scholarly journals Real-Time Nuclear Power Plant Monitoring with Adaptively Trained Neural Network

ICANN ’93 ◽  
1993 ◽  
pp. 863-863 ◽  
Author(s):  
K. Nabeshima ◽  
E. Türkcan ◽  
Ö. Ciftcioglu
1998 ◽  
Vol 35 (2) ◽  
pp. 93-100 ◽  
Author(s):  
Kunihiko NABESHIMA ◽  
Tomoaki SUZUDO ◽  
Katsuo SUZUKI ◽  
Erdinç TÜRKCAN

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hongyun Xie ◽  
Haixia Gu ◽  
Chao Lu ◽  
Jialin Ping

Real-time Simulation (RTS) has long been used in the nuclear power industry for operator training and engineering purposes. And, online simulation (OLS) is based on RTS and with connection to the plant information system to acquire the measurement data in real time for calibrating the simulation models and following plant operation, for the purpose of analyzing plant events and providing indicative signs of malfunctioning. OLS has been applied in certain industries to improve safety and efficiency. However, it is new to the nuclear power industry. A research project was initiated to implement OLS to assist operators in certain critical nuclear power plant (NPP) operations to avoid faulty conditions. OLS models were developed to simulate the reactor core physics and reactor/steam generator thermal hydraulics in real time, with boundary conditions acquired from plant information system, synchronized in real time. The OLS models then were running in parallel with recorded plant events to validate the models, and the results are presented.


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