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DC Field | Value | Language |
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dc.citation.title | KOREAN JOURNAL OF CHEMICAL ENGINEERING | - |
dc.contributor.author | Bae, Junyong | - |
dc.contributor.author | Lee, Seung Jun | - |
dc.date.accessioned | 2024-08-13T10:05:05Z | - |
dc.date.available | 2024-08-13T10:05:05Z | - |
dc.date.created | 2024-08-12 | - |
dc.date.issued | 2024-08 | - |
dc.description.abstract | Large-scale infrastructures, such as chemical plants and nuclear power plants (NPPs), are pivotal for modern civilization as they provide vital resources and energy. However, their operation introduces significant risks, as demonstrated by the tragic accidents at Bhopal and Fukushima. While extensive research has been conducted to improve the safety of these safety–critical systems, the human factor remains as a significant concern. In recent years, as artificial intelligence (AI) is being widely adopted in various fields, AI may be a solution for supporting operators and, ultimately, for reducing the overall risk of safety–critical systems such nuclear and chemical plants. This review discusses the application of AI in NPP operations, with a focus on event diagnosis, signal validation, prediction, and autonomous control. Various application examples are presented, highlighting the limitations of classical approaches and the potential for AI overcome such limitations to enhance the safety and efficiency of NPP operations. This work is expected to stimulate further investigation into the application of AI to support operators in not only NPPs but also other safety–critical systems, such as chemical plants. | - |
dc.identifier.bibliographicCitation | KOREAN JOURNAL OF CHEMICAL ENGINEERING | - |
dc.identifier.doi | 10.1007/s11814-024-00246-7 | - |
dc.identifier.issn | 0256-1115 | - |
dc.identifier.scopusid | 2-s2.0-85200504359 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/83477 | - |
dc.identifier.wosid | 001283861000003 | - |
dc.language | 영어 | - |
dc.publisher | KOREAN INSTITUTE CHEMICAL ENGINEERS | - |
dc.title | Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary;Engineering, Chemical | - |
dc.relation.journalResearchArea | Chemistry;Engineering | - |
dc.type.docType | Review | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Human factor | - |
dc.subject.keywordAuthor | Nuclear power plant | - |
dc.subject.keywordAuthor | Plant operation | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Safety–critical system | - |
dc.subject.keywordPlus | FUZZY NEURAL-NETWORKS | - |
dc.subject.keywordPlus | TRANSIENT IDENTIFICATION | - |
dc.subject.keywordPlus | FAULT-DETECTION | - |
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