Lightnin静态混合器内气液两相混合与传质强化特性

doi:10.16085/j.issn.1000-6613.2023-0183

摘要/Abstract

摘要：

采用计算流体力学（CFD）耦合群体平衡模型（PBM）研究Lightnin静态混合器（LSM）内气泡分散特性，使用不同的破碎核和聚并核函数系统研究雷诺数（Re)、气相体积分数（α_d）和元件数量对气泡分散行为以及混合效率的影响，采用气液界面积和体积传质系数（k_La）量化LSM和Kenics静态混合器（KSM）内气泡破碎性能以及传质速率，基于变异系数（CoV）和流体微元拉伸率分析LSM和KSM的分布混合性能和分散混合性能。结果表明：Luo聚并模型和Prince模型高估了LSM内气泡的聚并效率，通过Luo-Turbulent模型计算的气泡尺寸与实验数值具有很好的一致性；随着Re和α_d的增大，LSM内气泡的分散行为被进一步强化，在高Re的条件下增大元件数量可以显著提高气液传质效率；CoV曲线表明，LSM具有比KSM更好的分布混合性能，3个LSM元件可以保证混合程度大于95%。LSM的分散混合效率是KSM的1.06~1.16倍；基于压力波动信号时间序列，确定了LSM中流型从过渡区向非均匀区转变的临界表观速度。

关键词: 静态混合器, 气液两相流, 种群平衡公式, 计算流体力学, 混合

Abstract:

The bubble dispersion characteristics were investigated by computational fluid dynamics (CFD) coupled with population balance model (PBM) in the Lightnin static mixer (LSM). Different breakup and coalescence kernels were used to study the effect of Reynolds number (Re), gas volume fraction (α_d) and the element numbers on the bubble dispersion behavior and mixing efficiency. Bubble breakup capacity and mass transfer rate were quantized via gas-liquid interfacial area and volumetric mass transfer coefficient (k_La) in LSM and Kenics static mixer (KSM). The distributive mixing performance and dispersive mixing performance of LSM and KSM were analyzed based on CoV and fluid microelement stretching rate. The results showed that Luo coalescence model and Prince model overestimated the bubbles coalescence efficiency in LSM, and the bubble size calculated by the Luo-Turbulent model was in good agreement with the experimental data. With the increase of Re and α_d, the dispersion behavior of bubbles in LSM was further strengthened. Increasing the number of elements could significantly improve the gas-liquid mass transfer efficiency at higher Re condition. CoV curve showed that LSM had better distributive mixing performance than KSM, and three LSM elements could make the mixedness more than 95%. The dispersive mixing efficiency of LSM was 1.06 to 1.16 times that of KSM. The critical superficial velocities of flow pattern transition from pseudo-homogeneous to heterogeneous were identified based on pressure fluctuation signal time series in the LSM.

Key words: static mixer, gas-liquid flow, population balance equation, computational fluid dynamics (CFD), mixing

中图分类号:

TQ051.7

禹言芳, 李毓, 孟辉波, 刘桓辰. Lightnin静态混合器内气液两相混合与传质强化特性[J]. 化工进展, 2023, 42(12): 6180-6190.

YU Yanfang, LI Yu, MENG Huibo, LIU Huanchen. Enhancement of gas-liquid flow mixing and mass transfer in Lightnin static mixer[J]. Chemical Industry and Engineering Progress, 2023, 42(12): 6180-6190.

图/表 12

图1 LSM的模型结构

图2 LSM内气液两相流实验装置1—旋风分离器；2—水槽；3—Wilo多级离心泵；4—不锈钢转子流量计；5—LED光源；6—散光板；7—压力变送器；8—Dewetron-3021；9—高速相机；10—计算机；11—玻璃转子流量计；12—无油空气压缩机；13—阀门

图3 网格无关性测试

图4 不同聚并破碎模型预测的d32

表1 不同模型计算结果与实验值对比误差

模型	索特平均直径/mm	相对偏差/%
Luo-Turbulent	2.808	4.23
Luo-Liao	2.388	4.91
Luo-Prince	3.552	21.14
Luo-Luo	7.321	153.89
Lehr-Turbulent	1.492	48.97
Lehr-Liao	1.149	60.65
Lehr-Prince	2.132	27.05
Lehr-Luo	6.161	110.96

图5 数值模型有效性验证

图6 不同条件下的气液相界面积变化

图7 不同条件下的体积传质系数变化

图8 Re=8000条件下CoV和气液两相拉伸率的轴向变化

图9 不同雷诺数条件下G值与元件数量的关系

图10 不同UL和UG条件的压降曲线

图11 不同液相表观流速和气相表观流速条件下入口和出口压力标准偏差

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