A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO2 sequestration accelerated with machine learning-Reference-Cited by-同舟云学术

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A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO2 sequestration accelerated with machine learning

Published:2021-04 Issue: Volume:287 Page:116580
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Wu Hao,Lubbers Nicholas,Viswanathan Hari S.,Pollyea Ryan M.

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference76 articles.

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2. Geological storage of CO2 in saline aquifers – a review of the experience from existing storage operations;Michael;Int J Greenhouse Gas Control,2010

3. Determinants of carbon dioxide emissions: empirical evidence from 69 countries;Sharma;Appl Energy,2011

4. Prospects of carbon capture and storage (CCS) in India’s power sector – an integrated assessment;Viebahn;Appl Energy,2014

5. Prospects of carbon capture and storage (CCS) in China’s power sector – an integrated assessment;Viebahn;Appl Energy,2015

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