Optimization and simulation of adaptive mining cutting path in complex undulating coal seam
-
摘要:
采煤机自适应煤层起伏变化自主规划截割是实现煤矿智能无人开采的关键问题之一。然而，现有采煤机截割规划对复杂地质条件变化的自适应性相对薄弱。针对煤层分布信息特征考虑不全和缺乏适用于不同起伏条件的综采工作面采煤机截割路径规划模型以及连续规划精度差等问题，提出一种基于煤层起伏信息与采煤机滚筒高度预测的复杂起伏煤层自适应开采截割路径优化模型。首先，基于粒子群优化最小二乘支持向量机建立采煤机滚筒高度时间序列预测模型，实现采煤机滚筒高度精准超前预测。然后，分别构建以采煤机上下滚筒截割线与煤层上下分界线偏离最小为优化目标的近水平条件、俯斜开采条件和仰斜开采条件下的综采工作面自适应截割路径规划优化模型，利用多约束优化算法求解最优路径，从而实现适用于多种开采条件下的综采工作面自适应截割路径规划。通过数据仿真验证，采煤机滚筒截割高度预测精度达到84.11%以上，优化截割路径与模拟煤层分界线平均绝对百分比误差最大为3.13，所提方法能够在实现采煤机滚筒截割轨迹高精度预测的基础上完成复杂条件综采工作面的自适应截割路径连续规划，为复杂起伏变化工作面采煤机截割路径自适应规划应用提供参考。
Abstract:Adaptive cutting planning of shearer based on the fluctuation of coal seam is one of the key problems to realize intelligent unmanned mining in coal mine. However, the adaptability of existing shearer cutting planning scheme considering the changes of complex geological conditions is relatively weak. Aiming at the problems of incomplete consideration of coal seam distribution information characteristics, lack of appropriate shearer cutting path planning model for different undulating conditions and poor continuous planning accuracy in fully mechanized coal mining faces, an adaptive cutting path optimization model for complex undulating coal seams is proposed. Firstly, the time series prediction model of shearer drum height is established based on particle swarm optimization least squares support vector machine, which can realize accurate and advanced prediction of shearer drum height. Then, the optimization models of adaptive cutting path planning for fully mechanized working face under near horizontal conditions and inclined mining conditions are constructed respectively, with the objective of minimizing the deviation between the cutting lines of shearers and the boundary lines of coal seams. Finally, the optimal path is solved by multi-constraint optimization algorithm to realize the comprehensive mining working face adaptive cutting path planning for various mining conditions. Through data simulation, the accuracy of shearer drum cutting height prediction model is above 84.11%, and the maximum average absolute percentage error between the optimized cutting path and the simulated coal seam boundary is 3.13. The proposed method can achieve high-precision prediction of the cutting trajectory of the shearer drum and achieve continuous adaptive cutting path planning for complex conditions in fully mechanized mining faces, providing a reference for the application of adaptive cutting path planning for shearers in complex undulating working faces.
-
Keywords:
- intelligent mining /
- complex conditions /
- shearer /
- cutting path /
- adaptable planning
-
![]()
图 1 综采工作面自适应截割路径规划方案
Figure 1. Adaptive cutting path planning scheme for longwall mining face
![]()
图 5 采煤机第49刀的截割轨迹规划结果
Figure 5. Cutting trajectory planning results of the 49th cutter of the shearer
![]()
图 6 采煤机第50刀的截割轨迹规划结果
Figure 6. Cutting trajectory planning results of the 50th cutter of the shearer
表 1 训练数据集预测误差
Table 1 Training dataset errors
MAPE RMSE MAE 上滚筒 下滚筒 上滚筒 下滚筒 上滚筒 下滚筒 0.90 3.94 4.53 4.37 1.90 1.89 
下载: 导出CSV
表 2 验证数据集预测误差
Table 2 Validation dataset prediction error
采煤机截割刀 MAPE RMSE MAE 上滚筒 下滚筒 上滚筒 下滚筒 上滚筒 下滚筒 第49刀 1.25 9.67 5.20 3.85 2.08 1.25 第50刀 1.42 15.89 6.11 5.89 2.20 1.52
下载: 导出CSV
表 3 优化前后截割平均绝对误差
Table 3 MAE before and after optimization
采煤机截割刀 优化前的MAE 优化后的MAE 上滚筒 下滚筒 上滚筒 下滚筒 第49刀 3.13 1.27 2.04 1.24 第50刀 2.74 1.04 1.27 0.98
下载: 导出CSV
-
[1] WANG Guofa,XU Yongxiang,REN Huaiwei. Intelligent and ecological coal mining as well as clean utilization technology in China:review and prospects[J]. International Journal of Mining Science and Technology,2019,29:161−169. doi: 10.1016/j.ijmst.2018.06.005
[2] 王国法,杜毅博,徐亚军,等. 中国煤炭开采技术及装备50年发展与创新实践−纪念《煤炭科学技术》创刊50周年[J]. 煤炭科学技术,2023,51(1):1−18. doi: 10.13199/j.cnki.cst.2021.01.001 WANG Guofa,DU Yibo,XU Yajun,et al. Development and innovation practice of China coal mining technology and equipment for 50 years: Commemorate the 50th anniversary of the publication of Coal Science and Technology[J]. Coal Science and Technology,2023,51(1):1−18. doi: 10.13199/j.cnki.cst.2021.01.001
[3] 赵亦辉,赵友军,周 展. 综采工作面采煤机智能化技术研究现状[J]. 工矿自动化,2022,48(2):11−18,28 doi: 10.13272/j.issn.1671-251x.2021090024 ZHAO Yihui,ZHAO Youjun,ZHOU Zhan. Research status of intelligent technology of shearer in fully mechanized working face[J]. Industry and Mine Automation,2022,48(2):11−18,28. doi: 10.13272/j.issn.1671-251x.2021090024
[4] 任怀伟,巩师鑫,刘新华,等. 煤矿千米深井智能开采关键技术研究与应用[J]. 煤炭科学技术,2021,49(4):149−158. doi: 10.13199/j.cnki.cst.2021.04.018 REN Huaiwei,GONG Shixin,LIU Xinhua,et al. Research and application on key techniques of intelligent mining for kilo-meter deep coal mine[J]. Coal Science and Technology,2021,49(4):149−158. doi: 10.13199/j.cnki.cst.2021.04.018
[5] 权国通,谭 超,侯海潮,等. 基于粒子群三次样条优化的采煤机截割路径规划[J]. 煤炭科学技术,2011,39(3):77−79. doi: 10.13199/j.cst.2011.03.82.quangt.030 QUAN Guotong,TAN Chao,HOU Haichao,et al. Cutting path planning of coal shearer based on particle swarm triple spline optimization[J]. Coal Science and Technology,2011,39(3):77−79. doi: 10.13199/j.cst.2011.03.82.quangt.030
[6] 司 垒,王忠宾,刘新华,等. 基于煤层分布预测的采煤机截割路径规划[J]. 中国矿业大学学报,2014,43(3):464−471. doi: 10.13247/j.cnki.jcumt.000133 SI Lei,WANG Zhongbin,LIU Xinhua,et al. Cutting path planning of coal mining machine based on prediction of coal seam distribution[J]. Journal of China University of Mining and Technology,2014,43(3):464−471. doi: 10.13247/j.cnki.jcumt.000133
[7] 柴浩洛,邢存恩,华同兴. 基于双圆弧样条曲线的采煤机上滚筒割煤路径规划[J]. 工矿自动化,2020,46(12):84−89. doi: 10.13272/j.issn.1671-251x.2020060049 CHAI Haoluo,XING Cun’en,HUA Tongxing. Cutting coal path planning of shearer up-drum based on double arc spline curve[J]. Industry and Mine Automation,2020,46(12):84−89. doi: 10.13272/j.issn.1671-251x.2020060049
[8] 安葳鹏,徐玉平. 基于改进蚁群算法的滚筒截割轨迹规划[J]. 测控技术,2018,37(6):29−34. doi: 10.19708/j.ckjs.2018.06.006 AN Weipeng,XU Yuping. Roller cutting trajectory planning based on improved ant colony algorithm[J]. Measurement and Control Technology,2018,37(6):29−34. doi: 10.19708/j.ckjs.2018.06.006
[9] ZHANG Hong,HOU Yunbing,SUN Zhenming,et al. An optimal algorithm for planning shearer trailing drum cutting path[J]. Shock and Vibration,2021,1354705.
[10] 陈伟华,南鹏飞,闫孝姮,等. 基于深度学习的采煤机截割轨迹预测及模型优化[J]. 煤炭学报,2020,45(12):4209−4215. doi: 10.13225/j.cnki.jccs.2019.1779 CHEN Weihua,NAN Pengfei,YAN Xiaoheng,et al. Shearer cutting trajectory prediction and model optimization based on deep learning[J]. Journal of China Coal Society,2020,45(12):4209−4215. doi: 10.13225/j.cnki.jccs.2019.1779
[11] WANG Fuzhong,GAO Ying,ZHANG Fukai. Research on trajectory tracking strategy of roadheader cutting head using ILC[A]. Proceedings of the 2015 Chinese Intelligent Systems Conference. Berlin:Springer,2015:35-44.
[12] 赵丽娟,王雅东,张美晨,等. 复杂煤层条件下采煤机自适应截割控制策略[J]. 煤炭学报,2022,47(1):541−563. doi: 10.13225/j.cnki.jccs.yg21.1862 ZHAO Lijuan,WANG Yadong,ZHANG Meichen,et al. Research on self-adaptive cutting control strategy of shearer in complex coal seam[J]. Journal of China Coal Society,2022,47(1):541−563. doi: 10.13225/j.cnki.jccs.yg21.1862
[13] 邱呈祥. 基于GIS系统的采煤机导航自适应截割技术研究[J]. 煤矿现代化,2021,30(4):113−115. doi: 10.3969/j.issn.1009-0797.2021.04.036 QIU Chengxiang. Research on navigation adaptive cutting technology of shearer based on GIS system[J]. Coal Mine Modernization,2021,30(4):113−115. doi: 10.3969/j.issn.1009-0797.2021.04.036
[14] 孔 维. 基于煤层地理信息系统的采煤机截割路径规划方法[D]. 徐州:中国矿业大学,2019:9-44. KONG Wei. Planning method of shearer cutting path based on coal seam geographic information system [D] Xuzhou:China University of Mining and Technology,2019:9-44.
[15] 范森煜. 采煤机截割轨迹稳定性控制方案的优化研究[J]. 自动化应用,2020(7):125−126. doi: 10.19769/j.zdhy.2020.07.046 FAN Senyu. Optimization research on the stability control scheme of shearer cutting trajectory[J]. Automation Application,2020(7):125−126. doi: 10.19769/j.zdhy.2020.07.046
[16] 崔 耀,叶 壮. 基于5G+云边端协同技术的采煤机智能调高调速 控制系统设计与应用[J]. 煤炭科学技术,2023,51(6):205−216. doi: 10.13199/j.cnki.cst.2022-1017 CUI Yao,YE Zhuang. Research on cloud-edge-terminal collaborative intelligent control of coal shearer based on 5G communication[J]. Coal Science and Technology,2023,51(6):205−216. doi: 10.13199/j.cnki.cst.2022-1017
[17] 周 信,王忠宾,谭 超,等. 基于双坐标系的采煤机截割路径平整性控制方法[J]. 煤炭学报,2014,39(3):574−579. doi: 10.13225/j.cnki.jccs.2013.0429 ZHOU Xin,WANG Zhongbin,TAN Chao,et al. A smoothness control method for cutting path of the shearer based on double-coordinators[J]. Journal of China Coal Society,2014,39(3):574−579. doi: 10.13225/j.cnki.jccs.2013.0429
[18] LI Juanli,LIU Yang,XIE Jiacheng,et al. Cutting path planning technology of shearer based on virtual reality[J]. Applied Sciences,2020,10(3):771. doi: 10.3390/app10030771
[19] 董 刚,马宏伟,聂 真. 基于虚拟煤岩界面的采煤机上滚筒路径规划[J]. 工矿自动化,2016,42(10):22−26. doi: 10.13272/j.issn.1671-251x.2016.10.005 DONG Gang,MA Hongwei,NIE Zhen. Path planning of shearer up-drum based on virtual coal-rock interface[J]. Industry and Mine Automation,2016,42(10):22−26. doi: 10.13272/j.issn.1671-251x.2016.10.005
[20] SHI Hengbo,XIE Jiacheng,WANG Xuewen,et al. An operation optimization method of a fully mechanized coal mining face based on semi-physical virtual simulation[J]. International Journal of Coal Science & Technology,2020,7:147−163.
[21] 程建远,朱梦博,王云宏,等. 煤炭智能精准开采工作面地质模型梯级构建及其关键技术[J]. 煤炭学报,2019,44(8):2285−2295. doi: 10.13225/j.cnki.jccs.KJ19.0510 CHENG Jianyuan,ZHU Mengbo,WANG Yunhong,et al. Cascade construction of geological model of longwall panel for intelligent precision coal mining and its key technology[J]. Journal of China Coal Society,2019,44(8):2285−2295. doi: 10.13225/j.cnki.jccs.KJ19.0510
[22] 高士岗,高登彦,欧阳一博,等. 中薄煤层智能开采技术及其装备[J]. 煤炭学报,2020,45(6):1997−2007. doi: 10.13225/j.cnki.jccs.zn20.0246 GAO Shigang,GAO Dengyan,OUYANG Yibo,et al. Intelligent mining technology and its equipment for medium thickness thin seam[J]. Journal of China Coal Society,2020,45(6):1997−2007. doi: 10.13225/j.cnki.jccs.zn20.0246
[23] 毛善君,鲁守明,李存禄,等. 基于精确大地坐标的煤矿透明化智能综采工作面自适应割煤关键技术研究及系统应用[J]. 煤炭学报,2022,47(1):515−526. doi: 10.13225/j.cnki.jccs.2021.0005 MAO Shanjun,LU Shouming,LI Cunlu,et al. Key technologies and system of adaptive coal cutting in transparent intelligent fully mechanized coal mining face based on precise geodetic coordinates[J]. Journal of China Coal Society,2022,47(1):515−526. doi: 10.13225/j.cnki.jccs.2021.0005
[24] ZHANG Yagang,LI Ruixuan. Short term wind energy prediction model based on data decomposition and optimized LSSVM[J]. Sustainable Energy Technologies and Assessments,2022,52(A):102025.
[25] ZHOU Jie,LIN Haifei,JIN Hongwei,et al. Cooperative prediction method of gas emission from mining face based on feature selection and machine learning[J]. International Journal of Coal Science & Technology,2022,9(51):1−12.
[26] TANG Hong,LING Xiangzheng,LI Liangzhi,et al. One-shot pruning of gated recurrent unit neural network by sensitivity for time-series prediction[J]. Neurocomputing,2022,512:15−24. doi: 10.1016/j.neucom.2022.09.026
[27] 巩师鑫,任怀伟,杜毅博,等. 基于MRDA-FLPEM集成算法的综采工作面矿压迁移预测[J]. 煤炭学报,2021,46(S1):529−538. doi: 10.13225/j.cnki.jccs.2020.1361 GONG Shixin,REN Huaiwei,DU Yibo,et al. Transfer prediction of underground pressure for fully mechanized mining face based on MRDA-FLPEM integrated algorithm[J]. Journal of China Coal Society,2021,46(S1):529−538. doi: 10.13225/j.cnki.jccs.2020.1361
-
期刊类型引用(3)
1. 王忠宾，李福涛，司垒，魏东，戴嘉良，张森. 采煤机自适应截割技术研究进展及发展趋势. 煤炭科学技术. 2025(01): 296-311 . 
本站查看
2. 王国法，张金虎，任怀伟，杜毅博，张德生，闫汝瑜，于翔. 煤炭高效开采数智技术与成套装备研究及应用. 煤炭学报. 2025(01): 43-64 . 百度学术
3. 董博，史云，李杰，乔佳妮，冯港归. 基于矿压及顶板条件探测的综采面安全开采技术研究. 陕西煤炭. 2025(05): 145-150+156 . 百度学术
其他类型引用(2)
