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ZHAO Yanjun, FENG Guoqi, GAO Chengbin. Research on Dust Particle Size Measurement Based on Monte Carlo Algorithm[J]. Safety in Coal Mines, 2016, 47(11): 184-186.
Citation: ZHAO Yanjun, FENG Guoqi, GAO Chengbin. Research on Dust Particle Size Measurement Based on Monte Carlo Algorithm[J]. Safety in Coal Mines, 2016, 47(11): 184-186.

Research on Dust Particle Size Measurement Based on Monte Carlo Algorithm

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  • Published Date: November 19, 2016
  • Large amount of measured data will produce during the measurement process of light scattering particle size distribution, which could lead error results in terms of coal mine dust measurement. In order to improve the systematic stability and accuracy of coal dust distribution measurement, we try to track and analyze part of the measurement data through Monte Carlo method and distribute weight to establish Monte Carlo data soft measurement model and calculate some key parameters. The results show that the Monte Carlo algorithm can provide a stable and accurate data analysis method in the measurement system of light scattering particle size distribution, which can also provide an effective and reliable method for industrial application.
  • [1]
    郭红光,王飞,王凯.矿井下粉尘治理PM2.5标准初探[J].煤矿安全,2015,46(4):174-177.
    [2]
    黄成玉,赵立永,张全柱,等.煤矿粉尘浓度传感器及检测系统的研究[J].煤矿安全,2011,42(5):24-27.
    [3]
    王乃宁.颗粒粒径的光学测量技术及应用[M].北京:原子能出版社,2000.
    [4]
    蔡小舒,苏明旭,沈建琪,等.颗粒粒径测量技术及应用[M].北京:化学工业出版社,2010.
    [5]
    余慧.蒙特卡罗计算中颗粒型燃料的随机分布模型比较[J].强激光与粒子束,2013(1):143-146.
    [6]
    赵国艳,常海萍,金峰.基于蒙特卡洛方法的辐射光谱的计算[J].工程热物理学报,2009(3):471-474.
    [7]
    J. Ramella-Roman, S. Prahl and S.Jacques.Three Monte Carlo programs of polarized light transport into scattering media:partⅡ[J]. Optics express, 2005,13(25): 10392-10405.
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