Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Schweidtmann, AM, Clayton, AD orcid.org/0000-0002-4634-8008, Holmes, N orcid.org/0000-0002-3846-6493 et al. (3 more authors) (2018) Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives. Chemical Engineering Journal, 352. pp. 277-282. ISSN 1385-8947

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Copyright, Publisher and Additional Information: © 2018 The Authors. 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. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Automated flow reactor; Environmental chemistry; Machine learning; Reaction engineering; Sustainable chemistry
Dates:
  • Published: 15 November 2018
  • Accepted: 3 July 2018
  • Published (online): 4 July 2018
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering (Leeds) > School of Chemical & Process Engineering (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 05 Jul 2018 12:36
Last Modified: 21 Mar 2019 10:36
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.cej.2018.07.031

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