In-silico Formulation of Iterative Learning Control for Chromatographic Purification of Biopharmaceuticals
Espinoza, Daniel LU ; Andersson, Niklas LU
and Nilsson, Bernt
LU
(2022)
51.
p.1183-1188
- Abstract
- This study presents a novel application of multivariate iterative learning control (ILC) to the continuous, chromatographic ion-exchange purification of biopharmaceuticals for consistent and reliable production. The ILC algorithm used was based on a linearized model of the retention times of two compounds as functions of the starting and ending values of a linear salt gradient. Simulations of an ion-exchange purification process were used to identify the non-linear model behavior and thus suggest an appropriate linearization of the model for use in the ILC. Two control configurations were compared: one using direct inversion of the resulting linear model, and another using a least-squares, quadratic-criterion objective function for optimal... (More)
- This study presents a novel application of multivariate iterative learning control (ILC) to the continuous, chromatographic ion-exchange purification of biopharmaceuticals for consistent and reliable production. The ILC algorithm used was based on a linearized model of the retention times of two compounds as functions of the starting and ending values of a linear salt gradient. Simulations of an ion-exchange purification process were used to identify the non-linear model behavior and thus suggest an appropriate linearization of the model for use in the ILC. Two control configurations were compared: one using direct inversion of the resulting linear model, and another using a least-squares, quadratic-criterion objective function for optimal control in conjunction with the
model. The result was an ILC configuration based on a simple model with parameters that only required 3 experiments to compute, that was capable of controlling the retention times of two compounds simultaneously. This leads to more reliable and flexible operation of continuous and integrated biopharmaceutical purification in the future, and serves as a foundation for further development of other ILC-based control strategies within biopharmaceutical purification. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/39f8fa89-a3ce-45ad-948a-0ad73cd7e257
- author
- Espinoza, Daniel
LU
; Andersson, Niklas
LU
and Nilsson, Bernt
LU
- organization
- publishing date
- 2022-06-12
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- preparative chromatography, ion-exchange, simulation, Iterative learning control, model-based control
- host publication
- Computer Aided Chemical Engineering
- editor
- Montastruc, Ludovic and Negny, Stephane
- volume
- 51
- pages
- 6 pages
- publisher
- Elsevier Science Publishers B.V.
- external identifiers
-
- scopus:85135522541
- ISBN
- 978-0-323-95879-0
- DOI
- 10.1016/B978-0-323-95879-0.50198-3
- language
- English
- LU publication?
- yes
- additional info
- Part of volume: 32nd European Symposium on Computer Aided Process Engineering
- id
- 39f8fa89-a3ce-45ad-948a-0ad73cd7e257
- date added to LUP
- 2022-08-15 11:17:22
- date last changed
- 2023-12-20 03:45:14
@inproceedings{39f8fa89-a3ce-45ad-948a-0ad73cd7e257,
abstract = {{This study presents a novel application of multivariate iterative learning control (ILC) to the continuous, chromatographic ion-exchange purification of biopharmaceuticals for consistent and reliable production. The ILC algorithm used was based on a linearized model of the retention times of two compounds as functions of the starting and ending values of a linear salt gradient. Simulations of an ion-exchange purification process were used to identify the non-linear model behavior and thus suggest an appropriate linearization of the model for use in the ILC. Two control configurations were compared: one using direct inversion of the resulting linear model, and another using a least-squares, quadratic-criterion objective function for optimal control in conjunction with the<br/>model. The result was an ILC configuration based on a simple model with parameters that only required 3 experiments to compute, that was capable of controlling the retention times of two compounds simultaneously. This leads to more reliable and flexible operation of continuous and integrated biopharmaceutical purification in the future, and serves as a foundation for further development of other ILC-based control strategies within biopharmaceutical purification.}},
author = {{Espinoza, Daniel and Andersson, Niklas and Nilsson, Bernt}},
booktitle = {{Computer Aided Chemical Engineering}},
editor = {{Montastruc, Ludovic and Negny, Stephane}},
isbn = {{978-0-323-95879-0}},
keywords = {{preparative chromatography; ion-exchange; simulation; Iterative learning control; model-based control}},
language = {{eng}},
month = {{06}},
pages = {{1183--1188}},
publisher = {{Elsevier Science Publishers B.V.}},
title = {{In-silico Formulation of Iterative Learning Control for Chromatographic Purification of Biopharmaceuticals}},
url = {{http://dx.doi.org/10.1016/B978-0-323-95879-0.50198-3}},
doi = {{10.1016/B978-0-323-95879-0.50198-3}},
volume = {{51}},
year = {{2022}},
}