Abstract
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.
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