Dynamic prediction of NOx generation concentration based on Kolmogorov–Arnold Network integrated deep learning method for a 660 MW coal-fired boiler

Ziwei Wang, Wei Fan, Zixuan Lin, Haiquan Yu, Cong Yu, Yu Li, Wei Zhou,
Dynamic prediction of NOx generation concentration based on Kolmogorov–Arnold Network integrated deep learning method for a 660 MW coal-fired boiler,
Energy,
Volume 340,
2025,
139343,
ISSN 0360-5442,
https://doi.org/10.1016/j.energy.2025.139343.
(https://www.sciencedirect.com/science/article/pii/S0360544225049850)
Abstract: Nitrogen oxides (NOx) are among the most significant pollutants produced by coal-fired power plants. Accurate prediction of NOx concentrations at the boiler outlet is crucial for optimizing unit control and effectively reducing emissions. This paper investigates the integration of deep learning techniques with Kolmogorov–Arnold Networks (KANs) to model the relationship between operational parameters and NOx generation concentration in a 660 MW coal-fired boiler. By leveraging the strengths of both methodologies, the study aims to enhance the accuracy and robustness of NOx generation concentration predictions. Four deep learning methods including Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Units (GRU), and Temporal Convolutional Network (TCN) are employed to extract dynamic features from the auxiliary variables. An automated framework, Optuna, is utilized to optimize the hyperparameters of these deep learning algorithms. The extracted dynamic features are then used as inputs for KANs to predict NOx generation concentration. The results demonstrate that the proposed method outperforms conventional deep learning approaches in real-world NOx generation concentration prediction tasks, providing more accurate results. This approach opens new avenues for temporal forecasting models and underscores the potential of KANs as a powerful tool in predictive analytics.
Keywords: KAN; Deep learning method; Coal-fired boiler; NOx generation concentration prediction