摘要
以某330MW机组锅炉为对象,求得锅炉受热面吸热净收益最大化时的最佳吹灰频率及清洁系数,并以神经网络软件为基础,借助DCS中的实时数据监测锅炉对流受热面及热辐射受热面的积灰情况,实时比较监测清洁系数与临界清洁系数,以此进行吹灰操作。应用表明,系统可实时反映炉内灰污状态,指导受热面吹灰按需进行,减少了盲目吹灰造成的能量消耗。
Aiming at improving the economical efficiency of boiler soot blowing system,this paper takes a 330 MW unit boiler as the object to calculate the optimum soot blowing frequency and clean coefficient when the heat-absorbent net income of the heating surface reaches the maximization.Moreover,based on the neural network software,real-time monitoring for ash depositing situation on convective heating surface and radiant heating surface was carried out through data collected from DCS,so as to compare and monitor the clean coefficient and the critical value timely,thus to take initiative to soot blowing.Application results indicate that,the system with real-time reflecting of ash deposition condition in furnace can instruct the soot blowing be taken according to the demand.Therefore,the energy consumption caused by blind soot blowing is reduced.
出处
《热力发电》
CAS
北大核心
2012年第10期38-40,共3页
Thermal Power Generation
关键词
330
MW机组
锅炉
神经网络
智能吹灰
受热面
吹灰频率
清洁系数
neural network
intelligent
soot blowing
boiler
optimization
heating surface
blowing frequency
clean coefficient
