Faculty of Mining and Geology, Silesian University of Technology, 44-100 Gliwice, Poland.
Faculty of Organization and Management, Silesian University of Technology, 44-100 Gliwice, Poland.
Int J Environ Res Public Health. 2019 Apr 18;16(8):1406. doi: 10.3390/ijerph16081406.
Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards.
甲烷在开采过程中释放，对这个过程构成严重威胁。这是因为气体可能会点燃或引起爆炸。这两种现象都极其危险。高浓度的甲烷在矿山巷道中会扰乱采矿作业，并引发火灾或爆炸的风险。因此，有必要监测和预测正在进行的采矿作业区域中的甲烷浓度。本文介绍了为提高工作安全性而进行的测试结果。文章介绍了在一个矿区使用人工神经网络预测甲烷浓度值的方法。本文的目的是开发一种在采矿业中预测甲烷浓度的有效方法。为此目的应用神经网络是这方面的首次尝试之一。所开发的方法利用了位于开采区域的传感器系统测量的直接甲烷浓度值。预测模型是基于多层感知器（MLP）网络构建的。相应的计算是使用具有非线性激活函数的三层网络进行的。以甲烷浓度预测形式获得的结果与该浓度的记录值相比误差较小。这为智能系统在有效预测采矿危险方面的更广泛应用提供了机会。
