Automatic height adjustment technology of shearer based on cutting roof and floor height prediction model
-
摘要: 传统的煤层截割路径规划通过几何控制、规划计算等方式对采煤机滚筒高度进行预测，但存在预测的数据误差较大、无法适应地质条件变化的问题。针对上述问题，提出了一种基于截割顶底板高度预测模型的采煤机自动调高技术。首先，分析了影响截割顶底板高度的因素，指出影响顶底板高度的主要因素包括煤层的起伏变化数据、历史截割数据、刮板输送机的高程数据及人工操作记录，将上述4类数据融合处理，建立以长短期记忆（LSTM）模型和灰色马尔可夫模型为基础的截割顶底板高度预测模型，通过算法模型预测出截割顶底板的高度。然后，以截割顶底板的高度数据为基础，结合采煤机位姿和空间坐标，建立计算滚筒高度的几何模型，同时依据刮板输送机上窜下滑量及是否执行加减刀工艺等因素进行修正，最终将顶底板高度序列转换为滚筒高度序列，即将截割顶底板高度转换为采煤机滚筒的目标高度，由采煤机执行到目标高度，实现滚筒自动调高工业性试验结果表明：① 在自动调高技术的控制下，顶滚筒和底滚筒的预测高度与实际高度偏差值有90%的数据量均在10 cm以内，滚筒的预测高度和实际高度具有明显的一致性。② 与传统手动控制方式相比，中部截割一刀煤的人工干预调高次数由49次下降为21次，说明截割顶底板的高度预测模型和计算滚筒高度的几何模型是准确合理的，采煤机滚筒的自动调高技术是可行的。Abstract: The traditional coal seam cutting path planning predicts the height of the drum through geometric control, planning calculation, and other methods. But there are problems with large data errors in planning and prediction and inability to adapt to changes in geological conditions. In order to solve the above problems, a shearer automatic height adjustment technology based on a cutting roof and floor height prediction model is proposed. Firstly, the factors affecting the height of the cutting roof and floor are analyzed. It is pointed out that the main factors affecting the height of the cutting roof and floor include the fluctuation data of the coal seam, historical cutting data, elevation data of the scraper conveyor, and manual operation records. The above four types of data are fused and processed to establish a cutting roof and floor height prediction algorithm model based on long short term memory (LSTM) model and gray Markov model. The height of the cutting roof and floor is predicted through an algorithmic model. Secondly, based on the height data of the cutting roof and floor, combined with the position and posture and spatial coordinates of the shearer, a geometric model for calculating the height of the drum is established. At the same time, correction is made according to factors such as the sliding amount of the scraper conveyor and whether the addition and subtraction process is carried out. Finally, the height sequence of the roof and floor is converted into a drum height sequence. The cutting roof and floor height is converted into the target height of the shearer drum, which is executed by the shearer to the target height, achieving automatic adjustment of the drum height. The industrial test results show the following points. ① Under the control of automatic height adjustment technology, 90% of the predicted height deviation values of the roof and floor drums are within 10 cm of the actual height. The predicted height of the drums is significantly consistent with the actual height. ② Compared with traditional manual control methods, the number of manual intervention height adjustment times for cutting coal in the middle has decreased from 49 to 21. It indicates that the height prediction model for cutting the roof and floor and the geometric model for calculating the height of the drum are accurate and reasonable, and the automatic height adjustment technology for the shearer drum is feasible.
-
-
![]()
图 2 截割顶底板高度预测算法流程
Figure 2. Algorithm process for predicting the height of the cutting roof and floor
![]()
图 9 上位机控制软件和采煤机控制系统关系
Figure 9. Relationship between upper computer control software and shear control system
![]()
图 10 采煤机滚筒的实际高度与预测高度对比结果
Figure 10. Comparison between the actual height and predicted of the shearer drum
![]()
图 11 自动调高方式下司机的干预次数与传统手动调高方式下司机的干预次数对比
Figure 11. Comparison of the intervention frequency of drivers under automatic height adjustment mode and traditional manual height adjustment mode
表 1 试验工作面地质情况
Table 1 Geological conditions of the coal mining face
煤层厚度/m 煤层倾角/（°） 基本顶厚度/m 直接顶厚度/m 底板厚度/m 1.7～3.0 1～5 9.8 6.6 8.0 
下载: 导出CSV
表 2 预测高度和实际高度偏差情况
Table 2 Deviation between predicted height and actual height
位置 偏差/cm 占比/% 顶板 ≤5 76.69 ≤10 94.66 ≤15 100 底板 ≤5 91.74 ≤10 97.69 ≤15 100
下载: 导出CSV
-
[1] 张世龙,张民波,朱仁豪,等. 近5年我国煤矿事故特征分析及防治对策[J]. 煤炭与化工,2021,44(8):101-106,109. ZHANG Shilong,ZHANG Minbo,ZHU Renhao,et al. Analysis of the characteristics of China's mine accidents in the past five years and countermeasures for prevention and control[J]. Coal and Chemical Industry,2021,44(8):101-106,109.
[2] 谭震,王建文,王宏科,等. 煤矿灾害智能综合防治系统构建及关键技术[J]. 中国煤炭,2022,48(12):68-75. TAN Zhen,WANG Jianwen,WANG Hongke,et al. Construction and key technologies of intelligent comprehensive prevention and control system for coal mine disaster[J]. China Coal,2022,48(12):68-75.
[3] 张胜利,汤家轩,王猛. “双碳”背景下我国煤炭行业发展面临的挑战与机遇[J]. 中国煤炭,2022,48(5):1-5. ZHANG Shengli,TANG Jiaxuan,WANG Meng. Challenges and opportunities for the development of China's coal industry under the background of carbon peak and carbon neutrality[J]. China Coal,2022,48(5):1-5.
[4] 李浩荡. 减碳背景下煤炭如何直面挑战[N]. 中国煤炭报,2021-04-01(2). LI Haodang. How does coal face the challenge in the context of carbon reduction[N]. China Coal News,2021-04-01(2).
[5] 王国法. 煤矿高效开采工作面成套装备技术创新与发展[J]. 煤炭科学技术,2010,38(1):63-68,106. WANG Guofa. Innovation and development of completed set equipment and technology for high efficient coal mining face in underground mine[J]. Coal Science and Technology,2010,38(1):63-68,106.
[6] 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. 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.
[7] 王国法,徐亚军,张金虎,等. 煤矿智能化开采新进展[J]. 煤炭科学技术,2021,49(1):1-10. WANG Guofa,XU Yajun,ZHANG Jinhu,et al. New development of intelligent mining in coal mines[J]. Coal Science and Technology,2021,49(1):1-10.
[8] 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(2):161-169. DOI: 10.1016/j.ijmst.2018.06.005
[9] 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. 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.
[10] 王昕. 基于电磁波技术的煤岩识别方法研究[D]. 徐州:中国矿业大学,2017. WANG Xin. Study of coal-rock identification method based on electromagnetic wave technology[D]. Xuzhou:China University of Mining and Technology,2017.
[11] 杨文萃,邱锦波,张阳,等. 煤岩界面识别的声学建模[J]. 煤炭科学技术,2015,43(3):100-103. YANG Wencui,QIU Jinbo,ZHANG Yang,et al. Acoustic modeling of coal-rock interface identification[J]. Coal Science and Technology,2015,43(3):100-103.
[12] 孙继平,陈浜. 基于双树复小波域统计建模的煤岩识别方法[J]. 煤炭学报,2016,41(7):1847-1858. SUN Jiping,CHEN Bang. An approach to coal-rock recognition via statistical modeling in dual-tree complex wavelet domain[J]. Journal of China Coal Society,2016,41(7):1847-1858.
[13] 刘俊利,赵豪杰,李长有. 基于采煤机滚筒截割振动特性的煤岩识别方法[J]. 煤炭科学技术,2013,41(10):93-95,116. LIU Junli,ZHAO Haojie,LI Changyou. Coal-rock recognition method based on cutting vibration features of coal shearer drums[J]. Coal Science and Technology,2013,41(10):93-95,116.
[14] 孙振明,毛善君,祁和刚,等. 煤矿三维地质模型动态修正关键技术[J]. 煤炭学报,2014,39(5):918-924. SUN Zhenming,MAO Shanjun,QI Hegang,et al. Dynamic correction of coal mine three-dimensional geological model[J]. Journal of China Coal Society,2014,39(5):918-924.
[15] 殷大发. 煤矿三维地质模型精度评价及动态更新技术探讨[J]. 煤矿开采,2018,23(4):20-24. YIN Dafa. Exploration of precision evaluation and dynamic update technology of coal mine 3D geological model[J]. Coal Mining Technology,2018,23(4):20-24.
[16] 刘万里,张学亮,王世博. 采煤工作面煤层三维模型构建及动态修正技术[J]. 煤炭学报,2020,45(6):1973-1983. LIU Wanli,ZHANG Xueliang,WANG Shibo. Modeling and dynamic correction technology of 3D coal seam model for coal-mining face[J]. Journal of China Coal Society,2020,45(6):1973-1983.
[17] 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. 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.
[18] 卢新明,阚淑婷. 煤炭精准开采地质保障与透明地质云计算技术[J]. 煤炭学报,2019,44(8):2296-2305. LU Xinming,KAN Shuting. Geological guarantee and transparent geological cloud computing technology of precision coal mining[J]. Journal of China Coal Society,2019,44(8):2296-2305.
[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. 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.
[20] 陈尔奎,吴梅花. 基于改进遗传算法和改进人工势场法的复杂环境下移动机器人路径规划[J]. 科学技术与工程,2018,18(33):79-85. CHEN Erkui,WU Meihua. The path planning of mobile robots based on the improved genetic algorithm and the improved artificial potential field algorithm in complex environment[J]. Science Technology and Engineering,2018,18(33):79-85.
[21] 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. 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.
-
期刊类型引用(8)
1. 谢才秀，张永菊，龙涛，陈鹏，石开仪，王市委. 不同实验设计方法在高灰分煤泥浮选优化实验中的应用. 矿产综合利用. 2021(01): 72-76+56 . 
百度学术
2. 陈鹏，李子文，石开仪，张谌虎，王市委，孙维旭. 基于司盘80改性的不黏煤煤泥浮选条件探索. 矿业研究与开发. 2021(05): 146-149 . 百度学术
3. 孟正琴，陈宗素，张永菊. 正交试验法对六盘水某煤泥浮选条件优化的研究. 山东化工. 2020(01): 20-21+23 . 百度学术
4. 徐学卫. 田庄选煤厂二次浮选工艺的设计与实践. 选煤技术. 2020(03): 74-77 . 百度学术
5. 柴炳升，胡峰. 石槽村选煤厂重介质浅槽分选工艺探究. 煤炭加工与综合利用. 2020(08): 33-34+38 . 百度学术
6. 刘宸，李秋科，李大虎，高飞云，王建忠，王文才，曹钊. 基于响应曲面法的五虎山煤泥浮选条件优化研究. 选煤技术. 2020(05): 8-14 . 百度学术
7. 陈鹏，白新伟，石开仪，潘东，王成勇. 煤泥分级浮选精煤最高产率预测. 矿业研究与开发. 2020(05): 139-143 . 百度学术
8. 徐江婵，熊余，朱艳. 细粒煤泥浮选正交试验初步研究. 河南科技. 2019(34): 92-94 . 百度学术
其他类型引用(5)
计量
- 文章访问数: 505
- HTML全文浏览量: 56
- PDF下载量: 44
- 被引次数: 13
