NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm

Abstract

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Measuring the nitrogen oxides concentration accurately at the inlet of the selective catalytic reduction denitrification system plays an important role in controlling the nitrogen oxides concentration for coal-fired power plants, and a coupling relationship exists between nitrogen oxides concentration and multiple operational variables. Here, a modeling method based on feature fusion and long short-term memory network is proposed to mine the spatial and temporal coupling relationship between input variables for improving the prediction accuracy. First, the collected data were converted to image-like sequences. Then, the high-dimensional features of image-like sequences were fused by a convolutional neural network, and the spatial coupling features among the variables were mined. Finally, the constructed fusion features were input into the long short-term memory network to further explore the time coupling characteristics among the variables and complete the prediction of nitrogen oxides concentration at the inlet of the selective catalytic reduction denitrification system. The simulation results show that the prediction error of nitrogen oxides concentration at the inlet of selective catalytic reduction denitrification system based on CNN-LSTM model is 15.15% lower than that of traditional LSTM model.

Keywords

• coal-fired power plants
• deep learning
• feature fusion
• Prediction
• NOx concentration