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Research on optimization of incoming coal stacking in power station coal yard based on K-means clustering algorithm
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Jizhen AN, Heng CHEN, Shichao QIAO, Peiyuan PAN, Gang XU
Thermal Power Generation | 2023, 52(4) : 135 - 143
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Thermal Power Generation | 2023, 52(4): 135-143
Power generation technology forum
Research on optimization of incoming coal stacking in power station coal yard based on K-means clustering algorithm
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Jizhen AN, Heng CHEN, Shichao QIAO, Peiyuan PAN, Gang XU
Affiliations
  • Beijing Key Laboratory of Emission Surveillance and Control for Thermal Power Generation, North China Electric Power University, Beijing 102206, China
Published: 2023-04-25 doi: 10.19666/j.rlfd.202206115
Outline
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In order to effectively deal with the complex electricity and coal market, strengthening the smart fuel management has become an important part of thermal power plant management. Aiming at solving the problems that the coal yard of a coal-fired power station occupies small area, the types of incoming coal are complex, and the coal-fired coal stacking is chaotic, by extracting the coal quality information of historical incoming coal, K-means and DBSCAN clustering algorithms are used to analyze the low-level coal. The calorific value, volatile matter and sulfur content are clustered and analyzed, and the two clustering algorithms are compared from the perspective of silhouette coefficient, cluster stability and sample division fineness, and finally K-means with better clustering effect is selected as the calculation method for coal quality division. The K-means algorithm divides the selected historical coal quality information data set into four categories, the contour coefficient is 0.587, and the coal quality components in each category are similar. The incoming coal frequency and the incoming coal weight ratio under different cluster labels are counted, and the coal yard is divided into corresponding proportions. The incoming coal of the same classification is stacked in each partition, and on this basis, the incoming coal in the digital coal yard platform is designed. Coal stacking guidance and information storage process are of great significance to improving the utilization of storage yard space and the efficiency of coal yard management.

smart fuel management  /  coal yard division  /  incoming coal stacking  /  K-means clustering  /  best plan
Jizhen AN, Heng CHEN, Shichao QIAO, Peiyuan PAN, Gang XU. Research on optimization of incoming coal stacking in power station coal yard based on K-means clustering algorithm[J]. Thermal Power Generation, 2023 , 52 (4) : 135 -143 . DOI: 10.19666/j.rlfd.202206115
  • National Natural Science Foundation of China Youth Program(52106008)
  • National Natural Science Foundation of China Innovative Research Group Project(51821004)
Year 2023 volume 52 Issue 4
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Article Info
doi: 10.19666/j.rlfd.202206115
  • Receive Date:2022-06-14
  • Online Date:2026-01-23
  • Published:2023-04-25
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  • Received:2022-06-14
Funding
National Natural Science Foundation of China Youth Program(52106008)
National Natural Science Foundation of China Innovative Research Group Project(51821004)
Affiliations
    Beijing Key Laboratory of Emission Surveillance and Control for Thermal Power Generation, North China Electric Power University, Beijing 102206, China
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表12种不同金属材料的力学参数

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Number of
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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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