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Research and Analysis of Support Load Prediction Based on WOA-DBN Model
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Jie LU1, 2, Song ZHANG1, Zhiqiang YANG3, Shaochen WANG1, Zheng WEI1, Ze LIU1
Mining Research and Development | 2025, 45(3) : 222 - 228
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Mining Research and Development | 2025, 45(3): 222-228
Research and Analysis of Support Load Prediction Based on WOA-DBN Model
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Jie LU1, 2, Song ZHANG1, Zhiqiang YANG3, Shaochen WANG1, Zheng WEI1, Ze LIU1
Affiliations
  • 1.College of Coal Engineering, Shanxi Datong University, Datong, Shanxi 037009, China
  • 2.College of Mining Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
  • 3.Zhongtian-hechuang Energy Co., Ltd., Ordos, Inner Mongolia 017004, China
Published: 2025-03-25
Outline
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In mine production, the roof fall accident of working face is directly related to the hydraulic support. According to this theory, a prediction model based on multi-source data fusion was proposed to predict the load of hydraulic support. By studying the theory of pressure deformation characteristics of coal seam roof, the composition and working principle of hydraulic support, the bearing theory and the influence of working condition on hydraulic support, the influencing factors of load change were analyzed, and the data of key stress components were collected. The K-means clustering algorithm was used to cluster the characteristics of the data, and the load was classified and predicted. The whale optimization algorithm (WOA) was used to optimize the long short-term memory network (LSTM) and the deep belief neural network (DBN) respectively, and the WOA-LSTM series prediction model and the WOA-DBN series prediction model were established. The results are as follows. Firstly, the mean absolute error is reduced by 0.228 7, 0.206 4 and 0.067 7 respectively in the prediction of the front column load of 20# hydraulic support by WOA-DBN model. The root mean square error is reduced by 0.212 9, 0.195 3 and 0.072 5, respectively. Secondly, in the prediction of the rear column load of 20# hydraulic support by WOA-DBN model, the mean absolute error is reduced by 0.303 1, 0.244 6 and 0.205 4 respectively. The root mean square error is reduced by 0.291 9, 0.246 4 and 0.238 9, respectively. It can be seen that the WOA-DBN series prediction model is more suitable for load prediction and has higher accuracy.

Support load prediction  /  Multi-source data fusion  /  WOA-DBN  /  K-means clustering algorithm
鲁杰, 张松, 杨志强, 王劭琛, 魏征, 刘泽. 基于 WOA-DBN模型的支架载荷预测研究分析. 矿业研究与开发, 2025 , 45 (3) : 222 -228 .
Jie LU, Song ZHANG, Zhiqiang YANG, Shaochen WANG, Zheng WEI, Ze LIU. Research and Analysis of Support Load Prediction Based on WOA-DBN Model[J]. Mining Research and Development, 2025 , 45 (3) : 222 -228 .
Year 2025 volume 45 Issue 3
60
30
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  • Receive Date:2024-08-27
  • Online Date:2026-10-29
  • Published:2025-03-25
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  • Received:2024-08-27
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Affiliations
    1.College of Coal Engineering, Shanxi Datong University, Datong, Shanxi 037009, China
    2.College of Mining Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
    3.Zhongtian-hechuang Energy Co., Ltd., Ordos, Inner Mongolia 017004, China
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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
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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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