摘要
高效、低污染是电站锅炉燃烧优化的目标。该文基于最小二乘支持向量机,建立了电站锅炉燃烧模型,实现了飞灰含碳量、排烟温度、NOx排放量等参数的软测量和锅炉效率的预测;对比了最小二乘支持向量机和BP神经网络模型的性能,对比结果表明,最小二乘支持向量机具有训练时间短、泛化能力高等优点。提出2种锅炉燃烧优化方式,并以所建立的燃烧模型为基础,采用遗传算法对锅炉运行工况进行寻优,为分散控制系统基础控制层提供最佳的操作变量设定值。算例表明,文中所提出的燃烧优化方案可以有效提高电站锅炉效率和降低NOx排放量。
High efficiency and low NOx emission are the two main goals of boiler combustion optimization.This paper applied least square-support vector machine （LS-SVM） to build the utility boiler combustion model,for the prediction of carbon content of fly ash,exhaust flue gas temperature,NOx emission,boiler efficiency,and so on.As a novel modeling method,the performance of LS-SVM was compared with that of the traditional BP neural network;the corresponding results indicate that LS-SVM needs less training time and has better generalization ability.Based on the above boiler combustion model,two combustion optimization modes were proposed and genetic algorithm was selected to solve the multi-objective and multi-constrained boiler combustion optimization problem.The results of boiler combustion optimization process provide best settings of manipulate variables for DCS.Numerical examples show that the proposed optimization control scheme in this paper is effective on improving utility boiler efficiency and reducing its NOx emission.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2010年第17期91-97,共7页
Proceedings of the CSEE
关键词
燃烧优化
锅炉效率
NOX排放
最小二乘支持向量机
遗传算法
combustion optimization
boiler efficiency
NOx emission
least square-support vector machine （LS-SVM）
genetic algorithm
作者简介
顾燕萍（1986－），女，博士研究生，主要从事电站锅炉燃烧优化控制的研究；
赵文杰（1969－），男，博士，副教授，主要从事智能控制及应用的研究；
吴占松（1946－），男，博士，教授，博士生导师，主要从事热力系统建模与优化控制的研究，wzs@mail．tsinghua．edu．cn。
