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

Yan, Yongliang, Borhani, Tohid N, Subraveti, Sai Gokul, Pai, Kasturi Nagesh, Prasad, Vinay, Rajendran, Arvind, Nkulikiyinka, Paula, Asibor, Jude Odianosen, Zhang, Zhien, Shao, Ding, and others. (2021) Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art review. Energy & Environmental Science, 14 . pp. 6122-6157. ISSN 1754-5692. (doi:10.1039/D1EE02395K) (KAR id:91123)

PDF Publisher pdf Language: English This work is licensed under a Creative Commons Attribution 4.0 International License.
Download this file (PDF/3MB)	Preview
Request a format suitable for use with assistive technology e.g. a screenreader
Official URL: http://dx.doi.org/10.1039/D1EE02395K

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 ten recommendations for further work and research that will help develop the role that ML plays in CCUS and enable greater deployment of the technologies.

Item Type:	Article
DOI/Identification number:	10.1039/D1EE02395K
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User:	Yong Yan
Date Deposited:	27 Oct 2021 14:23 UTC
Last Modified:	09 Jan 2024 20:54 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/91123 (The current URI for this page, for reference purposes)

University of Kent Author Information

Yan, Yong.

Creator's ORCID:	https://orcid.org/0000-0001-7135-5456
CReDIT Contributor Roles:

Depositors only (login required):

Altmetric

Download Statistics

Total unique views for this document in KAR since July 2020. For more details click on the image.