Modeling the Behavior of Alumina Agglomerate in the Hall‐Héroult Process

Dassylva-Raymond, Véronique; Kiss, László I.; Poncsák, Sándor; Chartrand, Patrice; Bilodeau, Jean‐François; Guérard, Sébastien

doi:10.1002/9781118888438.ch102

Cited by 10 publications

(8 citation statements)

References 5 publications

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“…The electrical current is distributed to 20 to 48 parallel-connected anodes in the cell, resulting in a typical anodic current density of 0.8 to 1.0 A/cm 2 . Based on Faraday's law, the applied current controls the rate of reaction (2Al2O3 + 3C => 3CO2 + 4Al) for the dissolved alumina at the carbon anode that generates CO2 gas bubbles.…”

Section: Bubble Flowmentioning

confidence: 99%

“…Dissolution of solid alumina particles into the bath is a complex process involving particle agglomeration, particle heating, freezing and remelting of solid layers of bath around agglomerates and phase transformation of  to  alumina [2]. Heat transfer and mass transfer between the agglomerate and bath along with alumina moisture content and agglomerate size are the key factors controlling the dissolution rate.…”

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%

“…Complex models for a single alumina particle or agglomerate have been proposed, cf. Dassylva-Raymond et al [2], but incorporation into a CFD model would require tracking for each agglomerate; information such as diameter, radial temperature distribution, frozen bath layer thickness and chemical composition. Such an approach would greatly add to the computational demands and is beyond the scope of the present work.…”

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

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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: Bubble Flowmentioning

confidence: 99%

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

Section: Basic Electrolysis Reactionsmentioning

confidence: 99%

See 2 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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“…More details are presented in an earlier published paper [12]. Sample producing process insertion of cold probe to the bath in order to obtain the thickest possible crust insertion of cold probe into the molten bath, then waiting until the transient crust is completely re-melted and the temperature of the probe stabilizes close to the bulk temperature; near steady-state crust is formed by the circulation of dried and compressed cold air in the channels of the probe with constant flow rate Duration of insertion 3 min this time was estimated by our mathematical model [13]~3 0 min probes were removed when the temperature became constant inside the probe Carbon steel and stainless steel were chosen to build the transient and steady-state probes respectively due to their relatively good mechanical and chemical resistance to high temperature electrolyte while still having an acceptable thermal conductivity compared to the frozen bath. Higher thermal conductivity of ordinary steel was important to increase heat transfer during transient test.…”

Section: Solidified Bath Samples Produced Using Cold Finger Techniquementioning

confidence: 99%

Impact of the Solidification Rate on the Chemical Composition of Frozen Cryolite Bath

Poncsák

Kiss

Guérard

et al. 2017

Metals

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Solidification of cryolite (Na3AlF6)-based bath takes place at different rates along the sideledge, and around alumina rafts and new anodes. The solidification rate has a significant impact on the structure and the chemical composition that determine the thermal conductivity and thus the thickness of sideledge, or the duration of the existence of the temporary frozen bath layers in other cases. Unfortunately, samples that can be collected in industrial cells are formed under unknown, spatially and temporally varying conditions. For this reason, frozen bath samples were created under different heat flux conditions in a well-controlled laboratory environment using the so-called cold finger technique. The samples were analyzed by X-ray Diffractometer (XRD) and Scanning Electron Microscope (SEM) in Back Scattering (BS) mode in order to obtain spatial distribution of chemical composition. Results were correlated with structural analysis. XRD confirmed our earlier hypothesis of recrystallization of cryolite to chiolite under medium heat flux regime. Lower α-alumina, and higher γ-alumina content in the samples obtained with very high heating rate suggest that fast cooling reduces α-γ conversion. In accordance with the expectation, SEM-BS revealed significant variation of the Na/Al ratio in the transient sample.

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