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Mining Publication: Development of Multiple Regression Functions for Performance Prediction of Gob Gas Ventholes for Sealed and Active Longwall Mines
Keywords: Computer simulation Gob gas vent holes Longwall panels Numerical modeling
This page is archived for historical purposes and is no longer being maintained or updated.
Original creation date: February 2010
Authors: HN Dougherty, C� Karacan, GV Goodman
Conference Paper - February 2010
Download PDF Document (0.13 MB)
NIOSHTIC2 Number: 20036456
2010 SME Annual Meeting and Exhibit, February 28 - March 3, Phoenix, Arizona, preprint 10-201. Littleton, CO: Society for Mining, Metallurgy, and Exploration, Inc., 2010; :1-10
In the absence of values for each influencing parameter and a known relationship of input-output space, novel approaches should be implemented as proxy solutions to mathematically complicated problems. Proxy solutions can be in the form of linear or non-linear multiple regression equations. Predicting the performance of gob gas ventholes is one of these mathematically complicated problems as it is difficult to know the exact relationships between various parameters influencing gob gas venthole (GGV) performance from active and sealed (completed) longwall panels. In this paper, a discussion of the NIOSH-developed software suite for methane control and prediction (MCP) based on an artificial neural network (ANN) is presented. The application of the ANN-based software suite to develop algebraic relationships for gob gas venthole performance prediction in sealed and active longwall panels is sought. Output information obtained from the NIOSH-MCP software as a response to various input parameters were used to create an input-output database for total flow and methane percentage from GGVs operating in active mines during completed and sealed panel situations. Due to the multi-variable nature of the software, outputs generated using the software for various inputs were used to develop analytical equations first using linear and then second using non-linear multivariable regression techniques. The advantage of this method is its ability to better understand the significant parameters of the relationships between the inputs and outputs for gob gas venthole production in active and sealed panels and to be able to predict their performances using relatively simple analytical functions.
Image of publication Development of Multiple Regression Functions for Performance Prediction of Gob Gas Ventholes for Sealed and Active Longwall MinesConference Paper - February 2010
Download PDF Document (0.13 MB)
NIOSHTIC2 Number: 20036456
2010 SME Annual Meeting and Exhibit, February 28 - March 3, Phoenix, Arizona, preprint 10-201. Littleton, CO: Society for Mining, Metallurgy, and Exploration, Inc., 2010; :1-10
See Also
Coal Mine Subsidence Prediction Using a Boundary-Element Program
Comparisons Between Cross-Measure Boreholes and Surface Gob Holes
Critical Review of Numerical Stress Analysis Tools for Deep Coal Longwall Panels Under Strong Strata
Deep Coal Longwall Panel Design for Strong Strata: The Influence of Software Choice on Results
Effects of Far-Field Shearing Deformation on Fracturing Around an Underground Opening
Elastic and Shear Moduli of Coal Measure Rocks Derived from Basic Well Logs Using Fractal Statistics and Radial Basis Functions
Evaluation of Mine Illumination Systems Using Numerical Modeling
Improvement of a Mine Fire Simulation Program � Incorporation of Smoke Rollback into MFIRE 3.0
Mine Face Ventilation: A Comparison of CFD Results Against Benchmark Experiments for the CFD Code Validation
Stochastic Modeling of Gob Gas Venthole Production Performances in Active and Completed Longwall Panels of Coal Mines
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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