A hybrid deep neural network model for NOx emission prediction of heavy oil-fired boiler flames

Abstract

Accurate NOx emission monitoring is essential for an in-depth understanding of the combustion state. However, establishing an accurate emission prediction model based on the traditional data-driven method is difficult, limited by low robustness and insufficient labeled data. To mitigate these limitations, this study proposes a hybrid deep neural network model for NOx emission prediction. In this hybrid model, an adversarial denoising autoencoder (ADAE) is established to extract flame deep features, and then the least support vector regression (LSSVR) is utilized to analyze the extracted features for predicting NOx emission. A novel training strategy that includes adversarial mechanisms, denoising coding and dropout is introduced to enhance the feature learning ability. The feasibility of the hybrid model ADAE-LSSVR is verified by the 4.2 MW heavy oil-fired boiler flame images, and the model hyper-parameters are optimized for higher prediction performance. Experimental results demonstrated that the ADAE can automatically extract robust features from raw flame images without manual intervention. Moreover, the proposed ADAE-LSSVR model provides satisfactory prediction accuracy with a correlation coefficient (R2) of 0.97, outperforming other state-of-art models.