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Prediction model of coal spontaneous combustion based on SSA-RBF neural network
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Fei GAO1, 2, Ning LIANG1, Zhe JIA1, Qing HOU3
China Safety Science Journal | 2024, 34(8) : 128 - 137
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China Safety Science Journal | 2024, 34(8): 128-137
Safety engineering technology
Prediction model of coal spontaneous combustion based on SSA-RBF neural network
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Fei GAO1, 2, Ning LIANG1, Zhe JIA1, Qing HOU3
Affiliations
  • 1 School of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125130,China
  • 2 Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education,Huludao Liaoning 125130,China
  • 3 Jizhong Energy Group,Xingtai Hebei 054099,China
Published: 2024-08-28 doi: 10.16265/j.cnki.issn1003-3033.2024.08.1567
Outline
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To solve the problems of single prediction state and insufficient prediction accuracy of the traditional coal spontaneous combustion prediction model,a prediction model based on RBF neural network optimized by SSA was proposed. Firstly,the temperature programmed test was used to analyze the variation characteristics of the index gas of coal samples with temperature. The coal spontaneous combustion process was divided into slow oxidation stage (80≤ti<120 ℃),accelerated oxidation stage (120≤ti<160 ℃) and intense oxidation stage (ti≥160 ℃) with coal temperature as the node. At the same time,the grey correlation degree between the index gas and coal temperature in each stage of coal spontaneous combustion was analyzed. Secondly,the performance of Particle Swarm Optimization (PSO),Grey Wolf Optimization (GWO) and SSA algorithm was tested by different dimension test functions. Finally,the superiority of the RBF neural network optimized by SSA algorithm to the coal spontaneous combustion prediction model was verified by using six mining area data. The results show that the grey correlation coefficients of CO/ΔO2,CO and C2H4 with coal temperature are the largest in the slow oxidation stage. The grey correlation coefficient between C2H4/C2H6,CO/ΔO2,CO2/CO and coal temperature is the largest in the accelerated oxidation stage. The test results of three different dimensional functions show that SSA has better global search ability,stability and faster convergence speed compared with PSO and GWO. When the number of neurons is 5 and the number of iterations is 300,the prediction accuracy of the SSA-RBF neural network prediction model for the slow and accelerated oxidation stages reaches 99% and 93% respectively.

sparrow search algorithm (SSA)  /  radial basis function (RBF)  /  coal spontaneous combustion  /  prediction model  /  indicator gas  /  grey relational analysis
Fei GAO, Ning LIANG, Zhe JIA, Qing HOU. Prediction model of coal spontaneous combustion based on SSA-RBF neural network[J]. China Safety Science Journal, 2024 , 34 (8) : 128 -137 . DOI: 10.16265/j.cnki.issn1003-3033.2024.08.1567
Year 2024 volume 34 Issue 8
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.08.1567
  • Receive Date:2024-02-21
  • Online Date:2025-07-09
  • Published:2024-08-28
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  • Received:2024-02-21
  • Revised:2024-05-22
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Affiliations
    1 School of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125130,China
    2 Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education,Huludao Liaoning 125130,China
    3 Jizhong Energy Group,Xingtai Hebei 054099,China
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表12种不同金属材料的力学参数

Family
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Number of
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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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