A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process

In this study, a hybrid multi-scale model has been developed for a continuous fluid bed wet granulation process by dynamically coupling computational fluid dynamics (CFD) with a discrete element model (DEM) and population balance model (PBM). In this process, the granules are formed by spraying the...

Full description

Bibliographic Details
Main Authors: Maitraye Sen, Dana Barrasso, Ravendra Singh, Rohit Ramachandran
Format: Article
Language:English
Published: MDPI AG 2014-01-01
Series:Processes
Subjects:
Online Access:http://www.mdpi.com/2227-9717/2/1/89
id doaj-5ce8c8b85c944d7fb5b87522ffb33dde
record_format Article
spelling doaj-5ce8c8b85c944d7fb5b87522ffb33dde2020-11-25T01:11:34ZengMDPI AGProcesses2227-97172014-01-01218911110.3390/pr2010089pr2010089A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation ProcessMaitraye Sen0Dana Barrasso1Ravendra Singh2Rohit Ramachandran3Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USAIn this study, a hybrid multi-scale model has been developed for a continuous fluid bed wet granulation process by dynamically coupling computational fluid dynamics (CFD) with a discrete element model (DEM) and population balance model (PBM). In this process, the granules are formed by spraying the liquid binder on the fluidized powder bed. The fluid flow field has been solved implementing CFD principles and the behavior of the solid particles has been modeled using DEM techniques whereas the change in particle size has been quantified with the help of PBM. The liquid binder droplets have been modeled implicitly in DEM. A detailed understanding of the process aids in the development of better design, optimization and control strategies. The model predicts the evolution of important process variables (i.e., average particle diameter, particle size distribution (PSD) and particle liquid content) over time, which have qualitative similarity with experimentally observed trends. The advantage of incorporating the multi-scale approach is that the model can be used to study the distributions of collision frequencies, particle velocity and particle liquid content in different sections of the fluid bed granulator (FBG), in a more mechanistic manner.http://www.mdpi.com/2227-9717/2/1/89computational fluid dynamicsdiscrete element modelpopulation balance modelfluid bed granulationmulti-scalesimulation
collection DOAJ
language English
format Article
sources DOAJ
author Maitraye Sen
Dana Barrasso
Ravendra Singh
Rohit Ramachandran
spellingShingle Maitraye Sen
Dana Barrasso
Ravendra Singh
Rohit Ramachandran
A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process
Processes
computational fluid dynamics
discrete element model
population balance model
fluid bed granulation
multi-scale
simulation
author_facet Maitraye Sen
Dana Barrasso
Ravendra Singh
Rohit Ramachandran
author_sort Maitraye Sen
title A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process
title_short A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process
title_full A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process
title_fullStr A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process
title_full_unstemmed A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process
title_sort multi-scale hybrid cfd-dem-pbm description of a fluid-bed granulation process
publisher MDPI AG
series Processes
issn 2227-9717
publishDate 2014-01-01
description In this study, a hybrid multi-scale model has been developed for a continuous fluid bed wet granulation process by dynamically coupling computational fluid dynamics (CFD) with a discrete element model (DEM) and population balance model (PBM). In this process, the granules are formed by spraying the liquid binder on the fluidized powder bed. The fluid flow field has been solved implementing CFD principles and the behavior of the solid particles has been modeled using DEM techniques whereas the change in particle size has been quantified with the help of PBM. The liquid binder droplets have been modeled implicitly in DEM. A detailed understanding of the process aids in the development of better design, optimization and control strategies. The model predicts the evolution of important process variables (i.e., average particle diameter, particle size distribution (PSD) and particle liquid content) over time, which have qualitative similarity with experimentally observed trends. The advantage of incorporating the multi-scale approach is that the model can be used to study the distributions of collision frequencies, particle velocity and particle liquid content in different sections of the fluid bed granulator (FBG), in a more mechanistic manner.
topic computational fluid dynamics
discrete element model
population balance model
fluid bed granulation
multi-scale
simulation
url http://www.mdpi.com/2227-9717/2/1/89
work_keys_str_mv AT maitrayesen amultiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
AT danabarrasso amultiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
AT ravendrasingh amultiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
AT rohitramachandran amultiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
AT maitrayesen multiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
AT danabarrasso multiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
AT ravendrasingh multiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
AT rohitramachandran multiscalehybridcfddempbmdescriptionofafluidbedgranulationprocess
_version_ 1725170874277953536