New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system-Reference-Cited by-同舟云学术

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New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system

Published:2024-01 Issue: Volume:50 Page:1326-1337
ISSN:0360-3199
Container-title:International Journal of Hydrogen Energy
language:en
Short-container-title:International Journal of Hydrogen Energy

Author:

Gbadamosi Afeez,Adamu Haruna^ORCID,Usman Jamilu^ORCID,Usman A.G.,Jibril Mahmud M.^ORCID,Salami Babatunde Abiodun^ORCID,Gbadamosi Saheed Lekan,Oyedele Lukumon O.^ORCID,Abba S.I.^ORCID

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Condensed Matter Physics,Fuel Technology,Renewable Energy, Sustainability and the Environment

Reference51 articles.

1. A review of gas enhanced oil recovery schemes used in the North Sea;Gbadamosi;J Pet Explor Prod Technol,2018

2. Circular economy strategies for combating climate change and other environmental issues;Yang;Environ Chem Lett,2023

3. Environmental Science media – the scientific challenges;Hangx;Energy Environ Sci,2021

4. Hydrogen production, storage, utilisation and environmental impacts: a review;Osman;Springer Int Publish,2022

5. Wettability of shale – brine – H 2 system and H 2 -brine interfacial tension for assessment of the sealing capacities of shale formations during underground hydrogen storage;Al-mukainah;Energy Rep,2022

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