Planning coal drawing control system based on process engine
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摘要: 目前对综放智能化的研究主要聚焦于感知方面，对放煤过程智能化的研究较少，自动放煤控制技术存在自适应性不足、效率较低、放煤质量难以把控等问题。为了提升放煤过程的智能化水平与运行效率，设计了一种基于工艺引擎的规划放煤控制系统。该系统由放煤管控单元和窗口决策单元组成：规划放煤管控单元通过异步递进的放煤调度策略、柔性切换技术及规划放煤工艺编辑引擎，实现采煤机位置弱关联的自动顺序放煤及工艺在线编辑，通过关联后部刮板输送机负载，动态调整工艺启停，保障刮板输送机安全作业；窗口决策单元通过PID控制算法动态调节尾梁角度，实现放煤窗口反馈控制，采用遗传算法优化BP神经网络对放煤窗口大小进行智能决策，以适应不同工况，提高放煤质量。现场应用结果表明：基于异步递进的放煤调度策略与柔性切换技术提升了单刀自动运行效率，无需再手动接管；每一班组自动化运行刀数提升了33.3%；系统关联的后部刮板输送机负载、泵站等设备可动态调整工艺启停，每班后部刮板输送机平均停止次数下降了61.1%，可保障作业安全；工艺编辑引擎能适应多种场景下的应用，工艺调整用时大幅度降低；后部动作与前部动作相互叠加，使得单刀平均用时缩短了9.3%，提升了开采效率；倾角传感关联控制与规划放煤窗口智能决策将每日发热量提升了10.3%，改善了放煤质量。Abstract: Current research on intelligent fully mechanized coal caving mining primarily focuses on perception, with limited studies on the intelligence of the coal drawing process. Existing automatic coal drawing control technologies face issues such as insufficient adaptability, low efficiency, and difficulty in quality control. To enhance the intelligence and operational efficiency of the coal drawing process, a planning coal drawing control system based on a process engine was designed. This system consisted of a coal drawing management unit and a window decision-making unit. The planning coal drawing management unit employed an asynchronous progressive scheduling strategy, flexible switching technology, and a process editing engine to achieve automated sequential coal drawing with weak correlation to the mining machine's position and online process editing. By associating with the load of the rear scraper conveyor, the system dynamically adjusted process starts and stops, ensuring safe operation of the scraper conveyor. The window decision-making unit utilized a PID control algorithm to dynamically adjust the tail beam angle, implementing feedback control of the coal drawing window. A genetic algorithm optimized a BP neural network to make intelligent decision about the size of the coal drawing window to adapt to varying operating conditions and improve coal drawing quality. Field application results indicated that the asynchronous progressive scheduling strategy and flexible switching technology enhanced the efficiency of automatic operation, eliminating the need for manual intervention. The number of automated operations per shift increased by 33.3%. The system's associated rear scraper conveyor load, pump station, and other equipment could dynamically adjust process starts and stops, resulting in a 61.1% decrease in the average stopping frequency of the rear scraper conveyor per shift, ensuring operational safety. The process editing engine accommodated various applications, substantially reducing adjustment time. The overlap of rear and front actions shortened the average operation time by 9.3%, increasing extraction efficiency. The correlation control of the tilt angle and intelligent decision-making for the planning coal release window improved daily calorific value by 10.3%, enhancing coal drawing quality.
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图 9 规划放煤控制系统部署前后生产数据对比
Figure 9. Comparison of production data before and after deployment of the planning coal drawing control system
表 1 前部动作与后部动作叠加
Table 1 Superimposition of front and rear movements
动作 放煤 拉后溜 跟机移架 × × 跟机推溜 √ √ 伸伸缩梁护帮联动 √ √ 收伸缩梁护帮联动 √ √ 
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[1] 王国法,庞义辉,任怀伟. 煤矿智能化开采模式与技术路径[J]. 采矿与岩层控制工程学报,2020,2(1):5-19. WANG Guofa,PANG Yihui,REN Huaiwei. Intelligent coal mining pattern and technological path[J]. Journal of Mining and Strata Control Engineering,2020,2(1):5-19.
[2] 王国法,庞义辉. 特厚煤层大采高综采综放适应性评价和技术原理[J]. 煤炭学报,2018,43(1):33-42. WANG Guofa,PANG Yihui. Full-mechanized coal mining and caving mining method evaluation and key technology for thick coal seam[J]. Journal of China Coal Society,2018,43(1):33-42.
[3] 宋选民,朱德福,王仲伦,等. 我国煤矿综放开采40年:理论与技术装备研究进展[J]. 煤炭科学技术,2021,49(3):1-29. SONG Xuanmin,ZHU Defu,WANG Zhonglun,et al. Advances on longwall fully-mechanized top-coal caving mining technology in China during past 40 years:theory,equipment and approach[J]. Coal Science and Technology,2021,49(3):1-29.
[4] 王国法,庞义辉,马英. 特厚煤层大采高综放自动化开采技术与装备[J]. 煤炭工程,2018,50(1):1-6. WANG Guofa,PANG Yihui,MA Ying. Automated mining technology and equipment for fully-mechanized caving mining with large mining height in extra-thick coal seam[J]. Coal Engineering,2018,50(1):1-6.
[5] 王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术,2022,50(1):1-27. WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology,2022,50(1):1-27.
[6] 吴桐,尉瑞,刘清,等. 综放工作面智能放煤工艺研究及应用[J]. 工矿自动化,2021,47(3):105-111. WU Tong,YU Rui,LIU Qing,et al. Research and application of intelligent caving technology in fully mechanized working face[J]. Industry and Mine Automation,2021,47(3):105-111.
[7] 马英. 基于记忆放煤时序控制的智能放煤模式研究[J]. 煤矿机电,2015,36(2):1-5. MA Ying. Research on intelligent coal caving system based on memory coal caving sequential control[J]. Colliery Mechanical & Electrical Technology,2015,36(2):1-5.
[8] 于斌,徐刚,黄志增,等. 特厚煤层智能化综放开采理论与关键技术架构[J]. 煤炭学报,2019,44(1):42-53. YU Bin,XU Gang,HUANG Zhizeng,et al. Theory and its key technology framework of intelligentized fully-mechanized caving mining in extremely thick coal seam[J]. Journal of China Coal Society,2019,44(1):42-53.
[9] 王国法,刘峰,庞义辉,等. 煤矿智能化——煤炭工业高质量发展的核心技术支撑[J]. 煤炭学报,2019,44(2):349-357. WANG Guofa,LIU Feng,PANG Yihui,et al. Coal mine intellectualization:the core technology of high quality development[J]. Journal of China Coal Society,2019,44(2):349-357.
[10] 崔志芳,牛剑峰. 自动化记忆放煤控制研究[J]. 工矿自动化,2019,45(3):99-102,108. CUI Zhifang,NIU Jianfeng. Research on automatic memory coal caving control[J]. Industry and Mine Automation,2019,45(3):99-102,108.
[11] 韩秀琪,杨秀宇,孙峰,等. 智能综放工作面自动运行与人工干预分析系统[J]. 工矿自动化,2020,46(12):31-37. HAN Xiuqi,YANG Xiuyu,SUN Feng,et al. Automatic operation and manual intervention analysis system for intelligent fully mechanized caving face[J]. Industry and Mine Automation,2020,46(12):31-37.
[12] 张守祥,张学亮,刘帅,等. 智能化放顶煤开采的精确放煤控制技术[J]. 煤炭学报,2020,45(6):2008-2020. ZHANG Shouxiang,ZHANG Xueliang,LIU Shuai,et al. Intelligent precise control technology of fully mechanized top coal caving face[J]. Journal of China Coal Society,2020,45(6):2008-2020.
[13] 张文,周均忠,孙帅. 基于改进BP神经网络的综放开采放煤时间预测研究[J]. 煤炭科技,2023,44(5):24-28. ZHANG Wen,ZHOU Junzhong,SUN Shuai. Research on prediction of coal drawing time in fully-mechanized caving mining based on improved BP neural network[J]. Coal Science & Technology Magazine,2023,44(5):24-28.
[14] PRADO F,MINUTOLO M C,KRISTJANPOLLER W. Forecasting based on an ensemble autoregressive moving average- adaptive neuro-fuzzy inference system-neural network-genetic algorithm framework[J]. Energy,2020,197:117159. DOI: 10.1016/j.energy.2020.117159
[15] 王虹,尤秀松,李首滨,等. 基于遗传算法与BP神经网络的支架跟机自动化研究[J]. 煤炭科学技术,2021,49(1):272-277. WANG Hong,YOU Xiusong,LI Shoubin,et al. Research on automation of support based on genetic algorithm and BP neural network[J]. Coal Science and Technology,2021,49(1):272-277.
[16] 曹哲哲. 综采工作面智能化开采技术研究[J]. 陕西煤炭,2021,40(2):48-51. CAO Zhezhe. Research on intelligent mining technology in fully mechanized working face[J]. Shaanxi Meitan,2021,40(2):48-51.
[17] 刘军锋,高亮亮,尹春雷. 智能放煤技术在某矿综放工作面的研究与应用[J]. 煤炭技术,2022,41(2):58-60. LIU Junfeng,GAO Liangliang,YIN Chunlei. Research and application of intelligent caving technology in fully mechanized caving face in a mine[J]. Coal Technology,2022,41(2):58-60.
[18] 刘清,孟峰,牛剑峰. 放煤工作面支架姿态记忆控制方法研究[J]. 煤矿机械,2015,36(5):89-92. LIU Qing,MENG Feng,NIU Jianfeng. Research on memory control method for support postures of top coal caving mining face[J]. Coal Mine Machinery,2015,36(5):89-92.
[19] SCHMIDT L,ANDERSEN T O,PEDERSEN H C. On application of second order sliding mode control to electro-hydraulic systems[C]. ASME 12th Biennial Conference on Engineering Systems Design and Analysis,Copenhagen,2014. DOI: 10.1115/ESDA2014-20470.
[20] EL SAYED M A,HABIBI S. Inner-loop control for electro-hydraulic actuation systems[J]. Journal of Dynamic Systems,Measurement,and Control,2012,134(1). DOI: 10.1115/1.4001338.
[21] HAS Z,RAHMAT M F,HUSAIN A R,et al. Robust precision control for a class of electro-hydraulic actuator system based on disturbance observer[J]. International Journal of Precision Engineering and Manufacturing,2015,16(8):1753-1760. DOI: 10.1007/s12541-015-0230-y
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