Synergistic Use of Artificial Neural Network for the Detection of Underground Coal Fires

Abstract

The spontaneous heating of coal is responsible for releasing a number of combustible gases at characteristic temperatures. However, its accurate prediction is essential to plan efficient preventive measures for impending underground coal fires. For this purpose, artificial neural network can be used as a potential tool to precisely envisage the temperature on the basis of the concentration of various gases released during thermal gas analysis of coal samples. Keeping this in view, a three-layer feed forward back propagation model has been developed. The correlation among the gases released during the thermal gas analysis along with temperature has also been established by developing a dendrogram using statistical software. The results of the neural network model have been conformed to the hierarchy of the gas appearance taking place during thermal gas analysis.