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

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-a-vis MLP and FLANN.

[1]  Ran Vijay Kumar Singh,et al.  Spontaneous Heating and Fire in Coal Mines , 2013 .

[2]  Santanu Kumar Rath,et al.  Predicting Object-Oriented Software Maintainability using Hybrid Neural Network with Parallel Computing Concept , 2015, ISEC.

[3]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[4]  S. K. Ray,et al.  Assessment of self-heating susceptibility of Indian coal seams – a neural network approach , 2014 .

[5]  Jagdish C. Patra,et al.  Functional link artificial neural network-based adaptive channel equalization of nonlinear channels with QAM signal , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[6]  Lionel C. Briand,et al.  Resource modeling in software engineering , 2002 .

[7]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[8]  Hu Wen,et al.  Forecast of coal spontaneous combustion with artificial neural network model based on testing and monitoring gas indices , 2011 .

[9]  Lionel C. Briand,et al.  Explaining the cost of European space and military projects , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[10]  M. N. Tarafdar,et al.  Application of wet oxidation processes for the assessment of the spontaneous heating of coal , 1989 .

[11]  Martin T. Hagan,et al.  Neural network design , 1995 .

[12]  Jenlong Moh,et al.  A neural architecture applied to the enhancement of noisy binary images , 1992 .

[13]  Madan M. Gupta,et al.  Fuzzy Logic and Intelligent Systems , 1995 .

[14]  Zbigniew Ogonowski,et al.  Advanced control with MATLAB and SIMULINK , 1996 .

[15]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[16]  Santosh Kumar Nanda,et al.  A Novel Application of Artificial Neural Network for the Solution of Inverse Kinematics Controls of Robotic Manipulators , 2012 .

[17]  D. D. Banerjee,et al.  Applications of Crossing Point Temperature for Determining the Spontaneous Heating Characteristics of Coals , 1972 .

[18]  Santanu Kumar Rath,et al.  Fuzzy-class point approach for software effort estimation using various adaptive regression methods , 2013, CSI Transactions on ICT.

[19]  Mohamed Pourkashanian,et al.  Modelling coal combustion: the current position , 2002 .

[20]  Santosh Kumar Nanda,et al.  Application of Legendre Neural Network for Air Quality Prediction , 2011 .

[21]  Bijay S Singh,et al.  Spontaneous Combustibility Characterisation of the Chirimiri Coals, Koriya District, Chhatisgarh, India , 2011 .

[22]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic - basic concepts and applications , 1997 .

[23]  Santanu Kumar Rath,et al.  Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis , 2014 .

[24]  Dan Simon,et al.  Training radial basis neural networks with the extended Kalman filter , 2002, Neurocomputing.

[25]  Santosh Kumar Nanda,et al.  Development of Regression Models for Assessing Fire Risk of Some Indian Coals , 2013 .

[26]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[27]  Seref Sagiroglu,et al.  Neural Networks for the Calculation of Bandwidth of Rectangular Microstrip Antennas , 2003 .

[28]  Barbara A. Kitchenham,et al.  A Simulation Study of the Model Evaluation Criterion MMRE , 2003, IEEE Trans. Software Eng..

[29]  Hongfei Xiao,et al.  Prediction of mine coal layer spontaneous combustion danger based on genetic algorithm and BP neural networks , 2011 .

[30]  James L. Rogers,et al.  SIMULATING STRUCTURAL ANALYSIS WITH NEURAL NETWORK , 1994 .

[31]  Erkan Besdok,et al.  A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification , 2009, Sensors.

[32]  S. N. Sivanandam,et al.  Principles of soft computing , 2011 .

[33]  A. C. Smith,et al.  SPONCOM - a computer program for the prediction of the spontaneous combustion potential of an underground coal mine , 1900 .

[34]  H. H. Lowry.  Coal: its formation and composition , 1955 .