Machine learning directed multi-objective optimization of mixed variable chemical systems

Kershaw, OJ, Clayton, AD orcid.org/0000-0002-4634-8008, Manson, JA orcid.org/0000-0001-7392-3197 et al. (8 more authors) (2023) Machine learning directed multi-objective optimization of mixed variable chemical systems. Chemical Engineering Journal, 451 (1). 138443. ISSN 1385-8947

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Automated flow reactor; Machine learning; Mixed variable optimization; Multi-objective; Reaction engineering
Dates:
  • Accepted: 30 July 2022
  • Published (online): 2 August 2022
  • Published: 1 January 2023
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)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/V055089/1
EPSRC (Engineering and Physical Sciences Research Council)EP/S000380/1
Depositing User: Symplectic Publications
Date Deposited: 09 Aug 2022 15:11
Last Modified: 02 Aug 2023 00:13
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.cej.2022.138443

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