Development of Dust Emission Prediction Model for Open-Pit Mines Based on SHPB Experiment and Image Recognition Method

Autor: Shanzhou Du, Hao Chen, Xiaohua Ding, Zhouquan Liao, Xiang Lu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Atmosphere, Vol 15, Iss 9, p 1118 (2024)
Druh dokumentu: article
ISSN: 2073-4433
DOI: 10.3390/atmos15091118
Popis: Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations.
Databáze: Directory of Open Access Journals