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《Proceedings of the CSEE》 2009-17
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State Prediction of Slagging on Coal-fired Boilers Based on Least Squares-support Vector Machine for Regression

XU Zhi-ming1, WEN Xiao-qiang2, SUN Yuan-yuan1, SUN Ling-fang1 (1. School of Energy Resources and Mechanical Engineering, Northeast Dianli University, Jilin 132012, Jilin Province, China; 2. School of Energy and Power Engineering, North China Electric Power University, Changping District, Beijing 102206, China)  
Building a model to predict the state of slag on coal-fired boilers is a good way to optimize the coal combustion and reduce the risk of boiler slag. This paper built the least squares-support vector machine for regression (LS SVMR) to predict the state of slag on coal-fired boilers, in which there were six input vectors, which were softening temperature (tST), SiO2-Al2O3 ratio(w(SiO2)/w(Al2O3)), alkali acid ratio(J), percentage of silicon content(G), the dimens ionless average temperature furnace(φt) and the dimensionless inscribed circle diameter furnace(φd), and one output vectors, which was slagging degree. At the same time, to obtain the optimal combination of penalty parameter γ and nuclear parameter σ, the principle of microscope was used effectively. The feasibility of this method was proved by the result of predicting the state of slag on the five coal-fired boilers. Besides, the prediction system has been developed by object oriented high-level language accordingly.
【Fund】: 国家重点基础研究发展规划项目(2007CB206904);; 吉林省科技发展计划项目(20070529)~~
【CateGory Index】: TK229.6
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