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 …
Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities
Carbon capture, utilization, and sequestration (CCUS) is a promising solution to
decarbonize the energy and industrial sector to mitigate climate change. An integrated …
decarbonize the energy and industrial sector to mitigate climate change. An integrated …
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 …
Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based …
Geophysical reservoir characterization is a significant task in the oil and gas industry and
elastic logs prediction of subsurface formations is a fundamental aspect of this process …
elastic logs prediction of subsurface formations is a fundamental aspect of this process …
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 …
Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland
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 …
hydrogen storage. We introduce an innovative artificial intelligence framework that applies …
Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach
This study introduces machine learning (ML) algorithms to predict hydrogen (H 2)
thermodynamic properties for geological storage, focusing on its mixtures with natural gas …
thermodynamic properties for geological storage, focusing on its mixtures with natural gas …
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 …
Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production
Hydrogen offers significant potential as a sustainable energy source. However, its storage
and transportation pose challenges due to its volatility and low density. Subsurface …
and transportation pose challenges due to its volatility and low density. Subsurface …
Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models
Underground coal fires release substantial carbon dioxide (CO 2), posing significant
environmental and health threats. Accurate prediction of surface CO 2 emissions in these …
environmental and health threats. Accurate prediction of surface CO 2 emissions in these …