Calculation of hydrogen dispersion in cushion gases using machine learning
Abstract Hydrogen storage is a crucial technology for ensuring a sustainable energy transition. Underground Hydrogen Storage (UHS) in depleted hydrocarbon reservoirs, aquifers, and salt caverns provides a viable large-scale solution. However, hydrogen dispersion in cushion gases such as nitrogen (N2...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98613-9 |
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| author | Ali Akbari Mehdi Maleki Yousef Kazemzadeh Ali Ranjbar |
| author_facet | Ali Akbari Mehdi Maleki Yousef Kazemzadeh Ali Ranjbar |
| author_sort | Ali Akbari |
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| description | Abstract Hydrogen storage is a crucial technology for ensuring a sustainable energy transition. Underground Hydrogen Storage (UHS) in depleted hydrocarbon reservoirs, aquifers, and salt caverns provides a viable large-scale solution. However, hydrogen dispersion in cushion gases such as nitrogen (N2), methane (CH4), and carbon dioxide (CO2) lead to contamination, reduced purity, and increased purification costs. Existing experimental and numerical methods for predicting hydrogen dispersion coefficients (KL) are often limited by high costs, lengthy processing times, and insufficient accuracy in dynamic reservoir conditions. This study addresses these challenges by integrating experimental data with advanced machine learning (ML) techniques to model hydrogen dispersion. Various ML models—including Random Forest (RF), Least Squares Boosting (LSBoost), Bayesian Regression, Linear Regression (LR), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs)—were employed to quantify KL as a function of pressure (P) and displacement velocity (Um). Among these methods, RF outperformed the others, achieving an R2 of 0.9965 for test data and 0.9999 for training data, with RMSE values of 0.023 and 0.001, respectively. The findings highlight the potential of ML-driven approaches in optimizing UHS operations by enhancing predictive accuracy, reducing computational costs, and mitigating hydrogen contamination risks. |
| format | Article |
| id | doaj-art-f8a558abe71e45f9b6fce397ec9156ba |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-f8a558abe71e45f9b6fce397ec9156ba2025-08-20T02:19:07ZengNature PortfolioScientific Reports2045-23222025-04-0115112010.1038/s41598-025-98613-9Calculation of hydrogen dispersion in cushion gases using machine learningAli Akbari0Mehdi Maleki1Yousef Kazemzadeh2Ali Ranjbar3Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityAbstract Hydrogen storage is a crucial technology for ensuring a sustainable energy transition. Underground Hydrogen Storage (UHS) in depleted hydrocarbon reservoirs, aquifers, and salt caverns provides a viable large-scale solution. However, hydrogen dispersion in cushion gases such as nitrogen (N2), methane (CH4), and carbon dioxide (CO2) lead to contamination, reduced purity, and increased purification costs. Existing experimental and numerical methods for predicting hydrogen dispersion coefficients (KL) are often limited by high costs, lengthy processing times, and insufficient accuracy in dynamic reservoir conditions. This study addresses these challenges by integrating experimental data with advanced machine learning (ML) techniques to model hydrogen dispersion. Various ML models—including Random Forest (RF), Least Squares Boosting (LSBoost), Bayesian Regression, Linear Regression (LR), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs)—were employed to quantify KL as a function of pressure (P) and displacement velocity (Um). Among these methods, RF outperformed the others, achieving an R2 of 0.9965 for test data and 0.9999 for training data, with RMSE values of 0.023 and 0.001, respectively. The findings highlight the potential of ML-driven approaches in optimizing UHS operations by enhancing predictive accuracy, reducing computational costs, and mitigating hydrogen contamination risks.https://doi.org/10.1038/s41598-025-98613-9Hydrogen storageUnderground hydrogen storage (UHS)Cushion gasesDispersion coefficientsMachine learning |
| spellingShingle | Ali Akbari Mehdi Maleki Yousef Kazemzadeh Ali Ranjbar Calculation of hydrogen dispersion in cushion gases using machine learning Scientific Reports Hydrogen storage Underground hydrogen storage (UHS) Cushion gases Dispersion coefficients Machine learning |
| title | Calculation of hydrogen dispersion in cushion gases using machine learning |
| title_full | Calculation of hydrogen dispersion in cushion gases using machine learning |
| title_fullStr | Calculation of hydrogen dispersion in cushion gases using machine learning |
| title_full_unstemmed | Calculation of hydrogen dispersion in cushion gases using machine learning |
| title_short | Calculation of hydrogen dispersion in cushion gases using machine learning |
| title_sort | calculation of hydrogen dispersion in cushion gases using machine learning |
| topic | Hydrogen storage Underground hydrogen storage (UHS) Cushion gases Dispersion coefficients Machine learning |
| url | https://doi.org/10.1038/s41598-025-98613-9 |
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