Danish Esmatullah, Onder Mustafa
Kabul Polytechnic University, Underground Mining Engineering Department, Karte Mamorin, 5th Districts, Kabul, Afghanistan.
Eskisehir Osmangazi University, Mining Engineering Department, Eskisehir, 26040, Turkey.
Saf Health Work. 2020 Sep;11(3):322-334. doi: 10.1016/j.shaw.2020.06.005. Epub 2020 Jun 26.
Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage.
Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O, N, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB.
The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure.
The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.
煤炭自燃是导致直接或间接瓦斯和粉尘爆炸、矿井火灾、有毒气体释放、储量损失以及矿工生命损失的因素之一。为避免这些事故，煤炭自燃预测至关重要。如果能在早期对煤炭火灾进行预测，就能确保矿区矿工的安全。
选取采用长壁开采法的全机械化阿杜拉里地下煤矿作为案例研究区域。利用2017年十个气体监测站的传感器收集的数据，对煤炭火灾进行模拟和预测。在本研究中，由于数据存在不确定性、非线性和不精确变量，因此使用模糊逻辑模型。对于煤炭火灾预测，将一氧化碳（CO）、氧气（O）、氮气（N）和温度作为输入变量，而将火灾强度视为输出变量。该模型的模拟使用Mamdani推理系统，并通过MATLAB中的模糊逻辑工具箱运行。
结果表明，模糊逻辑系统在考虑数据的不确定性和非线性的情况下，对火灾强度的预测更可靠。这也表明1409和610/2B气体监测站位置发生自燃的可能性更大，因此需要采取预防措施。
与格雷厄姆指数相比，模糊逻辑模型在同时应用多个变量预测火灾强度方面显示出更高的概率。
