A review of underground hydrogen storage systems: Current status, modeling approaches, challenges, and future prospective

SO Bade, K Taiwo, UF Ndulue, OS Tomomewo… - International Journal of …, 2024 - Elsevier
Our increased reliance on fossil fuels and its environmental effects have led us to prioritize
transitioning to a carbon-free economy and using renewable sources of electric power …

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

Z Du, Z Dai, Z Yang, C Zhan, W Chen, M Cao… - … and Sustainable Energy …, 2024 - Elsevier
Hydrogen, as a clean and efficient energy source, is important in achieving zero-CO 2
targets. This paper explores the potential of hydrogen geologic storage (HGS) in China for …

Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland

R Derakhshani, L Lankof, A GhasemiNejad… - Scientific Reports, 2024 - nature.com
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 …

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

M Saeed, P Jadhawar - Gas Science and Engineering, 2024 - Elsevier
This review presents a state-of-the-art of geochemical, geomechanical, and hydrodynamic
modelling studies in the Underground Hydrogen Storage (UHS) domain. Geochemical …

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

A Latrach, ML Malki, M Morales, M Mehana… - Geoenergy Science and …, 2024 - Elsevier
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …

Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance analysis

F Yousefmarzi, A Haratian, J Mahdavi Kalatehno… - Scientific Reports, 2024 - nature.com
Interfacial tension (IFT) is a key physical property that affects various processes in the oil and
gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability …

A machine learning strategy for enhancing the strength and toughness in metal matrix composites

Z Zhong, J An, D Wu, N Gao, L Liu, Z Wang… - International Journal of …, 2024 - Elsevier
Particle-reinforced metal matrix composites (MMCs) are highly sought after for various
applications due to their robust mechanical properties containing high strength and high …

Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics

BO Taiwo, A Gebretsadik, HH Abbas, M Khishe… - Heliyon, 2024 - cell.com
Maximizing the use of explosives is crucial for optimizing blasting operations, significantly
influencing productivity and cost-effectiveness in mining activities. This work explores the …

Predictive Modeling of Energy Poverty with Machine Learning Ensembles: Strategic Insights from Socioeconomic Determinants for Effective Policy Implementation

S Gawusu, SA Jamatutu… - International Journal of …, 2024 - Wiley Online Library
This study aims to identify the key predictors of the multidimensional energy poverty index
(MEPI) by employing advanced machine learning (ML) ensemble methods. Traditional …

Enhanced desalination with polyamide thin-film membranes using ensemble ML chemometric methods and SHAP analysis

J Usman, SI Abba, FJ Abdu, LT Yogarathinam… - RSC …, 2024 - pubs.rsc.org
Addressing global freshwater scarcity requires innovative technological solutions, among
which desalination through thin-film composite polyamide membranes stands out. The …