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2020 ANS Virtual Winter Meeting
November 16–19, 2020
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U.S. reactor technologies to be featured at IAEA conference
A virtual side event at the 64th General Conference of the International Atomic Energy Agency will spotlight U.S. reactor technologies. The free event, US Reactor Technologies: Flexible Energy Security for Real-World Challenges, will be held this Thursday, September 24, from 9:00 a.m. to 10:30 a.m. (EDT).
The event will highlight the capabilities of small modular reactors and other innovative reactors for addressing countries’ current needs. It will also examine anticipated challenges in the future, as well as underscore the need to act now.
The event is sponsored by the U.S. Department of Energy’s Office of Nuclear Energy. Advanced registration is required.
Junyong Bae, Jeeyea Ahn, Seung Jun Lee
Nuclear Technology | Volume 206 | Number 7 | July 2020 | Pages 951-961
Technical Paper – Special section on the 2019 ANS Student Conference | dx.doi.org/10.1080/00295450.2019.1693215
Articles are hosted by Taylor and Francis Online.
Human operators always have the possibility to commit human errors, and in safety-critical infrastructures such as a nuclear power plant, human error could cause serious consequences. Since nuclear plant operations involve highly complex and mentally taxing activities, especially in emergency situations, it is important to detect human errors to maintain plant safety. This work proposes a method to predict the future trends of important plant parameters to determine whether a performed action is an error or not. To achieve this prediction, a recursive strategy is adopted that employs an artificial neural network as its prediction model. Two artificial neural networks were selected and compared: multilayer perceptron and long short-term memory (LSTM). Model training was accomplished using emergency operation data from a nuclear power plant simulator. From the comparison results, it was observed that the future trends of plant parameters were quite accurately predicted through the LSTM model. It is expected that the plant parameter prediction function proposed in this work can give useful information for detecting and recovering human errors.