Chia, D;
Duanmu, F;
Mazzei, L;
Sorensen, E;
Besenhard, M;
(2025)
The Smart HPLC Robot: Fully Autonomous Method Development Guided by A Mechanistic Model Framework.
In: Van Impe, J and Léonard, G and Bhonsale, SS and Polanska, M and Logist, F, (eds.)
Proceedings of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35).
(pp. pp. 1884-1889).
PSE Press: Hamilton, Canada.
Preview |
Text
Mazzei_LAPSE-2025.0455-1v1.pdf Download (1MB) | Preview |
Abstract
Developing ultra- or high-performance liquid chromatography (HPLC) methods for analysis or purification requires significant amounts of material and manpower, and typically involves time-consuming iterative lab-based workflows. This work demonstrates in two case studies that an autonomous HPLC platform coupled with a mechanistic model that self-corrects itself by performing parameter estimation can efficiently develop an optimized HPLC method with minimal experiments (i.e., reduced experimental costs and burden) and manual intervention (i.e., reduced manpower). At the same time, this HPLC platform, referred to as Smart HPLC Robot, can deliver a calibrated mechanistic model that provides valuable insights into method robustness.
| Type: | Proceedings paper |
|---|---|
| Title: | The Smart HPLC Robot: Fully Autonomous Method Development Guided by A Mechanistic Model Framework |
| Event: | ESCAPE 35 – 35th European Symposium on Computer Aided Process Engineering |
| Location: | Ghent, Belgium |
| Dates: | 7 Jul 2025 - 9 Jul 2025 |
| ISBN-13: | 978-1-7779403-3-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.69997/sct.116643 |
| Publisher version: | https://doi.org/10.69997/sct.116643 |
| Language: | English |
| Additional information: | This is an Open Access article published under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) Licence (https://creativecommons.org/licenses/by-sa/4.0/). |
| Keywords: | Industry 4.0, Modelling and Simulations, Optimization, Genetic Algorithm, Batch Process, Self-driving, Autonomous, Digital Twin, Mechanistic Model, Chromatography |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210900 |
Archive Staff Only
 |
View Item |
