热力发电Issue(12)：19-24,6.DOI:10.3969/j.issn.1002-3364.2014.12.019
基于果蝇优化算法的锅炉高效率低N Ox 燃烧建模
Fruit fly optimization algorithm based high efficiency and low NOx combustion modeling for a boiler
张振星 1孙保民 1信晶1
作者信息
- 1. 华北电力大学电站设备状态监测与控制教育部重点实验室，北京 102206
- 折叠
摘要
Abstract
In order to control NOx emissions and enhance boiler efficiency in coal-fired boilers,the thermal operating data from an ultra-supercritical 1000MW unit boiler were analyzed.On the basis of the support vector regression machine (SVM),the fruit fly optimization algorithm (FOA)was applied to optimize the penalty parameter C,kernel parameter g and insensitive loss coefficient of the model.Then,the FOA-SVM model was established to predict the NOx emissions and boiler efficiency,and the performance of this model was compared with that of the GA-SVM model optimized by genetic algorithm (GA).The results show the FOA-SVM model has better prediction accuracy and generalization capability,of which the maximum aver-age relative error of testing set lies in the NOx emissions model,which is only 3 .5 9%.The above models can predict the NOx emissions and boiler efficiency accurately,so they are very suitable for on-line modeling prediction,which provides a good model foundation for further optimization operation of large capacity boilers.关键词
超超临界/1 000MW机组/锅炉/效率/NOx 排放/支持向量机/果蝇优化算法Key words
ultra-supercritical/1 000 MW unit/boiler/efficiency/NOx emissions/support vector machine/fruit fly optimization algorithm分类
能源科技引用本文复制引用
张振星,孙保民,信晶..基于果蝇优化算法的锅炉高效率低N Ox 燃烧建模[J].热力发电,2014,(12):19-24,6.
