摘要
对常用的突水水源判别方法进行了概述,阐明了其适用条件,为选择合适的方法提供了依据。以石壕煤矿为例,利用BP神经网络方法,选择矿化度、p H值、总硬度、Ca2++Mg2+、K++Na+以及涌水量作为判别因子,建立了水源判别模型,经过样本训练和模型验证,其判别结果与实际基本一致,验证了人工神经网络方法在突水水源判别上的准确性。
Summarizing the commonly used methods of water bursting source discrimination, to clarify the conditions for its application, select the appropriate method to provide a basis. In Shihao coal mine, for example, using BP neural network method, selecting salinity, pH value, total hardness, Ca2＋＋Mg2＋, K＋＋Na＋ and water inflow as discrimination factors, establish a water discriminate model. Through training and model validation, the discrimination results are basically consistent with the actual, verified the accuracy of artificial neural network on water-bursting source discrimination.
出处
《煤炭技术》
CAS
北大核心
2016年第9期150-152,共3页
Coal Technology
关键词
矿井突水
水源判别
人工神经网络
mine water bursting
water source judgment
artificial neural network
