Hydrogen-consuming bacteria in underground hydrogen storage: Bacterial diversity and mathematical modeling of their impacts on storage efficiency

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 113, P. 115519 - 115519

Published: Feb. 1, 2025

Language: Английский

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Data-Driven Prediction of Storage Column Height for H2-Brine Systems: Accelerating Underground Hydrogen Storage

International Petroleum Technology Conference, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract A practical solution to energy transition and the increasing demand for is underground hydrogen storage (UHS). The contribution of (H2) as a clean source has proven be an effective substitute future use meet net-zero target reduce anthropogenic greenhouse gas emissions. One most important factors affecting H2 displacement capacity under geological circumstances column height. objective this study underscore importance large-scale reliable machine learning algorithms evaluate predict height varied thermophysical salinity conditions. In study, dataset 540 datapoints evaluation prediction generated, which involves three main parameters: density difference (Δρ), interfacial tension (IFT) contact angle (θ). correlation angles against various reservoir depths used evaluated. Thermophysical conditions include pressures (0.1-20 MPa), temperatures (25-70°C), salinities including deionized water, seawater brines 1 3 molar concentrations salts (NaCl, KCl, MgCl2, CaCl2, Na2SO4) from our experimental data. (h) predicted using (ML) models, viz., random forest (RF), decision tree (DT) gradient boosting (GB). Statistical data analysis performed generate distribution coefficient calculated while feature determined identify relationship each input parameter with output Pearson, Spearman, Kendall models. RF GB, demonstrated in have shown promising results providing accurate predictions maintaining generalizability. Various error assessment metrics MSE, RMSE, MAPE R2 are utilized evaluation. Prediction resulted values 0.995 training 0.999 testing model. Whereas GB model also superior performance 0.997 during phase phase. However, DT 0.994 phases respectively. While MSE value 0 obtained indicated overfitting. findings suggest that data-driven ML models can powerful tool accurately predicting effectively determine capacity, reducing time cost associated determination traditional methods. addition, advanced explored overcome challenges pertinent

Language: Английский

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Techno-economic analysis and site screening for underground hydrogen storage in Intermountain-West region, United States

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 109, P. 275 - 286

Published: Feb. 11, 2025

Language: Английский

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Hydrogeochemical modeling of hydrogen storage in depleted gas reservoirs: Insights from local and global sensitivity analysis

Applied Energy, Journal Year: 2025, Volume and Issue: 391, P. 125940 - 125940

Published: April 22, 2025

Language: Английский

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Theoretical prediction of novel phase, phonon dynamics and physical properties of KAlH4 hydride for hydrogen storage

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 131, P. 221 - 228

Published: April 28, 2025

Language: Английский

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