主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2020, Vol. 39 ›› Issue (10): 1465-1468.

• • 上一篇    下一篇

火电厂消防设计中冷凝器污垢累积规律的神经网络预测模型

周广宏1，任万英2   

  1. 1. 无锡职业技术学院控制技术学院，江苏无锡214121；2. 华北水利水电大学电力学院，河南郑州450003
  • 出版日期:2020-10-15 发布日期:2020-10-15
  • 作者简介:周广宏（1977-），男，江苏扬州人，无锡职业技术学院控制技术学院讲师，硕士，主要从事智能控制方面的工作，江苏省无锡市高浪西路1600 号，214121。

A neural network predicting model for condenser scaling cumulative law in fire protection design of coal power station

ZHOU Guang-hong1, REN Wan-ying2   

  1. 1. Department of Control Technology, Wuxi Institute of Technology, Jiangsu Wuxi 214121, China; 2. Department of Electrical Engineering, North China University of Water Resources and Electric Power, Henan Zhengzhou 450003, China
  • Online:2020-10-15 Published:2020-10-15

摘要： 为了防止火电厂锅炉消防设计中冷凝器因结垢而引起锅炉的火灾和爆炸事故，需要对冷凝器污垢系数的发展规律进行预测。设计了一种结合K-均值算法和Chebyshev 神经网络的污垢系数预测模型，针对Chebyshev 神经网络的弊端，应用K-均值算法对其进行改进，将污垢系数随时间发展的曲线分为启动阶段、粘附阶段和老化阶段3 类。结果表明，改进Chebyshev 神经网络模型有效地预测了冷凝器污垢系数发展规律，得到的输出结果比渐进预测和幂率预测模型的预测结果更准确，该模型具有算法简单、收敛速度快的特点。

关键词: 火电厂, 消防安全, 冷凝器, 神经网络, 污垢系数

Abstract: In order to prevent the condenser scaling from causing fire and explosion accidents, it is necessary to predict the law of development of fouling factor in the condenser. A fouling factor predicting model combining K-mean algorithm and Chebyshev neural network was designed. Aiming at the disadvantages of Chebyshev neural network, the K-mean algorithm can be used to improve the curve of fouling factor development over time, which can be divided into three stages: starting stage, adhesion stage and aging stage. Results showed that, the modified Chebyshev neural networks can predict the law of development of condenser fouling factor effectively, and is more accuracy than progressive prediction and power-law prediction; the algorithm is simple and has fast convergence speed.

Key words: coal power station, fire safety, condenser, neural network, fouling factor