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A soft measurement method of carbon content in fly ash under variable operating conditions of SSAE+BPNN
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Xinping LIU, Bo LI, Tuoyu DENG
Thermal Power Generation | 2023, 52(1) : 66 - 73
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Thermal Power Generation | 2023, 52(1): 66-73
• Thermal energy science research •
A soft measurement method of carbon content in fly ash under variable operating conditions of SSAE+BPNN
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Xinping LIU, Bo LI, Tuoyu DENG
Affiliations
  • School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Published: 2023-01-25 doi: 10.19666/j.rlfd.202205097
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The variable operating condition of thermal power units makes the data show multi-modal characteristics, which leads to the decrease of prediction accuracy of the regression soft sensor model based on shallow network structure. An improved BP neural network (back propagation neural network, BPNN) soft sensor method is studied. Firstly, the original data features are extracted by using the strong deep learning ability of stacked sparse autoencoder (SSAE), and then the extracted features are analyzed by BPNN. The experimental results show that, the mean square error of the SSAE+BPNN soft sensor method is 0.135 8×10–3 and the square correlation coefficient is 0.983 2. It is proved that its prediction accuracy and generalization ability are significantly better than those of BPNN. It is applied to the soft sensor of carbon content in fly ash of a flexible peak-shaving 660 MW ultra-supercritical generator set, and the average relative error of the prediction results is 0.91%, the overall relative error is less than±5%, indicating the method has good engineering application value.

stacked sparse autoencoder  /  feature extraction  /  soft measurement  /  variable operating conditions  /  carbon content in fly ash  /  deep learning
Xinping LIU, Bo LI, Tuoyu DENG. A soft measurement method of carbon content in fly ash under variable operating conditions of SSAE+BPNN[J]. Thermal Power Generation, 2023 , 52 (1) : 66 -73 . DOI: 10.19666/j.rlfd.202205097
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  • National Key Research and Development Program(2017YFB0902100)
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Year 2023 volume 52 Issue 1
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67
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Article Info
doi: 10.19666/j.rlfd.202205097
  • Receive Date：2022-05-10
  • Online Date：2026-01-23
  • Published：2023-01-25
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  • Received：2022-05-10
Funding
National Key Research and Development Program(2017YFB0902100)
Affiliations
    School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202205097
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Citations
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
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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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