Clayton, AD orcid.org/0000-0002-4634-8008, Schweidtmann, AM, Clemens, G et al. (8 more authors) (2020) Automated Self-Optimisation of Multi-Step Reaction and Separation Processes Using Machine Learning. Chemical Engineering Journal, 384. 123340. p. 123340. ISSN 1385-8947
Abstract
There has been an increasing interest in the use of automated self-optimising continuous flow platforms for the development and manufacture in synthesis in recent years. Such processes include multiple reactive and work-up steps, which need to be efficiently optimised. Here, we report the combination of multi-objective optimisation based on machine learning methods (TSEMO algorithm) with self-optimising platforms for the optimisation of multi-step continuous reaction processes. This is demonstrated for a pharmaceutically relevant Sonogashira reaction. We demonstrate how optimum reaction conditions are re-evaluated with the changing downstream work-up specifications in the active learning process. Furthermore, a Claisen-Schmidt condensation reaction with subsequent liquid-liquid separation was optimised with respect to three-objectives. This approach provides the ability to simultaneously optimise multi-step processes with respect to multiple objectives, and thus has the potential to make substantial savings in time and resources.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Published by Elsevier B.V. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Automated flow reactor; Environmental chemistry; Machine learning; Reaction engineering; Sustainable chemistry |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds) > Inorganic Chemistry (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds) > Organic Chemistry (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering RCSRF1920\9\38 |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Nov 2019 11:25 |
Last Modified: | 07 Jun 2023 14:00 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cej.2019.123340 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153007 |