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Numerical simulation study of a low-load biomass gas co-firing boiler optimized using artificial neural networks
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Zhihao WANG1, Xueyi HAN1, Huanting GAO2, Xuanlong CHEN1, Xun GONG2
Thermal Power Generation | 2026, 55(6) : 154 - 163
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Thermal Power Generation | 2026, 55(6): 154-163
Thermal energy science research
Numerical simulation study of a low-load biomass gas co-firing boiler optimized using artificial neural networks
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Zhihao WANG1, Xueyi HAN1, Huanting GAO2, Xuanlong CHEN1, Xun GONG2
Affiliations
  • 1.Hubei Huadian Power Generation Co., Ltd., Wuhan 430061, China
  • 2.State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Published: 2026-06-25 doi: 10.19666/j.rlfd.202509060
Outline
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[Objective]

To investigate the effects of biomass gas co-firing on combustion stability and in-furnace parameters under low-load conditions, a 660 MW tangentially fired boiler was taken as the research object to carry out the study. A stability index was proposed, and a combined approach of numerical simulation and artificial neural network (ANN) was employed.

[Methods]

Comparative analysis was conducted between pure coal and co-firing conditions at loads of 100%, 70%, 50%, and 30%.

[Results]

The results show that the deviation of the temperature stability coefficient (MT) under co-firing is less than 3.9%, indicating stable combustion at low load conditions. When the unit load decreases from 100% to 70%, the average temperature in the main combustion zone drops by 147.87 K for pure coal and 69.37 K for co-firing, indicating a slower temperature decay. At 30% load, the NOx volume fractions in the reduction and burnout zones under co-firing are 0.051 2% and 0.044 4%, which are lower than 0.093 3% and 0.078 6% under pure coal combustion condition, with smaller fluctuations of other parameters. Furthermore, an artificial neural network (ANN) model was developed to describe the complicated relationships among in-furnace parameters, and the results show that the regression coefficients R2 for temperature, CO2 volume fraction, and NOx volume fraction predictions are all greater than 0.96 in both pure coal and co-firing conditions.

[Conclusion]

This study provides support for optimization and prediction of low-load operation in biomass gasification co-firing boilers.

low-load combustion stability  /  biomass gas  /  co-firing  /  artificial neural network
Zhihao WANG, Xueyi HAN, Huanting GAO, Xuanlong CHEN, Xun GONG. Numerical simulation study of a low-load biomass gas co-firing boiler optimized using artificial neural networks[J]. Thermal Power Generation, 2026 , 55 (6) : 154 -163 . DOI: 10.19666/j.rlfd.202509060
  • Science and Technology Project of China Huadian Power Generation Co., Ltd.(CHDKJ23-02-79)
Year 2026 volume 55 Issue 6
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Article Info
doi: 10.19666/j.rlfd.202509060
  • Receive Date:2025-09-19
  • Online Date:2026-08-14
  • Published:2026-06-25
Article Data
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History
  • Received:2025-09-19
  • Revised:2025-11-04
  • Accepted:2025-11-18
Funding
Science and Technology Project of China Huadian Power Generation Co., Ltd.(CHDKJ23-02-79)
Affiliations
    1.Hubei Huadian Power Generation Co., Ltd., Wuhan 430061, China
    2.State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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