摘要
受热面灰污增长预测模型是研究吹灰优化方案的前提,分析了受热面灰污增长简化理论模型的基础上,基于小波变换的特征提取方法和ARMA(p,q)时序模型分析,建立锅炉受热面灰污增长综合预测模型。该模型无需额外投资,通过对电厂已有的实时数据进行分析和特征提取,便能准确评价和预测受热面的灰污状态。经某电厂省煤器灰污增长预测实例表明,该模型预测结果与实际状况相符,从而能为吹灰优化提供指导。
Based on the characteristic identification method of wavelet transformation and ARMA（p,q）model analysis of time-sequence information,a comprehensive prediction model of fouling growth on coal-fired utility boilers＇ heating surface is constructed in this paper after the analysis of simplified theoretical model of fouling growth,for it is the premise of the study on the soot blowing optimization.Through the calculation analysis and characteristic identification on real time date of boiler＇s operation,the accurate estimation and prediction of fouling state can be obtained with this model without any other extra invest.With the evidence of some economizer fouling growth,it can be concluded that the present model is validity and would be contributed to soot blowing optimization of boilers.
出处
《锅炉技术》
北大核心
2010年第3期16-20,共5页
Boiler Technology
关键词
电站锅炉
灰污增长预测
小波分析
ARMA(p
q)模型
coal-fired utility boilers
fouling growth prediction
wavelet transformation
ARMA（p
q）prediction model
