Monitoring and Control of Coal Dust Exposure Using Low-Cost PM Sensors and Computational Fluid Dynamics

<p>"Overexposure to respirable coal mine dust has been linked to causing serious health problems including coal workers pneumoconiosis (CWP) and chronic massive fibrosis. The prevalence of these health problems has been on the increase since the 2000s due to increased exposure levels with roof bolter operators having the highest exposure levels. The currently used PDM3700 is too expensive, heavy and bulky which limits their use only for regulatory monitoring failing to measure miners’ personal exposure levels. Also, since roof bolter operators are more prone to elevated coal dust levels, the canopy air curtain (CAC) was developed to protect then high coal dust concentrations. However, the current generation CAC only provides a 46% coal dust reduction efficiency leaving room for improvement. Insufficient coal dust monitors in mines make it challenging to effectively evaluate the performance of these CACs.</p> <p>The objectives of this research are therefore, to develop a small, lightweight, low-cost coal dust monitor using a low-cost PM sensor for personal coal dust monitoring in underground coal mines; to develop statistical and machine learning calibration models for low-cost PM sensors to accurately measure coal dust concentrations; and to optimize the design of the CAC using computational fluid dynamics for improved coal dust protection. This study has led to the development and calibration of a low-cost coal dust monitor which will potentially reduce monitoring cost by ~$15,000 per unit while achieving 95% the accuracy of the PDM3700. The CAC design was optimized using CFD simulations improving the uniformity of the CAC. Further, the low-cost sensors present an opportunity to effectively measure the dust control efficiency of the CACs"-- Abstract, p. iv</p>

Name thesis:degree_name

Ph. D. in Mining Engineering

Grantor

Missouri University of Science and Technology

Author dc:creator

• Amoah, Nana Kobina Amoako

• Air quality
• coal dust
• computational fluid dynamics
• dust
• low-cost pm sensors
• underground mine
• Engineering
• Mining Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/3295

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-4300