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Comparison of prediction models of carbon content of fly ash based on machine learning
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Zhiyuan CHEN1, Houzhang TAN2, Siyang CHENG3, Shixue ZHANG1, Xiaohe XIONG2, Renhui RUAN2
Thermal Power Generation | 2023, 52(7) : 64 - 73
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Thermal Power Generation | 2023, 52(7): 64-73
• Intelligent management technologies for coal-fired power plants •
Comparison of prediction models of carbon content of fly ash based on machine learning
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Zhiyuan CHEN1, Houzhang TAN2, Siyang CHENG3, Shixue ZHANG1, Xiaohe XIONG2, Renhui RUAN2
Affiliations
  • 1.School of Economics and Management, Wuhan University, Wuhan 430072, China
  • 2.MOE Key Laboratory of Thermo-Fluid Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
  • 3.Georgia Institute of Technology, Atlanta, Georgia 30332, America
Published: 2023-07-25 doi: 10.19666/j.rlfd.202305073
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The carbon content of fly ash in boilers is one of the important indicators of combustion efficiency. This study employs machine learning models to accurately predict the carbon content of fly ash. Firstly, random forest is employed to adjust the frequency of fly ash carbon content data to once per minute, aligning it with the input features to address the issue of imbalanced data collection frequency. Then, a recursive feature elimination method based on random forest is used to extract nine important features out of the original 30 features, reducing feature correlation and improving model accuracy. Subsequently, six machine learning models (linear regression, decision tree, K-nearest neighbors (KNN), random forest, Catboost and XGBoost) are compared for prediction. The results indicate that decision tree, KNN, random forest and XGBoost models perform well, MSE of which on the test are 0.010, 0.009, 0.006 and 0.006, respectively, while linear regression exhibits the poorest performance. The prediction models remain robust under low, medium, and high boiler loads.

carbon content of fly ash  /  random forest  /  XGBoost  /  recursive feature elimination
Zhiyuan CHEN, Houzhang TAN, Siyang CHENG, Shixue ZHANG, Xiaohe XIONG, Renhui RUAN. Comparison of prediction models of carbon content of fly ash based on machine learning[J]. Thermal Power Generation, 2023 , 52 (7) : 64 -73 . DOI: 10.19666/j.rlfd.202305073
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  • National Natural Science Foundation of China(71871166)
Appendix
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Year 2023 volume 52 Issue 7
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64
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Article Info
doi: 10.19666/j.rlfd.202305073
  • Receive Date：2023-05-24
  • Online Date：2026-01-26
  • Published：2023-07-25
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  • Received：2023-05-24
Funding
National Natural Science Foundation of China(71871166)
Affiliations
    1.School of Economics and Management, Wuhan University, Wuhan 430072, China
    2.MOE Key Laboratory of Thermo-Fluid Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
    3.Georgia Institute of Technology, Atlanta, Georgia 30332, America
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202305073
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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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