基于激光诱导荧光技术的矿井突水监测系统研究

发布时间：2018-04-01 06:16

本文选题：激光诱导荧光技术　切入点：水参数　出处：《安徽理工大学》2017年硕士论文

【摘要】：随着煤炭开采深度的不断增加,煤矿生产中会遇到各种安全及灾害问题,大量地下水突然涌入矿山井巷的现象,矿井突水一般来势凶猛,常会在短时间内淹没坑道,给矿山生产带来危害,造成人员伤亡,并导致严重的经济损失。如今,水害事故成为威胁煤矿生命财产安全的最主要风险之一,面对水害危害的严重性,迫切需要进行水害预警防治工作。针对此种情况,本论文采用化探与监测相结合,提出基于激光诱导荧光技术的矿井突水监测系统研究。本系统利用激光诱导荧光技术,实时测量矿井水源的荧光发光通量随发射光波长的变化而获得的光谱和光谱强度,识别所测物质的元素成分及含量,辅助以PH值和电导率、涌水量等水参数实时监测,根据参数突变再辅以常规的水化学判别水源类型。应用RBF神经网络(径向基神经网络)进行判别模型,建立综合煤矿突水水源判别模型。该系统目前已成功应用于大同煤矿燕子山矿8306、8401工作面,经过安装调试已正常运行达10个月,数据采集分站采集传感器信息,利用数据通信分站由光纤网络传输至地面上位机,实现矿井工作面水质信息的远程实时在线监测。经过测试表明系统设计合理,运行稳定,测量数据精度高,系统丢包率为0.93%,PH和电导率的相对误差为0.42%和0.52%。长时间的监测表明,8306、8401老空水PH稳定在6.5左右,电导率稳定在2105μs/cm左右,含氧度为2%,光谱特征稳定,通过RBF神经网络模型判定其为老空水,无明显的外部水源入侵。本系统首次采用在线多参数水质传感器,利用CAN网络设计了一套矿井水质监测系统,该系统通过传感器节点采集光谱特征、PH值和电导率、溶氧度等参数,利用总线技术网关将数据由网络将信息传输给地面上位机,实现矿井工作面水质信息的远程实时在线监测,并将该系统应用大同集团燕子山矿运行,效果良好,进行在线式的突水预警意义重大,为矿井突水灾害的预测提供决策依据,这对矿山灾害防治及煤矿安全生产的基础理论与方法的研究具有重要的科学意义。
[Abstract]:With the continuous increase of coal mining depth, coal production will encounter various safety and disaster problems, a large number of underground water suddenly poured into the mine roadway phenomenon, mine water inrush generally fierce, often in a short period of time submerged tunnel, It has brought harm to mine production, caused casualties, and caused serious economic losses. Nowadays, water disaster accidents have become one of the most important risks threatening the safety of life and property in coal mines. There is an urgent need for early warning and prevention of water hazards. In view of this situation, a study of mine water inrush monitoring system based on laser-induced fluorescence technique is put forward in this paper, which is based on the combination of geochemical exploration and monitoring. The spectrum and spectral intensity of the fluorescence flux of mine water source with the variation of emission wavelength are measured in real time, and the element composition and content of the measured substance are identified, and the real-time monitoring of water parameters such as PH value, conductivity, water discharge and so on is assisted. The RBF neural network (radial basis function neural network) is used to judge the water source type according to the parameter mutation and the conventional hydrochemistry. This system has been successfully applied to 8306D8401 working face of Yanzishan Coal Mine in Datong Coal Mine. After installation and debugging, it has been running normally for 10 months, and the data acquisition sub-station collects sensor information. The data communication sub-station is transmitted from the optical fiber network to the upper computer on the ground to realize the remote real-time on-line monitoring of the water quality information in the mine face. The test results show that the system is reasonable in design, stable in operation and high in accuracy of measurement data. The relative error between the pH and conductivity of the system is 0.42% and 0.52% respectively. The results of long time monitoring show that the PH of the old empty water is about 6.5, the conductivity is about 2105 渭 s/cm, the oxygen content is 2 and the spectral characteristics are stable. The model of RBF neural network is used to determine it as old empty water without obvious external water intrusion. An on-line multi-parameter water quality sensor and a set of mine water quality monitoring system based on CAN network are designed in this system for the first time. The system collects the parameters such as PH value, conductivity, dissolved oxygen degree and so on through sensor node, and transmits the data from network to the upper computer using bus technology gateway to realize the remote real-time on-line monitoring of water quality information of mine face. The application of the system to Yanzishan Mine of Datong Group has good effect, and it is of great significance to carry out on-line water inrush warning, which provides a decision basis for the prediction of mine water inrush disaster. This is of great scientific significance to the study of basic theories and methods of mine disaster prevention and mine safety production.
【学位授予单位】：安徽理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TD745.2

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