摘要
根据燃烧特性试验数据,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模型参数和锅炉的运行参数进行优化,找到了使得NOx质量浓度降低和热效率提高的运行参数组合,为实现电站锅炉高效低污染的优化目标提供了有效手段。
According to the experimental data of boiler combustion characteristics, response models of NOx emission and thermal efficiency during boiler combustion process were established using support vector machine （SVM）. By respectively optimizing the model parameters and boiler operating parameters with particle swarm optimization algorithm, an optimum combination of operating parameters was obtained, in which case the NOx mass concentration can be lowered and the thermal efficiency can be improved, which provides an effective means to realize the optimization objectives of high efficiency and low pollution for power station boilers.
出处
《发电设备》
2014年第2期81-85,共5页
Power Equipment
关键词
锅炉
燃烧优化
支持向量机
粒子群算法
Keywords.. boiler
combustion optimization
support vector machine
particle swarm optimizationalgorithm
