固体智能充填关键装备工况位态表征及自主识别调控方法

Condition state characterization and self-identification control method of key equipment for intelligent solid backfilling

固体充填开采技术最显著的特征是依靠机械夯实保障充填体的致密性,存在工作量大、自动化程度低、采充工序分散等特点,制约了固体充填开采技术的规模化推广应用,迫切需要向固体智能充填方向升级改造。基于固体智能充填技术研究现状的系统总结,明确其实现智能化的基础是充填液压支架等关键装备工况位态的精准表征及自主识别调控;通过构建以感知、识别为核心的固体智能充填工序流程,比较各表征方法的优缺点及适用条件,提出了基于MDH运动学建模的固体智能充填关键装备工况位态精准表征方法;通过采用理论实践结合、建模仿真并行的研究手段,针对充填液压支架、多孔底卸式输送机和机构干涉的工况位态进行界定,给出了各装备非正常工况的判别函数,揭示了各装备非正常工况的解调路径;并以河北邢东矿固体智能充填工作面建设为工程背景进行应用与验证,结果表明:相比非固体智能充填,在一个完整工序循环时间内,单组充填装备工况位态自主识别调控时间减少10 mins,充填效率提高40%以上,研究结果大大提高了固体充填开采的智能化程度及充填效率,为完善固体智能充填体系提供理论及技术依据。固体充填装备具有多种型号,各型号的结构、尺寸及机构间的相互配合关系均有所差异,本文所建立的工况位态表征及自主识别调控方法在应用到具体型号的固体充填装备时,需要对相关的模型结构参数进行对应调整。

The most significant feature of solid backfilling mining technology is to rely on mechanical compaction to ensure the compactness of infill, which has some shortcomings of large workload, low degree of automation, and scattered mining and backfilling processes. This restricts the large-scale application of solid backfilling mining technology, and urgently needs to be upgraded in the direction of intelligent solid backfilling. Based on the systematic anlysis of the research status of intelligent solid backfilling technology, it is clear that the basis of realizing intelligence is the accurate characterization and self-identification control of the working condition of key equipment such as backfilling hydraulic support. By constructing a solid intelligent backfilling process flow with perception and identification as the core, and comparing the advantages and disadvantages of each characterization method and the applicable conditions, an accurate characterization method for the working conditions of key equipment of intelligent solid backfilling based on MDH kinematics modeling is proposed. By means of combining theory with practice and parallel modeling and simulation, the working conditions of backfilling hydraulic support, multi-hole bottom discharge scraper conveyor and mechanism interference are defined, the discriminant function and the demodulation path of each equipment under abnormal conditions are revealed. The application and verification are carried out with the construction of intelligent solid backfilling working face in Hebei Xingdong Mine. The results show that compared with non-intelligent solid backfilling, in a complete process cycle time, the self-identification and control time of working condition of single group backfilling equipment is reduced by 10 mins, and the backfilling efficiency is increased by more than 40%. The research greatly improves the intelligent level and backfilling efficiency of solid backfilling technology, and provide a theoretical and technical basis for improving the solid backfilling system. Solid backfilling equipment has a variety of models, and the structure, size and inter-mechanism cooperation relationship of each model are different. The working condition characterization and self-identification control method proposed in this paper need to adjust the relevant model structure parameters when applied to some specific models of solid backfilling equipment.