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Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA
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Ruwei HOU, Fang TIAN, Hao CAI, Hua MA, Boyang LIU
Thermal Power Generation | 2025, 54(12) : 134 - 141
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Thermal Power Generation | 2025, 54(12): 134-141
Thermal energy science research
Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA
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Ruwei HOU, Fang TIAN, Hao CAI, Hua MA, Boyang LIU
Affiliations
  • Huaneng Nanjing Cogeneration Co, Ltd, Nanjing 210035, China
Published: 2025-12-25 doi: 10.19666/j.rlfd.202503038
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In view of the problems that conventional fly ash carbon content prediction models are prone to fall into local optimal solution traps and have insufficient generalization ability, based on the boiler hot-state multi-condition tests, 28 key characteristic parameters are selected through data collection, processing, Pearson correlation analysis of variables, and importance ranking, the sparrow search algorithm (SSA) is used to determine the optimal hyper-parameters of the random forest (RF) model, and an SSA-RF prediction model is constructed. The model verification results show that the root-mean-square error of the SSA-RF model in the training set and the test set decreases to 0.010 8 and 0.019 1 respectively, and the coefficient of determination R2 increases to 0.999 7 and 0.998 1 respectively, demonstrating the excellent prediction accuracy and generalization ability of the model. Furthermore, the ISSA-RF-SSA algorithm is proposed. The SSA is improved by integrating multiple strategies to achieve global extreme value optimization of combustion parameters. Engineering verification shows that after optimization, the carbon content in fly ash decreased from 2.500% to 1.345%, and the prediction error was only 0.003 percentage points, verifying the accuracy of the model. The research results indicate that the ISSA-RF-SSA method improved by multiple strategies significantly enhances the optimization performance of the algorithm, providing a new idea for the combustion optimization of coal-fired units.

coal-fired boiler  /  carbon content in fly ash  /  combustion optimization  /  sparrow search algorithm  /  random forest
Ruwei HOU, Fang TIAN, Hao CAI, Hua MA, Boyang LIU. Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA[J]. Thermal Power Generation, 2025 , 54 (12) : 134 -141 . DOI: 10.19666/j.rlfd.202503038
Year 2025 volume 54 Issue 12
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doi: 10.19666/j.rlfd.202503038
  • Receive Date:2025-03-03
  • Online Date:2026-01-13
  • Published:2025-12-25
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  • Received:2025-03-03
Affiliations
    Huaneng Nanjing Cogeneration Co, Ltd, Nanjing 210035, 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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