Modelling of industrial crystallizers, a compartmental approach using a dynamic flow-sheeting tool

Kramer, Herman J. M.; Dijkstra, Jelke; Neumann, Andreas M.; ÓMeadhra, R.; Rosmalen, G.M. van

doi:10.1016/0022-0248(96)00096-6

Cited by 18 publications

(10 citation statements)

References 4 publications

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“…This remains a challenging task. As an alternative, researchers (Benallou et al, 1986; Kramer et al, 1996) developed the compartmental modeling technique to achieve comparable results. The whole crystallizer is normally divided into several compartments.…”

Section: Population Balance Modelmentioning

confidence: 99%

Simultaneous prediction of crystal shape and size for solution crystallization

Zhang

Doherty

2004

AIChE Journal

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Section: Population Balance Modelmentioning

confidence: 99%

Simultaneous prediction of crystal shape and size for solution crystallization

Zhang

Doherty

2004

AIChE Journal

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“…Compartmental modelling is a well‐known technique which is frequently used for standard reactor engineering problems, and has been applied within crystallization for a number of years . Since the compartmental model allows a natural separation of kinetics and hydrodynamic mechanisms, it could make it possible to quantify kinetics and transport phenomena at the meso‐scale.…”

Section: Introductionmentioning

confidence: 99%

A compartmental CFD‐PBM model of high shear wet granulation

Hounslow

Reynolds

et al. 2016

AIChE Journal

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The conventional, geometrically lumped description of the physical processes inside a high shear granulator is not reliable for process design and scale-up. In this study, a compartmental Population Balance Model (PBM) with spatial dependence is developed and validated in two lab-scale high shear granulation processes using a 1.9L MiPro granulator and 4L DIOSNA granulator. The compartmental structure is built using a heuristic approach based on computational fluid dynamics (CFD) analysis, which includes the overall flow pattern, velocity and solids concentration. The constant volume Monte Carlo approach is implemented to solve the multi-compartment population balance equations. Different spatial dependent mechanisms are included in the compartmental PBM to describe granule growth.It is concluded that for both cases (low and high liquid content), the adjustment of parameters (e.g. layering, coalescence and breakage rate) can provide a quantitative prediction of the granulation process.

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“…Mixture properties were assumed to be constant. Slip and particle drag were not con- Kramer et al (1996Kramer et al ( , 2000, Bermingham et al (1998) andTen Cate et al (2000), just to name a few. All of the mentioned studies are dealing with ammonium sulphate.…”

Section: Introductionmentioning

confidence: 96%

“…All of the mentioned studies are dealing with ammonium sulphate. Kramer et al (1996) modelled crystallization process in a 970 dm 3 Draft Tube Baffle (DTB) crystallizer. They divided the crystallizer into four compartments which were dictated by the geometry of the crystallizer: a mixing zone at the bottom, the inner draft tube, the boiling zone near surface, the outer draft tube.…”

Section: Introductionmentioning

confidence: 99%