Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link


Centers for Disease Control and Prevention. CDC twenty four seven. Saving Lives, Protecting People
Search
            ×
                        Mining
                        Mining
                            NIOSH
                            All CDC
                    Submit
                For a full list of topics:A-Z Index
                Advanced Search
            Advanced Search


The National Institute for Occupational Safety and Health (NIOSH)





Mining Publication: Stochastic Modeling of Gob Gas Venthole Production Performances in Active and Completed Longwall Panels of Coal Mines
Keywords: Coal mining Gob gas vent holes Longwall panels Methane Numerical modeling
Original creation date: November 2010
Authors: C� Karacan, KD Luxbacher
Peer Reviewed Journal Article - November 2010
Download PDF Document (0.60 MB)
NIOSHTIC2 Number: 20037818
Int J Coal Geol 2010 Nov; 84(2):125-140
Gob gas ventholes (GGVs) are an integral part of longwall coal mining operations, enhancing safety by controlling methane in underground workings. As in many disciplines in earth sciences, uncertainties due to the heterogeneity of geologic formations exist. These uncertainties, and the wide range of mining and venthole operation parameters, lead to performance variability in GGVs. Random variations in parameters affecting GGV performance and influencing parameters that cannot be quantified sufficiently due to lack of information limit deterministic GGV models and even introduce error in severe cases. Therefore, evaluation of GGV performance data and the uncertainty in input parameters is valuable for understanding the variability in GGV production and for designing them accordingly. This paper describes a practical approach for implementing stochastic determination of GGV production performances and for generalizing the prediction capability of deterministic models. Deterministic sitespecific models were derived by using the GGV module in the recently developed MCP (Methane Control and Prediction) software suite. These models were generated using multi-parameter regression techniques and were then improved by inclusion of extra input parameters that eliminated the site dependency and improved the predictions. Statistical distributions of input parameters in these models were quantified and tested with the Kolmogorov-Smirnov goodness-of-fit technique. Next, Monte Carlo simulations were performed using these distributions and generalized results for GGV performances were generated. The results of this work indicate that this approach is a promising method of representing the variability in GGV performances and to improve the limited and site-specific character of the deterministic models.
Image of publication Stochastic Modeling of Gob Gas Venthole Production Performances in Active and Completed Longwall Panels of Coal MinesPeer Reviewed Journal Article - November 2010
Download PDF Document (0.60 MB)
NIOSHTIC2 Number: 20037818
Int J Coal Geol 2010 Nov; 84(2):125-140
See Also
Comparisons Between Cross-Measure Boreholes and Surface Gob Holes
Critical Review of Numerical Stress Analysis Tools for Deep Coal Longwall Panels Under Strong Strata
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
Development of Multiple Regression Functions for Performance Prediction of Gob Gas Ventholes for Sealed and Active Longwall Mines
Elastic and Shear Moduli of Coal Measure Rocks Derived from Basic Well Logs Using Fractal Statistics and Radial Basis Functions
Forecasting Gob Gas Venthole Production Performances Using Intelligent Computing Methods for Optimum Methane Control in Longwall Coal Mines
Predicting Methane Emissions from Longer Longwall Faces by Analysis of Emission Contributors
Remote Methane Sensors
Reservoir Rock Properties of Coal Measure Strata of the Lower Monongahela Group, Greene County (Southwestern Pennsylvania), from Methane Control and Production Perspectives
Page last reviewed: September 20, 2012
Page last updated: September 20, 2012
Content source: National Institute for Occupational Safety and Health, Mining Program
Facebook
        Twitter
        LinkedIn
        Syndicate Mining
        Site BrowserSafety and Health TopicsData & StatisticsMethodologyMine DisastersMSHA Data FilesMine & Mineworker ChartsTools & PublicationsTools You Can UsePublicationsNews & ArticlesResearch ProgramNORA Mining CouncilStrategic PlanMining Program, 2019-2023Miner Health Program, 2020-2030ProjectsContractsFunding OpportunitiesMining LinksAbout UsContact NIOSH MiningEmploymentFacilitiesHistoryVisitor Information NIOSH Homepage
 NIOSH A-Z
Workplace Safety & Health Topics
Publications and Products
Programs


About CDC
					Contact Us
					800-232-4636
 Facebook Twitter Instagram LinkedIn Youtube Pinterest Snapchat
 CONTACT CDC
				Contact Us
							Call 800-232-4636
							Email Us
			ABOUT CDC
				About CDC
							Jobs
							Funding
			POLICIES
				Accessibility
							External Links
							Privacy
							Policies
							No Fear Act
							FOIA
							OIG
							Nondiscrimination
							Vulnerability Disclosure Policy
			CONNECT WITH US
				Facebook
								Twitter
								Instagram
								LinkedIn
							Youtube
								Pinterest
								Snapchat
								Email
			LANGUAGES
				Español
							繁體中文
							Tiếng Việt
							한국어
							Tagalog
							Русский
							العربية
							Kreyòl Ayisyen
							Français
							Polski
							Português
							Italiano
							Deutsch
							日本語
							فارسی
							English


U.S. Department of Health & Human Services
			Accessibility
			External Links
			Privacy
			Policies
			No Fear Act
			FOIA
			Nondiscrimination
			OIG
			Vulnerability Disclosure Policy
			USA.gov


Exit Notification / Disclaimer Policy Close
 Links with this icon  indicate that you are leaving the CDC website. The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
				Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
				You will be subject to the destination website's privacy policy when you follow the link.
				CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website. Website Disclaimers
 Cancel Continue