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

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.

关键词： NOx emission support vector machines soft sensor modeling power station boilers

作者: FENG Lei-hua GUI Wei-hua YANG Feng

作者单位: School of Information Science and Engineering Central South University Changsha, China School of Ene School of Information Science and Engineering Central South University Changsha, China JME (HuNan) Automation Engineering Co. Ltd.Changsha, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 1414-1418

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）