宋嘉梁,阮圣奇,陈永东,吴晓红,李翔
Song Jialiang,Ruan Shengqi,Chen Yongdong,Wu Xiaohong,LI Xiang

• 1:合肥通用机械研究院有限公司传热技术与装备研究所
• 2:中国大唐集团科学技术研究院有限公司华东电力试验研究院

摘要(Abstract)：

冷却塔是热力发电厂中重要的冷端设备。人工神经网络是研究冷却塔热力性能的一个重要方法,然而目前关于自然通风逆流湿式冷却塔热力特性的神经网络相关研究还不够充分。本文基于电厂领域36座自然通风逆流湿式冷却塔的实测热力数据,使用BP神经网络模型,对冷却塔的出塔水温、冷却数、蒸发损失水率进行预测研究。结果表明利用BP神经网络可以对冷却塔的热力性能进行较好的预测,出塔水温、冷却数、蒸发损失水率的预测均方误差以及平均相对误差分别为0.278、0.076、0.003和1.565%、18.153%、3.599%。研究结果对电厂冷却塔的热力设计及运行监测具有重要的参考价值。
Cooling tower is a critical equipment in thermal power plant. Artificial neural network is an important method to study the thermal performance of cooling tower,however,the neural network research on the thermal performance of natural draft counterflow wet cooling tower is still not sufficient. In this paper,based on the measured thermal data of 36 natural draft counterflow wet cooling towers in the field of power plant,the BP artificial neural network model was used to predict the leaving water temperature,cooling number and evaporation loss rate of cooling tower. The results show that BP neural network can better predict the thermal performance of the cooling tower. The predicted mean square error and average relative error of leaving water temperature,cooling number and evaporation loss rate are 0.278,0.076,0.003 and 1.565%,18.153% and 3.599%,respectively. The research results provide important reference value for thermal design and operation monitoring of cooling towers in power plants.

关键词(KeyWords)： 自然通风冷却塔;BP神经网络;热力性能;预测;电厂
natural draft cooling tower;BP artificial neural network;thermal performance;prediction;power plant

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划课题“典型接触式热交换设备能效评价关键技术研究”资助项目(2017YFF0209803)

作者(Author): 宋嘉梁,阮圣奇,陈永东,吴晓红,李翔
Song Jialiang,Ruan Shengqi,Chen Yongdong,Wu Xiaohong,LI Xiang

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