The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler

Abstract

This paper presents a methodology for predicting NOx emissions of a coal-fired boiler by using real operation data, coal properties and CNN (Convolutional Neural Network). Two building blocks are carefully designed following the practical guidelines for the light weight CNN architecture design. Furthermore, the building blocks are used to develop the deep CNN-based model for NOx prediction. A comprehensive comparison among different prediction models based on DL (Deep Learning) shows that the proposed deep CNN-based prediction model outperforms other prediction models in terms of RMSE (Root Mean Square Error) criteria. The results indicate that the developed deep CNN-based prediction model has more excellent accuracy and better numerical stability. Besides, the architecture design of the DL-based prediction model has a significant impact on the performance of the prediction model.