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

Yongliang Yan; Tohid N. Borhani; Sai Gokul Subraveti; Kasturi Nagesh Pai; Vinay Prasad; Arvind Rajendran; Paula Nkulikiyinka; Jude Odianosen Asibor; Zhien Zhang; Ding Shao; Lijuan Wang; Wenbiao Zhang; Yong Yan; William Ampomah; Junyu You; Meihong Wang; Edward J. Anthony; Vasilije Manovic; Peter T. Clough

doi:10.1039/D1EE02395K

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

Yongliang Yan,

*^ab Tohid N. Borhani,

^c Sai Gokul Subraveti,^d Kasturi Nagesh Pai,^d Vinay Prasad,

^d Arvind Rajendran,^d Paula Nkulikiyinka,

^a Jude Odianosen Asibor,

^a Zhien Zhang,^e Ding Shao,

^f Lijuan Wang,

^g Wenbiao Zhang,

^f Yong Yan,

^g William Ampomah,^h Junyu You,^hi Meihong Wang,

^j Edward J. Anthony,

^a Vasilije Manovic

^a and Peter T. Clough

*^a

Author affiliations

* Corresponding authors

^a Energy and Power Theme, Cranfield University, Bedfordshire, UK
E-mail: yongliang.yan@newcastle.ac.uk, p.t.clough@cranfield.ac.uk

^b Materials, Concept and Reaction Engineering (MatCoRE) Group, School of Engineering, Newcastle University, Merz Court, Newcastle Upon Tyne, UK

^c School of Engineering, Division of Chemical Engineering, University of Wolverhampton, Wolverhampton, UK

^d Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street NW, Edmonton, Alberta, Canada

^e Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA

^f School of Control and Computer Engineering, North China Electric Power University, Beijing, P. R. China

^g School of Engineering, University of Kent, Canterbury, Kent, UK

^h Petroleum Recovery Research Centre, New Mexico Tech, Socorro NM, USA

ⁱ School of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing, China

^j Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S1 3JD, UK

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.

Energy & Environmental Science

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

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