Study on the Prediction of Dust Migration and Distribution Characteristics in Open Pits under the Influence of Different Vehicle Speeds

Abstract Because an open-pit mine is an open operating environment, mining and stripping equipment inevitably pollutes the environment to some extent during the operation process. Therefore, the current dust concentration monitoring methods and technologies are relatively simple, and the dust distribution characteristics and diffusion laws of each production link in open-pit mines are not clear. Taking the transportation link of the Anjialing open-pit mine as the research object, a set of integrated monitoring methods combining ground and space and fixed and mobile technology is proposed. First, aiming at the complex transportation system of an open-pit mine, a physical model of dump truck movement in an open pit mine was constructed. On the basis of the principle of gas‒solid two-phase flow, a starting model of dust particles under impact and a dynamic model of dust particles under wind pressure were constructed, and the dusting, movement and diffusion mechanisms of dust particles were defined. Second, in view of the missing data and noise caused by the stability of the acquisition system and the coverage of the mining area signal, a method of dust concentration prediction based on time series and sample data correction processing of background noise was proposed. To fully restore and characterize the characteristics of the induction and influence of mining trucks on dust particles under different environmental conditions, a simulation method based on fluid mechanics, which effectively reveals the characteristics of air flow field migration and dust diffusion during the driving of mining trucks, was. Finally, on the basis of field monitoring data and numerical simulation data, the law of dust diffusion and migration of mining trucks under different speed conditions was revealed, and the characteristics of dust diffusion and migration prediction models were constructed. The relative error of the model prediction accuracy was 1.2%~10.6%. The mean square error in the z-axis direction was 13.8%, whereas in the x-axis direction, it was 52.0%. The research results provide data support and a theoretical basis for follow-up dust concentration prediction in open-pit mines and the formulation of dust control programs.