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AuthorSleiti, Ahmad K.
AuthorKapat, Jayanta S.
AuthorVesely, Ladislav
AuthorAl-Khawaja, Mohammed
Available date2021-10-18T06:52:12Z
Publication Date2021
Publication NameQatar University Annual Research Forum and Exhibition (QUARFE 2021)
CitationSleiti A. K., Kapat J. S., Vesely L., Al-Khawaja M., "Digital Twin for Power Plants, Energy Savings and other Complex Engineering Systems", Qatar University Annual Research Forum and Exhibition (QUARFE 2021), Doha, 20 October 2021, https://doi.org/10.29117/quarfe.2021.0003
URIhttps://doi.org/10.29117/quarfe.2021.0003
URIhttp://hdl.handle.net/10576/24270
AbstractDigital Twin (DT) is a digital representation of a machine, service, or production system that consists of models, information, and data used to characterize properties, conditions, and behavior of the system. Renewable energy integration will make future power plants more complex with addition of varieties of Power-to-X technologies, Electrolysis to green hydrogen, onsite storage and transport of hydrogen, and use of pure or blended hydrogen, etc. These future power plants need robust DT architecture to achieve high Reliability, Availability and Maintainability at lower cost. In this research work, a comprehensive and robust DT architecture for power plants is proposed that also can be implemented in other similar complex capital-intensive large engineering systems. The novelty and advantages of the proposed DT is asserted by reviewing the state-of-the-art of DT in energy industries and its potential to transform these industries. Then the proposed DT architecture and its five components are explained and discussed. More specifically, the main contributions of the present work include: 1. Overview of DT key research and development for energy savings applications to consider important findings, research gaps and the needed future development for the proposed DT for power plants. 2. Overview of DT key research for power plants including applications, frameworks and architectures to consider important findings and to confirm the novelty and robustness of the proposed DT. 3. Proposing and demonstrating new robust DT architecture for power plants and other similar complex capital-intensive large engineering systems.
Languageen
PublisherQatar University Press
SubjectDigital twin
Power plant
Dynamic system model (DSM)
Anomaly detection and deep Learning (ADL)
Sensor network
Energy cyber-physical systems
TitleDigital Twin for Power Plants, Energy Savings and other Complex Engineering Systems
TypePoster
dc.accessType Open Access


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