Prediction of NOx Concentration from Coal Combustion Using LS-SVR
Ligang Zheng, Hailin Jia, Shuijun Yu, Minggao Yu School of Safety Science and Engineering and Key Lab of Gas Geology and Gas Control, Henan Polytechnic University, Jiaozuo Henan, China zhengligang97@
Abstract—Nitrogen oxide (NOx) is one of main pollutants emitted other relevant system variables by using suitable algorithms. from coal fired power plants and is a significant pollutant source That means stack gases from bustion chamber are in the environment. Therefore, the monitoring or prediction of possible to be predicted indirectly. NOx emissions is an indispensable process in coal-fired power Currently, the techniques for predicting or modeling the plant so as to control NOx emissions. In this paper, NOx emissions modeling for real-time operation and control of a 300MWe coal- NOx emission consists of empirical model, statistical model [1], fired power generation plant is studied. A least square support neuro-fuzzy modeling [2], and work models such as vector regression (LS-SVR) model was proposed to establish a back propagation work (BPNN) [3-5], time delay non-linear model between the parameters of the boiler and the work (TDNN) [6], generalized regression neural NOx emissions. The results show that the LS-SVR work (GRNN) [7]. The state of the art methods for pr
