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Dynamic modelling of alumina feeding in an aluminium electrolysis cell

Dynamic modelling of alumina feeding in an aluminium electrolysis cell

Bojarevics, Valdis (2019) Dynamic modelling of alumina feeding in an aluminium electrolysis cell. In: Chesonis, Corleen, (ed.) Light Metals 2019. The Minerals, Metals & Materials Series . Springer Nature Switzerland AG, pp. 1-8. ISBN 978-3030058630 (In Press) (doi:https://doi.org/10.1007/978-3-030-05864-7_83)

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Abstract

Alumina feeding at regular intervals requires optimization for the feeder positions, their number, the feed amount and the timing. The specific composition of the feed material could be variable in the particulate material sizes and the specific density. The addition of particles of various sizes is treated by Lagrangian methods following the tracks of inertial particles subject to drag in the turbulent electrolyte flow. The emphasis is on the large scale circulation which is essential to achieve the desired uniform alumina composition over the whole cell. Each particle is permitted to gradually dissolve in dependence of its individual size and the local concentration field value below the saturation level. This time variable source is used to follow the concentration field development on the Eulerian grid. The newly developed modelling technique is implemented as an add-in to the specialised MHD software for commercial cell modelling and optimisation.

Item Type: Book Section
Uncontrolled Keywords: Aluminium electrolysis cell - Alumina feeding - Particle tracking - Turbulent mixing - Dissolution
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA)
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Science & Engineering Group (CSEG)
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 24 Jan 2019 12:13
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/22525

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