SearcharxivSearch

arXiv · 2106.12606

Oscillating currents stabilize aluminium cells for efficient, low carbon production

Abstract

Humankind produced 63.7 million metric tons of aluminium in 2019, nearly all via an electrochemical process in which electrical current liberates molten Al from dissolved alumina. That year, Al production required 848 TWh of electricity1, 3% of the worldwide total, and caused 1% of human greenhouse gas emissions. Much of the electricity and emissions originate from energy loss in the poorly conducting electrolyte where aluminum oxide is dissolved. Thinning the electrolyte layer could decrease loss but has been limited by the Metal Pad Instability (MPI), which causes Al cells to slosh out of control if the electrolyte is not sufficiently thick. Here we show that adding an oscillating component to the current disrupts the MPI in realistic simulations, allowing stable operation with electrolyte layers at least 12% thinner. This occurs when oscillation excites standing waves, which decouple the resonance that drives a growing traveling wave, characteristic of the MPI. Maintaining oscillation can prevent MPI; initiating oscillation can halt an MPI in progress. Our findings could significantly increase the efficiency of virtually all aluminium refining cells without the need for expensive reconstruction, thereby decreasing energy use by 34 TWh/year (2.1 MJ/kg Al) or more and greenhouse gas emissions by 13 Mton/year or more.

Explore related subjects

Keep this discovery

BibTeXRIS

Ibrahim Mohammad, Marc Dupuis, Paul D. Funkenbusch, Douglas H. Kelley. 2021-06-23. Oscillating currents stabilize aluminium cells for efficient, low carbon production. https://arxiv.org/abs/2106.12606

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Correlative effects of induced magnetic field-buoyancy on reactive solute dispersion dynamics in couple-stress fluids

We investigate the dispersion of a reactive solute in a couple-stress fluid flowing between two parallel plates under the combined effects of pressure-driven flow, buoyancy, and an induced magnetic field. The model incorporates first-order heterogeneous reactions at both channel walls alongside a bulk reaction. Using Mei's multiscale homogenization technique accurate to third order, we develop a higher-order asymptotic formulation to determine the effective longitudinal dispersion coefficient and concentration field. Analytical predictions are complemented by Brownian dynamics simulations and finite-difference solutions, while the Aris method of moments quantifies transient mean displacement, spatial variance, and effective dispersivity. The hydrodynamic analysis reveals a singular branch in the velocity solution when the Hartmann number equals half the couple-stress parameter and identifies a characteristic quarter-power scaling between the Hartmann number and couple-stress parameter, separating couple-stress- and magnetically dominated regimes. The model recovers classical Taylor-dispersion behavior in the non-reactive Newtonian limit and agrees well with experimental measurements. Couple-stress rheology and magnetic damping suppress shear-induced dispersion, whereas buoyancy enhances dispersion through additional transverse velocity gradients. A distinct saturation regime of the dispersion coefficient emerges with an increasing couple-stress parameter, while unequal wall absorption induces persistent transverse asymmetry, and stronger absorption enhances solute removal near the source. Numerical and stochastic results validate the analytical framework while resolving higher-order concentration structures and particle-scale wall adsorption.

physics.flu-dyn

DiffSWE2d: a differentiable Shallow Water Equations solver for end-to-end flood and tsunami modelling

Solving inverse and optimisation problems with traditional shallow water equations (SWE) solvers can be computationally expensive, particularly when gradients with respect to model inputs or parameters must be estimated through repeated forward simulations. In this paper, we introduce DiffSWE2d, an open-source differentiable shallow water equations solver for end-to-end flood and tsunami modelling implemented in PyTorch. By leveraging automatic differentiation, DiffSWE2d represents the time-marching physics as a differentiable computational graph, enabling gradients to be propagated directly through the numerical solver. We validate the solver against two established benchmark cases and demonstrate its application to tsunami waveform inversion, showing its ability to infer model inputs through gradient-based optimisation. DiffSWE2d provides a flexible framework for integrating physics-based hydrodynamic modelling with modern optimisation and machine learning methods. The source code and reproducible examples are publicly available at: https://github.com/ZhonghouXu/DiffSWE2d

physics.flu-dyn

Low inertia limit of elasto-inertial turbulence

Pipe and channel flows of viscoelastic fluids display chaotic dynamics at unusually low speeds, a phenomenon referred to as elasto-inertial turbulence, EIT. First reported in experiments a century ago, recent theoretical studies and model computations predict a variety of scenarios for the phenomenon's origin, ranging from hoop stress modes to center modes and to Tollmien-Schlichting waves. Lacking experimental confirmation, the relevant scenario in actual flows of polymer solutions remains unknown. We here determine the transition threshold of EIT in pipe experiments, covering three decades in elasticity number. Across this entire parameter range, the transition features center mode structures at onset. Eventually the instability diverges at a lower inertia (upper elasticity) limit, which is a robust signature of this center mode scenario. Finally, we report the first experimental observation of a traveling wave in viscoelastic pipe flow, and the sequences of localized structures found, are in excellent agreement with a center mode traveling wave, the "arrowhead" solution, discovered in model simulations.

physics.flu-dyn