Dust dispersion law and high-pressure air curtain control technology of crossheading during the process of ore unloading

Wang, Jiuzhu; Du, Cuiwei; Du, Shaowu; Jin, Weibo; Fan, Dezhen

doi:10.1016/j.jweia.2022.105179

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…32−34 Wang utilized CFD to investigate the dispersion behavior of dust during the unloading process and the high-pressure air curtain control technology. 35 Du et al investigated the dispersion characteristics of dust during dumping in an open-pit mine using CFD. 36 Similar experiments, compared with field experiments, can obtain reliable conclusions with smaller experimental scales and lower costs.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the difficulties of conducting on-site experiments under certain specific circumstances, numerical simulation methods have been widely employed in research. , The computational fluid dynamics (CFD) method has been widely employed for simulating the dispersion of particulate pollutants. − Wang utilized CFD to investigate the dispersion behavior of dust during the unloading process and the high-pressure air curtain control technology . Du et al.…”

Section: Introductionmentioning

confidence: 99%

“… 32 − 34 Wang utilized CFD to investigate the dispersion behavior of dust during the unloading process and the high-pressure air curtain control technology. 35 Du et al. investigated the dispersion characteristics of dust during dumping in an open-pit mine using CFD.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Recirculation Flow on the Dispersion Pattern of Blasting Dust in Deep Open-Pit Mines

Chen,

Du,

Wang

et al. 2023

ACS Omega

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The recirculation within a deep open-pit mine is a significant factor contributing to the deterioration of the atmospheric environment. However, the underlying mechanisms of how recirculation influences the dispersion pattern of dust clouds within the deep open-pit mine have not been clearly elucidated. In this research, the dispersion patterns of blast dust clouds were investigated in a deep open-pit mine located in northern China. This research initially conducted a similar experiment to verify the existence of recirculation flow in the experimental mine, which can cause dust particles to aggregate toward the upwind slope. In response to the dust pollution issue in deep open-pit mine blasting operations, this study conducted a numerical simulation analysis based on on-site measurement data to investigate the effects of varying natural wind velocity, natural wind direction, and blast location on the diffusion pattern of blasting dust. The results indicate that natural wind velocity (v), natural wind direction (α), and blast location (d) affect the distance between the blast location and the recirculation center point (D recir), subsequently influencing the diffusion pattern of blasting dust. The recirculation flow effect influences the diffusion of dust toward the upwind slope under smaller D recir values, leading to widespread and long-term pollution within the mine. Under larger D recir values, dust diffuses toward the downwind slope with the straight flow of wind, resulting in less pollution within the mine. Through orthogonal experiments, the equation D recir = −120.61v 2 + 237.27v + 0.82d – 0.07α2 + 6.75α + 151.08 was established in this deep open-pit mine, which provides a basis for predicting the diffusion pattern of blasting dust and control strategy in this deep open-pit mine.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Recirculation Flow on the Dispersion Pattern of Blasting Dust in Deep Open-Pit Mines

Chen,

Du,

Wang

et al. 2023

ACS Omega

Self Cite

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“…Therefore, the research on coal mine dust prevention and control is not only conducive to preventing coal dust explosion accidents but also conducive to promoting the occupational safety and health development of coal mine employees. At present, the commonly used dust prevention technologies in coal mines mainly include coal seam dust suppression by water injection [4][5][6], ventilation dust removal [7][8][9], foam dust removal [10,11], spray dust removal [12,13], air curtain dust control [14][15][16], dust removal by a dust collector [17][18][19], etc. Among them, coal dust wettability directly affects the effects of coal seam water dust suppression and spray dust removal.…”

Section: Introductionmentioning

confidence: 99%

“…The study of Zhao et al [29] showed that with the increase in the quartz content, the wettability of coal dust first decreases and then increases. Based on the relationship between the functional group content and the contact angle, Zhen [30] found that the relationship between the content of cyclic hydroxyl groups and the size of the contact angle was the most At present, the commonly used dust prevention technologies in coal mines mainly include coal seam dust suppression by water injection [4][5][6], ventilation dust removal [7][8][9], foam dust removal [10,11], spray dust removal [12,13], air curtain dust control [14][15][16], dust removal by a dust collector [17][18][19], etc. Among them, coal dust wettability directly affects the effects of coal seam water dust suppression and spray dust removal.…”

Section: Introductionmentioning

confidence: 99%

Research on Coal Dust Wettability Identification Based on GA–BP Model

Zheng

Shi

Jiang

et al. 2022

IJERPH

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Aiming at the problems of the influencing factors of coal mine dust wettability not being clear and the identification process being complicated, this study proposed a coal mine dust wettability identification method based on a back propagation (BP) neural network optimized by a genetic algorithm (GA). Firstly, 13 parameters of the physical and chemical properties of coal dust, which affect the wettability of coal dust, were determined, and on this basis, the initial weight and threshold of the BP neural network were optimized by combining the parallelism and robustness of the genetic algorithm, etc., and an adaptive GA–BP model, which could reasonably identify the wettability of coal dust was constructed. The extreme learning machine (ELM) algorithm is a single hidden layer neural network, and the training speed is faster than traditional neural networks. The particle swarm optimization (PSO) algorithm optimizes the weight and threshold of the ELM, so PSO–ELM could also realize the identification of coal dust wettability. The results showed that by comparing the four different models, the accuracy of coal dust wettability identification was ranked as GA–BP > PSO–ELM > ELM > BP. When the maximum iteration times and population size of the PSO algorithm and the GA algorithm were the same, the running time of the different models was also different, and the time consumption was ranked as ELM < BP < PSO–ELM < GA–BP. The GA–BP model had the highest discrimination accuracy for coal mine dust wettability with an accuracy of 96.6%. This study enriched the theory and method of coal mine dust wettability identification and has important significance for the efficient prevention and control of coal mine dust as well as occupational safety and health development.

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Insulation performance of a new annular heated air curtain in cold-region tunnels: numerical modeling, effects analysis, and prediction

Sun,

Wei,

Zhou

et al. 2024

J Therm Anal Calorim

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Dust dispersion law and high-pressure air curtain control technology of crossheading during the process of ore unloading

Cited by 9 publications

References 24 publications

Influence of Recirculation Flow on the Dispersion Pattern of Blasting Dust in Deep Open-Pit Mines

Influence of Recirculation Flow on the Dispersion Pattern of Blasting Dust in Deep Open-Pit Mines

Research on Coal Dust Wettability Identification Based on GA–BP Model

Insulation performance of a new annular heated air curtain in cold-region tunnels: numerical modeling, effects analysis, and prediction

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