CCTEG Xi’an Research Institute (Group) Co., Ltd.
China Coal Research Institute
School of Automаtion, Chinа University of Geosciences

煤矿坑道钻进施工环境恶劣、复杂的煤层结构和繁杂的操作工序造成钻进效率低、钻进成本高。开展煤矿坑道钻进过程优化与控制技术的研究势在必行。围绕煤矿坑道钻进控制过程关键技术，从含煤地层岩性智能识别、钻进参数智能优化和智能控制3方面展开。首先，为了准确判断含煤地层类型，建立基于BP-Adaboost的含煤地层岩性智能识别模型。然后，在不同含煤地层条件下，建立基于机械比能和钻速的智能优化模型，为司钻人员提供最优给进压力和转速参考值。进而，提出一种基于模糊PID的给进压力控制策略，实现给进压力的有效控制。最后，基于煤矿坑道钻机智能钻进系统在淮南矿区某煤矿井下进行了现场试验。试验结果表明：所提含煤地层岩性智能识别方法的识别准确率达到96.75%；智能优化方法显著提升现场钻速，消耗的机械比能降低，在提高钻进效率的同时降低了钻进成本；给进压力控制策略使给进压力稳定运行在最优值附近，减小给进系统超调的同时，提升系统的响应速度，使给进压力的动态响应更加平稳。煤矿坑道钻进过程智能优化与控制技术能够保障钻进过程安全高效运行，促进煤矿坑道钻进技术智能化发展。

The harsh environment for drilling construction of coal mine tunnel, complex coal seam structure and complicated operation procedures can cause low drilling efficiency and high drilling cost. For this reason, it is imperative to carry out studies on the optimization and control technology for the drilling process of coal mine tunnel. Herein, study was conducted from intelligent identification of lithology of coal-bearing formation, intelligent optimization of drilling parameters and their intelligent control, focusing on the key technology for control of drilling process of coal mine tunnel. First, in order to accurately determine the type of coal-bearing formation, an intelligent identification model of coal-bearing formation lithology based on BP-Adaboost was established. Then, an intelligent optimization model based on mechanical specific energy and drilling speed was established under different condition of coal-bearing formations, to provide the driller with the reference values of optimal feed pressure and rotational speed. Next, a fuzzy PID-based feed pressure control strategy was proposed to realize the effective control of feed pressure. Finally, field test was implemented in a coal mine in Huainan with the intelligent drilling system of drill rig for coal mine tunnel. The test results indicate that: the identification accuracy of the proposed intelligent identification model for coal-bearing formation lithology is up to 96.75%. The intelligent optimization method improves the drilling rate significantly and reduces the mechanical specific energy, thereby improving the drilling efficiency and reducing the drilling cost. The feed pressure control strategy could stabilize the feed pressure near the optimal value, reduce the overshoot of the feed system while speeding up the system response, thereby ensuring the dynamic response of the feed pressure smoother. Intelligent optimization and control technology for drilling process of coal mine tunnel can effectively guarantee the safe and efficient operation of the drilling process and promote the intelligent development of the drilling technology for coal mine tunnel.

国家自然科学基金面上项目(62273317)；教育部高等学校学科创新引智计划项目(B17040)

姚宁平，吴敏，陈略峰，等. 煤矿坑道钻进过程智能优化与控制技术[J]. 煤田地质与勘探，2023，51(9)：1−9.

YAO Ningping，WU Min，CHEN Luefeng，et al. Intelligent optimization and control technology for drilling process of coal mine tunnels[J]. Coal Geology & Exploration，2023，51(9)：1−9.