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Abstract

Explosions of coal dust are a major safety concern within the coal mining industry. The explosion and subsequent fires caused by coal dust can result in significant property damage, loss of life in underground coal mines and damage to coal processing facilities. The United States Bureau of Mines conducted research on coal dust explosions until 1996 when it was dissolved. In the following years, the American Society for Testing and Materials (ASTM) developed a test standard, ASTM E1226, to provide a standard test method characterizing the “explosibility” of particulate solids of combustible materials suspended in air. The research presented herein investigates the explosive characteristic of Pulverized Pittsburgh Coal dust using the ASTM E1226-12 test standard. The explosibility characteristics include: maximum explosion pressure, (Pmax); maximum rate of pressure rise, (dP/dt)max; and explosibility index, (Kst). Nine Pulverized Pittsburgh Coal dust concentrations, ranging from 30 to 1,500 g/m3, were tested in a 20-Liter Siwek Sphere. The newly recorded dust explosibility characteristics are then compared to explosibility characteristics published by the Bureau of Mines in their 20 liter vessel and procedure predating ASTM E1126-12. The information presented in this paper will allow for structures and devices to be built to protect people from the effects of coal dust explosions.
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Abstract

To investigate the effect of different proximate index on minimum ignition temperature(MIT) of coal dust cloud, 30 types of coal specimens with different characteristics were chosen. A two-furnace automatic coal proximate analyzer was employed to determine the indexes for moisture content, ash content, volatile matter, fixed carbon and MIT of different types of coal specimens. As the calculated results showed that these indexes exhibited high correlation, a principal component analysis (PCA) was adopted to extract principal components for multiple factors affecting MIT of coal dust, and then, the effect of the indexes for each type of coal on MIT of coal dust was analyzed. Based on experimental data, support vector machine (SVM) regression model was constructed to predicate the MIT of coal dust, having a predicating error below 10%. This method can be applied in the predication of the MIT for coal dust, which is beneficial to the assessment of the risk induced by coal dust explosion (CDE).
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