Machine learning-based performance predictions for steels considering manufacturing process parameters: a review
W Fang, J Huang, T Peng, Y Long, F Yin - Journal of Iron and Steel …, 2024 - Springer
Steels are widely used as structural materials, making them essential for supporting our lives
and industries. However, further improving the comprehensive properties of steel through …
and industries. However, further improving the comprehensive properties of steel through …
[HTML][HTML] Enhancing energy carrier gas storage: Novel MOF-decorated carbons with high affinity toward methane and hydrogen
S Mirzaei, L LotfiKatooli, A Ahmadpour… - … Research and Design, 2024 - Elsevier
The low volumetric density of alternative energy sources, like methane and hydrogen,
makes their efficient storage challenging. This issue hinders their widespread adoption as …
makes their efficient storage challenging. This issue hinders their widespread adoption as …
Machine Learning in Operating of Low Voltage Future Grid
B Mroczek, P Pijarski - Energies, 2022 - mdpi.com
The article is a continuation of the authors' ongoing research related to power flow and
voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use …
voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use …
Optimal Energy Storage System Selection: A Decision Support Framework
OI Rozhdestvenskiy, PB Bobba… - E3S Web of …, 2024 - e3s-conferences.org
This study enhances the domain of optimum energy storage system selection by offering a
complete decision support framework that incorporates technical, economic, and …
complete decision support framework that incorporates technical, economic, and …