Prediction of NOx Emissions from a Coal-Fired Boiler Based on Convolutional Neural Networks with a Channel Attention Mechanism

Nan Li (), You Lv and Yong Hu
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Nan Li: School of Information and Electrical Engineering, Lu Dong University, Yantai 264025, China
You Lv: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Yong Hu: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Energies, 2022, vol. 16, issue 1, 1-11

Abstract: This paper presents a small and efficient model for predicting NOx emissions from coal-fired boilers. The raw data collected are processed by the min–max scale method and converted into a multivariate time series. The overall model’s architecture is mainly based on building blocks consisting of separable convolutional neural networks and efficient channel attention (ECA) modules. The experimental results show that the model can learn good representations from sufficient data covering different operation conditions. These results also suggest that ECA modules can improve the model’s performance. The comparative study shows our model’s strong performance compared to other NOx prediction models. Then, we demonstrate the effectiveness of the model proposed in this paper in terms of predicting NOx emissions.

Keywords: coal-fired boiler; separable convolutional neural network; channel attention mechanism; NOx emissions; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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