Yan, Y., Borhani, T.N., Subraveti, S.G. et al. (16 more authors) (2021) Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review. Energy and Environmental Science, 14 (12). pp. 6122-6157. ISSN 1754-5692
Abstract
Carbon capture, utilisation and storage (CCUS) will play a critical role in future decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of climate change. Whilst there are many well developed CCUS technologies there is the potential for improvement that can encourage CCUS deployment. A time and cost-efficient way of advancing CCUS is through the application of machine learning (ML). ML is a collective term for high-level statistical tools and algorithms that can be used to classify, predict, optimise, and cluster data. Within this review we address the main steps of the CCUS value chain (CO2 capture, transport, utilisation, storage) and explore how ML is playing a leading role in expanding the knowledge across all fields of CCUS. We finish with a set of recommendations for further work and research that will develop the role that ML plays in CCUS and enable greater deployment of the technologies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Royal Society of Chemistry 2021. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. (http://creativecommons.org/licenses/by/3.0/) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/M001458/2 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 May 2022 13:17 |
Last Modified: | 25 May 2022 13:17 |
Status: | Published |
Publisher: | Royal Society of Chemistry |
Refereed: | Yes |
Identification Number: | 10.1039/d1ee02395k |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187308 |