Study on air flow control parameters of long-pressure and short-extraction ventilation air outlets in heading face
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摘要: 掘进工作面长压短抽通风方式下出风口风流不能根据掘进过程的实际需求进行动态调控,进而造成风流分布不合理,粉尘聚集严重。现有针对掘进工作面长压短抽通风出风口风流分布及降尘效果的研究都只是单一地分析了风筒出风口参数变化对掘进工作面风流分布及降尘效果的影响,未考虑各参数之间对粉尘场运移分布的交互影响,且对在不同掘进阶段,出风口参数如何综合变化才能达到最佳的通风降尘效果的研究不深入。针对上述问题,以陕西榆林神木柠条塔矿S1204掘进工作面为研究对象,建立了出风口参数可以变化的风流调控有限元模型,模拟分析了风筒出风口参数变化对风流及粉尘浓度运移分布的影响,通过数值分析选取了风筒出风口口径、水平右偏角度和垂直上偏角度作为出风口风流动态调控参数,提取了不同风流调控参数调控后司机位置处及回风侧行人位置处的风速及粉尘浓度数据。通过小生境遗传算法,以司机位置处及回风侧行人位置处的粉尘浓度同时最低为优化目标对提取的风流调控数据进行挖掘分析,获取了S1204掘进工作面出风口距掘进端面最近距离5 m和最远距离10 m时的最佳风流调控参数:在5 m处,出风口口径为1.1~1.2 m,水平右偏角度为10~15°,垂直上偏角度为3~6°;在10 m处,出风口口径为0.8~0.9 m,水平右偏角度为0~5°,垂直上偏角度为0~3°。搭建了1∶5相似模拟的S1204掘进工作面风流智能调控实验测试平台,对最佳风流调控参数进行了测试分析,结果表明:司机位置处的粉尘浓度最高降低了52.3%,回风侧行人位置处的粉尘浓度最高降低了60.6%,验证了最佳风流调控参数的准确性。Abstract: The air flow at the air outlet cannot be dynamically adjusted according to the actual needs of the tunneling process under the long-pressure and short-extraction ventilation mode of the heading face, resulting in unreasonable air flow distribution and serious dust accumulation. The existing studies on the air flow distribution and dust reduction effect of long-pressure and short-extraction ventilation outlet at the heading face are only single analyses of the effect of the change of the air duct air outlet parameters on the air flow distribution and dust reduction effect in the heading face, without considering the interactive effect of each parameter on the dust field migration distribution. Moreover, there is no in-depth research on how the parameters of the air outlet can be changed comprehensively to achieve the optimal ventilation and dust reduction effect in different tunneling stages. In order to solve the above problems, taking the S1204 heading face of Shenmu Ningtiaota Mine in Yulin, Shaanxi Province as the research object, a finite element model of air flow control with variable air outlet parameters is established. The effect of the change of air duct air outlet parameters on the air flow and dust concentration migration distribution is simulated and analyzed. Through numerical analysis, the air outlet diameter, horizontal right deviation angle and vertical up deviation angle of the air duct are selected as the air flow dynamic control parameters of the air outlet, and the air speed and dust concentration data of the driver position and the pedestrian position on the return air side after the adjustment of different air flow control parameters are extracted. Through the niche genetic algorithm, the extracted air flow control data are mined and analyzed with the lowest dust concentration at the driver position and the pedestrian position on the return air side as the optimization object. And the optimal air flow control parameters are obtained for the closest distance of 5 m and the farthest distance of 10 m between the air outlet of S1204 heading face and the driving end face. At 5 m, the air outlet diameter is 1.1-1.2 m, the horizontal right deviation angle is 10-15°, and the vertical up deviation angle is 3-6°. At 10 m, the air outlet diameter is 0.8-0.9 m, the horizontal right deviation angle is 0-5° and vertical up deviation angle is 0-3°. A 1∶5 similar simulation test platform for intelligent control of air flow of the heading face of S1204 is built, and the optimal air flow control parameters are tested and analyzed. The results show that the dust concentration at the driver position is reduced by up to 52.3%, and the dust concentration at pedestrian position on the return air side is reduced by up to 60.6%, which verifies the accuracy of the optimal air flow control parameters.
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