Machine-Learning-Based Prediction of Gas Hydrate Dynamics: A Comparison with a Fundamental Model against Experimental Data
Author:
Affiliation:
1. Energy and Process Engineering Laboratory, Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
Funder
Council of Scientific and Industrial Research, India
Publisher
American Chemical Society (ACS)
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.4c02271
Reference33 articles.
1. Thermodynamic features-driven machine learning-based predictions of clathrate hydrate equilibria in the presence of electrolytes
2. Formulating formation mechanism of natural gas hydrates
3. Evaluating CO2 hydrate kinetics in multi-layered sediments using experimental and machine learning approach: Applicable to CO2 sequestration
4. The climate change mitigation potential of bioenergy with carbon capture and storage
5. Methane hydrate formation in mixed-size porous media with gas circulation: Effects of sediment properties on gas consumption, hydrate saturation and rate constant
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