基于灰色神经网络的凝汽器水侧清洁系数预测

王建国; 汪勇华

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基于灰色神经网络的凝汽器水侧清洁系数预测

热力发电 2013年42卷第9期页码：95-99

发电技术论坛 | 更新时间：2025-09-28

- 基于灰色神经网络的凝汽器水侧清洁系数预测
- Gray neural network based prediction of water side clean coefficient of the condenser tubes
- 作者机构：
  
  东北电力大学自动化工程学院
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TK264.11
- 纸质出版：2013
- 稿件说明：
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王建国, 汪勇华. 基于灰色神经网络的凝汽器水侧清洁系数预测[J]. 热力发电, 2013,42(9):95-99.

WANG Jianguo, WANG Yonghua. Gray neural network based prediction of water side clean coefficient of the condenser tubes[J]. Thermal Power Generation, 2013, 42(9): 95-99.
王建国, 汪勇华. 基于灰色神经网络的凝汽器水侧清洁系数预测[J]. 热力发电, 2013,42(9):95-99. DOI：

WANG Jianguo, WANG Yonghua. Gray neural network based prediction of water side clean coefficient of the condenser tubes[J]. Thermal Power Generation, 2013, 42(9): 95-99. DOI：

摘要

分别采用灰色模型、神经网络以及串联灰色神经网络对某机组凝汽器水侧清洁系数进行预测。结果表明

串联灰色神经网络模型优于单一预测模型

其预测值更接近真实值

在网络训练过程中实现了误差可控

将该模型用于凝汽器水侧清洁系数的预测可行。在实际应用中

可根据前3个清洗周期某时刻的数据来预测下1个周期该时刻的清洁系数

依据所需的输入参数

即可实现在线预测凝汽器的清洁系数。

Abstract

The gray model

neural network model and series gray neural network model were employed to predict the water side clean coefficient of the condenser tubes in a power plant.The results showed that

the series gray neural network model was superior to the single prediction model.Its predicted value was closer to the true value

and the error was controllable during the network training process.So it is feasible and effective to predict the water side clean coefficient of the condenser tubes by applying this model.In practical applications

we can predict the clean coefficient at some time according to the one at the time in the first three cleaning cycles.By the required input parameters

the online prediction of water side clean coefficient of the condenser tubes can be realized.

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