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Visual and algorithm-based spray dust reduction platform: Development and experimental research
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AI
DOI：10.1016/j.psep.2025.107729.png)
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Abstract
En 中文
Coal mine dust endangers safety production and occupational health. The existing research lacks a comprehensive experimental platform and is mostly limited to numerical simulation or simple experimental devices. This study innovatively integrates three-axis controllable spray, dual-camera visual processing and self-cleaning cycle purification system, and develops a mine intelligent spray dust reduction experimental platform that supports algorithm development and multi-parameter coupling analysis. For the first time, machine vision was used to study the nozzle performance under 1～3 MPa pressure. It was found that the atomization angle was restricted by structural parameters. The effective distance and water consumption were positively correlated with water pressure and increased with the increase of pore size. The angle of the rotating spray device can significantly improve the dust removal efficiency. It is found that the entrainment effect of roadway wind speed on dust decreases with the decrease of particle size. The platform adaptive system dynamically adjusts the parameters according to the dust concentration, which is suitable for low dust concentration places. The self-cleaning system can filter and purify experimental waste gas and sewage, reduce energy consumption and reduce secondary pollution. This study has important value for the engineering application of coal mine dust control technology.
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