摘要
为预防水冷凝汽器结垢、腐蚀,本文基于某电厂实际工况,通过循环冷却水水质智能在线监控系统监测的换热污垢特性参数和多个水质参数,利用范数灰色关联法,对于影响循环冷却水系统中换热器污垢、腐蚀的问题因素,进行了关联分析,以获得主要的影响因素作为在线预测模型的输入量,利用基于粒子群算法(PSO)的小波神经网络(WNN)建立了换热器污垢热阻、腐蚀速率的在线预测模型。同时,由模型计算的污垢热阻、腐蚀速率预测值与实际监测值进行了效果对比,验证了预测模型的准确性。
For preventing fouling and corrosion of condenser, water parameters which based on actual working conditions of a power plant are monitored by self-made dynamic experimental apparatus. This paper adopt grey correlation of the norm to analyse correlation between impact factors and fouling, corrosion of heat exchanger in circulating cooling water system. Fouling and corrosion on-line predictive model of heat exchanger is established by PSO-WNN. Main factors of above is used to as input values of on-line predictive model. The predictive value of fouling and corrosion that calculated by this model can fit with the experimental results well.
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2014年第2期324-328,共5页
Journal of Engineering Thermophysics
基金
国家自然科学基金资助项目(No.51376042)
吉林省科技发展计划资助项目(No.20125050)
吉林市科技发展计划资助项目(No.201262504)
关键词
污垢热阻
腐蚀速率
水质参数
范数灰色关联
预测模型
fouling resistance
corrosion rate
water quality parameters
grey correlation of the norm
predictive model
