HTTR operation monitoring with neural network in 30 days operation at 850C

Shimizu, Atsushi ; Nabeshima, Kunihiko  ; Nakagawa, Shigeaki  

The High temperature engineering test reactor (HTTR) executed the rated power driving for 30 days of the first time (850C in temperature of the nuclear reactor exit coolant) until March, 27th through April, 26th, 2007. In this operation, HTTR was observed according to the operation monitoring model with the neural network, and the detection performance of neural network was verified during slight changes of reactor state at rated power. The neural network used for the operation monitoring was an auto-associative network, where 31 input 31 outputs and the hidden layers were connected with 20 units by the hierarchy of three layer structure. Back-propagation algorithm is used for study rule. The operation monitoring model in initial study was constructed by using the power up data between 30% and rated power, which are randomly studied. The adjustment study during the operation monitoring changes the internal structure of the initial study model to follow the changes of reactor status, such as the combustion of the nuclear fuel for the rated power driving. As a monitoring result, slight changes of reactor state by the control system operation were correctly detected, and the on-line application to an early anomaly diagnosis for HTTR facilities will be expected.