Application of Deep Learning for Quantifying Small RCS Leakage

Abstract

In nuclear power plants, coolant leakage occurs for various reasons. Leak detection is important to ensure safety of nuclear power plants. Currently, a detection system for an unidentified reactor coolant system(RCS) leakage of less than 0.5gpm is being developed in Korea. The RCS leakage is detected through changes in radioactivity, humidity, and temperature in the containment air, and water level of sump. For small leaks, the change in humidity and temperature due to water vapor is very small, making the leak very difficult to detect until the leak accumulates in the instrument.BR In order to solve these problems and increase the leak detection speed, it is necessary to develop a system capable of real-time detection using artificial intelligence. In this study, long short-term memory and bidirectional long short-term memory, which are types of recurrent neural networks among artificial intelligence methods, were applied to perform initial relative humidity prediction for leakage quantification. Also, an optimization technique that reduces learning time and improves prediction performance for the optimization of learning was applied. Finally, the prediction performance was evaluated using the developed model.