Hydrogen production and pollution mitigation: Enhanced gasification of plastic waste and biomass with machine learning & storage for a sustainable future
The pursuit of carbon neutrality confronts the twofold challenge of meeting energy demands
and reducing pollution. This review article examines the potential of gasifying plastic waste …
and reducing pollution. This review article examines the potential of gasifying plastic waste …
[HTML][HTML] Modelling underground hydrogen storage: A state-of-the-art review of fundamental approaches and findings
This review presents a state-of-the-art of geochemical, geomechanical, and hydrodynamic
modelling studies in the Underground Hydrogen Storage (UHS) domain. Geochemical …
modelling studies in the Underground Hydrogen Storage (UHS) domain. Geochemical …
Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables
Hydrogen (H 2) absorption percentage by porous carbon media (PCM) is important for
identifying efficient H 2 storage media. PCM with H 2-uptakes of greater than 5 wt% are …
identifying efficient H 2 storage media. PCM with H 2-uptakes of greater than 5 wt% are …
Machine learning-based shale wettability prediction: Implications for H2, CH4 and CO2 geo-storage
Shale wettability determines shale gas productivities and gas (H 2, CH 4 and CO 2) geo-
storage efficiencies. However, shale wettability is a complex parameter which depends on …
storage efficiencies. However, shale wettability is a complex parameter which depends on …
Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques
Pyrolysis, as a thermochemical conversion of biomass, is a superior biofuel production
procedure. The determining procedure for the optimal operational parameters, biomass …
procedure. The determining procedure for the optimal operational parameters, biomass …
Recent progress on advanced solid adsorbents for CO2 capture: from mechanism to machine learning
Environmental pollution has become a serious issue due to the rapid development of
urbanization, industrialization, and vehicle traffic. Notably, fossil fuel combustion significantly …
urbanization, industrialization, and vehicle traffic. Notably, fossil fuel combustion significantly …
Data-driven machine learning models for the prediction of hydrogen solubility in aqueous systems of varying salinity: Implications for underground hydrogen storage
Hydrogen is a clean and sustainable renewable energy source with significant potential for
use in energy storage applications because of its high energy density. In particular …
use in energy storage applications because of its high energy density. In particular …
Enhancing carbon sequestration: innovative models for wettability dynamics in CO2-brine-mineral systems
This study investigates the application of machine learning techniques—specifically
convolutional neural networks, multilayer perceptrons and cascaded forward neural …
convolutional neural networks, multilayer perceptrons and cascaded forward neural …
Integrating capacity and efficiency for optimal hydrogen storage site selection in saline aquifers
Hydrogen (H2) energy is a promising transition pathway from conventional fossil fuels to
sustainable clean energy. However, H2 requires a large storage capacity because of its low …
sustainable clean energy. However, H2 requires a large storage capacity because of its low …
A critical review of physics-informed machine learning applications in subsurface energy systems
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …
computer vision, natural language processing, and speech recognition. It can unravel …