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聂玲, 马卫国, 阳婷. 真空筛分多孔介质床流动阻力系数预测及影响因素分析[J]. 真空科学与技术学报. DOI: 10.13922/j.cnki.cjvst.202404008
引用本文: 聂玲, 马卫国, 阳婷. 真空筛分多孔介质床流动阻力系数预测及影响因素分析[J]. 真空科学与技术学报. DOI: 10.13922/j.cnki.cjvst.202404008
NIE Ling, MA Weiguo, YANG Ting. Prediction of Flow Resistance Coefficient and Analysis of Influencing Factors during Vacuum Screening of Porous Media Beds[J]. CHINESE JOURNAL OF VACUUM SCIENCE AND TECHNOLOGY. DOI: 10.13922/j.cnki.cjvst.202404008
Citation: NIE Ling, MA Weiguo, YANG Ting. Prediction of Flow Resistance Coefficient and Analysis of Influencing Factors during Vacuum Screening of Porous Media Beds[J]. CHINESE JOURNAL OF VACUUM SCIENCE AND TECHNOLOGY. DOI: 10.13922/j.cnki.cjvst.202404008

真空筛分多孔介质床流动阻力系数预测及影响因素分析

Prediction of Flow Resistance Coefficient and Analysis of Influencing Factors during Vacuum Screening of Porous Media Beds

  • 摘要: 探究真空筛分流体流动特性对于揭示真空筛分机理至关重要。然而,真空筛分过程中筛网截留的固体颗粒形成的过滤床难以采用微观结构描述,通常假设为多孔介质床。为了精确描述多孔介质床的流体流动特性,深入研究真空筛分性能,本研究基于实验与机器学习方法预测真空筛分多孔介质床流动阻力系数,分析了BP神经网络,随机森林和XGBoost模型特征重要性排序结果,识别出影响多孔介质床流动阻力系数因素的敏感性顺序为气流速度>颗粒床>颗粒配比>颗粒层厚度>筛网目数。最后,采用遗传算法GA,以XGBoost预测模型作为多目标优化的适应度函数,建立真空筛分多孔介质床多目标优化模型,得到最优真空筛分工艺参数和流动阻力系数。本研究为真空筛分流动阻力系数的研究提供了一种新方法,研究结果对揭示真空筛分机理具有重要意义。

     

    Abstract: Investigating fluid flow characteristics during vacuum screening is crucial for revealing its underlying mechanisms. However, the filter bed formed by solid particles retained by the screen during vacuum screening is difficult to describe using microstructures and is often assumed to be a porous media bed. To accurately describe the fluid flow characteristics of the porous media bed and further investigate the performance during vacuum screening, this study predicted the flow resistance coefficient of the porous media bed based on experimental and machine learning methods. The study analyzed the feature importance ranking results of the BP neural network, random forest, and XGBoost models, identifying the sensitivity order of factors affecting the flow resistance coefficient of the porous medium bed as air flow rate > particle bed > particle ratio > particle layer thickness > screen mesh. Ultimately, utilizing the genetic algorithm (GA) with the XGBoost prediction model as the fitness function for multi-objective optimization, a multi-objective optimization model was established, yielding the optimal vacuum screening process parameters and flow resistance coefficient. This study provides a new method for the study of the vacuum screening resistance coefficient, and the results are of great significance for analyzing the vacuum screening mechanism.

     

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