摘要
研究引入BP神经网络模型对保护层开采条件下冒裂高度进行快速、准确预测。根据卸压开采冒裂运动的特点,选取了覆岩岩性、采高、煤层倾角等6个主要影响因素作为评判指标,建立了保护层开采条件下卸压高度的神经网络预测模型。研究结果表明,BP神经网络模型能够较准确地预测保护层开采条件下上覆岩层的冒裂高度,该模型易于操作、实用性强、预测精度高,是预测保护层卸压高度的新的技术途径。
BP neural network model is introduced to predict the caving height of the protective layer quickly and accurately.According to the characteristics of caving movement in pressure relief mining,six main influencing factors such as overburden lithology,mining height and coal seam dip angle are selected as evaluation indexes,and a neural network prediction model of pressure relief height under the condition of protective layer mining is established.The results show that the BP neural network model can accurately predict the caving height of the overlying strata under the mining conditions of the protective layer.The model is easy to operate,practical and accurate,which is a new technical way to predict the relief height of the protective layer.
作者
和树栋
HE Shu-dong(State Key Laboratory of Gas Monitoring and Emergency Technology,Chongqing 400037,China;Chongqing Research Institude,China Coal Technology and Engineering Group,Chongqing 400037,China)
出处
《煤炭技术》
CAS
2020年第6期73-75,共3页
Coal Technology
基金
国家重点研发计划资助(2018YFC0808305)。
关键词
BP神经网络
保护层
卸压高度
理论计算
预测
BP neural network
protective layer
pressure relief heigt
theoretical calculation
prediction
