Modeling and Simulation of Solid‐Bowl Centrifuges as an Aspect of the Advancing Digitization in Solid‐Liquid Separation

Hammerich, Simon; Gleiß, Marco; Nirschl, Hermann

doi:10.1002/cben.201900011

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…The assumption of a plug flow in these areas is no longer applicable. One possibility to characterise this behaviour is a complex flow simulation using Computational Fluid Dynamics (CFD) [15]. An alternative possibility is the use of a grey box model.…”

Section: Introductionmentioning

confidence: 99%

Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

et al. 2021

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Continuously operating decanter centrifuges are often applied for solid-liquid separation in the chemical and mining industries. Simulation tools can assist in the configuration and optimisation of separation processes by, e.g., controlling the quality characteristics of the product. Increasing computation power has led to a renewed interest in hybrid models (subsequently named grey box model), which combine parametric and non-paramteric models. In this article, a grey box model for the simulation of the mechanical dewatering of a finely dispersed product in decanter centrifuges is discussed. Here, the grey box model consists of a mechanistic model (as white box model) presented in a previous research article and a neural network (as black box model). Experimentally determined data is used to train the neural network in the area of application. The mechanistic approach considers the settling behaviour, the sediment consolidation, and the sediment transport. In conclusion, the settings of the neural network and the results of the grey box model and white box model are compared and discussed. Now, the overall grey box model is able to increase the accuracy of the simulation and physical effects that are not modelled yet are integrated by training of a neural network using experimental data.

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Section: Introductionmentioning

confidence: 99%

Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

et al. 2021

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“…The complexity of WBMs range from simple equations to nonlinear partial differential equations. While computational fluid dynamics (CFD) methods allow for fully resolved flow simulations of decanter centrifuges, [ 2 ] the computation time is significant, which makes them unsuitable for real‐time applications, such as model‐predictive control. On the other hand, so‐called real‐time models simplify complex systems through assumptions, reducing the computational effort significantly.…”

Section: Introductionmentioning

confidence: 99%

Enhancing decanter centrifuge process design with data‐driven material parameters in multi‐compartment modeling

Zhai,

Ehret,

Rhein

et al. 2024

J Adv Manuf & Process

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Predicting the separation performance of decanter centrifuges is challenging due to dynamic events within the apparatus. Current methods for designing decanter centrifuges rely on simplified models, often leading to inaccuracies. Consequently, manufacturers must perform time‐intensive pilot scale experiments to derive their own correction factors. Growing computing power sparks interest in alternative modeling strategies. Grey box models (GBM) combine mechanistic white box models (WBM) and data‐driven black box models (BBM), with the optimal structure (parallel or serial) varying by application. For modeling decanter centrifuges, we propose a serial GBM that comprises an artificial neural network that outputs unknown material parameters into a first‐principle multi‐compartment model. Comparing this approach to alternative data‐driven modeling strategies (pure BBM, parallel GBM), we conclude that the serial GBM excels in terms of extrapolation, prediction ability, and transparency while also enabling a better comprehension of the separation process.

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Mechanical Solid–Liquid Separation

Anlauf

2021

Kirk-Othmer Encyclopedia of Chemical Technology

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The separation of a suspension into its continuous liquid and disperse solid phase can be realized principally by thermal or mechanical means, whereby the emphasis is put here on the mechanical separation methods. Mechanical solid–liquid separation represents a cross‐sectional technology and is relevant for a range of particle sizes from some millimeters down to some nanometers. One can distinguish systematically into density separation processes, such as sedimentation and flotation; filtration processes, such as depth filtration, cake, cross‐flow, and sieve/blocking filtration; and processes that are using electric or magnetic properties of the particles. The huge variety of solid–liquid separation apparatuses and machines can be classified clearly by a physically based scheme. Various special tasks can be in the center of interest, such as thickening, clarification, deliquoring, washing, three‐phase separation and extraction, sorting, and classification. Several measures are available to enhance the basic separation processes, such as mechanical–thermal hybrid processes, batchwise or continuously operating processes, apparatus combinations, and slurry pretreatment. Last but not least, far important aspects of progress in solid–liquid separation technology can be summed up under the keyword digitalization and simulation. Most modern methods of numerical simulation enable to create digital twins of separation apparatuses, which can be used in many different ways.

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Modeling and Simulation of Solid‐Bowl Centrifuges as an Aspect of the Advancing Digitization in Solid‐Liquid Separation

Cited by 5 publications

References 41 publications

Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

Enhancing decanter centrifuge process design with data‐driven material parameters in multi‐compartment modeling

Mechanical Solid–Liquid Separation

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