摘要
针对可再生能源并网后,大型循环流化床(CFB)机组在超低负荷下实现NO_(x)超低排放的需求,利用BP神经网络算法构建了NO_(x)排放质量浓度预测模型,并采用遗传算法优化了BP神经网络的权值和阈值。综合考虑机组负荷、给煤量、一次风量、二次风量、床层温度、旋风分离器入口温度、锅炉省煤器出口氧体积分数、尿素溶液母管流量和炉膛床层平均压力9个运行参数对350 MW机组CFB锅炉NO_(x)排放质量浓度的影响行为,获得了机组100 MW超低负荷下的主要优化参数,即质量百分浓度为20%的尿素溶液流量为0.5 m^(3)/h,总一次风量和总二次风量优化比值为2.8,CFB锅炉床层操作温度为855℃,旋风分离器入口运行温度为778℃。
In order to meet the requirement of realizing ultra-low emission of NO_(x) from large-scale circulating fluidized bed(CFB)units at under ultra-low load after the renewable energy connected to the power grid,the BP neural network algorithm is applied to establish the model for predicting NO_(x) emission mass concentration,and genetic algorithm is used to optimize the weight and threshold of the BP neural network.The effects of nine operational parameters,including boiler load,feed coal quantity,primary air,secondary air,bed temperature,flue temperature at inlet of the cyclone separator,oxygen volume fraction at the economizer outlet,main pipe flow of urea solution,and furnace bed average pressure,on the NO_(x) emission mass concentration of a 350 MW unit CFB boiler,are comprehensively considered,and the major optimal parameters of the unit at ultra-low load(100 MW)are obtained,which are:the main pipe flow of 20%(mass fraction)urea solution is 0.5 m3/h,the ratio of primary air volume to secondary air volume is 2.8,the bed temperature of the CFB boiler is 855℃,and the flue temperature at inlet of the cyclone separator is 778℃.
作者
张媛媛
曲江源
王鹏程
李圳
王珂
张锴
ZHANG Yuanyuan;QU Jiangyuan;WANG Pengcheng;LI Zhen;WANG Ke;ZHANG Kai(School of New Energy,North China Electric Power University,Beijing 102206,China;Beijing Key Laboratory of Emission Surveillance and Control for Thermal Power Generation,North China Electric Power University,Beijing 102206,China;Shanxi Hepo Power Generation Co.,Ltd.,Yangquan 045001,China)
出处
《热力发电》
CAS
CSCD
北大核心
2021年第12期35-42,共8页
Thermal Power Generation
基金
国家重点研发计划项目(2020YFB0606203)。
