---
_id: '7985'
abstract:
- lang: eng
text: Bioprocess modeling has become a useful tool for prediction of the process
future with the aim to deduce operating decisions (e.g. transfer or feeds). Due
to variabilities, which often occur between and within batches, updating (re-estimation)
of model parameters is required at certain time intervals (dynamic parameter estimation)
to obtain reliable predictions. This can be challenging in the presence of low
sampling frequencies (e.g. every 24 h), different consecutive scales and large
measurement errors, as in the case of cell culture seed trains. This contribution
presents an iterative learning workflow which generates and incorporates knowledge
concerning cell growth during the process by using a moving horizon estimation
(MHE) approach for updating of model parameters. This estimation technique is
compared to a classical weighted least squares estimation (WLSE) approach in the
context of model updating over three consecutive cultivation scales (40–2160 L)
of an industrial cell culture seed train. Both techniques were investigated regarding
robustness concerning the aforementioned challenges and the required amount of
experimental data (estimation horizon). It is shown how the proposed MHE can deal
with the aforementioned difficulties by the integration of prior knowledge, even
if only data at two sampling points are available, outperforming the classical
WLSE approach. This workflow allows to adequately integrate current process behavior
into the model and can therefore be a suitable component of a digital twin.
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Christoph
full_name: Posch, Christoph
last_name: Posch
- first_name: Ralf
full_name: Pörtner, Ralf
last_name: Pörtner
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Hernández Rodriguez T, Posch C, Pörtner R, Frahm B. Dynamic parameter estimation
and prediction over consecutive scales, based on moving horizon estimation - applied
to an industrial cell culture seed train. Bioprocess and Biosystems Engineering.
2021;44(4):793-808. doi:10.1007/s00449-020-02488-1
apa: Hernández Rodriguez, T., Posch, C., Pörtner, R., & Frahm, B. (2021). Dynamic
parameter estimation and prediction over consecutive scales, based on moving horizon
estimation - applied to an industrial cell culture seed train. Bioprocess and
Biosystems Engineering, 44(4), 793–808. https://doi.org/10.1007/s00449-020-02488-1
bjps: Hernández Rodriguez T et al. (2021) Dynamic Parameter Estimation
and Prediction over Consecutive Scales, Based on Moving Horizon Estimation - Applied
to an Industrial Cell Culture Seed Train. Bioprocess and Biosystems Engineering
44, 793–808.
chicago: 'Hernández Rodriguez, Tanja, Christoph Posch, Ralf Pörtner, and Björn Frahm.
“Dynamic Parameter Estimation and Prediction over Consecutive Scales, Based on
Moving Horizon Estimation - Applied to an Industrial Cell Culture Seed Train.”
Bioprocess and Biosystems Engineering 44, no. 4 (2021): 793–808. https://doi.org/10.1007/s00449-020-02488-1 .'
chicago-de: 'Hernández Rodriguez, Tanja, Christoph Posch, Ralf Pörtner und Björn
Frahm. 2021. Dynamic parameter estimation and prediction over consecutive scales,
based on moving horizon estimation - applied to an industrial cell culture seed
train. Bioprocess and Biosystems Engineering 44, Nr. 4: 793–808. doi:10.1007/s00449-020-02488-1
, .'
din1505-2-1: 'Hernández Rodriguez, Tanja
; Posch, Christoph ; Pörtner,
Ralf ; Frahm, Björn: Dynamic
parameter estimation and prediction over consecutive scales, based on moving horizon
estimation - applied to an industrial cell culture seed train. In: Bioprocess
and Biosystems Engineering Bd. 44. Berlin ; Heidelberg [u.a.], Springer (2021),
Nr. 4, S. 793–808'
havard: T. Hernández Rodriguez, C. Posch, R. Pörtner, B. Frahm, Dynamic parameter
estimation and prediction over consecutive scales, based on moving horizon estimation
- applied to an industrial cell culture seed train, Bioprocess and Biosystems
Engineering. 44 (2021) 793–808.
ieee: 'T. Hernández Rodriguez, C. Posch, R. Pörtner, and B. Frahm, “Dynamic parameter
estimation and prediction over consecutive scales, based on moving horizon estimation
- applied to an industrial cell culture seed train,” Bioprocess and Biosystems
Engineering, vol. 44, no. 4, pp. 793–808, 2021, doi: 10.1007/s00449-020-02488-1 .'
mla: Hernández Rodriguez, Tanja, et al. “Dynamic Parameter Estimation and Prediction
over Consecutive Scales, Based on Moving Horizon Estimation - Applied to an Industrial
Cell Culture Seed Train.” Bioprocess and Biosystems Engineering, vol. 44,
no. 4, 2021, pp. 793–808, https://doi.org/10.1007/s00449-020-02488-1 .
short: T. Hernández Rodriguez, C. Posch, R. Pörtner, B. Frahm, Bioprocess and Biosystems
Engineering 44 (2021) 793–808.
ufg: 'Hernández Rodriguez, Tanja u. a.: Dynamic parameter estimation and
prediction over consecutive scales, based on moving horizon estimation - applied
to an industrial cell culture seed train, in: Bioprocess and Biosystems Engineering
44 (2021), H. 4, S. 793–808.'
van: Hernández Rodriguez T, Posch C, Pörtner R, Frahm B. Dynamic parameter estimation
and prediction over consecutive scales, based on moving horizon estimation - applied
to an industrial cell culture seed train. Bioprocess and Biosystems Engineering.
2021;44(4):793–808.
date_created: 2022-05-05T13:06:12Z
date_updated: 2023-08-21T07:51:38Z
department:
- _id: DEP4021
doi: '10.1007/s00449-020-02488-1 '
intvolume: ' 44'
issue: '4'
keyword:
- Dynamic parameter estimation
- Bioprocess
- Cell cultures
- Moving horizon estimation
- Prior knowledge
language:
- iso: eng
page: 793-808
place: Berlin ; Heidelberg [u.a.]
publication: Bioprocess and Biosystems Engineering
publication_identifier:
eissn:
- 1615-7605
issn:
- 1615-7591
publication_status: published
publisher: Springer
quality_controlled: '1'
status: public
title: Dynamic parameter estimation and prediction over consecutive scales, based
on moving horizon estimation - applied to an industrial cell culture seed train
type: scientific_journal_article
user_id: '83781'
volume: 44
year: '2021'
...
---
_id: '3349'
abstract:
- lang: eng
text: "Model-based concepts and simulation techniques in combination with digital
tools emerge as a key to explore the full potential of biopharmaceutical production
processes, which contain several challenging development and process steps. One
of these steps is the time- and cost-intensive cell proliferation process (also
called seed train) to increase cell number from cell thawing up to production
scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities
in cell behavior, and influences of changes in cultivation conditions necessitate
adequate digital solutions to provide information about the current and near future
process state to derive correct process decisions.\r\nFor this purpose digital
seed train twins have proved to be efficient, which digitally display the time-dependent
behavior of important process variables based on mathematical models, strategies,
and adaption procedures.\r\nThis chapter will outline the needs for digitalization
of seed trains, the construction of a digital seed train twin, the role of parameter
estimation, and different statistical methods within this context, which are applicable
to several problems in the field of bioprocessing. The results of a case study
are presented to illustrate a Bayesian approach for parameter estimation and prediction
of an industrial cell culture seed train for seed train digitalization."
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: 'Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods.
In: Herwig C, Pörtner R, Möller J, eds. Digital Twins Tools and Concepts for
Smart Biomanufacturing. Vol 176. Advances in Biochemical Engineering/Biotechnology.
Springer; 2021:97-131. doi:https://doi.org/10.1007/10_2020_137'
apa: Hernández Rodriguez, T., & Frahm, B. (2021). Digital Seed Train Twins and
Statistical Methods. In C. Herwig, R. Pörtner, & J. Möller (Eds.), Digital
Twins Tools and Concepts for Smart Biomanufacturing (Vol. 176, pp. 97–131).
Springer. https://doi.org/10.1007/10_2020_137
bjps: 'Hernández Rodriguez T and Frahm B (2021) Digital Seed Train Twins
and Statistical Methods. In Herwig C, Pörtner R and Möller J (eds), Digital
Twins Tools and Concepts for Smart Biomanufacturing, vol. 176. Berlin, Heidelberg:
Springer, pp. 97–131.'
chicago: 'Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins
and Statistical Methods.” In Digital Twins Tools and Concepts for Smart Biomanufacturing,
edited by Christoph Herwig, Ralf Pörtner, and Johannes Möller, 176:97–131.
Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer,
2021. https://doi.org/10.1007/10_2020_137.'
chicago-de: 'Hernández Rodriguez, Tanja und Björn Frahm. 2021. Digital Seed Train
Twins and Statistical Methods. In: Digital Twins Tools and Concepts for Smart
Biomanufacturing, hg. von Christoph Herwig, Ralf Pörtner, und Johannes Möller,
176:97–131. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg:
Springer. doi:https://doi.org/10.1007/10_2020_137,
.'
din1505-2-1: 'Hernández Rodriguez, Tanja
; Frahm, Björn: Digital Seed Train
Twins and Statistical Methods. In: Herwig,
C. ; Pörtner, R. ; Möller, J. (Hrsg.): Digital Twins Tools
and Concepts for Smart Biomanufacturing, Advances in Biochemical Engineering/Biotechnology.
Bd. 176. Berlin, Heidelberg : Springer, 2021, S. 97–131'
havard: 'T. Hernández Rodriguez, B. Frahm, Digital Seed Train Twins and Statistical
Methods, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digital Twins Tools and
Concepts for Smart Biomanufacturing, Springer, Berlin, Heidelberg, 2021: pp. 97–131.'
ieee: 'T. Hernández Rodriguez and B. Frahm, “Digital Seed Train Twins and Statistical
Methods,” in Digital Twins Tools and Concepts for Smart Biomanufacturing,
vol. 176, C. Herwig, R. Pörtner, and J. Möller, Eds. Berlin, Heidelberg: Springer,
2021, pp. 97–131. doi: https://doi.org/10.1007/10_2020_137.'
mla: Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins and
Statistical Methods.” Digital Twins Tools and Concepts for Smart Biomanufacturing,
edited by Christoph Herwig et al., vol. 176, Springer, 2021, pp. 97–131, https://doi.org/10.1007/10_2020_137.
short: 'T. Hernández Rodriguez, B. Frahm, in: C. Herwig, R. Pörtner, J. Möller (Eds.),
Digital Twins Tools and Concepts for Smart Biomanufacturing, Springer, Berlin,
Heidelberg, 2021, pp. 97–131.'
ufg: 'Hernández Rodriguez, Tanja/Frahm, Björn: Digital Seed Train Twins and
Statistical Methods, in: Herwig, Christoph/Pörtner, Ralf/Möller, Johannes (Hgg.):
Digital Twins Tools and Concepts for Smart Biomanufacturing, Bd. 176, Berlin,
Heidelberg 2021 (Advances in Biochemical Engineering/Biotechnology), S. 97–131.'
van: 'Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods.
In: Herwig C, Pörtner R, Möller J, editors. Digital Twins Tools and Concepts for
Smart Biomanufacturing. Berlin, Heidelberg: Springer; 2021. p. 97–131. (Advances
in Biochemical Engineering/Biotechnology; vol. 176).'
date_created: 2020-08-19T07:14:11Z
date_updated: 2023-08-16T06:48:35Z
department:
- _id: DEP4021
doi: https://doi.org/10.1007/10_2020_137
editor:
- first_name: 'Christoph '
full_name: 'Herwig, Christoph '
last_name: Herwig
- first_name: 'Ralf '
full_name: 'Pörtner, Ralf '
last_name: Pörtner
- first_name: 'Johannes '
full_name: 'Möller, Johannes '
last_name: Möller
intvolume: ' 176'
keyword:
- Bayes
- Digital twin
- Parameter estimation
- Seed train
- Uncertainty
language:
- iso: eng
page: 97–131
place: Berlin, Heidelberg
publication: Digital Twins Tools and Concepts for Smart Biomanufacturing
publication_identifier:
eisbn:
- 978-3-030-71660-8
eissn:
- 1616-8542
isbn:
- 978-3-030-71659-2
issn:
- 0724-6145
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Advances in Biochemical Engineering/Biotechnology
status: public
title: Digital Seed Train Twins and Statistical Methods
type: book_chapter
user_id: '83781'
volume: 176
year: '2021'
...