Browse by author
Lookup NU author(s): Dr Yongliang YanORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 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
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
