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Modeling of the Unburned Carbon in Fly Ash

Modeling of the Unburned Carbon in Fly Ash
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摘要 Numerical simulation of the content of unburned carbon in fly ash on the 300MW tangentially pulverized coal fired boiler is performed by the numerical simulation software COALFIRE, which is based on international advanced TASCFLOW software platform. Firstly, take the result of calculation of number value as the sample, and then set up the support vector machine model of unburned carbon content on the boiler. The relative error between the predicted output and measured value is 0.00186%, which proves the modeling is good for the unburned carbon in fly ash predict. Numerical simulation of the content of unburned carbon in fly ash on the 300MW tangentially pulverized coal fired boiler is performed by the numerical simulation software COALFIRE, which is based on international advanced TASCFLOW software platform. Firstly, take the result of calculation of number value as the sample, and then set up the support vector machine model of unburned carbon content on the boiler. The relative error between the predicted output and measured value is 0.00186%, which proves the modeling is good for the unburned carbon in fly ash predict.
机构地区 不详
出处 《Energy and Power Engineering》 2009年第2期90-93,共4页 能源与动力工程(英文)
关键词 NUMERICAL simulation unburned CARBON CONTENT support VECTOR MACHINE numerical simulation unburned carbon content support vector machine
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  • 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
  • 3Liu K. Comparison of very short-term load forecasting technique[J]. IEEE Trans. Power Systems, 1996,11(2): 877-882.
  • 4Hippert 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.
  • 5Muller 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.
  • 6Papadakis 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.
  • 7Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing[M].Cambridge, MA, MIT Press, 1997, 281-287.
  • 8Smola A J. Regression estimation with support vector learning machines[D]. Technische Universit"at M" unchen.1996.
  • 9Vapnik V N. The nature of statistical learning theory[M]. New York:Springer, 1995.
  • 10Mukherjee S, Osuna E, Girosi F. Nonlinear prediction of chaotic time series using support vector machines[C]. Proceedings of NNSP '97,Amelia Island,FL,1997.

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