Mining Publication: Forecasting Gob Gas Venthole Production Performances Using Intelligent Computing Methods for Optimum Methane Control in Longwall Coal Mines
Original creation date: September 2009
Gob gas ventholes (GGV) are used to control methane inflows into a longwall operation by capturing it within the overlying fractured strata before it enters the work environment. Thus, it is important to understand the effects of various factors, such as drilling parameters, location of borehole, applied vacuum by exhausters and mining/panel parameters in order to be able to evaluate the performance of GGVs and to predict their effectiveness in controlling methane emissions. However, a practical model for this purpose currently does not exist. In this paper, the total gas flow rates and methane percentages were analyzed from 10 GGVs located on three adjacent panels operated in Pittsburgh coalbed in Southwestern Pennsylvania section of Northern Appalachian basin. The ventholes were drilled from different surface elevations and were located at varying distances from the start-up ends of the panels and from the tailgate entries. Exhauster pressures, casing diameters, location of longwall face, and mining rates and production data were also recorded. These data were incorporated into a multilayer-perceptron (MLP) type artificial neural network (ANN) to model venthole production. The results showed that the two-hidden layer model predicted total production and the methane content of the GGVs with more than 90% accuracy. The ANN model was further used to conduct sensitivity analyses about the mean of the input variables to determine the effect of each input variable on the predicted production performance of GGVs.
Authors: CĂ Karacan
Peer Reviewed Journal Article - September 2009
NIOSHTIC2 Number: 20035774
Int J Coal Geol 2009 Sep; 79(4):131-144
See Also
- A CART Technique to Adjust Production from Longwall Coal Operations under Ventilation Constraints
- Comparisons Between Cross-Measure Boreholes and Surface Gob Holes
- 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
- MCP - Methane Control and Prediction - 2.0
- Methane Drainage and Migration
- Methane Emission Rate Studies in a Central Pennsylvania Mine
- 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
- Prediction of Longwall Methane Emissions and the Associated Consequences of Increasing Longwall Face Lengths: A Case Study in the Pittsburgh Coalbed
- Stochastic Modeling of Gob Gas Venthole Production Performances in Active and Completed Longwall Panels of Coal Mines
- Content source: National Institute for Occupational Safety and Health, Mining Program