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Multi-model dynamic prediction and collaborative deployment of NOx mass concentration at inlet of SCR denitration system in coal-fired power units
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Deyong LU1, Zhiyou WEI2, Yubo LIU1, Haiqiang LI1, Delong DING2, Bo SHEN2, Lai LI2
Thermal Power Generation | 2026, 55(4) : 156 - 165
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Thermal Power Generation | 2026, 55(4): 156-165
Power generation techonology forum
Multi-model dynamic prediction and collaborative deployment of NOx mass concentration at inlet of SCR denitration system in coal-fired power units
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Deyong LU1, Zhiyou WEI2, Yubo LIU1, Haiqiang LI1, Delong DING2, Bo SHEN2, Lai LI2
Affiliations
  • 1.Zhejiang Zheneng Lanxi Power Generation Co., Ltd., Jinhua 321102, China
  • 2.Zhejiang Zheneng Technology & Environment Group Co., Ltd., Hangzhou 311121, China
Published: 2026-04-25 doi: 10.19666/j.rlfd.202507085
Outline
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To meet the high requirements of selective catalytic reduction (SCR) systems in coal-fired power plants for accurate and low-latency prediction of nitrogen oxides (NOx) mass concentrations, this study designs and proposes a soft sensing and deployment framework that balances high accuracy and real-time performance. Using more than 110 000 sets of high-dimensional operational data from a 660 MW coal-fired unit, a systematic comparison of deep learning (DL) and XGBoost models was conducted on a unified platform. Time series cross-validation combined with grid search was employed to optimize hyperparameters, and model performance was comprehensively evaluated in terms of predictive accuracy, computational efficiency, and interpretability via local interpretable model-agnostic explanations. On this basis, an “edge-embedded” collaborative deployment strategy was proposed, in which the DL model is deployed on edge servers to deliver high-accuracy predictions, while the XGBoost model is embedded into the distributed control system (DCS) to ensure real-time responsiveness. The results show that the DL model outperforms XGBoost in dynamic response and predictive accuracy, achieving root mean square errors approximately 10% lower than that of the XGBoost, and maintaining stability under highly fluctuating conditions. Variable importance analysis highlights flue gas oxygen content, burner wall temperature, and total air volume as the dominant factors affecting NOx formation. The proposed collaborative architecture can theoretically achieve millisecond-level inference and provide offline fault tolerance, offering a practical pathway for intelligent ammonia injection control and combustion optimization.

NOx mass concentration prediction  /  deep learning  /  variable importance  /  edge computing  /  model deployment
Deyong LU, Zhiyou WEI, Yubo LIU, Haiqiang LI, Delong DING, Bo SHEN, Lai LI. Multi-model dynamic prediction and collaborative deployment of NOx mass concentration at inlet of SCR denitration system in coal-fired power units[J]. Thermal Power Generation, 2026 , 55 (4) : 156 -165 . DOI: 10.19666/j.rlfd.202507085
  • Science and Technology Project of Zhejiang Energy Group Co., Ltd.(ZNKJ-2025-034)
Year 2026 volume 55 Issue 4
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Article Info
doi: 10.19666/j.rlfd.202507085
  • Receive Date:2025-07-31
  • Online Date:2026-08-14
  • Published:2026-04-25
Article Data
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History
  • Received:2025-07-31
  • Revised:2025-10-20
  • Accepted:2025-11-04
Funding
Science and Technology Project of Zhejiang Energy Group Co., Ltd.(ZNKJ-2025-034)
Affiliations
    1.Zhejiang Zheneng Lanxi Power Generation Co., Ltd., Jinhua 321102, China
    2.Zhejiang Zheneng Technology & Environment Group Co., Ltd., Hangzhou 311121, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202507085
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表12种不同金属材料的力学参数

Family
属数
Number of
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种数
Number of
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占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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