AAAR 36th Annual Conference October 16 - October 20, 2017 Raleigh Convention Center Raleigh, North Carolina, USA
Abstract View
Experimental Evaluation of Opto-Dielectrometric Sensors for Monitoring of Total Incombustible Content (TIC) in Underground Coal Mines
OMID MAHDAVIPOUR, John Sabino, Michael R. Shahan, Clara E. Seaman, Larry D. Patts, Paul Wright, Igor Paprotny, University of Illinois at Chicago
Abstract Number: 619 Working Group: Instrumentation and Methods
Abstract Finely divided coal dust produced during underground coal-mining, i.e. float dust, which deposits throughout the coal mine can be feedstock for coal dust explosions. To prevent such explosions, inert rock dust (limestone dust) is applied in underground areas of a coal mine. The ratio of total incombustible mass (rock dust + incombustible content of coal dust) divided by total mass of the deposited dust is called the Total Incombustible Content (TIC) of dust deposited within the mine. Regulations require that TIC ratios be maintained above 80% to promote safe working conditions within the mine.
This paper presents the results from the evaluation of the distributed wireless sensing module called Sensor for Automated Control of Coal Dust (SACCD), developed by the University of Illinois at Chicago and the University of California, Berkeley. This sensing module uses optical reflectance (through the application of a modified Bouguer-Beer-Lambert Law) and dielectrometric spectrometry to probe the total incombustible content (TIC) of the deposited float dust/rock dust stack. The SACCD sensors were extensively evaluated in the laboratory, using both a float dust deposition setup at UIC, as well as in the float dust deposition gallery at the National Institute for Occupational Safe and Health (NIOSH) laboratory facilities in Pittsburgh, PA. Both sets of experiments confirmed the ability of our sensor to discern the TIC content to within 20%. Improvements to the opto-dielectrometric sensing setup, based on the obtained experimental results, are discussed, which will further lower the detection limit. Inverse solution to a computational fluid dynamic model (CFD) of the airflow through the mine is discussed, which will potentially allow the SACCD sensors to be used for predicting TIC content throughout the entire mine using a relatively sparse sensor data.