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《Journal of Chinese Society of Power Engineering》 2010-10
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Prediction for Ash Fusion Point Based on Partial Least Square Regression

XU Zhi-ming1,ZHENG Jiao-li1,WEN Xiao-qiang2(1.Institute of Energy and Power Engineering,Northeast Dianli University,Jilin 132012,China;2.School of Energy and Power Engineering,North China Electric Power University, Beijing 102206,China)  
Adopting the components of coal ash as the input variables and the distortion temperature of coal ash as the output variable,the model for ash fusion point was set up by use of partial least-square(PLS) regression method.The model was trained by 60 samples,then the obtained PLS regression equation was used to predict the ash fusion points of 20 samples of blend coal.The predicted results with different numbers of ash component as the input variables were compared.Results show that the set up PLS regression model can well predict the ash fusion point.When ten components of coal ash are used as input variables,the mean relative and the maximum relative forecasting error are 1.05%,1.91% respectively,which is more accurate than the predicting results using seven components of coal ash as input variables.
【Fund】: 国家重点研究发展计划(973计划)资助项目(2007CB206904);; 国家自然科学基金资助项目(50576009)
【CateGory Index】: TK227
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