Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

Separation and Purification Technology, Journal Year: 2023, Volume and Issue: 316, P. 123807 - 123807

Published: April 10, 2023

Language: Английский

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Machine learning - based shale wettability prediction: Implications for H2, CH4 and CO2 geo-storage

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 56, P. 1384 - 1390

Published: Jan. 4, 2024

Language: Английский

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Exploring hydrogen geologic storage in China for future energy: Opportunities and challenges

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 196, P. 114366 - 114366

Published: March 15, 2024

Language: Английский

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Prediction of hydrogen−brine interfacial tension at subsurface conditions: Implications for hydrogen geo-storage

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 58, P. 485 - 494

Published: Jan. 25, 2024

Underground hydrogen storage (UHS) offers a promising approach for the of significant volumes gas (H2) within deep geological formations, which can later be utilized energy generation when necessary. Interfacial tension (IFT) between H2 and formation brine plays vital role in influencing distribution at pore scale and, ultimately, capacity. In this research, we developed four intelligent models: Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP). These models were designed to predict IFT utilizing pressure, temperature, molality. Additionally, fine-tuned three explicit correlations previously our research. To assess influence each parameter on IFT, conducted comprehensive analysis raw data exclude doubtful samples. This was followed by rigorous model development, including hyperparameter tuning, finally, an examination using testing data. The results clearly demonstrate superiority RF model, achieving high accuracy reliability with coefficients determination (R2), root mean square error (RMSE), average absolute relative deviation (AARD) values 0.96, 1.50, 1.84 %, respectively. exemplary performance attributed its inherent characteristics. ensemble excels capturing complex relationships, thereby enhancing predictive solidifying over other study. Furthermore, feature importance revealed that temperature has most influence, molality pressure. Moreover, assessed these through external not used initial training stages. Our study highlights exceptional power emphasizing practical selecting enhanced reliability. proposed method shows potential industrial applications, especially optimizing underground storage.

Language: Английский

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A critical review of physics-informed machine learning applications in subsurface energy systems

Geoenergy Science and Engineering, Journal Year: 2024, Volume and Issue: 239, P. 212938 - 212938

Published: May 22, 2024

Language: Английский

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Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning

Materials Today Sustainability, Journal Year: 2024, Volume and Issue: 27, P. 100900 - 100900

Published: June 29, 2024

Language: Английский

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Estimating the hydrogen adsorption in depleted shale gas reservoirs for kerogens in underground hydrogen storage using machine learning algorithms

Fuel, Journal Year: 2025, Volume and Issue: 388, P. 134534 - 134534

Published: Feb. 5, 2025

Language: Английский

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Improving wettability estimation in carbonate formation using machine learning algorithms: Implications for underground hydrogen storage applications

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 111, P. 781 - 797

Published: Feb. 27, 2025

Language: Английский

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Data-driven machine learning models for the prediction of hydrogen solubility in aqueous systems of varying salinity: Implications for underground hydrogen storage

International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 55, P. 1422 - 1433

Published: Dec. 21, 2023

Language: Английский

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Modelling underground hydrogen storage: A state-of-the-art review of fundamental approaches and findings

Gas Science and Engineering, Journal Year: 2023, Volume and Issue: 121, P. 205196 - 205196

Published: Dec. 16, 2023

This review presents a State-of-Art of geochemical, geomechanical, and hydrodynamic modelling studies in the Underground Hydrogen Storage (UHS) domain. Geochemical assessed reactivity hydrogen respective fluctuations losses using kinetic reaction rates, rock mineralogy, brine salinity, integration redox reactions. Existing geomechanics offer an array coupled hydro-mechanical models, suggesting decline failure during withdrawal phase aquifers compared to injection phase. Hydrodynamic evaluations indicate critical importance relative permeability hysteresis determining UHS performance. Solubility diffusion gas appear have minimal impact on UHS. Injection production cushion deployment, reservoir heterogeneity however significantly affect performance, stressing need for thorough experimental studies. However, most current efforts focuses assessing aspects which are crucial understanding viability safety In contrast, lesser-explored geochemical geomechanical considerations point potential research gaps. Variety software tools such as CMG, Eclipse, COMSOL, PHREEQC evaluated those underlying effects, along with few recent application data-driven based Machine Learning (ML) techniques enhanced accuracy. identified several unresolved challenges modelling: pronounced lack expansive datasets, leading gap between model predictions their practical reliability; robust methodologies capable capturing natural subsurface while upscaling from precise laboratory data field-scale conditions; demanding intensive computational resources novel strategies enhance simulation efficiency; addressing geological uncertainties environments, that oil simulations could be adapted comprehensive offers synthesis prevailing approaches, challenges, gaps domain UHS, thus providing valuable reference document further efforts, facilitating informed advancements this towards realization sustainable energy solutions.

Language: Английский

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