SAMPLING, MODELING AND ANALYSIS OF DIESEL PARTICULATE MATTER DISTRIBUTION IN UNDERGROUND POLYMETALLIC MINES

Abstract

This thesis presents a comprehensive study on the modeling and analysis of Diesel Particulate Matter (DPM) distribution in underground polymetallic mines, integrating experimental sampling with Computational Fluid Dynamics (CFD) to enhance the accuracy of DPM dispersion models. Conducted at the Dolinnyy Mine, this research aimed to refine our understanding of the spatial and temporal distribution of particulate matter and assess its impacts on miners' health. The methodology included detailed real-time experimental sampling of air parameters such as PM1 concentrations, airflow velocity, and temperature. This data was analyzed to identify patterns and correlations, forming the basis for subsequent CFD simulations performed using ANSYS FLUENT. These simulations attempted to model the complex environmental dynamics observed within the mine. Additionally, a comprehensive risk analysis using ‘Palisade@Risk’ software assessed the variability and predictability of DPM concentrations and airflow velocity, further supporting the CFD findings. Despite successfully predicting airflow velocities close to the actual measurements (approximately 1.3 m/s), the CFD model significantly underestimated DPM concentrations. The simulated values averaged around 465 μg/m3, which was about four times lower than the observed values of approximately 1560 μg/m3. This substantial discrepancy underscores the need for further refinement of the CFD models to enhance their predictive accuracy. The findings indicate a critical need for improving both sampling strategies and modeling techniques to bolster the accuracy and reliability of DPM assessments in underground mining environments. By addressing these issues, the research supports the development of more effective ventilation and monitoring systems, ultimately aiming to improve worker safety by reducing health risks associated with prolonged exposure to particulate matter. This thesis advocates for continued advancements in monitoring, risk analysis, and modeling approaches as crucial steps towards ensuring safer mining operations.