期刊文献+

基于数学模型的煤粉锅炉管束磨损预测与分析 被引量:5

Prediction and Analysis on Tube Bank Erosion of Pulverized Coal-fired Boilers Based on Mathematical Model
下载PDF
导出
摘要 根据飞灰质量浓度与烟气速度的计算式建立了以易测取量为输入参数的磨损预测数学模型,通过测取预测模型的输入参数,对受热面的磨损量进行预测,并将其表示为过量空气系数与飞灰细度的函数.结果表明:过量空气系数和飞灰细度对磨损量影响显著;控制过量空气系数与煤粉细度,可以有效地降低磨损量;结合输入参数的在线测量,该模型亦可作为锅炉对流管束磨损的监测模型. Based on formulas for calculation of fly ash mass concentration and flue gas velocity,a mathematical model for erosion prediction of heat surfaces was built up by taking quantities easy to get as input parameters.After acquisition of the input parameters,corresponding erosion prediction was then completed,of which the results were expressed in a function of excess air ratio and fly ash fineness.Results show that both the excess air ratio and fly ash fineness have an appreciable impact on erosion of boiler tubes,therefore controlling excess air ratio and fly ash fineness at an appropriate level can effectively reduce the erosive wear,and if combined with online measurement of input parameters,the model can also be used to monitor erosion condition of boiler convection tubes.
出处 《动力工程学报》 CAS CSCD 北大核心 2012年第3期187-191,共5页 Journal of Chinese Society of Power Engineering
关键词 锅炉管束 磨损预测 过量空气系数 飞灰细度 烟气速度 boiler tube erosion prediction excess air ratio fly ash fineness flue gas velocity
  • 相关文献

参考文献4

二级参考文献92

  • 1刘柏谦,刘武彬.飞灰对省煤器磨损的模拟研究及新的防磨措施[J].东北电力技术,2005,26(4):20-22. 被引量:7
  • 2岑可法.锅炉和热交换器的积灰、结渣、磨损和腐蚀的防止原理与计算[M].科学出版社,1998..
  • 3[1]Hancke G P,Malan R.On-line particle size distribution analysis of pulverised coal[C]//Industrial Electronics,ISIE'96,Proceedings of the IEEE International Symposium on 1996,2:1066-1070.
  • 4[2]Hancke G P,Malan R.A modal analysis technique for the on-line particle size measurement of pneumatically conveyed pulverized coal[J].IEEE Transactions On Instrumentation And Measurement,1998,47(1):114-122.
  • 5[3]Casali A,Gonzalez G,Vallebuona G,et al.Grindablity soft-sensors based on lithological composition and on-line measurements[J].Minerals Engineering,2001,14(7):689-700.
  • 6[4]Gonzalez G D,Orchard M,Cerda J L,et al.Local models for soft-sensors in a rougher flotation bank[J].Minerals Engineering,2003,16(5):441-453.
  • 7[6]Suykens J A K,Vandewalle J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
  • 8[7]Rameswar Debnath,Masakazu Muramatsu,Haruhisa Takahashi.An efficient support vector machine learning method with second-order cone programming for large-scale problem[J].Applied Intelligence,2005,23(3):219-239.
  • 9[8]Gestel T V,Suykens J A K,Baesens B,et al.Benchmarking least squares support vector machine classifiers[J].Machine Learning,2004,54(1):5-32.
  • 10岑可法.锅炉燃烧试验研究方法及测量技术[M].水利电力出版社,1995..

共引文献217

同被引文献34

引证文献5

二级引证文献8

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部