About Modeling and Optimization of Solid Bowl Centrifuges

Many processes involve solid bowl centrifuges as a solid–liquid separation step, typically used for clarification, thickening, classification, degritting, mechanical dewatering, and screening. In order to operate solid bowl centrifuges safely, with minimum resource consumption and reduced setup time...

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Bibliographic Details
Published in:KONA Powder and Particle Journal 2024/01/10, Vol.41, pp.58-77
Main Authors: Gleiss, Marco, Nirschl, Hermann
Format: Article
Language:English
Subjects:
Centrifuges
Dewatering
gray-box modeling
Influence
Laboratories
Liquid phases
material characterization
Measurement techniques
Modelling
multiphase flow
Optimization
Particle size
process analytics
process modeling
Reynolds number
Sediment transport
Sedimentation
Separation
solid bowl centrifuges
Surface roughness
Velocity
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Summary:Many processes involve solid bowl centrifuges as a solid–liquid separation step, typically used for clarification, thickening, classification, degritting, mechanical dewatering, and screening. In order to operate solid bowl centrifuges safely, with minimum resource consumption and reduced setup times, modeling and optimization are necessary steps. This is a challenge due to the complex process behavior, which can be overcome by developing advanced physical models and process analysis. This review provides an overview of solid bowl centrifuge applications, their modeling, and addresses future optimization potentials through digital tools. The impact of dispersed phase properties such as particle size, shape, surface roughness, structure, composition, and continuous liquid phase is the reason for the lack of generally applicable models. Laboratory-scale batch sedimentation centrifuges are used to predict material behavior and develop material functions describing separation-related properties such as sedimentation, sediment build-up and sediment transport. The combination of material functions and modeling allows accurate simulation of solid bowl centrifuges from laboratory to industrial scale. Since models usually do not cover all influencing variables, there are often deviations between predictions and the real process behavior. Gray-box modeling and on-line or in-situ process analytics are tools to improve centrifuge operation.
ISSN:0288-4534
2187-5537
DOI:10.14356/kona.2024010