SMART_TC: an R&D Programme on uses of artificial intelligence techniques for tritium monitoring in complex ITER-like tritium plant systems
The realization of nuclear fusion energy is nowadays based on the concept of tritium breeding and the success of the ITER experiment. The latter relies today on a static monitoring approach to fulfill the emission limits imposed by the regulatory institutions. Artificial intelligence applications fo...
| Authors: | , , , , , |
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| Format: | article |
| Publication Date: | 2021 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/367794 |
| Online Access: | https://hdl.handle.net/2117/367794 https://dx.doi.org/10.1016/j.fusengdes.2021.112409 |
| Access Level: | Open access |
| Keyword: | Tritium Artificial intelligence Machine learning ITER Fault detection and diagnosis Triti Intel·ligència artificial Aprenentatge automàtic Àrees temàtiques de la UPC::Física |
| Summary: | The realization of nuclear fusion energy is nowadays based on the concept of tritium breeding and the success of the ITER experiment. The latter relies today on a static monitoring approach to fulfill the emission limits imposed by the regulatory institutions. Artificial intelligence applications for fault diagnosis and process monitoring anticipate potential for the dynamic management of tritium in complex plant systems. This paper explores the dynamic tritium inventory management issue in complex systems, reviews the diverse artificial intelligence techniques and discusses the most promising approaches for ITER-like plant system match balance monitoring. |
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