摘要
为降低锅炉燃烧系统氮氧化物(nitrogen oxide,NO_(x))的排放浓度,基于某1000MW火电机组采集的真实历史运行数据,采用堆叠降噪自编码器(stacked denoising auto-encoder,SDAE)建立了NO_(x)排放浓度的预测模型,进而提出一种基于改进麻雀搜索算法(sparrow search algorithm,SSA)的锅炉配风配粉优化策略。为提高SSA的寻优能力,提出一种引入萤火虫扰动的混沌优化麻雀搜索算法(chaotic optimized sparrow search algorithm with the introduction of firefly perturbation,FCOSSA),该算法采用Tent混沌映射使初始个体尽可能分布均匀,以增加初始种群的多样性,利用萤火虫扰动方式对所有麻雀位置进行更新。经典测试函数优化试验表明了FCOSSA的优越性。针对某给定负荷稳态运行工况,以降低NO_(x)排放为目标,利用方法对锅炉各磨煤机的给煤量及二次风门开度等进行寻优,结果表明优化后锅炉的NO_(x)排放浓度可有效降低,验证了方法的有效性。
In order to reduce the nitrogen oxide(NO_(x)) emission of the boiler combustion system,a prediction model of NO_(x) emission was established with the stacked denoising auto-encoder(SDAE) by using the real historical operation data collected from a 1000MW thermal power unit,and then an optimization strategy for boiler air distribution and coal powder distribution based on an improved sparrow search algorithm(SSA) was proposed.To improve the optimization ability of SSA,a chaotic optimization sparrow search algorithm with firefly perturbation(FCOSSA) was proposed.The FCOSSA used Tent chaotic mapping to make the initial individuals as evenly distributed as possible to increase the diversity of the initial population,and then updated the positions of all sparrows through firefly disturbance.The superiority of FCOSSA was proved by optimization tests with representative benchmark functions.For a given load steady-state operation condition,the presented method was employed to optimize the boiler air distribution and coal powder distribution with the goal of reducing NO_(x) emission.The results show that the NO_(x) emission can be effectively reduced after optimization,which verifies the effectiveness of the method.
作者
马良玉
孙佳明
MA Liangyu;SUN Jiaming(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2022年第14期5194-5201,共8页
Proceedings of the CSEE
