Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS)-a state-of-the-art review
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Borhani, Tohid N
Subraveti, Sai Gokul
Pai, Kasturi Nagesh
Asibor, Jude Odianosen
Anthony, Edward J
Clough, Peter T
AffiliationSchool of Engineering, Division of Chemical Engineering, University of Wolverhampton, Wolverhampton, UK
MetadataShow full item record
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. This journal is
CitationYan, Y., Borhani, T. N., Subraveti, S. G., Pai, K. N. 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, 14 (12), pp. 6122-6157. DOI: 10.1039/D1EE02395K
PublisherRoyal Society of Chemistry (RSC)
JournalEnergy and Environmental Science
Description© 2021 The Authors. Published by the Royal Society of Chemistry. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1039/D1EE02395K
SponsorsPN, PTC, and VM acknowledge the financial support from the UK Engineering and Physical Sciences Research Council Doctoral Training Partnership (EPSRC DTP) grant no. EP/R513027/1. Yongliang Yan would like to acknowledge the financial support from the Cranfield University Energy and Power research bursary. JOA is grateful to the Petroleum Technology Development Fund (PTDF), Nigeria, for doctoral study scholarship, award number: PTDF/ED/OSS/PHD/JOA/077/19 and the University of Benin, Benin City, Nigeria. WZ and Yong Yan acknowledge the financial support from the National Natural Science Foundation of China (No. 61973113 and No. 62073135). WA and JY would like to acknowledge grant support provided by the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) through the Southwest Regional Partnership on Carbon Sequestration (SWP) under Award No. DE-FC26-05NT42591. SGS, KNP, VP and AR acknowledge funding from Canada First Research Excellence Fund through University of Alberta Future Energy systems, MW would like to thank the financial support from UK EPSRC (EP/M001458/2 and EP/N024540/1).
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/