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DYNAMICS OF PARTICLE LOADING IN DEEP-BED FILTER. TRANSPORT, DEPOSITION AND REENTRAINMENT

机译:深层过滤器中颗粒载荷的动力学。运输,存放和再入库

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摘要

Deep bed filtration is an effective method of submicron and micron particle removal from the fluid stream. There is an extensive body of literature regarding particle deposition in filters, often using the classical continuum approach. However, the approach is not convenient for studying the influence of particle deposition on filter performance (filtration efficiency, pressure drop) when nonsteady state boundary conditions have to be introduced. For the purposes of this work the lattice- Boltzmann model describes fluid dynamics, while the solid particle motion is modeled by the Brownian dynamics. For aggregates the effect of their structure on displacement is taken into account. The possibility of particles rebound from the surface of collector or reentrainment of deposits to fluid stream is calculated by energy balanced oscillatory model derived from adhesion theory. The results show the evolution of filtration efficiency and pressure drop of filters with different internal structure described by the size of pores. The size of resuspended aggregates and volume distribution of deposits in filter were also analyzed. The model enables prediction of dynamic filter behavior. It can be a very useful tool for designing filter structures which optimize maximum lifetime with the acceptable values of filtration efficiency and pressure drop.
机译:深床过滤是从流体流中除去亚微米和微米级颗粒的有效方法。关于过滤器中颗粒沉积的文献很多,通常使用经典的连续谱方法。但是,当必须引入非稳态边界条件时,该方法不适用于研究颗粒沉积对过滤器性能(过滤效率,压降)的影响。出于这项工作的目的,格子-玻尔兹曼模型描述了流体动力学,而固体粒子运动则通过布朗动力学建模。对于骨料,要考虑其结构对位移的影响。粒子从收集器表面反弹或沉积物重新夹带到流体流中的可能性,是通过由粘附理论得出的能量平衡振荡模型计算得出的。结果表明,不同的内部结构的过滤器的过滤效率和压降的变化由孔的大小描述。还分析了重悬聚集体的大小和过滤器中沉积物的体积分布。该模型可以预测动态过滤器行为。它对于设计过滤器结构是一个非常有用的工具,它可以通过可接受的过滤效率和压降值来优化最大使用寿命。

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