Fluidization research on the FCC-assisted nanoparticle hybrid system based on the multicomponent DQMOM model

doi:10.11949/0438-1157.20241322

Abstract

Abstract:

According to the influencing factors of nanoparticle agglomeration and breakage, the calculation method of Re was modified based on the Gidaspow drag model, and a multi-component DQMOM model was proposed. The flow process of nanoparticles in a micro fluidized bed, as well as the changes in the number and diameter of agglomerates, are numerically simulated. The experimental values are in good agreement with the simulation results, verifying the accuracy and reliability of the micro fluidized bed model. The mixed nanoparticles of SiO₂ and ZnO are simulated to explore the effect of adding large particles to improve fluidization. The results show that a higher content of particles with better fluidization performance can enhance the fluidization performance of the mixed particles. When the content of SiO₂ particles is relatively high, smaller-sized particles have a better effect on improving the fluidization performance; when the content of ZnO particles is relatively high, particles with larger density have a better effect. When the content of particles with good fluidization performance is relatively high, adding a small amount of large particles can effectively improve the fluidization performance of the mixed particles.

Key words: DQMOM method, coalescence and fragmentation, nanoparticles, multi-component, micro fluidized bed

摘要：

根据纳米颗粒团聚和破碎的影响因素，基于Gidaspow曳力模型，修正了Re的计算方法，提出多组分DQMOM模型。数值模拟微型流化床内纳米颗粒流动过程及聚团数量与聚团直径的变化，实验值与模拟结果吻合较好，验证了微型流化床模型的准确性和可靠性。对SiO₂和ZnO混合纳米颗粒进行模拟，探究添加大颗粒改善流化效果。结果表明，混合颗粒中流化性能较好的颗粒占比较高时，整体的流化性能会有所提升。当SiO₂颗粒含量较高时，小粒径颗粒改善流化性能的效果更好；当ZnO颗粒含量较高时，大密度颗粒效果更好。当流化性能好的颗粒含量较高时，添加少量大颗粒就可以有效改善混合颗粒的流化性能。

关键词: DQMOM方法, 聚合与破碎, 纳米颗粒, 多组分, 微型流化床

CLC Number:

TK 229

Juhui CHEN, Ke CHEN, Dan LI, Tianyi YANG, Michael ZHURAVKOV, Siarhel LAPATSIN, Wenrui JIANG. Fluidization research on the FCC-assisted nanoparticle hybrid system based on the multicomponent DQMOM model[J]. CIESC Journal, 2025, 76(6): 2616-2625.

陈巨辉, 陈轲, 李丹, 杨天一, ZHURAVKOV Michael, LAPATSIN Siarhel, 姜文锐. 基于多组分DQMOM模型的FCC辅助纳米颗粒混合体系流化研究[J]. 化工学报, 2025, 76(6): 2616-2625.

Figures/Tables 9

Fig.1 Structure scheme of fluidized bed

Table 1 Parameters used in the simulation

参数	实验值	模拟值
初始床高/ mm	30	30
气体密度/(kg/m³)	1.225	1.225
气体黏度/(Pa·s)	—	1.7894×10^-5
弹性恢复系数	—	0.5
气体入口速度/(cm/s)	8	8
温度/K	298.15	298.15

Table 2 Particle parameter

颗粒	粒径/μm	密度/(kg/m³)
SiO₂	0.03	2560
ZnO	0.02	5600
FCC-1	130	1470
FCC-2	150	1020
FCC-3	190	990

Table 3 Grid independence test

网格数量/个	35 mm床高处颗粒体积分数	相对误差/%
1000	0.410
2000	0.389	5.40
4000	0.374	4.01
8000	0.372	0.53
16000	0.371	0.27

Fig.2 Pressure drop curves of mixed particle beds with different blending ratios of SiO2 and ZnO

Fig.3 Pressure drop curve of bed after adding FCC particles

Fig.4 Size distribution of agglomerates of mixed nanoparticles

Fig.5 Pressure drop curve of bed under different FCC addition ratio

Fig.6 Size distribution of agglomerates under different FCC addition ratio

References 31

[1]	侯旺君, 闫翎鹏, 曹哲勇, 等. 煤基零维纳米碳材料的合成、性能及其在能源转换和存储应用中的研究进展[J]. 化工学报, 2022, 73(11): 4791-4813.
	Hou W J, Yan L P, Cao Z Y, et al. Research progress of synthesis and properties of coal-based zero-dimensional nanocarbon materials and their applications in energy conversion and storage[J]. CIESC Journal, 2022, 73(11): 4791-4813.
[2]	赵之端, 赵蒙, 刘道银, 等. 振动和搅拌对SiO₂纳米颗粒聚团流化的影响对比研究[J]. 工程热物理学报, 2021, 42(1): 136-142.
	Zhao Z D, Zhao M, Liu D Y, et al. Comparative study on the effect of vibration and stirring on the fluidization of SiO₂ nanoparticle agglomerates[J]. Journal of Engineering Thermophysics, 2021, 42(1): 136-142.
[3]	郭婷, 何川, 李海念, 等. 声场对导向管喷流床环隙区流化质量的影响[J]. 化学反应工程与工艺, 2019, 35(6): 501-508.
	Guo T, He C, Li H N, et al. Effect of an acoustic field on fluidization quality in the annulus of a spout-fluidized bed with a draft tube[J]. Chemical Reaction Engineering and Technology, 2019, 35(6): 501-508.
[4]	王荘, 吕潇, 邵媛媛, 等. 流态化的往昔寻觅及未来启示[J]. 化工学报, 2021, 72(12): 5904-5927.
	Wang Z, Lyu X, Shao Y Y, et al. Early exploration of fluidization theory and its inspiration to the future[J]. CIESC Journal, 2021, 72(12): 5904-5927.
[5]	曾玺, 王芳, 余剑, 等. 微型流化床反应分析的方法基础与应用研究[J]. 化工进展, 2016, 35(6): 1687-1697.
	Zeng X, Wang F, Yu J, et al. Fundamentals and applications of micro fluidized bed reaction analysis[J]. Chemical Industry and Engineering Progress, 2016, 35(6): 1687-1697.
[6]	许可, 韩梦琪, 张海萍, 等. C类颗粒添加纳米细粉后的流态化行为研究[J]. 高校化学工程学报, 2019, 33(6): 1361-1368.
	Xu K, Han M Q, Zhang H P, et al. Study on flow behaviors of group C particles blended with nano additives[J]. Journal of Chemical Engineering of Chinese Universities, 2019, 33(6): 1361-1368.
[7]	Karimi F, Haghshenasfard M, Sotudeh-Gharebagh R, et al. Multiscale characterization of nanoparticles in a magnetically assisted fluidized bed[J]. Particuology, 2020, 51: 64-71.
[8]	Karimi F, Haghshenasfard M, Sotudeh-Gharebagh R, et al. Enhancing the fluidization quality of nanoparticles using external fields[J]. Advanced Powder Technology, 2018, 29(12): 3145-3154.
[9]	Ajbar A, Alhumazi K, Asif M. Improvement of the fluidizability of cohesive powders through mixing with small proportions of group A particles[J]. The Canadian Journal of Chemical Engineering, 2005, 83(6): 930-943.
[10]	Zhou Y, Zhu J. A review on fluidization of Geldart group C powders through nanoparticle modulation[J]. Powder Technology, 2021, 381: 698-720.
[11]	Duan H, Liang X Z, Zhou T, et al. Fluidization of mixed SiO₂ and ZnO nanoparticles by adding coarse particles[J]. Powder Technology, 2014, 267: 315-321.
[12]	于明州, 林建忠. 纳米颗粒多相流体动力学研究及应用[J]. 力学与实践, 2010, 32(3): 1-9.
	Yu M Z, Lin J Z. The dynamics of nanoparitcle-laden multiphase flow and its applications[J]. Mechanics in Engineering, 2010, 32(3): 1-9.
[13]	刘演华, 林建忠. 两相流中颗粒参数分布的矩方法研究[J]. 空气动力学学报, 2009, 27(6): 656-663.
	Liu Y H, Lin J Z. Research on method of momens of particulate parameter distribution in multiphase flow[J]. Acta Aerodynamica Sinica, 2009, 27(6): 656-663.
[14]	Icardi M, Ronco G, Marchisio D L, et al. Efficient simulation of gas-liquid pipe flows using a generalized population balance equation coupled with the algebraic slip model[J]. Applied Mathematical Modelling, 2014, 38(17/18): 4277-4290.
[15]	Duan X X, Feng X, Yang C, et al. CFD modeling of turbulent reacting flow in a semi-batch stirred-tank reactor[J]. Chinese Journal of Chemical Engineering, 2018, 26(4): 675-683.
[16]	魏利平, 江国栋, 滕海鹏. 双组分黏性颗粒相间曳力模型[J]. 工程热物理学报, 2019, 40(1): 114-117.
	Wei L P, Jiang G D, Teng H P. Cohesive particle-particle drag model[J]. Journal of Engineering Thermophysics, 2019, 40(1): 114-117.
[17]	Ding J M, Gidaspow D. A bubbling fluidization model using kinetic theory of granular flow[J]. AIChE Journal, 1990, 36(4): 523-538.
[18]	王垚, 金涌, 魏飞, 等. 纳米级SiO₂聚团散式流化中聚团参数及曳力系数[J]. 清华大学学报(自然科学版), 2001, 41(S1): 32-35.
	Wang Y, Jin Y, Wei F, et al. Agglomeration parameters and drag coefficients in agglomerate particulate fluidization of SiO₂ nanoparticles[J]. Journal of Tsinghua University (Science and Technology), 2001, 41(S1): 32-35.
[19]	李清, 夏珉, 何慧灵, 等. 水平管内气液两相流中气泡滑移速度的数值模拟[J]. 石油化工, 2011, 40(10): 1078-1082.
	Li Q, Xia M, He H L, et al. Numerical simulation of bubble slip velocity in gas-liquid two-phase flow within horizontal pipe[J]. Petrochemical Technology, 2011, 40(10): 1078-1082.
[20]	Fan R, Marchisio D L, Fox R O. Application of the direct quadrature method of moments to polydisperse gas-solid fluidized beds[J]. Powder Technology, 2004, 139(1): 7-20.
[21]	Mazzei L, Marchisio D L, Lettieri P. Direct quadrature method of moments for the mixing of inert polydisperse fluidized powders and the role of numerical diffusion[J]. Industrial & Engineering Chemistry Research, 2010, 49(11): 5141-5152.
[22]	Kong B, Fox R O. A moment-based kinetic theory model for polydisperse gas-particle flows[J]. Powder Technology, 2020, 365: 92-105.
[23]	Marchisio D L, Vigil R D, Fox R O. Quadrature method of moments for aggregation-breakage processes[J]. Journal of Colloid and Interface Science, 2003, 258(2): 322-334.
[24]	Ramachandran R, Immanuel C D, Stepanek F, et al. A mechanistic model for breakage in population balances of granulation: theoretical kernel development and experimental validation[J]. Chemical Engineering Research and Design, 2009, 87(4): 598-614.
[25]	Vigil R D. On equilibrium solutions of aggregation-fragmentation problems[J]. Journal of Colloid and Interface Science, 2009, 336(2): 642-647.
[26]	Jiang Y Y, Xu Z H, Zhang M Z, et al. Interactions between gas flow and reversible chemical reaction in porous media[J]. Journal of Central South University, 2017, 24(5): 1144-1154.
[27]	Ma H Y, Yu M Z, Jin H H. A study of the evolution of nanoparticle dynamics in a homogeneous isotropic turbulence flow via a DNS-TEMOM method[J]. Journal of Hydrodynamics, 2020, 32(6): 1091-1099.
[28]	郑建祥, 许帅, 王京阳. 超细颗粒聚团模型及湍流聚并器聚团研究[J]. 中国电机工程学报, 2016, 36(16): 4389-4395, 4524.
	Zheng J X, Xu S, Wang J Y. Simulation study of ultrafine particle aggregation models and agglomerator coagulation[J]. Proceedings of the CSEE, 2016, 36(16): 4389-4395, 4524.
[29]	Marchisio D L, Soos M, Sefcik J, et al. Role of turbulent shear rate distribution in aggregation and breakage processes[J]. AIChE Journal, 2006, 52(1): 158-173.
[30]	Shrestha S, Wang B, Dutta P. Nanoparticle processing: understanding and controlling aggregation[J]. Advances in Colloid and Interface Science, 2020, 279: 102162.
[31]	Hosseinibalam F, Hassanzadeh S, Mirmohammadi M. Simulation of tidal energy extraction by using FLUENT model[J]. Iranian Journal of Science and Technology, Transactions A: Science, 2019, 43: 2035-2042.