Multi-scale, Multi-physics Simulation Approach for Aluminium Electrolysis

Aluminium Electrolysis

Ingo Eick

1

, Jinsong Hua

2

, Kristian E. Einarsrud

3

, Peter Witt

4

, Wei Bai

5

1 Hydro Aluminium, 41468 Neuss, Germany

2 Institute for Energy Technology (IFE), 2027 Kjeller, Norway

3 Sør Trøndelag University College (HiST), 7004 Trondheim, Norway 4 CSIRO Minerals Resources Flagship, 3169 Melbourne, Australia 5 SINTEF Materials and Chemistry, 7465 Trondheim, Norway

• Hydro Aluminium metal production (03-06)

• Aluminium electrolysis process (07-14)

• Modelling assumptions (15-18)

• Steady state metal pad profile and MHD prediction (19-27)

• Transient bubble and chemical reaction flow model (28-38)

• Steady state full cell bath flow model (39-44)

• Transient Alumina reaction and distribution model (45-50)

All production figures are from 2012

Bauxite/alumina Smelters

Remelters

North America

•114 000 mt smelting capacity in 1 smelter

•369 000 mt remelt capacity in 6 remelters

Australia:

•228 000 mt primary smelting capacity in 2 smelters

Carribean / South America:

•1 890 000 mt alumina production in 2 alumina refineries

•2 700 000 mt bauxite production in 2 bauxite mines

•1 MoU new alumina JV project (Hydro share: 20%)

Europe:

•1 457 000 mt primary smelting capacity in 7 smelters

•359 000 mt remelt capacity in 6 remelters

Middle East:

• 300 000 mt smelting capacity in 1 smelter

Location Share Start-up Technology Layout amperage Cells Operating amperage Increase Ardal AI 100% 1970 HAL150 160 kA 216 209 kA 131% Ardal AII 100% 1978 HAL170 172 kA 110 221 kA 128% Hoyanger L2 100% 1981 HAL230 230 kA 80 282 kA 123% Husnes L1/2 100% 1965 EPT10 140 kA 400 150 kA 107% Karmoy L3 100% 1987 AP18 175 kA 288 232 kA 133% Sunndalsora SU3 100% 1968 HAL150 175 kA 184 203 kA 116% Sunndalsora SU4 100% 2003 HAL275 275 kA 340 314 kA 114% Rheinwerk L1 100% 1962 CA120 120 kA 162 180 kA 150% Rheinwerk L2 100% 1965 CA125 125 kA 156 180 kA 144% Rheinwerk L3 100% 1965 CA125 125 kA 156 180 kA 144% Qatar L1/2 50% 2010 HAL300 300 kA 704 355 kA 118% Ziar Nad Hronom L3 50% 1996 HAL230 230 kA 226 255 kA 111% Sept-Iles L1 20% 1992 AP30 295 kA 288 365 kA 124% Sept-Iles L2 20% 2005 AP35 335 kA 312 365 kA 109% Tomago L1 12% 1983 AP18 180 kA 280 225 kA 125% Tomago L2 12% 1993 AP18 180 kA 280 225 kA 125% Tomago L3 12% 1998 AP20 197 kA 280 225 kA 114%

Operational excellence

Capacity creep New design

Simulation

, a main tool, supporting the continuous improvement process

• Reduce variations • Reduce emissions • Reduce energy consumption • Step change • New design features

• Very low energy consumption

• Neutral CO2 foot print

• Increase production on same technology base

Main principle:

cell 1

cell 2 ...

Al₂O₃ powder Al₂O₃ powder

Cryolite melt (bath) (960°C) CO₂ (g) CO₂ (g) Current 150 – 600 kA Carbon anode Al (l) Cathode Cryolite melt (960°C) Carbon anode Al (l) Cathode

Magnetic effects:

A series of 150 – 350 cells are connected to current cycle in a potroom 150 to 600 kA applied to the conducting system (busbar) of the potline  Strong magnetic field of busbar

 Magnetisation of steel shell

 Lorentz forces on liquid metal and bath  Induced fields

 Induced current

Time scale: < 1 milli second

Length scales: 1 km long potroom, 15 mm thick steel shell

Magneto-Hydrodynamics (MHD): 200 150 100 50 0 cm 1:20 1:10 1:5 1:10 50 40 30 25 20 10 0 cm 1) Generates a manifold overlaying magnetic field partly shielded by the shell

2) Resulting in Lorenz forces, stirring the metal and electrolyte and piling up a metal pad profile

A complex current path across different material designed to reduce magnetic effects

Main reaction: reduction of alumina based on 6 species (Al₂O_3(sol), Na₂Al₂O₂F₄, Na₂Al₂OF₆, AlF₃, NaF, Na)

 Alumina feeding process  Alumina dissolution

 Equilibrium reaction

 Anode boundary layer reaction  Cathode boundary layer reaction

Local species concentration will affect properties of bath like viscosity, resistivity, ... Time scale: Built-up concentration layer at anode / cathode < milli seconds,

Feeding cycles ≈ hours

Length scale: Concentration layer < 1 mm, species transport = cell length

Aluminium Electrolysis Process

Al2O3(sol)+ 3 NaF + AlF3 →Na2Al2O2F4 Na2Al2O2F42-+ 2 NaF + 2 AlF

3 ↔ 2 Na2Al2OF6

2-2 Na₂Al₂OF₆2-_{+ C →4e}- _{+ 4 AlF}

3 + 4 Na + CO2

Bubble flow:



Bubble nucleation (not fully understood)



Bubble growth



Bubble coalescence



Bubble transport



Bubble release

Releasing CO₂ with a strong stirring effect

Time scale: Release frequency ~ 2-5 seconds

Length scale: nucleon ≈ 0.5 mm, rising plug > 10cm

Metal layer

Carbon anode Bath

Bath flow: based on bubble draft and MHD



Metal surface profile



Metal surface movement



Bubble draft



Lorentz forces around bubble



Convection flow an ledge sides



Bath film around metal (not fully understood)

Time scale: bath speed ~ 10 – 20 cm/s

Length scale: cell length ~ 10 - 20 m, height of reaction zone < 40mm Aluminium Electrolysis Process

Time and length scales

•

Magnetic field and species reaction quite fast  separable

•

Bath and bubble flow in same domain  how to interlink

•

Species transport with longer time scale  separable 10 decades in time

1)

Magneto-hydrodynamics (MHD)

•

Magnetic field is establishing fast

•

Overall current distribution in cell long term stable (anode cycle ≈ 24 days)

•

Metal pad is mainly defined by overall current distribution

•

Metal flow speed is mainly defined by overall current distribution But

•

bubble movement => local current distribution below anode

•

MHD instabilities can initiate surface waves on metal pad

 Steady state approach for magnetic field, metal pad profile and speed. Output forwarded to next step.

2) Chemical reaction of alumina reduction

•

Fast reactions and large diffusion coefficients

 Concentration layers at anode and cathode can be solved as boundary equations.

But

•

Transient feeding pattern (underfeeding / overfeeding) required for cell controll

•

Distribution of species concentration by advection across 20 m long cell

3) Bubble flow

•

Transient approach on bubble formation, coalescence and draft required

•

Detailed anode geometry of rounding and slots are relevant

•

Bubble draft forces in bath stronger than advection from metal surface

 Flow pattern at single anode, based on overall stable global metal pad and magnetic field, can be simulated separately and transferred as volume draft forces and turbulent viscosity into a full cell model

60% 65% 70% 75% 80% 85% 90% 95% 100% 105% 110% 0 4 8 12 16 20 24 Current pick-up

Operation days of anode 4) Full cell bath flow

•

Due to stationary MHD and bubble draft steady state approach sufficient

•

Detailed geometry of ledge profile required

profile and MHD

prediction

Modelling approach:

Flow field

• Two phase flow model approach for bath layer and metal pad

• The volume of fluid (VOF) method is used to simulate the dynamics of the immiscible two-fluid system

Magnetic field:

• Electric current density J is calculated

• Electrical potential distribution is given as current conservation

• Induced magnetic field (B_ind) is obtained by solving magnetic potential vector (A)

• The Lorentz force calculated Turbulence model:

• Standard k-ε turbulence model is solved for the turbulent viscosity that is included in the effective viscosity of fluids.

Modelling approach:

Flow field

• Two phase flow model approach for bath layer and metal pad

• The volume of fluid (VOF) method is used to simulate the dynamics of the immiscible two-fluid system

where __ ____ __, and F_E is the Lorentz force.

• The secondary phase volume fraction _ is accomplished by solving, the continuity equation, and the primary phase volume fraction _ is obtained by the continuity constraint

Steady state metal pad profile and MHD prediction

Magnetic field:

• Electric current density J is calculated as

where  = electrical potential, B = magnetic flux density and = electrical conductivity

• Electrical potential distribution is given as current conservation

• Induced magnetic field (B_ind) is obtained by solving magnetic potential vector (A)

• Induced magnetic field:

Turbulence model:

• Standard k-ε turbulence model is solved for the turbulent viscosity that is included in the effective viscosity of fluids.

Programming structure:

Implementation in ANSYS Fluent by

• User defined scalar for: , A_X, A_Y, A_Z

• User defined functions for: B, J, F_E

• User defined memory for variable storage

Steady state metal pad profile and MHD prediction

Implementation

Mesh adjustments:

• Dynamic tracking of Bath/Metal interface using Fluent VOF (volume fraction 0.5) and sliding mesh approach to adjust anode bottom shape to metal pad profile

Boundary conditions:

• Top surface of bath with wall function

• Realistic side and end ledge profile with standard wall function

• Rigid surface between metal and bath assumed

• Mesh: 35 mm wide gaps between anodes resolved for 4 x 12 m cell geometry

Steady state metal pad profile and MHD prediction

Simulation result:

Metal pad profile (interface between bath and metal layer)

• Introduces an inclination of anode bottom due current dependent anode consumption

• Metal pad profile transferred to full cell bath flow model

Velocity field:

Metal surface speed

• Metal pad surface speed transferred to full cell bath flow model

Steady state metal pad profile and MHD prediction

Result verification:

• Generally good agreement

• Realistic geometry significant

• Induced current significant

• Induced fields time consuming and less significant

• Measurement approach with steel rods

− Rod dissolution profile flow direction

− Dissolved mass / time  flow speed

− Very rough integral method

chemical reaction flow

model

Cooperation with

SINTEF, HIST

Modelling approach

This modelling multi-scale and multi-field approach, aiming to fully resolve the behavior of macroscopic bubbles. Unresolved fields are treated by

applicable sub-grid models

In the model the different physical phenomena are considered:

1) Current  Magnetic field, Lorentz forces

 CO2 generation, Faraday law

2) Species concentrations, especially CO₂

3) Phase fractions, PBM

4) Realized bubbles with VOF method

5) Flow field

Microscopic properties (conductivity, surface tension and contact angles) are dependent upon dissolved, dispersed and continuous fields

Modelling approach

This modelling multi-scale and multi-field approach, aiming to fully resolve the behavior of macroscopic bubbles. Unresolved fields are treated by

applicable sub-grid models

In the model the different physical phenomena are considered:

1) Current  Magnetic field, Lorentz forces

 CO2 generation, Faraday law

2) Species concentrations

3) Phase fractions

4) Realized bubbles with VOF method

5) Flow field

Only metal pad profile is given as geometry profile and velocity boundary condition

Transient bubble and chemical reaction flow model

1) Electro magnetic fields

•

The current density is obtained by solving a Laplace equation for the electrical potential

0

where is the electrical conductivity, both phases (gas, bath) and depending upon local species concentrations (_Bath = f(c_i)).

•

Lorentz forces F_E = J x B_o are obtained by the current density J = - . Induced fields and currents are not considered.

•

CO₂ gas generation is defined to the current density as of Faraday’s equations.

2) Dissolved fields

•

Seven species are considered; Al₂O₃, NaF, AlF₃, Na₂Al₂O₂F₆, Na₂Al₂OF₄, Na+ _{and CO}

2.

•

The behavior of all species is governed by a generic advection-diffusion equation:

⋅

on a mass fraction basis, where is the effective

(turbulent) diffusivity and and are source and sink terms due to consumption and production of the th _specie.

• The saturation of the bath with dissolved CO₂ enables

Transient bubble and chemical reaction flow model

3) Dispersed fields

•

The dispersed field is concerned with small scale bubbles, typically ranging from a diameter of 0.4 mm (nuclei) and up to sizes dictated by the numerical resolution.

•

Dispersed bubbles are modelled by means of a discrete population balance model (PBM), which describes the evolution of number densities under coalescence and mass transfer.

•

Ten classes were used with a ghost class for transition into the two phase model.

Typical 2-5 mm

4 + 5) Continuous fields toward flow fields

•

Continuous (resolved) fields are treated by means of the Volume of Fluid (VOF) method, allowing for

direct simulation of the complex bubble topology present on the anode surface.

•

The VOF-approach is in the current formulation extended in order to allow for dynamic contact angles.

•

Turbulence is modelled by the realizable -model. Transient bubble and chemical reaction flow model

•

Large scale properties, i.e. density and viscosity are dependent upon the distribution of continuous fields.

•

Microscopic properties (conductivity, surface tension and contact angles) are dependent upon dissolved, dispersed and continuous fields.

•

Only restricted information and knowledge on

concentration dependent microscopic properties are available.

Implementation

ANSYS Fluent used as simulation software

•

UDF calculating

− Lorentz forces

− Species concentration based on reactions

− Material properties based on Species concentration

•

Random bubble nucleation points feed by CO₂ of oversaturated bath

•

PBM with 10 classes plus ghost class to reflect sub-grid bubbles

•

VOF bubble reconstruction with enhanced contact angle and surface tension

•

Anode rounding and metal pad profile is given as geometry profile, while velocity from metal interface as boundary condition

Simulation results

Typical simulation on 30 seconds of anode bubble flow with variation of:

•

Current density (CD = 8000/11000 A/m2₎

•

Anode inclination ( = 2° / 4° )

•

Anode cathode distance

CD = 8000 A/m2 _{CD = 11000 A/m}2



2°

4° Gas phase

distribution after 30 sec of flow time Investigation of process parameter

outside today operation conditions possible...

... but huge CPU time of 300 h*) _{with fix}

time steps of 0.001s per simulation needed.

Result verification

Measurement techniques for flow pattern and species concentration are missing in the harsh environment of industrial cells.

Laboratory cell experiment with “large” 10x10 cm anode carried out

•

90 measurement in lab cell with different current density, anode tilting and ACD carried out.

•

Recorded bubble release and cell voltage used for comparison

Transient bubble and chemical reaction flow model

Lab cell experiment:

•

Six simulations have been performed with constant surface tension and contact

angles, with conditions corresponding to a sub-set of the experiments

Lab cell experiment:

•

Voltage signal with similar behavior e.g. at CD=0.8 A/m2_{, the predicted frequency is}

0.54 Hz with amplitude 97 mV, while

corresponding numbers from experiments are 0.44 and 115 mV

•

Overall comparison good

•

Simulations show overall higher release frequencies (bubble speed) and lower amplitudes (smaller bubbles), indicating suppression of certain surface phenomena

Transient bubble and chemical reaction flow model

Modelling approach:

Bath Flow Model – Steady State

• Eulerian-Eulerian or two-fluid model

• Conservation equations for phase mass and phase momentum.

• MHD forces included

• Modified κ- turbulence model in liquid phase only.

• Bubble draft and phase turbulence from zero equation model.

• Time averaged gas distributions, gas & liquid velocities and turbulence quantities.

• Chemical reaction model with 6 species applied

Steady state full cell bath flow model

see P. Witt et al, 10th_{Int Conf on CFD in Oil & Gas,}

Modelling implementation:

Realistic geometry required:

• Anodes of different age considered

• Ledge profile of sides and ends

• Metal pad profile

Anode with deep, flat and no slots Alumina  Feed Area Free Surface Treated as a degassing  boundary Symmetry  Plane Anode Base Gas inlet through 

Simulation results:

• Velocity field stable against temperature changes

• Velocity field stable against viscosity changes

• Turbulent viscosity 1000 time higher than bath viscosity

• High cross flow speed in area with no slots

Steady state full cell bath flow model

see P. Witt et al, 10th_{Int Conf on CFD in Oil & Gas,}

Simulation results:

• Gas accumulation below anode and in slot visible

• High cross flow speed in area with no slots

• Simulation indicating performance deficit of anode toward end of anode cycle

Results verification:

Extensive work was done in the past with water models but not all effects (MHD, contact behavior, surface tension) could be represented.

Actually

• Only point wise measurement of flow speed possible with steel rod method

• No direct measurement of gas accumulation in cryolite bath available

• No tomographic approach for measuring flow fields available at 960°C

More effort needed on measurement technique

Steady state full cell bath flow model

see P. Witt et al, 10th_{Int Conf on CFD in Oil & Gas,}

model

Modelling approach:

Transient transport model

• Time averaged fluxes used to transport and reaction of all species

• Steady state bath flow field is fixed boundary condition.

• A time marching solution gives spatial and temporal variations in alumina and other species concentration and reaction rates

Transient alumina reaction and distribution model

see P. Witt et al, 10th_{Int Conf on CFD in Oil & Gas,}

Modelling implementation:

Based on bath flow model:

• Geometry

• Mesh

• Flow field as boundary condition

• Alumina feed: 0.25 kg every 80 seconds at 5 spots

• Explicit alumina feeding curve can be applied at every feeding spots

Initial mass

fraction of species needs several iteration to stabiles

Simulation results:

1) High undissolved alumina in feeding area

2) Well distribution of dissolved alumina

3) NaF concentration indicating good performance

Transient alumina reaction and distribution model

see P. Witt et al, 10th_{Int Conf on CFD in Oil & Gas,}

Metallurgical and Process Industries, Trondheim, Norway

1

2 3

Simulation results:

• Undissolved alumina is rapidly distributed in the bath around the anode

• 60 seconds after feeding no fluctuation visible

• Dissolved alumina for anode reaction is stable during feeding cycle 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 20000 20010 20020 20030 20040 20050 20060 20070 20080 20090 20100 Mas s   Fr ac ti o n Time [s]

Alumina Point 1 Point 2 Point 3 Point 4 Point 5 Point 6 Point 7 Point 8 Point 9 0.0862 0.0863 0.0864 0.0865 0.0866 0.0867 0.0868 20000 20010 20020 20030 20040 20050 20060 20070 20080 20090 20100 Mas s   Fr ac ti o n Time [s]

Na2Al2OF6 Point 1 Point 2 Point 3 Point 4 Point 5 Point 6 Point 7 Point 8 Point 9

Results verification:

• No direct measurement technique of species in cryolite bath available

• No tomographic approach for measuring species concentration available at 960°C

 Indirect approach by measuring the changing electrical

resistivity at each anode is a possible option for verification

Transient alumina reaction and distribution model

see P. Witt et al, 10th_{Int Conf on CFD in Oil & Gas,}

For simulation of multi-scale, multi-physic process of aluminium electrolysis it was required to:

• Separating effects and apply as initial condition • Reducing complexity and apply as boundary conditions • Couple models by applying volume sources or

Acknowledgement

This work was supported by RCN project ES497436