The use of the neuro-fuzzy model to predict the methane hazard during the underground coal mining production process

作     者：Brodny, Jaroslaw Felka, Dariusz Tutak, Magdalena 

作者机构：Silesian Tech Univ Akad 2A PL-44100 Gliwice Poland Lukasiewicz Res Network Inst Innovat Technol EMAG Leopolda 31 PL-40189 Katowice Poland 

出 版 物：《JOURNAL OF CLEANER PRODUCTION》 (清洁化生产杂志)

年 卷 期：2022年第368卷第0期

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 

基　　金：The aim of the research was to develop a methodology for diagnosing and forecasting the methane hazard state in the area of the ongoing mining exploitation based on the fuzzy-neural model and real ventilation data registered continuously by sensors of the automated gasmetry system. This methodology and  in particular  the developed model will be used to build an IT tool to support the process of ventilation safety management in mining companies 

主　　题：Methane hazard Mining production Neuro-fuzzy model Neural networks Fuzzy clustering ANFIS algorithm 

摘      要：Coal mining production is marked with a number of different types of hazards, of which the most dangerous are natural hazards resulting from disturbances in the rock mass balance due to mining operations. One of the most frequent and dangerous to the continuity, effectiveness and safety of this process is a methane hazard. It is cause by methane, a gas naturally occurring in coal beds. In order to limit this threat, the article presents a developed methodology of its diagnosis and prognosis together with an example of its practical application. The methodology is based on the neural-fuzzy model, which, using the results of measurements of real ventilation parameters, enables a diagnosis (fuzzy model) and forecast (neural-fuzzy model) the degree of methane hazard in the area of exploitation. The measure of this hazard is the value of the methane hazard index (MHI), which takes into account the relation between the absolute and criterial methane-bearing capacity in the examined region. The developed models and methodology enable the determination of short-term methane susceptibility prognosis, which provides an opportunity for current and effective methane susceptibility control in a given mining exploitation area. This in turn, by taking appropriate actions (e.g. change of ventilation parameters), makes it possible to improve safety and thus the effectiveness of the whole mining production process. The presented example of using this methodology in real conditions confirms its effectiveness. This mainly concerns the pace and reliability of the obtained results and the possibility of continuous learning of the developed system based on new data. The paper is an example of the original application of advanced methods of modeling of physical phenomena to solve a practical problem of improving the safety of the production process.