基于BP神经网络的矿井淋水井筒风温预测  

BP Neural Network for Predicting Air Temperature of Watering Well in Mine

作　　者：吕品[1] 左金宝[1] 倪小军[1] 

机构地区：[1]安徽理工大学能源与安全学院煤矿安全高效开采省部共建教育部重点实验室,安徽淮南232001

出　　处：《煤矿安全》2008年第12期11-13,共3页Safety in Coal Mines

基　　金：安徽自然科学基金重点科研计划项目(2006KJ006A);安徽省高校科技创新团队计划资助项目(2006KJ005TD)

摘　　要：应用BP神经网络方法,建立了一种新的井底风温预测模型。它利用BP神经网络的函数拟合能力,通过拟合井筒风流温度、相对湿度,地面大气压力及井筒深度等参数,来预测淋水井筒井底风温,为矿井的井底风温预测提供了一种新的预测精度较高方法。This article applied the method of neural network, established a new model of predicting the air temperature in the bottom of the well. It utilized the function fitting ability of BP neural network, by fitted specific isoperimetric, such as ground air temperature, air relative humidity, ground atmospheric pressure and mine shaft depth. Used this method to predict the air temperature in the bottom of the well. It provided a new higher precision method of predicting the air temperature in the bottom of the wen.

关 键 词：神经网络 风温预测 淋水 井筒 

分 类 号：TD727[矿业工程—矿井通风与安全]