摘要
近年来,随着煤炭价格的不断攀升和环保要求的日益严格,我国的电站锅炉运行面临着降低运行成本和降低污染物排放的双重要求,如何实现高效低污染燃烧优化日益引起了人们的关注。支持向量机作为一种新的统计学方法,在建模方面具有良好的性能。借助优化燃烧特性试验数据,利用支持向量机建立锅炉燃烧过程NOx排放与热效率的响应特性模型,结合粒子群算法分别对支持向量机的结构参数及锅炉运行参数进行了优化。优化数值表明,该方法可以实现锅炉燃烧高效低污染的整体优化。
With the rising of coal price and the strict controls on the environmental pollution during recent years, the power stations of our country are faced with the double reducing both in operating costs and the pollutant emission, how to achieve the burning optimization that of high efficiency and low pollution has drawn a widely public attention. As a new method in statistics, Support Vector Machine performs satisfact-orily when modeling. With the aid of test data in optimize burning experiment, this test takes full advanta-ge of Support Vector Machine to establish the model of responses between NO~ emissions and thermal eff- iciency in the process of flue boiler, integrated with Particle Swarm Optimization Algorithm try to optimize the structure parameter and boiler operation parameter belongs to Support Vector Machine. According to the results from the experiment, it indicates that this method can lead the burning optimization to be high efficiency and low pollution.
出处
《锅炉技术》
北大核心
2014年第4期13-17,共5页
Boiler Technology
关键词
锅炉
燃烧优化
支持向量机
粒子群算法
flue boiler
burning optimization
support vector machine
particle swarm optimization algorithm
