独头巷道掘进过程中排烟时间预测

分析了炮烟在独头巷道中的运移情况，从质量守恒角度建立了炮烟抛掷区中的炮烟排出数学模型。对归来庄金矿-118 m穿脉巷道爆破作业的现场炮烟监测，监测点炮烟浓度先增加后减小，炮烟排出过程中符合指数衰减。从爆破、炸药、通风条件、巷道情况4个方面分析了影响排烟时间的因素，利用BP神经网络模型，将炸药量、炮眼数目、风筒到掘进面的距离、监测点到掘进面的距离、风筒风量、风流温度、巷道温度、巷道相对湿度等因素的10组实验数据作为输入量，排烟时间作为输出量，获得稳定的网络结构。再将5组输入量实验数据代入BP网络训练，预测的排烟时间与实测的排烟时间相对误差在7%以内，运用BP网络模型取得了较好的预测效果。准确预测排烟时间，可以合理安排掘进作业，避免炮烟中毒事件发生，对矿山安全高效生产有重要的意义。

Prediction of Drain-fume Time of Single-end Roadway in Tunneling Process

The migration of blasting fume in single-end roadway is analyzed，and the mathematical model of blasting fume diffusion in the casting area is established from the viewpoint of mass conversation.Blasting fume monitoring in the -118 m tunnel in Guilaizhuang gold mine showed that the density of blasting fume at monitoring point increases firstly and then decreases.The process of blasting fume diffusion basically coincides with the exponential decay.The factors for fume-drain time are analyzed from four aspects of blasting，explosive，ventilation condition and tunnel.With BP neural network model，a stable network structure is obtained，regarding 10 groups data of explosive quantity，hole number，distance between fan drum and heading face，distance between monitoring point and heading face，air output，air temperature，roadway temperature，relative humidity of roadway as input，and fume-drain time as output.5-group of experimental input data was introduced into the BP network，obtaining that relative errors between measured results and network-training results are lower than 7%.Better prediction effect is achieved with BP neural network model.Accurate prediction of fume-drain time can not only arrange tunneling in a reasonable way，but also avoid occurrence of blasting fume poisoning incident，which creates significant meanings to safe and high-efficient production of mining.