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Mining Publication: Modeling and Prediction of Ventilation Methane Emissions of U.S. Longwall Mines Using Supervised Artificial Neural Networks
Keywords: Computer simulation Gas emissions Longwall mining Methane Methane control 
 Ventilation
Original creation date: February 2008
Authors: C� Karacan
Peer Reviewed Journal Article - February 2008
Download PDF Document (0.82 MB)
NIOSHTIC2 Number: 20033212
Int J Coal Geol 2008 Feb; 73(3-4):371-387
Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production. Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principal component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines. Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.
Image of publication Modeling and Prediction of Ventilation Methane Emissions of U.S. Longwall Mines Using Supervised Artificial Neural NetworksPeer Reviewed Journal Article - February 2008
Download PDF Document (0.82 MB)
NIOSHTIC2 Number: 20033212
Int J Coal Geol 2008 Feb; 73(3-4):371-387
See Also
Artificial Neural Networks to Determine Ventilation Emissions and Optimum Degasification Strategies for Longwall Mines
Development and Application of Reservoir Models and Artificial Neural Networks for Optimizing Ventilation Air Requirements in Development Mining of Coal Seams
Evaluation of the Relative Importance of Coalbed Reservoir Parameters for Prediction of Methane Inflow Rates During Mining of Longwall Development Entries
Investigation of Methane Occurrence and Outbursts in the Cote Blanche Domal Salt Mine, Louisiana
Methane Content of Gulf Coast Domal Rock Salt
Methane Drainage and Migration
A New Methane Control and Prediction Software Suite for Longwall Mines
Probabilistic Modeling Using Bivariate Normal Distributions for Identification of Flow and Displacement Intervals in Longwall Overburden
Reservoir Modeling-Based Prediction and Optimization of Ventilation Requirements During Development Mining in Underground Coal Mines
Reservoir Rock Properties of Coal Measure Strata of the Lower Monongahela Group, Greene County (Southwestern Pennsylvania), from Methane Control and Production Perspectives
Technology News 465 - Method for Predicting Methane Emissions on Extended Longwall Faces
Page last reviewed: September 20, 2012
Page last updated: September 20, 2012
Content source: National Institute for Occupational Safety and Health, Mining Program
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