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- Hazard prevention and control:
- Ventilation
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Results 1 - 10 of 83
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7/24/2020 - PC softwareTry the newly rewritten version of the MFIRE software program to model fire and contaminant spread in underground mines.
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5/1/2012 - Reports of InvestigationsA new test method has been developed by the National Institute for Occupational Safety and Health (NIOSH) and Clean Air Filter (CAF) for quantifying the outside air leakage into environmental cab filtration systems.
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1/1/2012 - Peer reviewed journal articlesTo assess the effectiveness of commonly applied ventilation strategies for improving air distribution and ventilation controls to meet statutory requirements.
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10/1/2011 - Peer reviewed journal articlesThis study evaluated CO and CO2 emissions from spontaneous heating of a U.S. coal sample in an isothermal oven under different airflow ventilation rates from 100 to 500 cm3/min. Experiments were conducted at oven temperatures of 70, 90, and 100 °C.
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1/1/2011 - Peer reviewed journal articlesStudy results of the role of various ventilation rates in the spontaneous heating of coal in underground mines found that there is an optimum ventilation flow to produce the maximum rate of temperature rise at the critical ambient temperature.
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6/1/2010 - Conference papersIn this NIOSH study, a computational fluid dynamics (CFD) model developed in previous work is used to model the effect of seal leakage on spontaneous heating of coal in longwall gob behind the seal.
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8/1/2008 - Peer reviewed journal articlesTo provide insights for the optimization of bleederless ventilation systems for U.S. underground coal mines, a computational fluid dynamics study was conducted to model spontaneous heating in longwall gob areas using a bleederless ventilation system.
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6/1/2008 - Conference papersThe results of field tests to understand how ventilation and gob characteristics affect the spontaneous heating process causing spontaneous combustion fires in longwall or worked-out gob areas are presented.
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2/1/2008 - Peer reviewed journal articlesThis 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.
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11/1/2007 - Peer reviewed journal articlesUsing coalbed methane reservoir modeling and an artificial neural network (ANN) design for prediction and optimization of methane inflows and ventilation air requirements to maintain methane concentrations below statutory limits.
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Content source: National Institute for Occupational Safety and Health, Mining Program