工矿自动化2025，Vol.51Issue(3)：22-31,38,11.DOI:10.13272/j.issn.1671-251x.2024120033
极薄煤层破碎顶板条件下液压支架带压移架残余支撑力决策方法
Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams
摘要
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.关键词
极薄煤层/液压支架/带压移架/残余支撑力/改进蜣螂算法/深度混合核极限学习机Key words
ultra-thin coal seam/hydraulic support/pressurized moving/residual support force/improved dung beetle optimization(IDBO)/deep hybrid kernel extreme learning machine(DHKELM)分类
矿业与冶金引用本文复制引用
张传伟,张刚强,路正雄,李林岳,何正伟,龚凌霄,黄骏峰..极薄煤层破碎顶板条件下液压支架带压移架残余支撑力决策方法[J].工矿自动化,2025,51(3):22-31,38,11.基金项目
陕西省重点研发计划项目(2022GD-TSLD-63,2022GD-TSLD-64) （2022GD-TSLD-63,2022GD-TSLD-64）
陕西省教育厅青年创新团队科研计划项目(23JP100). （23JP100）
