摘要
当前电站锅炉吹灰普遍根据运行规程定时吹灰,未考虑负荷变动对受热面灰污状况的影响。为实现锅炉不同受热面按需吹灰,采用机理建模与数据分析耦合的方法建立锅炉智能吹灰决策模型并开发智能吹灰系统。建立锅炉受热面灰污监测模型,结合烟温、汽温、金属壁温等关键表征参数,对锅炉受热面吹灰器的吹灰敏感性进行分析,基于此,建立多维度综合评判的吹灰决策模型,开发智能吹灰系统并在某350 MW超临界对冲燃烧锅炉上实现应用。系统应用后,吹灰策略优化后的机组排烟温度没有上升,表明受热面积灰结渣情况没有恶化,同时由于减少了吹灰频次,相较于原吹灰方案,系统投运后4个月智能吹灰系统节省蒸汽量约3805 t,比原方案减少47.2%。
At present,the soot blowing is generally scheduled according to the operation regulations in power plant boilers,while the influence of load changes on the soot condition of the heating surface is not considered.In order to realize on-demand soot blowing on different heating surfaces of the boiler,the method of coupling mechanism modeling and data analysis is used to establish an intelligent soot blowing decision-making model of the boiler and develop an intelligent soot blowing system.A boiler heating surface ash pollution monitoring model is established,and combined with key characterization parameters such as flue gas temperature,steam temperature,and metal wall temperature,the soot blowing sensitivity of the boiler heating surface soot blower is analyzed.Based on this,a soot blowing decision-making model for multi-dimensional comprehensive evaluation is established,and an intelligent soot blowing system is developed and applied in a 350 MW supercritical hedging combustion boiler.With the application of the system,the exhaust gas temperature of the unit doesn’t rise after the soot blowing strategy is optimized,indicating that the ash/slagging situation in the heated area doesn’t deteriorate.At the same time,because the frequency of soot blowing is reduced,after the system is put into operation for four months,the intelligent soot blowing system saves about 3,805 t of steam,which is 47.2%less than the original soot blowing scheme.
作者
黄书益
陈鸿
廖勇
白彬
谭鹏
张成
HUANG Shuyi;CHEN Hong;LIAO Yong;BAI Bin;TAN Peng;ZHANG Cheng(Ledong Power Plant,CHN Energy,Ledong,Hainan 572599,China;State Key Laboratory of Coal Combustion,School of Energy and Power Engineering,Huazhong University of Science and Technology,Wuhan,Hubei 430074,China)
出处
《广东电力》
2022年第7期114-121,共8页
Guangdong Electric Power
基金
国家自然科学基金项目(52106011)。
关键词
吹灰优化
敏感性分析
电站锅炉
soot blowing optimization
sensitivity analysis
power station boiler
