Assessment of Fire Risk of Indian Coals Using Artificial Neural Network Techniques

Devidas S. Nimaje^1, and Debi P. Tripathy¹

¹Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha, India

Cite this paper:
Devidas S. Nimaje and Debi P. Tripathy. Assessment of Fire Risk of Indian Coals Using Artificial Neural Network Techniques. American Journal of Mining and Metallurgy. 2015; 3(2):43-53. doi: 10.12691/ajmm-3-2-2

Abstract

Spontaneous heating of coal is a major problem in the global mining industry. It has been known to pose serious problems on account of coal loss due to fires and affects not only the coal production but also creates environmental pollution over the years. It is well known that the intrinsic properties and susceptibility indices play a vital role to assess the spontaneous heating susceptibility of coal. In this paper, best correlated parameters from the intrinsic properties with the susceptibility indices were used as input to the different Artificial Neural Network (ANN) techniques viz. Multilayer Perceptron (MLP), Functional Link Artificial Neural Network (FLANN), and Radial Basis Function (RBF) to predict in advance the fire risk of Indian coals. This can help the mine management to adopt appropriate strategies and effective action plans to prevent occurrence and spread of fire. From the proposed ANN techniques, it was observed that Szb provides better fire risk prediction with RBF model vis-à-vis MLP and FLANN.

Keywords:
coal; spontaneous heating ANN MLP FLANN RBF

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