摘要
核电站对板式换热器使用需求正逐步上升,现有的污垢热阻预测模型泛化能力较低,时序序列角度设计方案较少。针对国内某核电站1号机组的RRI/SEC换热器的实验数据进行主成分分析,优化长短期记忆神经网络设计模型来预测瞬时污垢热阻,覆盖12条管道温度和4条管道流量等变量。模型可精确预测未来25天内的污垢清洗需求,精度可达99.35%,能够在实际使用中,减少换热器监测的人力成本,以提前对板式换热器部分机组停机清洗,增加使用周期和整体机组换热效率。
Nuclear power plants have gradually increasing demand for the use of plate heat exchangers.Existing fouling thermal resistance prediction models have low generalization capabilities and few design options from the time series angle.Through the principal component analysis of the experimental data of the RRI/SEC heat exchanger of Unit 1 of the nuclear power plant,the long short-term memory neural network design model was optimized to predict the instantaneous fouling thermal resistance,covering variables such as the temperature of 12 pipelines and the flow rate of 4 pipelines.The model can accurately predict the demand for dirt cleaning in the next 25 days with an accuracy of 99.35%.In actual use,it can reduce the labor cost of heat exchanger monitoring,so as to stop and clean some units of plate heat exchangers in advance,extend the life cycle and improve heat exchange efficiency.
作者
唐健
肖明轩
侯晔
沈超
徐华
冯春
Tang Jian;Xiao Mingxuan;Hou Ye;Shen Chao;Xu Hua;Feng Chun(School of Physics and Electronic Engineering,Yancheng Teachers University,Yancheng 224007,China;Daya Bay Nuclear Operation Management Company,Shenzhen 518124,China;Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China)
出处
《电子测量技术》
北大核心
2021年第22期102-107,共6页
Electronic Measurement Technology
基金
国家自然科学基金(61771417)项目资助。
关键词
板式换热器
长短期记忆神经网络
污垢热阻预测模型
plate heat exchanger
long short-term memory
fouling thermal resistance prediction model
