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
Abstract
Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance criteria. Herein we report the implementation of a new multi-objective machine learning optimization algorithm for self-optimization, and demonstrate it in two exemplar chemical reactions performed in continuous flow. The algorithm successfully identified a set of optimal conditions corresponding to the trade-off curve (Pareto front) between environmental and economic objectives in both cases. Thus, it reveals the complete underlying trade-off and is not limited to one compromise as is the case in many other studies. The machine learning algorithm proved to be extremely data efficient, identifying the optimal conditions for the objectives in a lower number of experiments compared to single-objective optimizations. The complete underlying trade-off between multiple objectives is identified without arbitrary weighting factors, but via true multi-objective optimization.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jul 2018 12:36 |
Last Modified: | 25 Jun 2023 21:25 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cej.2018.07.031 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132922 |