Evaluation of the effect of the moisture content of coal dust on the prediction of the coal dust explosion index

Abstract

Exploring the mechanism of coal dust explosions is essential for the development of safety techniques to prevent coal dust explosions. An explosion index can be used to estimate explosion severity. In this study, the moisture content parameter, one of the intrinsic characteristics of coal dust, was used to estimate the explosion index. For this purpose, 32 samples of coal with different moisture content were collected from different mines in Iran and were prepared as coal rounds. The coal dust explosion process was carried out in a 2-litre closed chamber. After determining the most important and influential parameters, prediction models of the explosion index were presented using linear regression. For this purpose, 75 percent of data was randomly assigned for training and 25 percent of data was used for testing and validation. The performance of these models was assessed through the root mean square error (RMSE), the proportion of variance accounted for (VAF), the mean absolute percentage error (MAPE) and the mean absolute error (MAE). Then the results of the laboratory method were compared with the results of the regression model. The results show that there is a good correlation between the laboratory results and the predictive model obtained through linear regression analysis.