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

Lookup NU author(s): Dr Yongliang YanORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© The Royal Society of Chemistry.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. This journal is


Publication metadata

Author(s): Yan Y, Borhani TN, Subraveti SG, Pai KN, Prasad V, Rajendran A, Nkulikiyinka P, Asibor JO, Zhang Z, Shao D, Wang L, Zhang W, Yan Y, Ampomah W, You J, Wang M, Anthony EJ, Manovic V, Clough PT

Publication type: Review

Publication status: Published

Journal: Energy and Environmental Science

Year: 2021

Volume: 14

Issue: 12

Pages: 6122-6157

Print publication date: 01/12/2021

Online publication date: 01/11/2021

Acceptance date: 01/11/2021

ISSN (print): 1754-5692

ISSN (electronic): 1754-5706

Publisher: Royal Society of Chemistry

URL: https://doi.org/10.1039/d1ee02395k

DOI: 10.1039/d1ee02395k


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