Combined Deep Learning and Optimization for Hydrogen-Solubility Prediction in Aqueous Systems Appropriate for Underground Hydrogen Storage Reservoirs DOI
Promise O. Longe, Shadfar Davoodi, Mohammad Mehrad

et al.

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(22), P. 22031 - 22049

Published: Oct. 30, 2024

The widespread use of fossil fuels drives greenhouse gas emissions, prompting the need for cleaner energy alternatives like hydrogen. Underground hydrogen storage (UHS) is a promising solution, but measureing (H2) solubility in brine complex and costly. Machine learning can provide accurate reliable predictions H2 by analyzing diverse input variables, surpassing traditional methods. This advancement crucial improving UHS, making it viable component sustainable infrastructure. Given its importance, this study utilized convolutional neural network (CNN) long–short-term memory (LSTM) deep algorithms combination with growth optimization (GO) gray wolf (GWO) to predict solubility. A total 1078 data points were collected from laboratory results, including variables temperature (T), pressure (P), salinity (S), salt type (ST). After removing 97 points, which identified as outliers duplicates, remaining 981 divided into training testing sets using best separation ratio selected based on sensitivity analysis. Standalone hybrid forms then applied develop predictive models optimized control parameters both algorithms. Among developed models, CNN-GO has lowest root-mean-square error (RMSE, train: 0.00006 mole fraction test: 0.00021 fraction) compared other standalone models. application scoring regression characteristic (REC) curve analysis showed that model generated prediction performance. Shapley additive explanation indicated P was most important factor influencing solubility, followed S, T, ST, order. Partial dependency revealed ability capture nonlinear relationships between features target variable.

Language: Английский

Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models DOI
Hemeng Zhang,

Pengcheng Wang,

Mohammad Rahimi

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 441, P. 141043 - 141043

Published: Jan. 31, 2024

Language: Английский

Citations

24
Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state DOI Creative Commons
Behnam Amiri-Ramsheh, Aydin Larestani,

Saeid Atashrouz

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104035 - 104035

Published: Jan. 1, 2025

Language: Английский

Citations

2
Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage DOI
Ahmed A. Ewees, Hung Vo Thanh, Mohammed A. A. Al‐qaness

et al.

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(2), P. 112210 - 112210

Published: Feb. 14, 2024

Language: Английский

Citations

14
Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland DOI Creative Commons
Reza Derakhshani, Leszek Lankof, Amin GhasemiNejad

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 20, 2024

Abstract This study explores the feasibility of utilizing bedded salt deposits as sites for underground hydrogen storage. We introduce an innovative artificial intelligence framework that applies multi-criteria decision-making and spatial data analysis to identify most suitable locations storing in caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms—KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, MLR—creating rock deposit suitability maps The performance these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Absolute (MAE), Percentage (MAPE), Root Square (RMSE), Correlation Coefficient (R 2 ), compared against actual dataset. CatBoost model demonstrated exceptional performance, achieving R 0.88, MSE 0.0816, MAE 0.1994, RMSE 0.2833, MAPE 0.0163. novel methodology, leveraging advanced machine learning techniques, offers unique perspective assessing potential is valuable asset stakeholders, government bodies, geological services, renewable energy facilities, chemical/petrochemical industry, aiding them identifying optimal

Language: Английский

Citations

12
Prediction of hydrogen solubility in aqueous solution using modified mixed effects random forest based on particle swarm optimization for underground hydrogen storage DOI
Grant Charles Mwakipunda,

Norga Alloyce Komba,

Allou Koffi Franck Kouassi

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 87, P. 373 - 388

Published: Sept. 9, 2024

Language: Английский

Citations

11
Investigation of Wettability and IFT Alteration during Hydrogen Storage Using Machine Learning DOI Creative Commons

Mehdi Maleki,

Mohammad Rasool Dehghani,

Ali Akbari

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e38679 - e38679

Published: Sept. 30, 2024

Language: Английский

Citations

10
Low-Carbon Advancement through Cleaner Production: A Machine Learning Approach for Enhanced Hydrogen Storage Predictions in Coal Seams DOI
Yongjun Wang, Hung Vo Thanh, Hemeng Zhang

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122342 - 122342

Published: Jan. 1, 2025

Language: Английский

Citations

1
Identification of Stable Intermetallic Compounds for Hydrogen Storage via Machine Learning DOI Open Access

A. S. Athul,

Aswin V. Muthachikavil,

Venkata Sudheendra Buddhiraju

et al.

Energy Storage, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 6, 2025

ABSTRACT Hydrogen is one of the most promising alternatives to fossil fuels for energy as it abundant, clean and efficient. Storage transportation hydrogen are two key challenges faced in utilizing a fuel. Storing H 2 form metal hydrides safe cost effective when compared its compression liquefaction. Metal leverage ability metals absorb stored can be released from hydride by applying heat needed. A multi‐step methodology proposed identify intermetallic compounds that thermodynamically stable have high storage capacity (HSC). It combines compound generation, thermodynamic stability analysis, prediction properties ranking discovered materials based on predicted properties. The US Department Energy (DoE) Materials Database Open Quantum (OQMD) utilized building testing machine learning (ML) models enthalpy formation compounds, formation, equilibrium pressure HSC hydrides. here require only attributes elements involved compositional information inputs do no need any experimental data. Random forest algorithm was found accurate amongst ML algorithms explored predicting all interest. total 349 772 hypothetical were generated initially, out which, 8568 stable. highest these 3.6. Magnesium, Lithium Germanium constitute majority compounds. predictions using present not DoE database reasonably close data published recently but there scope improvement accuracy with HSC. findings this study will useful reducing time required development discovery new used check practical applicability

Language: Английский

Citations

1
Explainable Artificial Intelligence in modelling hydrogen gas solubility in n-Alkanes DOI Creative Commons
Afshin Tatar, Abbas Zeinijahromi, Manouchehr Haghighi

et al.

Separation and Purification Technology, Journal Year: 2025, Volume and Issue: 362, P. 131741 - 131741

Published: Jan. 22, 2025

Language: Английский

Citations

1
Long-term stability forecasting for energy storage salt caverns using deep learning-based model DOI
Kai Zhao, Shinong Yu, Louis Ngai Yuen Wong

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134854 - 134854

Published: Feb. 1, 2025

Language: Английский

Citations

1