期刊文献+

基于预数值计算的锅炉飞灰可燃物含量建模 被引量:5

Modeling of the Unburned Carbon in Fly Ash Based on Numerical Simulation in the Utility Boiler
原文传递
导出
摘要 运用四角切圆燃烧煤粉锅炉专用数值模拟计算软件COALFIRE,对某电厂300MW四角切圆煤粉锅炉飞灰可燃物含量排放特性进行了数值模拟。以数值计算结果为样本,建立基于支持向量机的四角切圆燃烧锅炉飞灰可燃物含量预测模型,其预测输出与数值计算结果的最小相对误差为1.01%,说明基于预数值计算和支持向量机算法的四角切圆煤粉锅炉飞灰可燃物含量模型能够较好地对锅炉飞灰可燃物含量进行预测。为将计算结果精确但计算过程耗时较长的数值模拟用于锅炉燃烧工况在线监测,提供了新的思路。 Numerical simulation of the characters of unburned carbon in fly ash on the 300 MW tangentially pulverized coal fired boiler was performed by the numerical simulation software COALFIRE. Taking the result of calculation of number value as the sample, the support vector machine model of unburned carbon content on the 300 MW tangentially pulverized coal fired boiler was set up. Relative error between the predicted output and measured value is 1.01%, it proves the modeling is good for the unburned carbon in fly ash predict. It also provides the new method to use numerical simulation with the accurate result but computational process consuming time longer for combustion operating mode monitor online.
出处 《中国电机工程学报》 EI CSCD 北大核心 2009年第17期32-37,共6页 Proceedings of the CSEE
关键词 四角切圆煤粉锅炉 数值模拟 飞灰可燃物含量 支持向量机 tangentially pulverized coal fired boiler numerical simulation unbunied carbon content support vector machine
  • 相关文献

参考文献21

  • 1Fan Maohong, Brown R C. Precision and accuracy of photo acoustic measurements of unburned carbon in fly ash[J]. Fuel, 2001, 80(11):1545-1554.
  • 2Styszko G K, Golas J, Jankowski H, et al. Characterization of the coal fly ash for the purpose of improvement of industrial on-line measurement of unburned carbon content[J]. Fuel, 2004, 83(13): 1847-1853.
  • 3Ouazzane A K, Castagner J L, Jones A R, et al. Design of an optical instrument to measure the carbon content of fly ash[J]. Fuel, 2002, 81(15): 1907-1911.
  • 4周昊,朱洪波,曾庭华,廖宏楷,岑可法.基于人工神经网络的大型电厂锅炉飞灰含碳量建模[J].中国电机工程学报,2002,22(6):96-100. 被引量:76
  • 5方湘涛,叶念渝.基于BP神经网络的电厂锅炉飞灰含碳量预测[J].华中科技大学学报(自然科学版),2003,31(12):75-77. 被引量:25
  • 6Sebastia M, Olmo I F, lrabien A. Neural network prediction of unconfined compressive strength of coal fly ash . cement mixtures[J]. Cement and Concrete Research, 2003, 33(8): 1137-1145.
  • 7张国云,章兢.基于模糊支持向量机的多级二叉树分类器的水轮机调速系统故障诊断[J].中国电机工程学报,2005,25(8):100-104. 被引量:36
  • 8李元诚,方廷健,于尔铿.短期负荷预测的支持向量机方法研究[J].中国电机工程学报,2003,23(6):55-59. 被引量:279
  • 9Lin Kuanming, Lin Chihen. A study on reduced support vector machines[J]. IEEE Transaction on Neural Networks, 2003, 14(6): 1449-1459.
  • 10Anguita D, Boni A, Ridella S. A digital architecture for support vector machines: Theory, algorithm, and FPGA implementation [J]. IEEE Transactions on Neural Networks, 2003, 14(5): 993-1009.

二级参考文献79

  • 1占勇,丁屹峰,程浩忠,曾德君.电力系统谐波分析的稳健支持向量机方法研究[J].中国电机工程学报,2004,24(12):43-47. 被引量:60
  • 2张国云,章兢.基于模糊支持向量机的多级二叉树分类器的水轮机调速系统故障诊断[J].中国电机工程学报,2005,25(8):100-104. 被引量:36
  • 3陈学俊 陈听宽.锅炉原理(第二版)[M].西安:西安交通大学出版社,1991..
  • 4Liu K. Comparison of very short-term load forecasting technique[J]. IEEE Trans. Power Systems, 1996,11(2): 877-882.
  • 5Hippert H S, Pefreira C E, Souza R C. Neural network for short-term load forecasting: A review and evaluation[J].IEEE Trans. Power System. 2001,16(2): 44-54.
  • 6Muller K R, Smola A J, Ratsch G, et al.Prediction time series with support vector machines[C].In Proc of ICANN'97., Springer LNCS 1327, Bedin,1997, 999-1004.
  • 7Papadakis S E, Theocharis J B, Kiartzis S J, et al. A novel approach to short-term load forecasting using fuzzy neural net-works[J].IEEE Trans. Power Systems, 1998,13(2):480-492.
  • 8Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing[M].Cambridge, MA, MIT Press, 1997, 281-287.
  • 9Smola A J. Regression estimation with support vector learning machines[D]. Technische Universit"at M" unchen.1996.
  • 10Vapnik V N. The nature of statistical learning theory[M]. New York:Springer, 1995.

共引文献2846

同被引文献49

引证文献5

二级引证文献40

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