高瓦斯矿井掘进通风瓦斯浓度预测模型研究

摘要

为了对不同瓦斯涌出量和通风配置下的高瓦斯矿井掘进通风瓦斯浓度进行准确预测,文中在对掘进工作面瓦斯浓度的各种通风影响因素分析基础上,设计了两种掘进通风瓦斯浓度预测神经网络模型.利用MATLAB软件及煤矿现场获得的实测样本数据,建立了瓦斯浓度BP和RBF神经网络预测模型.通过预测结果对比分析可知,RBF神经网络预测模型能够对掘进通风瓦斯浓度进行准确地动态预测,为不同掘进阶段、不同瓦斯涌出量下的掘进通风方案选择提供了一定的理论依据.%To predict accurately excavation ventilation gas concentration in the different gas emission quantity and different parameter of ventilation allocation in high gas coal mine, this paper based on analysis of ventilation factors of gas concentration,designed two predictive models for excavation ventilation gas concentration. Using MATLAB and online measured sample data,BP and RBF predictive model were established based on neural network technology. Analysis on results shows that the RBF model has higher prediction precision and can accurately forecast gas concentration of various ventilation schemes under different gas emission quantity and different parameter of ventilation allocation, providing theoretical basis for ventilation scheme choosing under different driving conditions.