Modeling NOx Emissions with an Intelligent Combinatorial Algorithm

Abstract

Coal combustion is considered to be the key source of nitrogen oxide (NOx) emissions in thermal power plants. Methods for effective reduction in these emissions are critically sought on the national and global levels. Such methods typically achieve this goal through accurate modeling and prediction. However, such modeling process is difficult because of the complexity of the NOx emission mechanisms and the influence of many factors. Furthermore, real-operation data of power plants tend to be centralized in some local areas because of working condition experiment so that no single model can deal with the complicated and changeable boiler production processes. In this paper, we address this problem and propose a model intelligent combinatorial algorithm (MICA). First, the actual production data are preprocessed by a wavelet denoising algorithm, and the model input variables are selected based on a random forest algorithm. Then, several models for NOx emission prediction are constructed by various data-driven algorithms. Finally, a C4.5 algorithm is applied to intelligently combine these models. The experimental results indicate that the proposed algorithm can construct an accurate prediction model for NOx emissions based on actual operating data. The mean absolute percentage errors are within 1%. Moreover, a correlation of 0.98 between predicted and measured values was obtained by applying the MICA model.