Selection of Factors Affecting NOx Emissions Concentration Forecast Modeling Based on BP Neural Network

Abstract

At present, most coal-fired units use selective catalytic reduction of (SCR) and selective non-catalytic reduction of (SNCR) denitrification technology for NOx removal. Accurate prediction of NOx emission concentration of coal-fired units will not only help to further improve the regulation quality of denitrification control system, but also evaluate whether the data collected in the current site are true and accurate, and provide a basis for environmental protection departments to supervise and enforce the law of NOx emission from power plants. In this paper, based on the historical operation data of a 200MW circulating fluidized bed unit, by analyzing the factors affecting the NOx emission concentration, firstly, the correlation coefficient method is used to analyze the delay between each factor and the NOx emission concentration, then the BP neural network is used to model the two-stage intersection, the established NOx emission concentration prediction model is compared, and a more accurate NOx emission concentration prediction model is selected. Finally, the factors affecting NOx emission concentration are selected from a more accurate model. The results show that the root mean square error of the first kind of modeling is 0.023 less than that of the second kind of modeling, so the six input factors in the first kind of model are regarded as the best factors affecting the NOx emission concentration. The selected factors can be used to accurately predict the NOx emission concentration for a period of time in the future, which lays a foundation for more accurate control of SNCR denitrification control system.