Dynamic NO x Emission Modeling in a Utility Circulating Fluidized Bed Boiler Considering Denoising and Multi-Frequency Domain Information

Qianyu Li, Guanglong Wang, Xian Li, Qing Bao, Wei Li, Yukun Zhu, Cong Yu and Huan Ma ()
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Qianyu Li: School of Energy and Environment, Southeast University, Nanjing 210096, China
Guanglong Wang: Inner Mongolia Jingtai Power Generation Co., Ltd., Ordos 010399, China
Xian Li: Inner Mongolia Jingtai Power Generation Co., Ltd., Ordos 010399, China
Qing Bao: Inner Mongolia Jingtai Power Generation Co., Ltd., Ordos 010399, China
Wei Li: Inner Mongolia Jingtai Power Generation Co., Ltd., Ordos 010399, China
Yukun Zhu: School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China
Cong Yu: School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China
Huan Ma: School of Energy and Environment, Southeast University, Nanjing 210096, China

Energies, 2025, vol. 18, issue 4, 1-16

Abstract: Climate change poses a significant global challenge that necessitates concerted efforts toward carbon neutrality. Circulating fluidized bed (CFB) boilers have gained prominence in various industries due to their adaptability and reduced emissions. However, many current control systems rely heavily on manual operator intervention and lack advanced automation, which constrains the operational efficiency. This study addressed the need for dynamic models capable of monitoring and optimizing NO x emissions in CFB boilers, especially under fluctuating loads and strict regulatory standards. We introduced the TimesNet model, which utilizes fast Fourier transform (FFT) to extract key frequency components, transforming 1D time series data into 2D tensors for enhanced feature representation. The model employs Inception blocks for multi-scale feature extraction and incorporates residual connections with amplitude-weighted aggregation to mitigate catastrophic forgetting during training. The results indicated that TimesNet achieved R 2 values of 0.98, 0.97, and 0.95 across training, validation, and testing datasets, respectively, surpassing conventional models with a reduced MAE of 1.63 mg/m 3 and RMSE of 3.35 mg/m 3 . Additionally, it excelled in multi-step predictions and effectively managed long-term dependencies. In conclusion, TimesNet provides an innovative solution for the precise monitoring of NO x emissions in CFB boilers by enhancing predictive stability and robustness and addressing salient limitations in existing models to optimize combustion efficiency and regulatory compliance.

Keywords: power plant; circulating fluidized bed boilers; NO x emissions; dynamic prediction modeling; deep learning (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: 2025
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