Machine-learning based prediction of hydrogen/methane mixture solubility in brine
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Machine-learning based prediction of hydrogen/methane mixture solubility in brine ; volume:14 ; number:1 ; day:4 ; month:12 ; year:2024 ; pages:1-23 ; date:12.2024
Scientific reports ; 14, Heft 1 (4.12.2024), 1-23, 12.2024
- Classification
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Ingenieurwissenschaften und Maschinenbau
- Creator
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Altalbawy, Farag M. A.
Al-saray, Mustafa Jassim
Vaghela, Krunal
Nazarova, Nodira
K. N., Raja Praveen
Kumari, Bharti
Kaur, Kamaljeet
Alsaadi, Salima B.
Jumaa, Sally Salih
Al-Ani, Ahmed Muzahem
Al-Farouni, Mohammed
Khalid, Ahmad
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41598-024-80959-1
- URN
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urn:nbn:de:101:1-2502182117527.259669882666
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:25 AM CEST
Data provider
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Altalbawy, Farag M. A.
- Al-saray, Mustafa Jassim
- Vaghela, Krunal
- Nazarova, Nodira
- K. N., Raja Praveen
- Kumari, Bharti
- Kaur, Kamaljeet
- Alsaadi, Salima B.
- Jumaa, Sally Salih
- Al-Ani, Ahmed Muzahem
- Al-Farouni, Mohammed
- Khalid, Ahmad
- SpringerLink (Online service)
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Effects of ultrasonic waves on carbon dioxide solubility in brine at different pressures and temperatures
Solubility and thermodynamic study of deferiprone in propylene glycol and ethanol mixture
Development of solubility selective mixed-matrix membranes for n-butane/methane separation
Combination of empirical multi-parameter Helmholtz-energy mixture models with Gibbs-energy based Brine models
Franz Brine zu Bontkirchen
Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study
Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
The influence of carbon content in the mixture of substrates on methane production
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Surfactant and a mixture of surfactant and nanoparticles to stabilize CO2/brine foam, control gas mobility, and enhance oil recovery
TASTE Lab. Beer, Beats and Brine
Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state
Effects of ultrasonic waves on carbon dioxide solubility in brine at different pressures and temperatures
Solubility and thermodynamic study of deferiprone in propylene glycol and ethanol mixture
Development of solubility selective mixed-matrix membranes for n-butane/methane separation
Combination of empirical multi-parameter Helmholtz-energy mixture models with Gibbs-energy based Brine models
Franz Brine zu Bontkirchen
Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study
Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
The influence of carbon content in the mixture of substrates on methane production
Coro: Sparso di puro brine
Surfactant and a mixture of surfactant and nanoparticles to stabilize CO2/brine foam, control gas mobility, and enhance oil recovery