Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art review

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dc.contributor.authorYan, Yongliang
dc.contributor.authorBorhani, Tohid N.
dc.contributor.authorSubraveti, Sai Gokul
dc.contributor.authorPai, Kasturi Nagesh
dc.contributor.authorPrasad, Vinay
dc.contributor.authorRajendran, Arvind
dc.contributor.authorNkulikiyinka, Paula
dc.contributor.authorAsibor, Jude Odianosen
dc.contributor.authorZhang, Zhien
dc.contributor.authorShao, Ding
dc.contributor.authorWang, Lijuan
dc.contributor.authorZhang, Wenbiao
dc.contributor.authorYan, Yong
dc.contributor.authorAmpomah, William
dc.contributor.authorYou, Junyu
dc.contributor.authorWang, Meihong
dc.contributor.authorAnthony, Edward J.
dc.contributor.authorManovic, Vasilije
dc.contributor.authorClough, Peter T.
dc.date.accessioned2021-11-17T16:34:35Z
dc.date.available2021-11-17T16:34:35Z
dc.date.issued2021-11-01
dc.description.abstractCarbon 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.en_UK
dc.identifier.citationYan Y, Borhani TN, Subraveti SG,et al., (2021) Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art review. Energy and Environmental Science, Volume 14, Issue 12, December 2021, pp. 6122-6157en_UK
dc.identifier.issn1754-5692
dc.identifier.urihttps://doi.org/10.1039/D1EE02395K
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17275
dc.language.isoenen_UK
dc.publisherRoyal Society of Chemistryen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleHarnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art reviewen_UK
dc.typeArticleen_UK

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