A bimodal population balance method for the dynamic process of engineered nanoparticles

被引:5
作者
Shen, Jindong [1 ]
Jiang, Honghui [1 ]
Yu, Mingzhou [1 ]
Kong, Bo [2 ]
机构
[1] China Jiliang Univ, Lab Aerosol Sci & Technol, Hangzhou 310018, Peoples R China
[2] Guangdong Technion Israel Inst Technol GTIIT, Dept Chem Engn, Shantou 515063, Peoples R China
基金
中国国家自然科学基金;
关键词
Engineered nanoparticles dynamics; Inverse Gaussian distribution; Method of moments; Population balance equation; PARTICLE-SIZE DISTRIBUTION; MONTE-CARLO METHOD; AEROSOL DYNAMICS; BROWNIAN COAGULATION; KINETIC COAGULATION; QUADRATURE METHOD; MOMENT METHOD; MODEL; SIMULATION; EVOLUTION;
D O I
10.1016/j.ijheatmasstransfer.2022.122605
中图分类号
O414.1 [热力学];
学科分类号
摘要
The evolution of size distribution of engineered nanoparticles (ENPs) from the gas phase to the prod-uct in chemical reactors is a complicated heat and mass transfer process, whose mathematical descrip-tion is commonly characterized by the population balance equation (PBE) in terms of particle number concentration. This study presents a new population balance model (PBM) for resolving the evolution of PBE for ENPs using a bimodal inverse Gaussian distributed method of moments (BIGDMOM). In this method, engineered nanoparticles' size distribution is constructed by superposing two inverse Gaussian subdistribution. A close model for arbitrary moments is then obtained for achieving the final solution of the population balance equation for ENPs. The precision of BIGDMOM, which is verified in the test cases of two representative ENPs dynamics, is acceptable compared with widely used methods, such as log-normal MOM (log MOM), third-order Taylor-series expansion MOM, and unimodal IGDMOM, for key statistical quantities determining ENP size distributions, including kth moments ( k = 0, 1/3, 2/3, and 2), shape factor, mean, and variance, sometimes is even better. Therefore, this study provides a new bimodal particle size distribution for the moment method, which can be used as an option to explore the specific bimodal practical problems of ENPs in the future. (c) 2022 Elsevier Ltd. All rights reserved.
引用
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页数:14
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