approach where dual Kalman filters are used to estimate the states of the adsorption modifier, salt, and the components to be separated. We performed offline tuning of the Kalman filters on real chromatogram data from a linear gradient, ion-exchange separation of two proteins. The tuning was then validated by running the Kalman filters in parallel with a chromatographic separation in real time. The resulting, tuned, dual Kalman filters improved the L2 norm by 53% over the open-loop model prediction, when compared to the true elution profiles. The Kalman filters were also applicable in real-time with a signal sampling frequency of 5 seconds, enabling accurate and robust estimation and paving the way for future applications beyond monitoring, such as real-time optimal pooling control. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/696343b6-a605-48d7-b320-750698185c7b
- author
- Zandler-Andersson, Gusten
LU
; Espinoza, Daniel
LU
; Andersson, Niklas
LU
and Nilsson, Bernt
LU
- organization
- publishing date
- 2024-07-14
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Chromatography A
- volume
- 1731
- article number
- 465161
- pages
- 11 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85198971010
- ISSN
- 0021-9673
- DOI
- 10.1016/j.chroma.2024.465161
- language
- English
- LU publication?
- yes
- id
- 696343b6-a605-48d7-b320-750698185c7b
- date added to LUP
- 2024-07-18 10:59:59
- date last changed
- 2024-08-08 11:39:07
@article{696343b6-a605-48d7-b320-750698185c7b,
abstract = {{Real-time state estimation in chromatography is a useful tool to improve monitoring of biopharmaceutical downstream processes, combining mechanistic model predictions with real-time data acquisition to obtain an estimation that surpasses that of either approach individually. One common technique for real-time state estimation is Kalman filtering. However, non-linear adsorption isotherms pose a significant challenge to Kalman filters, which are dependent on fast algorithm execution to function. In this work, we apply Kalman filtering of non-constant elution conditions using a non-linear adsorption isotherm using a novel approach where dual Kalman filters are used to estimate the states of the adsorption modifier, salt, and the components to be separated. We performed offline tuning of the Kalman filters on real chromatogram data from a linear gradient, ion-exchange separation of two proteins. The tuning was then validated by running the Kalman filters in parallel with a chromatographic separation in real time. The resulting, tuned, dual Kalman filters improved the L2 norm by 53% over the open-loop model prediction, when compared to the true elution profiles. The Kalman filters were also applicable in real-time with a signal sampling frequency of 5 seconds, enabling accurate and robust estimation and paving the way for future applications beyond monitoring, such as real-time optimal pooling control.}},
author = {{Zandler-Andersson, Gusten and Espinoza, Daniel and Andersson, Niklas and Nilsson, Bernt}},
issn = {{0021-9673}},
language = {{eng}},
month = {{07}},
publisher = {{Elsevier}},
series = {{Journal of Chromatography A}},
title = {{Real-time monitoring of gradient chromatography using dual Kalman-filters}},
url = {{http://dx.doi.org/10.1016/j.chroma.2024.465161}},
doi = {{10.1016/j.chroma.2024.465161}},
volume = {{1731}},
year = {{2024}},
}