Development of a multi-compartment population balance model for high-shear wet granulation with discrete element method

被引：22

作者：

Lee, Kok Foong ^{[1
]}

Dosta, Maksym ^{[2
]}

McGuire, Andrew D. ^{[1
]}

Mosbach, Sebastian ^{[1
]}

Wagner, Wolfgang ^{[3
]}

Heinrich, Stefan ^{[4
]}

Kraft, Markus ^{[1
,2
]}

机构：

[1] Univ Cambridge, Dept Chem Engn & Biotechnol, New Museums Site,Pembroke St, Cambridge CB2 3RA, England

[2] Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore

[3] Weierstrass Inst Appl Anal & Stochast, Mohrenstr 39, D-10117 Berlin, Germany

[4] Hamburg Univ Technol, Inst Solids Proc Engn & Particle Technol, Denickestr 15, D-21073 Hamburg, Germany

来源：

COMPUTERS & CHEMICAL ENGINEERING | 2017年 / 99卷

基金：

新加坡国家研究基金会;

关键词：

Granulation; Stochastic Weighted Algorithm; Compartmental model; Discrete element method; Majorant kernel; WEIGHTED PARTICLE METHOD; PLOWSHARES MIXER; MULTISCALE; POWDER; ALGORITHM; DEM; COALESCENCE; SIMULATION; EQUATIONS; LACTOSE;

D O I：

10.1016/j.compchemeng.2017.01.022

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper presents a multi-compartment population balance model for wet granulation coupled with DEM (discrete element method) simulations. Methodologies are developed to extract relevant data from the DEM simulations to inform the population balance model. First, compartmental residence times are calculated for the population balance model from DEM. Then, a suitable collision kernel is chosen for the population balance model based on particle-particle collision frequencies extracted from DEM. It is found that the population balance model is able to predict the trends exhibited by the experimental size and porosity distributions by utilising the information provided by the DEM simulations. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：171 / 184

页数：14

共 40 条

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