Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network-Reference-Cited by-同舟云学术

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Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network

Published:2017-01 Issue: Volume:185 Page:916-928
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Kim Youngmin,Jang Hochang,Kim Junggyun,Lee Jeonghwan

Funder

Energy Efficiency and Resources of the Korea Institute of Energy Technology Evaluation and Planning

Korea government Ministry of Trade, Industry and Energy

Publisher

Elsevier BV

Subject

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

Reference44 articles.

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2. Numerical investigation of the influence of vertical permeability heterogeneity in stratified formation and of injection/production well perforation placement on CO2 geological storage with enhanced CH4 recovery;Luo;Appl Energy,2013

3. Assessing innovation in emerging energy technologies: socio-technical dynamics of carbon capture and storage (CCS) and enhanced geothermal systems (EGS) in the USA;Stephens;Energy Policy,2010

4. Large CO2 sinks: their role in the mitigation of greenhouse gases from an international, national (Canadian) and provincial (Alberta) perspective;Gunter;Appl Energy,1998

5. Perceptions of opinion leaders towards CCS demonstration projects in China;Liang;Appl Energy,2011

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