AI and Digital Twins Technologies for Hydrogen (H2) Production Power Plants
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A5: Hydrogen Energy".
Deadline for manuscript submissions: closed (25 December 2023) | Viewed by 1032
Special Issue Editors
Interests: artificial intelligence; machine learning; digital twins; predictive analytics/maintenance; diagnostics; prognostics; condition monitoring; RAMS; development of model-based and data-driven approaches; digitization; Bayesian statistical machine learning techniques for forecasting
2. Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy
Interests: risk assessment; reliability analysis; simulation; artificial intelligence; nuclear systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hydrogen is emerging as a sustainable carbon-free energy source and, as such, its use is attracting growing interest as a key enabler for the achievement of the objective of a reduction in CO2 emissions. On the other hand, digital-twin technology is offering a concrete opportunity to build a virtual model of the complete dynamics of the assets of a production facility, including its process equipment. Digital Twin uses mechanistic models combined with sensor data to create a digital model of the industrial assets and its production line. Furthermore, in recent years, Applied Artificial Intelligence has been widely adopted in production lines for monitoring and predictive control. Companies from various sectors worldwide are already adopting smart and intelligent technologies that allow them to automate, and improve processes efficiency, also from the point of view of energy consumption. The developments in Artificial Intelligence for Digital Twin are quite promising, as they can be effectively utilized to integrate Internet of Things (IoT), automation, and environmental and safety mitigation measures. Various Artificial Intelligence/Machine Learning models and algorithms can improve the automation, control, reliability and safety.
In this view, the Special Issue invites papers concerning a broad range of methods and applications of Artificial Intelligence, Machine Learning, Digital Twins for hydrogen energy production plants and other topics related to the industrial use of hydrogen.
Dr. Madhav Mishra
Prof. Dr. Enrico Zio
Guest Editors
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Keywords
- Artificial Intelligence
- Machine Learning
- Digital Twins
- Hydrogen (H2) Energy
- Dynamics Models
- Data-Driven Models
- Digital Models
- Predictive Maintenance
- Green Energy
