Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams
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摘要:
在破碎顶板条件下，液压支架带压移架过程中残余支撑力的精准决策对于提高极薄煤层智能化开采效率和保障作业安全至关重要。为实现极薄煤层破碎顶板条件下液压支架带压移架残余支撑力的准确决策，提出了一种基于改进蜣螂算法（IDBO）优化深度混合核极限学习机（DHKELM）的液压支架带压移架残余支撑力决策方法。在混合核极限学习机（HKELM）基础上引入极限学习机自动编码器（ELM−AE）结构来构建DHKELM模型，以增强对复杂输入的特征提取和非线性映射能力；引入ICMIC混沌映射、Lévy飞行和贪婪策略对蜣螂算法（DBO）进行改进，形成具备更高寻优精度和更快收敛速度的IDBO算法；利用IDBO算法优化DHKELM模型的超参数，建立IDBO−DHKELM模型。结合极薄煤层综采工作面液压支架带压移架实测数据，通过可视化和相关性分析，确定支架号、带压移架前支架支撑力、推移油缸进液压力和推移油缸行程变化速度作为影响残余支撑力的关键特征，并构建残余支撑力决策样本数据集，最终完成IDBO−DHKELM模型的训练与评估。实验结果表明：基于IDBO−DHKELM模型的液压支架带压移架残余支撑力决策结果的均方根误差（RMSE）、平均绝对误差（MAE）及决定系数（R2）分别为0.143，0.119，0.971，具有较高的决策精确度。
Abstract:Accurate decision-making on the residual support force of hydraulic supports during pressurized moving under fragmented roof conditions is crucial for improving intelligent mining efficiency in ultra-thin coal seams and ensuring operational safety. To address this challenge, this study proposed a novel decision-making method based on a Deep Hybrid Kernel Extreme Learning Machine (DHKELM) optimized by an Improved Dung Beetle Optimization (IDBO) algorithm. The DHKELM model was constructed by incorporating an Extreme Learning Machine Autoencoder (ELM-AE) into the Hybrid Kernel Extreme Learning Machine (HKELM) framework, enhancing its feature extraction capability and nonlinear mapping efficiency for complex inputs. Furthermore, the Dung Beetle Optimization (DBO) algorithm was enhanced with ICMIC chaotic mapping, Lévy flight, and a greedy strategy, yielding the IDBO algorithm with improved optimization accuracy and faster convergence. The IDBO algorithm was further employed to optimize the hyperparameters of the DHKELM model, forming the IDBO-DHKELM model. Using field-measured data from hydraulic supports during pressurized moving in a fully mechanized ultra-thin coal seam mining face, key influencing factors of residual support force—including support number, support force before pressurized moving, pushing cylinder inlet pressure, and pushing cylinder stroke variation speed—were identified through visualization and correlation analysis. A residual support force decision-making dataset was subsequently constructed, and the IDBO-DHKELM model was trained and evaluated. Experimental results demonstrate that the proposed IDBO-DHKELM model achieves high decision-making accuracy, with a root mean square error (RMSE) of 0.143, a mean absolute error (MAE) of 0.119, and a coefficient of determination (R2) of 0.971.
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图 3 基于IDBO−DHKELM模型的液压支架带压移架残余支撑力决策流程
Figure 3. Decision-making process of residual support force of hydraulic supports during pressurized moving based on IDBO-DHKELM model
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图 5 50号液压支架带压移架时数据变化曲线
Figure 5. Data variation curves of No.50 hydraulic support during pressurized moving
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图 8 不同隐含层数下的决策结果
Figure 8. Decision-making results under different hidden layers configurations
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图 9 不同算法优化DHKELM模型迭代曲线
Figure 9. Iteration curves of different algorithms for optimizing DHKELM model
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图 10 不同算法优化DHKELM模型决策结果对比
Figure 10. Comparison of decision-making performance among different algorithms optimizing DHKELM model
表 1 残余支撑力与各影响因素相关性分析结果
Table 1 Correlation analysis between residual support force and influencing factors
参数 皮尔逊相关系数 推移油缸
进液压力带压移架前
支架支撑力推移油缸行程
变化速度支架号 工作面倾角 带压移架
残余支撑力0.41 0.33 0.18 0.15 0.07 
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表 2 不同模型的决策结果评价
Table 2 Evaluation of different models in decision-making
模型 RMSE MAE R2 ELM 0.376 0.312 0.788 HKELM 0.310 0.253 0.864 DHKELM 0.251 0.207 0.913 IDBO−DHKELM 0.143 0.119 0.971
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表 3 测试函数
Table 3 Test functions
函数 维度 取值范围 最优解 Schwefel 30 [−100，100] 0 Rastrigin 30 [−5.12，5.12] 0
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表 4 不同算法优化DHKELM模型决策结果评价
Table 4 Evaluation of different optimization algorithms for DHKELM model
模型 RMSE MAE R2 PSO−DHKELM 0.242 0.203 0.925 DBO−DHKELM 0.220 0.176 0.943 IDBO−DHKELM 0.143 0.119 0.971
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