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Mining Publication: Artificial Neural Networks to Determine Ventilation Emissions and Optimum Degasification Strategies for Longwall Mines
Keywords: Computer simulation Degasification Longwall mining Methane Ventilation
This page is archived for historical purposes and is no longer being maintained or updated.
Original creation date: June 2008
Authors: C� Karacan, GV Goodman
Conference Paper - June 2008
Download PDF Document (0.16 MB)
NIOSHTIC2 Number: 20034161
Proceedings of the 12th U.S./North American Mine Ventilation Symposium, Reno, Nevada, June 9-11, 2008. Wallace, Jr. KG. ed., Reno, NV: University of Nevada-Reno, 2008; :321-329
In longwall mining, premining prediction of methane emission rate depends on a number of geological factors, geographical factors, and operational factors. These same factors also can impact the selection of a specific degasification system. This study proposes a principle component analysis (PCA) and artificial neural network (ANN) approach to predict the ventilation methane emission rates of U.S. longwall mines and the optimum combination of degasification boreholes based on the given characteristics of the mine. Artificial neural networks, more than conventional computer models, are adaptable systems that can solve problems such as nonpolynomial or very complex relationships that are difficult to describe mathematically. Data on ventilation emissions and degasification system design were obtained from 63 longwall mines in 10 states and were combined with corresponding geological, geographical, and operational parameters for the period 1985-2005. PCA was used to determine the variables that most influence ventilation emissions and degasification system design. Different combinations of variables in the data set and network structures were used for network training, cross-validation, and testing. The resultant ANN models predicted ventilation methane emissions with a 90-95% accuracy and were superior to multi-linear and second order non-linear models. The ANN-based expert classification system was able to classify the design of the degasification system (no degasification, gob vent borehole, horizontal and gob vent borehole, and horizontal, vertical and gob vent borehole) with more than 80% accuracy. The models can be used as prediction and decision tools for the ventilation emissions and degasification system selection for specific site and mine-design conditions.
Image of publication Artificial Neural Networks to Determine Ventilation Emissions and Optimum Degasification Strategies for Longwall MinesConference Paper - June 2008
Download PDF Document (0.16 MB)
NIOSHTIC2 Number: 20034161
Proceedings of the 12th U.S./North American Mine Ventilation Symposium, Reno, Nevada, June 9-11, 2008. Wallace, Jr. KG. ed., Reno, NV: University of Nevada-Reno, 2008; :321-329
See Also
A CART Technique to Adjust Production from Longwall Coal Operations under Ventilation Constraints
Coal Bed Discontinuity Effects on the Production of Degasification Boreholes and on Emissions During Longwall Mining
Degasification System Selection for U.S. Longwall Mines Using an Expert Classification System
Development and Application of Reservoir Models and Artificial Neural Networks for Optimizing Ventilation Air Requirements in Development Mining of Coal Seams
Field Study of Longwall Coal Mine Ventilation and Bleeder Performance
Methane Control for Underground Coal Mines
Modeling and Prediction of Ventilation Methane Emissions of U.S. Longwall Mines Using Supervised Artificial Neural Networks
A New Methane Control and Prediction Software Suite for Longwall Mines
Technology News 448 - Improving the Performance of Longwall Gob Gas Ventholes
Underground Gob Gas Drainage During Longwall Mining
Page last reviewed: September 21, 2012
Page last updated: September 21, 2012
Content source: National Institute for Occupational Safety and Health, Mining Program
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