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.
Highlights
About 70% of China’s electricity requirement comes from coal-based power plants, which are predicted to stay as the main electricity source for a long future span [1]
The errors of the model intelligent combinatorial algorithm (MICA)-based model clearly vary within a smaller range. e worst results are provided by the Elman algorithm as the predicted curve does not reflect the true data
We propose a model intelligent combinatorial algorithm to create a nitrogen oxide (NOx) emission model for coal-fired power plants based on real-operation data. e input data samples are firstly preprocessed to remove outliers. en, wavelet denoising is employed to reduce the noise. e random forest (RF) algorithm is exploited to select the best input variables for improving the model accuracy
Summary
About 70% of China’s electricity requirement comes from coal-based power plants, which are predicted to stay as the main electricity source for a long future span [1]. Boiler combustion optimization has received special attention as a way to minimize NOx emissions in coal-based power plants. This optimization approach is more cost effective and easier to implement than equipment retrofit [5]. The combustion optimization approach has two major stages: modeling and optimization. A model for the relationship of NOx emissions and the operating parameters is constructed. Is stage involves the selection of the modeling approach and the operating parameters. The operating parameters of the constructed model are optimized
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