摘要
以东古城煤业大量通风监测数据为基础,建立了通风数据算法模型,通过MGM(1,n)灰色预测模型和BP神经网络模型预测了瓦斯体积分数。实际结果显示,瓦斯体积分数实测值与预测值的平均残差为0.0193,证实了通风数据算法模型的可靠性,在实现数据高效利用的同时,为矿井进行危险防范提供了重要依据。
Based on a large number of ventilation monitoring data of Donggucheng Coal Industry Co.,Ltd.,this paper established the ventilation data algorithm model,and forecasted the gas volume fraction through MGM(1,n)grey prediction model and BP neural network model.The actual results show that the average residual between the measured and predicted gas volume fraction is 0.0193,which proves the reliability of the ventilation data algorithm model,and provides important basis for mine hazard prevention while realizing the efficient utilization of data.
作者
张超
ZHANG Chao(Donggucheng Coal Industry Co.,Ltd.of Zuoyun,Shanxi Coal Imp.&Exp.Group,Zuoyun 037100,Shanxi,China)
出处
《能源与节能》
2021年第6期133-134,209,共3页
Energy and Energy Conservation
关键词
通风数据算法模型
灰色预测模型
BP神经网络模型
平均残差
危险防范
ventilation data algorithm model
grey prediction model
BP neural network model
average residual
risk prevention
