Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

Construction of multi-factor identification model for real-time monitoring and early warning of mine water inrush

As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.

开采过程中矿井涌水量动态预测研究

为解决矿井涌水量预计误差较大、矿山排水能力无法适应矿井突水的问题,以山东龙固矿井为对象,根据矿井涌水量多年动态变化规律分析影响矿井涌水量大小的主要因素,揭示采动过程中充水因素变化对涌水量大小的影响,并在此基础上,采用数值法实时预测采掘进程中矿井涌水量的动态变化。结果表明,与以往采区范围固定不变的模拟方法相比,考虑按工作面开采的时间先后顺序进行涌水量的预测,实时调整新采区内的虚拟开采井,此种预测方法使矿井涌水量预测结果更逼近真实,减小了计算误差。

灵新煤矿高矿化度矿井水井下分级处理技术研究

根据灵新煤矿矿井水水文地质条件、涌水量和涌水水质资料等综合考虑,对灵新煤矿高矿化度矿井水提出了井下分级处理技术和浓盐水封存技术,改变了在地面建设传统的矿井水处理模式,有效的实现了"地面清水零入井、井下污水零升井"的双零目标,节约了地面生活水,保护了地下水资源,有效保障了矿区的可持续发展,为矿井水的处理利用技术开辟了新的技术途径。

深部矿井工作面开采与灾害耦合关系分析

针对深部矿井工作面开采与灾害耦合关系,采用现场观测及数值模拟方法进行研究。研究结果表明:工作面微震事件数目、能量变化受工作面回采影响明显;工作面涌水量随工作面推进距离增加而增大;工作面硫化氢浓度随工作面后方采空区顶板垮落范围加大,整体呈现上升趋势;工作面回风流CO与回风隅角CO变化有一定关联性,在推采至200m时回风流CO达到最大值,为23×10-6,随后出现下降。该研究成果对类似条件矿井具有一定的参考价值。