TY - JOUR
T1 - Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS)-a state-of-the-art review
AU - Yan, Yongliang
AU - Borhani, Tohid N.
AU - Subraveti, Sai Gokul
AU - Pai, Kasturi Nagesh
AU - Prasad, Vinay
AU - Rajendran, Arvind
AU - Nkulikiyinka, Paula
AU - Asibor, Jude Odianosen
AU - Zhang, Zhien
AU - Shao, Ding
AU - Wang, Lijuan
AU - Zhang, Wenbiao
AU - Yan, Yong
AU - Ampomah, William
AU - You, Junyu
AU - Wang, Meihong
AU - Anthony, Edward J.
AU - Manovic, Vasilije
AU - Clough, Peter T.
N1 - Publisher Copyright:
© The Royal Society of Chemistry.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85121222663
U2 - 10.1039/d1ee02395k
DO - 10.1039/d1ee02395k
M3 - Review article
AN - SCOPUS:85121222663
SN - 1754-5692
VL - 14
SP - 6122
EP - 6157
JO - Energy and Environmental Science
JF - Energy and Environmental Science
IS - 12
ER -
