Prediction and analysis of gas emission in advancing process of stope working face
-
摘要: 针对现有回采工作面瓦斯涌出预测方法的数据大都是基于回采工作面单一传感器的瓦斯浓度序列，存在无法将工作面持续推进过程中空间位置变化的监测点位置进行记录的问题，提出了以回采工作面传感器各监测点瓦斯浓度序列数据为基础，结合工作面实际推进距离，运用BP神经网络模型综合预测工作面瓦斯涌出量的方法。该方法利用回采工作面瓦斯分源辨识方法，分别分析采空区瓦斯涌出和煤壁瓦斯涌出的变化规律；利用BP神经网络预测法，结合表征采空区瓦斯涌出和巷道煤壁瓦斯涌出规律的特征值对工作面日均瓦斯涌出进行预测。 实例应用验证了该方法的正确性。Abstract: The data of existing gas emission prediction methods of stop working face are mostly based on gas concentration sequence of single sensor in stope working face, and these methods can not record position of monitoring point in process of continuous advancement of the working face.In view of above problems, a method that used BP neural network model to predict gas emission in the working face was proposed, which was based on data of gas concentration sequence data of monitoring point of sensor and actual advance distance on stope working face. The method uses gas source identification method of the working face to analyze variation law of gas emission of in goaf and coal wall respectively; and uses BP neural network prediction method to predict average daily gas emission combining with characteristic values of variation law of gas emission of in goaf and coal wall. The example application verifies correctness of the method.
-
-
期刊类型引用(2)
1. 赵晟，赵旭亮，孟小文，赵欣，申化波. 煤矿综采工作面本质安全型组合开关控制与仿真研究. 电子设计工程. 2024(24): 138-142 . 
百度学术
2. 周坪，马国庆，周公博，马天兵，李远博. 智能化带式输送机健康监测技术研究综述. 仪器仪表学报. 2023(12): 1-21 . 百度学术
其他类型引用(0)
计量
- 文章访问数: 51
- HTML全文浏览量: 8
- PDF下载量: 9
- 被引次数: 2
下载:
