Prediction of NOx Concentration from Coal Combustion Using LS-SVR

Nitrogen oxide (NO_x) is one of main pollutants emitted from coal fired power plants and is a significant pollutant source in the environment. Therefore, the monitoring or prediction of NO_x emissions is an indispensable process in coal-fired power plant so as to control NO_x emissions. In this paper, NO_x emissions modeling for real-time operation and control of a 300MWe coal-fired power generation plant is studied. A least square support vector regression (LS-SVR) model was proposed to establish a non-linear model between the parameters of the boiler and the NO_x emissions. The results show that the LS-SVR model predicted NO_x emissions with good accuracy. LS-SVR model is much more accurate than the GRNN model previously reported by the authors. LS-SVR model will be a good alternative to a neural network based model which is commonly used to implement the predictive emission monitoring system (PEMS).