If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.

如果在发生冷却剂损失事故 (LOCA) 时安全注入系统 (SIS) 不起作用，则事故可能会发展为反应堆堆芯暴露和反应堆容器发生故障的严重事故。因此，认为需要为 SIS 驱动提供可恢复的最大时间的技术来防止这种进展。在本研究中，相应的时间被定义为黄金时间。为了实现准确预测黄金时间的目标，使用具有规则丢失的深度模糊神经网络（DFNN）进行预测。带有规则丢弃的 DFNN 具有连接许多模糊神经网络 (FNN) 的架构，并且是一种方法，其中模糊规则数与 FNN 中影响推理性能的节点数直接相关，由遗传算法适当调整。带有规则丢弃的 DFNN 模型的黄金时间预测性能优于支持向量回归模型。通过所提出的带有规则丢弃的 DFNN 的预测结果，有望通过为 SIS 恢复提供最大剩余时间来防止事故的恶化，该恢复在 LOCA 情况下失败。