换热器是一种把热量从一种介质传递到另一种介质的装置。由于换热表面污垢的存在，换热器的性能随着时间的推移而恶化。为了保持换热器的高效率，有必要定期对换热器的性能进行评估，在线监测的工艺参数能够帮助对换热器换热性能进行预测。本文利用温度和流量等参数计算表征换热器性能相关的指标，并基于共享权重长短时记忆网络（SWLSTM）建立预测模型，利用历史运行数据对其进行训练。通过与验证数据比较，验证了所建立模型预测的高精度和快速性；同时与传统神经网络模型进行比较，可见本模型在预测精度的优越性。通过换热性能参数的预测，能够合理规划停机清洗时间，降低成本。
The performance of heat exchangers deteriorates over time due to fouling on the surface of heat exchangers. In order to maintain the high efficiency of heat exchanger, it is necessary to evaluate the performance of heat exchanger regularly. The process parameters monitored online can help predict the heat transfer performance of heat exchanger. In this paper, parameters such as temperature and flow rate are used to calculate the indicators related to the performance of heat exchanger, and a prediction model is established based on the shared weight short and long time memory network (SWLSTM), which is trained with historical operation data. By comparing with the validation data, the accuracy and rapidity of prediction of the established model are verified. Moreover, compared with the traditional neural network model, it can be seen that the superiority of this model in prediction accuracy. Through the prediction of heat exchange performance parameters, maintenance personnel can reasonably plan the downtime cleaning time and reduce the cost of production loss.
2023,45(21): 153-157 收稿日期：2022-7-5
DOI：10.3404/j.issn.1672-7649.2023.21.028
分类号：U664.5+3
基金项目：中国核动力研究设计院核反应堆系统设计技术重点实验室基金资助项目（LRSDT2017302）
作者简介：余文敏(1988-)，女，博士研究生，研究方向为核设施健康管理技术
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