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Volume 105
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Boiler NOx emission prediction based on ensemble learning and extreme learning machine optimization
Ze Dong a b, Jun Li a b, Xinxin Zhao a b, Wei Jiang a b *, Mingshuai Gao c
a School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China
b Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding, 071003, China
c Beijing Huake Tonghe Technology Co., LTD, Beijing, 102206, China
10.1016/j.partic.2025.07.023
Volume 105, October 2025, Pages 123-139
Received 5 June 2025, Revised 23 July 2025, Accepted 29 July 2025, Available online 13 August 2025, Version of Record 21 August 2025.
E-mail: 51652617@ncepu.edu.cn
Highlights

• A prediction method is proposed to improve the accuracy of ELM full condition object modeling.

• A multi strategy improved dingo optimization algorithm (MS-DOA) is given to optimize hyperparameters of model.

• A MS-DOA-ELM model is proposed for the construction of individual learner.

• Case study of NOx prediction in SCR denitrification system validates effectiveness and practicality of the method.


Abstract

The nitrogen oxides (NOx) emission measurement of selective catalytic reduction (SCR) denitrification system has issues that insufficient live processing and irregular purge readings. Therefore, establishing an accurate NOx concentration prediction model can significantly advance the timeliness and precision of NOx measurement. The study proposes a prediction method based on ensemble learning and extreme learning machine (ELM) optimization to build a NOx concentration prediction model for SCR denitrification system outlet. Firstly, to enhance the modeling precision of ELM for complex feature objects under all working conditions, the ensemble learning framework was introduced and an ensemble learning model based on ELM was designed. Secondly, to alleviate the impact of random initialization of ELM network learning parameters on the stability of modeling performance, the multi strategy improved dingo optimization algorithm (MS-DOA) is given by introducing Tent chaotic mapping, Lévy flight and adaptive t-distribution strategy to ameliorate the initial solution and position update process of population. Finally, the SCR denitrification operating data from 660 MW coal-fired power plant was opted for experimental validation. The findings demonstrate that the established SCR denitrification system outlet NOx concentration prediction model has high modeling accuracy and prediction accuracy, and provides a reliable approach for achieving accurate prediction of boiler NOx emissions.

Graphical abstract
Keywords
NOx emission prediction; Extreme learning machine (ELM); Ensemble learning; Dingo optimization algorithm
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