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Review of applications of machine learning in nitrogen oxides reduction in thermal power plants
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Longhui ZHANG1, Dehai LIN1, Ying WANG2, Guanghui JI3, Shaodan MA1, Zixiong CAO1, Wei LIU1, Zilin LIU1, Ziran MA1, Baodong WANG1
Thermal Power Generation | 2023, 52(1) : 7 - 17
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Thermal Power Generation | 2023, 52(1): 7-17
• Technical and economic review •
Review of applications of machine learning in nitrogen oxides reduction in thermal power plants
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Longhui ZHANG1, Dehai LIN1, Ying WANG2, Guanghui JI3, Shaodan MA1, Zixiong CAO1, Wei LIU1, Zilin LIU1, Ziran MA1, Baodong WANG1
Affiliations
  • 1.National Institute of Clean and Low Carbon Energy, Beijing 102209, China
  • 2.North Engineering Design and Research Institute Co., Ltd., Shijiazhuang 050000, China
  • 3.Hebei Guohua Dingzhou Power Generation Co., Ltd., Dingzhou 073000, China
Published: 2023-01-25 doi: 10.19666/j.rlfd.202206132
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With the completion of ultra-low emission transformation of thermal power plants, problems such as increased costs and excessive ammonia injection have arisen. Modeling and optimization of power plant operation data through machine learning has become an important means to solve the above problems. This article reviews the commonly used machine learning algorithms and their application scenarios in reducing nitrogen oxides. In terms of algorithm, the main algorithms of data preprocessing, modeling prediction and parameter optimization and their applicability to nitrogen oxides removal are summarized. The research directions of multi-operating condition data preprocessing method and the construction method of the objective function in multi-objective optimization are proposed. For the application level of the machine learning methods, such as low nitrogen combustion in the furnace, optimization of SCR denitration system, and comprehensive energy saving and consumption reduction of the whole system, the implementation methods and corresponding effects are summarized. The future research directions of long-period dynamic modeling control and multi-power plant joint modeling have prospected.

NOx emission  /  SCR  /  model predictive control  /  big data  /  machine learning
Longhui ZHANG, Dehai LIN, Ying WANG, Guanghui JI, Shaodan MA, Zixiong CAO, Wei LIU, Zilin LIU, Ziran MA, Baodong WANG. Review of applications of machine learning in nitrogen oxides reduction in thermal power plants[J]. Thermal Power Generation, 2023 , 52 (1) : 7 -17 . DOI: 10.19666/j.rlfd.202206132
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  • National Key Research and Development Program(2019YFC1907500)
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Year 2023 volume 52 Issue 1
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Article Info
doi: 10.19666/j.rlfd.202206132
  • Receive Date：2022-06-02
  • Online Date：2026-01-23
  • Published：2023-01-25
Article Data
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History
  • Received：2022-06-02
Funding
National Key Research and Development Program(2019YFC1907500)
Affiliations
    1.National Institute of Clean and Low Carbon Energy, Beijing 102209, China
    2.North Engineering Design and Research Institute Co., Ltd., Shijiazhuang 050000, China
    3.Hebei Guohua Dingzhou Power Generation Co., Ltd., Dingzhou 073000, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202206132
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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