基于改进PSO的电站锅炉低NO_X燃烧优化  

Improved Particle Swarm Optimization to Optimize Combustion of Power Plant Boiler for Low NO_x Emission

作　　者：冯磊华[1,2] 桂卫华[1] 杨锋 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]长沙理工大学能源与动力工程学院,长沙410077 [3]湖南江河机电自动化工程有限公司,长沙410013

出　　处：《系统仿真学报》2011年第12期2812-2815,共4页Journal of System Simulation

基　　金：国家973计划资助项目(2009CB219803-03);长沙理工大学"可再生能源电力技术"湖南省重点实验室(2009)

摘　　要：燃煤锅炉NOx排放是造成环境污染的重要因素,影响NOx排放的因素众多而且复杂。首先建立了锅炉NOx排放支持向量机模型,并利用实炉热态数据对模型进行了校验。接着应用一种改进的粒子群优化算法,对锅炉运行参数进行了优化,并与一般线性粒子群优化算法进行了对比。结果表明,NOx排放量均有明显的降低,但改进的优化算法收敛性更好,为优化锅炉燃烧提供了更好的方式。NOx emission of coal-fired boiler is a main factor that has great impacts on the environment.It was affected by many factors and they are complicated.Firstly,a support vector machine（SVM） model of NOx emission was developed and verified by the data on-spot.Then,operating parameters were optimized by an improved linear particles swarm optimization（ILPSO）,and compared with the linear particles swarm optimization（LPSO）.The results show that NOx emissions are significantly lower,and ILPSO algorithm has better convergence.It has improved a better way to optimize boiler combustion.

关 键 词：锅炉 燃烧 NOX排放 支持向量机 改进粒子群优化 

分 类 号：TK323[动力工程及工程热物理—热能工程]