摘要
通过控制变量法对混流闭式冷却塔进行测试,采用灰色关联分析法对影响出水温度的因素进行筛选,将关联度较大的5个因子作为输入参数,进而建立灰色_BP神经网络预测模型,对混流闭式冷却塔的出水温度进行预测。操作参数包括进水温度、湿球温度、补水温度、循环水流量和风量,输出值为出水温度。网络采用三层结构,隐含层神经元数为4个,迭代次数为30 000次,使用不涉及训练阶段的实验数据来验证所建立的模型。结果表明,灰色_BP神经网络模型比传统BP神经网络模型的预测结果更加准确,其预测值与实际值的相关系数、平均相对误差、均方根误差,分别为0.998 9、 0.293 4%和0.152 9,因而可认为灰色_BP神经网络是预测混流闭式冷却塔出水温度的有效工具。
In this study, a mixed flow closed cooling tower was tested with a control variable method. The factors affecting outlet water temperature were screened with a grey correlation analysis method. Five most important factors were taken as input features in a gray_BP neural network which was developed to predict outlet water temperature of the mixed flow closed cooling tower. These factors include inlet water temperature, wet bulb temperature, water refill temperature, circulating water flow rate and air volume, and the prediction output is outlet water temperature. The network adopts a three-layer structure, four-hidden layer neurons, and 30 000 iterations. Experimental data that involve no training set were used to validate the developed model. It shows that the gray neural network model outperforms the traditional BP neural network model. The correlation coefficient, average relative error, root mean square error are 0.998 9, 0.293 4% and 0.152 9, respectively. We concluded that the gray_BP neural network is a promising algorithm for predicting water temperature of a mixed flow closed cooling tower.
作者
李洪
章立新
任燕
高明
刘婧楠
LI Hong;ZHANG Lixin;REN Yan;GAO Ming;LIU Jingnan(Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《计算物理》
CSCD
北大核心
2022年第1期53-59,共7页
Chinese Journal of Computational Physics
基金
国家自然科学基金(51976127)资助项目。
关键词
混流闭式冷却塔
出水温度
灰色关联分析
BP神经网络
mixed flow closed cooling tower
outlet water temperature
grey correlation analysis
BP neural network
