Solution of population balance equations using the direct quadrature method of moments

2005 ◽  
Vol 36 (1) ◽  
pp. 43-73 ◽  
Author(s):  
Daniele L. Marchisio ◽  
Rodney O. Fox
2018 ◽  
Vol 365 ◽  
pp. 243-268 ◽  
Author(s):  
Maxime Pigou ◽  
Jérôme Morchain ◽  
Pascal Fede ◽  
Marie-Isabelle Penet ◽  
Geoffrey Laronze

Author(s):  
Mohsen Shiea ◽  
Antonio Buffo ◽  
Marco Vanni ◽  
Daniele Marchisio

This review article discusses the solution of population balance equations, for the simulation of disperse multiphase systems, tightly coupled with computational fluid dynamics. Although several methods are discussed, the focus is on quadrature-based moment methods (QBMMs) with particular attention to the quadrature method of moments, the conditional quadrature method of moments, and the direct quadrature method of moments. The relationship between the population balance equation, in its generalized form, and the Euler-Euler multiphase flow models, notably the two-fluid model, is thoroughly discussed. Then the closure problem and the use of Gaussian quadratures to overcome it are analyzed. The review concludes with the presentation of numerical issues and guidelines for users of these modeling approaches.


AIChE Journal ◽  
2003 ◽  
Vol 49 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Daniele L. Marchisio ◽  
Jesse T. Pikturna ◽  
Rodney O. Fox ◽  
R. Dennis Vigil ◽  
Antonello A. Barresi

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Dan Zheng ◽  
Wei Zou ◽  
Chuanfeng Peng ◽  
Yuhang Fu ◽  
Jie Yan ◽  
...  

A coupled numerical code of the Euler-Euler model and the population balance model (PBM) of the liquid-liquid dispersions in a spray fluidized bed extractor (SFBE) has been performed to investigate the hydrodynamic behavior. A classes method (CM) and two representatively numerical moment-based methods, namely, a quadrature method of moments (QMOM) and a direct quadrature method of moments (DQMOM), are used to solve the PBE for evaluating the effect of the numerical method. The purpose of this article is to compare the results achieved by three methods for solving population balance during liquid-liquid two-phase mixing in a SFBE. The predicted results reveal that the CM has the advantage of computing the droplet size distribution (DSD) directly, but it is computationally expensive if a large number of intervals are needed. The MOMs (QMOM and DQMOM) are preferable to coupling the PBE solution with CFD codes for liquid-liquid dispersions simulations due to their easy application, reasonable accuracy, and high reliability. Comparative results demonstrated the suitability of the DQMOM for modeling the spray fluidized bed extractor with simultaneous droplet breakage and aggregation. This work increases the understanding of the chemical engineering characteristics of multiphase systems and provides a theoretical basis for the quantitative design, scale-up, and optimization of multiphase devices.


Author(s):  
Roel Belt ◽  
Olivier Simonin

In this work, Eulerian transport equations are derived for a polydispersed cloud of droplets in a turbulent carrier flow, which take into account the effects of polydispersion and coalescence. The approach is an extension of the Direct Quadrature Method Of Moments (DQMOM) formalism initially proposed by Marchisio and Fox (2005, J. Aerosol Sci., 36, pp. 43–95). In the initial DQMOM approach of Marchisio and Fox, the effects of polydispersion and coalescence can only be accounted for in the mass balance equations. By combining the DQMOM approach and the joint fluid-particle pdf approach of Simonin (1996, Von Karman Lecture Notes), Eulerian transport equations can be written in the frame of the DQMOM formalism for the velocity, agitation and fluid-particle covariance, which quantities are required to predict the behavior of a cloud of droplets in a turbulent flow. It is formally shown that the Eulerian transport equations in the DQMOM framework are the same equations as those in the multi-class Eulerian approach, except that now there is a collision term in the equations. The collision term due to coalescence can be easily expressed due to the assumptions made in the DQMOM framework, and it is shown that it couples the transport equations of the different classes and dispersed phase statistics, according to the change of number, mass and momentum during coalescence.


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