NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm - Yin, Zhe; Yang, Chunlai; Yuan, Xiaolei; Jin, Fei; Wu, Bin (Frontiers in Energy Research, 2023)
Measuring the nitrogen oxides (NOx) concentration accurately at the inlet of the selective catalytic reduction (SCR) denitrification system plays an important role in controlling the NOx concentration for coal-fired power plants, and a coupling relationship exists between NOx concentration and multiple operational variables. Here, a modeling method based on feature fusion and long short-term memory (LSTM) 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 (CNN), and the spatial coupling features among the variables were mined. Finally, the constructed fusion features were input into the LSTM network to further explore the time coupling characteristics among the variables and complete the prediction of NOx concentration at the inlet of the SCR denitrification system. Simulation results showed that the prediction of NOx concentration at the inlet of the SCR denitrification system based on CNN–LSTM model has a higher accuracy than those of other prediction models.