NOx emission prediction using a lightweight convolutional neural network for cleaner production in a down-fired boiler

Abstract

The selective catalytic reduction (SCR) denitrification efficiency of coal-fired boilers is used to assess the clean production of power plants. Accurate predictions of NOx emissions at the SCR inlet can help improve the denitrification efficiency. However, the generation and distribution of NOx in a furnace involves complex combustion, thermodynamics, and fluid mechanics processes. The physical-based method for NOx emission models is complex and requires expensive computational resources. To address these issues, deep learning was employed to develop a prediction approach based on the random forest (RF) algorithm and lightweight convolutional neural network (CNN). To avoid curse of dimensionality, the random forest algorithm was utilized to select the importance of the candidate variables. The selected variables were in line with the boiler NOx generation and combustion control trends. The lightweight CNN was developed to build the model that predicts NOx emissions. The key was that a Cross-Channel Communication (C3)-Block was introduced into the lightweight CNN to resolve the issue in which there was no communication between feature maps in the same convolutional layer. This model was named C3–CNN. The predictive effectiveness of the C3–CNN-based model was assessed on the real historical dataset of a 600 MW down-fired boiler. The experimental results indicated that the proposed method reduced the model depth of the CNN (saved 3 convolutional layers, 1.5 times parameters, and 1 times FLOPs than 6-layer CNN) while also ensuring the model prediction accuracy (RMSE = 13.52704 ± 0.21036 mg/m3, MAE = 9.89313 ± 0.18288 mg/m3, R2 = 0.93337 ± 0.00222), which was suitable for the online optimization of industrial pollutant control and may assist cleaner production.