基于多模型聚类集成的锅炉烟气NO_x排放量预测模型
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摘要
电站锅炉烟气NO_x排放量的预测控制对电站的经济效益和环境污染治理有重要影响。为了提高NO_x排放量预测模型的精度,本文提出了一种基于多模型聚类集成的锅炉烟气NO_x排放量建模方法。首先根据输出NO_x排放量的高低划分数据空间,通过基于相关性分析的变量权重和基于信息熵的分层聚类确定参与聚类的变量,然后利用提出的多模型聚类集成(VMSC)算法聚类得到各子空间的隶属度矩阵,最后采用融合隶属度的最小二乘法对各子空间的最小二乘支持向量机(LS-SVM)模型进行集成。仿真结果表明,通过集成模糊C均值聚类(FCM)和有监督的遗传算法-软模糊聚类(GA-SFCM)的VMSC算法提高了建模的精度,比单一模型的仿真性能更好。
Predictive control of NO_x emission from flue gas of utility boilers has important influence on power plants' economic benefits and environmental pollution control. To improve the accuracy of the NO_x emission prediction model, a modeling method of boiler NO_x emission based on multi-model clustering ensemble was proposed. In this method, the data space is firstly divided according to the level of NO_x emission, and the variables that participate in clustering are determined by using the variable weight based on relevant analysis and hierarchical clustering utilized information entropy. Then, the proposed algorithm VMSC is used to obtain the new membership degree matrix of each subspace. Finally, the multiple least squares support vector machine(LS-SVM) model of each subspace is integrated by the least-squares method fused membership degree. The simulation results show that, the VMSC algorithm integrating the soft fuzzy C-means clustering(SFCM) with the genetic algorithm-soft fuzzy clustering(GA-SFCM) improves the accuracy of the clustering, and the simulation performance is better than the single model.
Predictive control of NO_x emission from flue gas of utility boilers has important influence on power plants' economic benefits and environmental pollution control. To improve the accuracy of the NO_x emission prediction model, a modeling method of boiler NO_x emission based on multi-model clustering ensemble was proposed. In this method, the data space is firstly divided according to the level of NO_x emission, and the variables that participate in clustering are determined by using the variable weight based on relevant analysis and hierarchical clustering utilized information entropy. Then, the proposed algorithm VMSC is used to obtain the new membership degree matrix of each subspace. Finally, the multiple least squares support vector machine(LS-SVM) model of each subspace is integrated by the least-squares method fused membership degree. The simulation results show that, the VMSC algorithm integrating the soft fuzzy C-means clustering(SFCM) with the genetic algorithm-soft fuzzy clustering(GA-SFCM) improves the accuracy of the clustering, and the simulation performance is better than the single model.
引文
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[2]宋清昆,侯玉杰.基于MFOA的锅炉热效率及NOx排放建模与优化[J].计算机仿真,2018,35(1):98-102.SONG Qingkun,HOU Yujie.Boiler thermal efficiency and NOx emissions modeling and optimization based on MFOA[J].Computer Simulation,2018,35(1):98-102.
[3]顾燕萍,赵文杰,吴占松.基于最小二乘支持向量机的电站锅炉燃烧优化[J].中国电机工程学报,2010,30(17):91-97.GU Yanping,ZHAO Wenjie,WU Zhansong.Combustion optimization for utility boiler based on least square support vector machine[J].Proceedings of the CSEE,2010,30(17):91-97.
[4]赵志宏,韩超,赵文杰.主元分析及多变量过程监测联合预测NOx质量浓度[J].热力发电,2016,45(7):98-103.ZHAO Zhihong,HAN Chao,ZHAO Wenjie.NOx content prediction based on principal component analysis and multivariable process monitoring[J].Thermal Power Generation,2016,45(7):98-103.
[5]赵文杰,吕猛.基于多LS-SVM集成模型的锅炉NOx排放量建模[J].电子测量与仪器学报,2016,30(7):1037-1044.ZHAO Wenjie,LV Meng.Modeling of NOx emission of boiler based on multiple LS-SVM integrated model[J].Journal of Electronic Measurement and Instrument,2016,30(7):1037-1044.
[6]王广龙,吕猛,赵文杰.基于遗传算法的电站锅炉NOx排放量LS-SVM建模[J].自动化与仪器仪表,2016(2):70-72.WANG Guanlong,LV Meng,ZHAO Wenjie.LS-SVMmodeling for NOx emission of power plant boiler based on genetic algorithm[J].Automation&Instrumentation,2016(2):70-72.
[7]张振星,孙保民,信晶.基于自适应遗传算法的锅炉低NOx燃烧建模及其优化[J].热力发电,2014,43(9):60-64.ZHANG Zhenxing,SUN Baomin,XIN Jing.Adaptive genetic algorithm based low NOx combustion modeling and optimization for boilers[J].Thermal Power Generation,2014,43(9):60-64.
[8]彭琛,钱晓山.基于聚类的多模型蒸发过程软测量建模[J].系统仿真学报,2015,27(9):2050-2055.PENG Chen,QIAN Xiaoshan.Multi-model evaporation process soft-measuring modeling based on clustering[J].Journal of System Simulation,2015,27(9):2050-2055.
[9]牛培峰,刘超,李国强,等.基于双层聚类与GSA-LSSVM的汽轮机热耗率多模型预测[J].电机与控制学报,2016,20(3):90-95.NIU Peifeng,LIU Chao,LI Guoqiang,et al.Multi-model for turbine heat rate forecasting based on double layer clustering algorithm and GSA-LSSVM[J].Electric Machines and Control,2016,20(3):90-95.
[10]孙建平,苑一方.复杂过程的多模型建模方法研究[J].仪器仪表学报,2011,32(1):132-137.SUN Jianping,YUAN Yifang.Research on multi-model modeling method for complex processes[J].Chinese Journal of Scientific Instrument,2011,32(1):132-137.
[11]索幸仪,侍洪波.基于多模型模糊核聚类方法的污水处理过程软测量建模[J].华东理工大学学报(自然科学版),2010,36(5):732-736.SUO Xingyi,SHI Hongbo.Soft measurement model of sewage process based on multi-model fuzzy kernel clustering method[J].Journal of East China University of Science and Technology(Natural Science Edition),2010,36(5):732-736.
[12]刘明波,胡朝举.层次聚类结合空间金字塔的图像分类[J].计算机应用研究,2018(12):1-2.LIU Mingbo,HU Chaoju.Hierarchical clustering combined with spatial pyramid image classification[J].Application Research of Computers,2018(12):1-2.
[13]张静静,杨燕,王红军,等.一种新的软聚类投票法及其并行化实现[J].中国科学技术大学学报,2016,46(3):173-179.ZHANG Jingjing,YANG Yan,WANG Hongjun,et al.Anew soft clustering voting method and its parallel implementation[J].Journal of University of Science and Technology of China,2016,46(3):173-179.
[14]TOPCHY A P,LAW M H C,JAIN A K,et al.Analysis of consensus partition in cluster ensemble[C]//Proceeding of the 4th IEEE International Conference on Data Mining.Brighton,UK:ACM Press,2004:225-232.
[15]IAM-ON N,BOONGOEN T.Comparative study of matrix refinement approaches for ensemble clustering[M].Kluwer Academic Publishers,2015:29.