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Thermochemical water-splitting structures for hydrogen production: Thermodynamic, economic, and environmental impacts
Thermochemical water-splitting (TWS) processes are regarded as one of the most
environmentally friendly strategies, capable of harnessing high-temperature waste heat from …
environmentally friendly strategies, capable of harnessing high-temperature waste heat from …
Digital twins in safety analysis, risk assessment and emergency management
Digital twins (DTs) represent an emerging technology that is currently leveraging the
monitoring of complex systems, the implementation of autonomous control systems, and …
monitoring of complex systems, the implementation of autonomous control systems, and …
Machine learning in energy economics and finance: A review
Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …
energy economics and finance. We critically review the burgeoning literature dedicated to …
Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …
essential if it is desired to simultaneously take advantage of both engineering principles and …
[HTML][HTML] Digital twins in pharmaceutical and biopharmaceutical manufacturing: a literature review
The development and application of emerging technologies of Industry 4.0 enable the
realization of digital twins (DT), which facilitates the transformation of the manufacturing …
realization of digital twins (DT), which facilitates the transformation of the manufacturing …
[HTML][HTML] A review and perspective on hybrid modeling methodologies
The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …
from knowledge about the system) and nonparametric models (typically deduced from data) …
[HTML][HTML] Advancing hydrogen storage predictions in metal-organic frameworks: a comparative study of LightGBM and random forest models with data enhancement
The escalating consumption of fossil fuels has given rise to a substantial upsurge in
greenhouse gas concentrations and global temperatures, which, in turn, has triggered …
greenhouse gas concentrations and global temperatures, which, in turn, has triggered …
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 …
[HTML][HTML] Machine learning for industrial sensing and control: A survey and practical perspective
With the rise of deep learning, there has been renewed interest within the process industries
to utilize data on large-scale nonlinear sensing and control problems. We identify key …
to utilize data on large-scale nonlinear sensing and control problems. We identify key …
[HTML][HTML] Energy modeling and model predictive control for HVAC in buildings: A review of current research trends
Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas
emissions, which may significantly impact climate change. Heating, ventilation, and air …
emissions, which may significantly impact climate change. Heating, ventilation, and air …