CFD simulation of dissolution process of alumina in an aluminum reduction cell with two-particle phase population balance model

Zhan, Shuiqing; Li, Mao; Zhou, Jiemin; Yang, Jianhong; Zhou, Yiwen

doi:10.1016/j.applthermaleng.2014.08.040

Cited by 35 publications

(18 citation statements)

References 18 publications

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“…Future work will involve improved description for alumina dissolution, for instance by introducing PBM-concepts also for alumina particles and agglomeration as in [2,37], thermo-electrical and magnetic models, allowing for more realistic calculations of magnetic fields, and increasing the extent of coupling between the sub-models developed. Special attention will be given to usage of the meso-scale bubble model as a numerical lab in order to establish new and extend the validity of existing correlations for gas-bath momentum exchange and to impose bubble drag forces calculated in the bath flow model into the metal pad model to allow bubble effects on the metal pad to be accounted for.…”

Section: Discussionmentioning

confidence: 99%

“…To stabilise the model time steps on the order 0.001 s were used. Using this approach [37] were able to simulate 144 s or one to two feeding cycles.…”

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

“…A complete mathematical model of the dissolution process is not available in the open literature (cf. [2] and [37]). Complex models for a single alumina particle or agglomerate have been proposed, cf.…”

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

“…An alternate approach using a population balance model for the agglomerate size in a forty anode cell was proposed by Zhan [37] using heat and mass transfer coefficients based on agglomerate size to calculate the dissolution rate. Agglomerates were divided into two sizes, large and small, with each size represented by twelve size classes.…”

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

“…Our aim is to model thousands of feeding cycles and the resultant changes in alumina concentration thus we need to simulate up to 20,000 s therefore complex approaches such as [2] and [37] are unaffordable and to justify their level of complexity require prediction of the bath temperature and superheat distribution in the cell. Frovlov et al [14] carried out dissolution experiments for both a modified and a typical industrial bath chemistry at a bath temperature of 960°C and over a range of dissolved alumina concentrations.…”

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

See 4 more Smart Citations

Towards a coupled multi-scale, multi-physics simulation framework for aluminium electrolysis

Einarsrud

Eick

Bai

et al. 2017

Applied Mathematical Modelling

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Aluminium metal production through electrolytic reduction of alumina in a cryolite bath is a complex, multi-physics, multi-scale process, including magneto-hydrodynamics (MHD), bubble flow, thermal convection, melting and solidification phenomena based on a set of chemical reactions.Through interactions of the different forces applied to the liquid bath combined with the different time and length scales, self-organised fluctuations occur. Moreover, the MHD behaviour causes a complex metal pad profile and a series of surface waves due to the meta-stable condition of the metal / cryolite interface.The large aspect ratio of an industrial cell, with a footprint of 20 by 4 m and at the same time having dimensions approaching just 30 mm of height for the reaction zone, prevents an integrated approach where all relevant physics are included in a single mathematical model of this large degree of freedom system. In order to overcome these challenges, different modelling approaches have been established in ANSYS FLUENT; Three models are used to predict details of specific physics: one to predict the electro-magnetic forces and hence the metal pad profile, a second that resolves details of the local bubble dynamics around a single anode and a third for the full cell bath flow. Results from these models are coupled to allow integration of the different phenomena into a full cell alumina distribution model. The current paper outlines each of the approaches and presents how the coupling between them can be realized in a complete framework, aiming to provide new insight into the process. 2 Highlights• Scales of the process in length and time are identified for aluminium electrolysis.• Four modelling approaches describing essential features are presented.• Coupling between modelling approaches is discussed and demonstrated.

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Section: Discussionmentioning

confidence: 99%

“…To stabilise the model time steps on the order 0.001 s were used. Using this approach [37] were able to simulate 144 s or one to two feeding cycles.…”

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

See 3 more Smart Citations

Towards a coupled multi-scale, multi-physics simulation framework for aluminium electrolysis

Einarsrud

Eick

Bai

et al. 2017

Applied Mathematical Modelling

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Alumina Quality, HF Removal, Dissolution and Aluminum Purity

Lindsay¹,

Lavoie

2022

Springer Series in Materials Science

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Advanced Alumina Dissolution Modelling

Bojarevics

Dupuis²

2022

Light Metals 2022

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Alumina feeding requires optimization for the feed amount, timing, and point feeder locations. The alumina raft formation and dispersion are the essential stages leading to the particles of various sizes travel with the bath flow along anodes in the central channel, in the bath volume beneath the anodes, and gradually dissolving. The raft motion and particles are traced accounting for their inertia, drag in the turbulent flow, the dome-shaped anode bottom shape. Large-scale MHD-driven circulation in the cell is modelled using specific inputs corresponding to real commercial cells of various types. The feed material forming rafts and particles is added periodically, moves with the flow, and gradually dissolves depending on the local turbulence and instantaneous concentration in a location. The newly developed modelling technique is applied to illustrate possible optimization of the feeder locations, the variable mass, and feeding time intervals for possible adjustments suitable for commercial cells.

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CFD simulation of dissolution process of alumina in an aluminum reduction cell with two-particle phase population balance model

Cited by 35 publications

References 18 publications

Towards a coupled multi-scale, multi-physics simulation framework for aluminium electrolysis

Towards a coupled multi-scale, multi-physics simulation framework for aluminium electrolysis

Alumina Quality, HF Removal, Dissolution and Aluminum Purity

Advanced Alumina Dissolution Modelling

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