Prediction of the internal states of a nuclear power plant containment in LOCAs using rule-dropout deep fuzzy neural networks

Abstract A serious threat to the integrity of the reactor core, reactor coolant system, or containment is incurred if proper and essential actions to mitigate accidents cannot be taken owing to insufficient information about the internal states of the nuclear power plant (NPP). Therefore, this study was carried out to develop a model capable of mitigating the risk of severe accidents by accurately predicting the internal states of an NPP containment. A deep fuzzy neural network (DFNN) is a method in which syllogistic fuzzy reasoning is relatively efficient and inference capability is enhanced. In this study, the internal states of an NPP containment, hydrogen concentration and pressure, are predicted using a rule-dropout DFNN, as little NPP information is available under the circumstances of severe accidents. In addition, the performance of the proposed rule-dropout DFNN model is compared with that of other fuzzy neural network variations to verify the enhancement in the accuracy of the DFNN. The developed rule-dropout DFNN model is expected to be capable of providing accident monitoring information in advance for accident mitigation, as its prediction error for the hydrogen concentration and pressure in the containment is low.