Nuclear Power AI Applications: Status, Challenges and Opportunities

Zhang Heng; Lyu Xue; Liu Dong; Wang Guoyin; Hang Qin; Sha Rui; Guo Bin

doi:10.13832/j.jnpe.2023.01.0001

Volume 44 Issue 1

Feb. 2023

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Zhang Heng, Lyu Xue, Liu Dong, Wang Guoyin, Hang Qin, Sha Rui, Guo Bin. Nuclear Power AI Applications: Status, Challenges and Opportunities[J]. Nuclear Power Engineering, 2023, 44(1): 1-8. doi: 10.13832/j.jnpe.2023.01.0001

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Zhang Heng, Lyu Xue, Liu Dong, Wang Guoyin, Hang Qin, Sha Rui, Guo Bin. Nuclear Power AI Applications: Status, Challenges and Opportunities[J]. Nuclear Power Engineering, 2023, 44(1): 1-8. doi: 10.13832/j.jnpe.2023.01.0001

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Nuclear Power AI Applications: Status, Challenges and Opportunities

doi: 10.13832/j.jnpe.2023.01.0001

1.
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
2.
Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China
3.
China Nuclear Energy Association, Beijing, 100048, China
4.
Hangzhou Bolean IoT Tech Co., Ltd., Hangzhou, 311100, China

Received Date: 2022-08-16
Rev Recd Date: 2022-10-16
Publish Date: 2023-02-15

Abstract

Abstract

In recent years, artificial intelligence (AI) technology has been widely used in the field of nuclear power to promote nuclear power plants to achieve the goal of improving production efficiency, reducing operating costs and improving operating safety through self diagnosis, self optimization and self adaptation. This paper introduces the AI technology often used in the nuclear power field, summarizes its research status in four typical application scenarios of the nuclear industry, namely, intelligent mine, intelligent design, intelligent manufacturing and intelligent operation and maintenance. Finally, it analyzes the challenges and development trends of the application of AI technology in the nuclear power field from three aspects: data samples, network security, and the explanatory nature of deep learning.
- Nuclear power AI applications,
- Trustworthy AI,
- Explainable deep learning,
- Nuclear safety

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References(57)

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