Soft-Sensor Modeling on NOx Emission of Power Station Boilers Based on Least Squares Support Vector Machines

被引：1
作者：
Feng Lei-hua [1 ,2 ]
Gui Wei-hua [1 ,3 ]
Yang Feng [4 ]
机构：
[1] Cent S Univ, Sch Informat Sci & Engn, Changsha, Peoples R China
[2] Univ Sci & Technol, Sch Energy & Power Engn, Changsha, Hunan, Peoples R China
[3] Cent South Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China
[4] JME HuNan Automat Engn Co Ltd, Changsha, Hunan, Peoples R China
来源：
ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL II, PROCEEDINGS | 2009年
关键词：
NOx emission; support vector machines; soft sensor; modeling; power station boilers;
D O I：
10.1109/ICICTA.2009.347
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The online monitoring for NOx emission of coal-fired boilers in power plants is more difficult to achieve. The soft-sensor technology of artificial neural network (ANN) method that was commonly used has not strong generalization ability, but support vector machine modeling-method can solve the problem better. In this paper, a soft-sensor modeling on NOx emission of power station boilers based on least squares support vector machines (LS-SVM) was built. The model can predict NOx emission in different conditions. The comparative analysis of forecast-results between LS-SVM model and ANN model showed that LS-SVM has more strong generalization ability and higher calculation speed.
引用
收藏
页码：462 / +
页数：2
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