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

Transcription

1 Multi-scale, Multi-physics Simulation Approach for Aluminium Electrolysis Ingo Eick 1, Jinsong Hua 2, Kristian E. Einarsrud 3, Peter Witt 4, Wei Bai 5 1 Hydro Aluminium, 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 (1)

2 Content 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) Conclusions (51-52) Page 2/TC

3 (3) Hydro Aluminium Metal Production

4 Hydro Aluminium Metal production facilities North America mt smelting capacity in 1 smelter mt remelt capacity in 6 remelters Europe: mt primary smelting capacity in 7 smelters mt remelt capacity in 6 remelters Middle East: mt smelting capacity in 1 smelter Australia: mt primary smelting capacity in 2 smelters Bauxite/alumina Smelters Remelters All production figures are from 2012 Carribean / South America: mt alumina production in 2 alumina refineries mt bauxite production in 2 bauxite mines 1 MoU new alumina JV project (Hydro share: 20%) 4

5 Hydro Aluminium Primary Production (2012) Location Share Start-up Technology Layout amperage Cells Operating amperage Increase Ardal AI 100% 1970 HAL ka ka 131% Ardal AII 100% 1978 HAL ka ka 128% Hoyanger L2 100% 1981 HAL ka ka 123% Husnes L1/2 100% 1965 EPT ka ka 107% Karmoy L3 100% 1987 AP ka ka 133% Sunndalsora SU3 100% 1968 HAL ka ka 116% Sunndalsora SU4 100% 2003 HAL ka ka 114% Rheinwerk L1 100% 1962 CA ka ka 150% Rheinwerk L2 100% 1965 CA ka ka 144% Rheinwerk L3 100% 1965 CA ka ka 144% Qatar L1/2 50% 2010 HAL ka ka 118% Ziar Nad Hronom L3 50% 1996 HAL ka ka 111% Sept-Iles L1 20% 1992 AP ka ka 124% Sept-Iles L2 20% 2005 AP ka ka 109% Tomago L1 12% 1983 AP ka ka 125% Tomago L2 12% 1993 AP ka ka 125% Tomago L3 12% 1998 AP ka ka 114% 5

6 Continuous improvement in metal production Step change New design features Very low energy consumption Neutral CO2 foot print New design Operational excellence Reduce variations Reduce emissions Reduce energy consumption Capacity creep Increase production on same technology base Simulation, a main tool, supporting the continuous improvement process 6

7 (7) Aluminium Electrolysis Process

8 Aluminium Electrolysis Process Main principle: Al 2 O 3 powder CO 2 (g) Al 2 O 3 powder CO 2 (g) Current ka Carbon anode Carbon anode Cryolite melt (bath) (960 C) Al (l) Cryolite melt (960 C) Al (l) Cathode Cathode cell 1 cell

9 Aluminium Electrolysis Process Magnetic effects: A series of 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 9

10 Aluminium Electrolysis Process Magneto-Hydrodynamics (MHD): A complex current path across different material designed to reduce magnetic effects 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 cm cm 1:20 1:10 1:5 1:10 10

11 Aluminium Electrolysis Process Main reaction: reduction of alumina based on 6 species (Al 2 O 3(sol), Na 2 Al 2 O 2 F 4, Na 2 Al 2 OF 6, AlF 3, NaF, Na) Alumina feeding process Alumina dissolution Equilibrium reaction Anode boundary layer reaction Cathode boundary layer reaction Al 2 O 3(sol) + 3 NaF + AlF 3 Na 2 Al 2 O 2 F 4 Na 2 Al 2 O 2 F NaF + 2 AlF 3 2 Na 2 Al 2 OF Na 2 Al 2 OF C 4e AlF Na + CO 2 AlF Na + 3e - Al + 3 NaF 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 11

12 Aluminium Electrolysis Process Bubble flow: Bubble nucleation (not fully understood) Bubble growth Bubble coalescence Bubble transport Bubble release Releasing CO 2 with a strong stirring effect Bath layer Carbon anode Metal layer Time scale: Release frequency ~ 2-5 seconds Length scale: nucleon 0.5 mm, rising plug > 10cm 12

13 Aluminium Electrolysis Process 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 Streamlines in reaction zone Bath film around metal (not fully understood) Time scale: bath speed ~ cm/s Length scale: cell length ~ m, height of reaction zone < 40mm 13

14 Modelling assumptions Time and length scales 10 decades in time 7 decades in length 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 14

15 Modelling assumptions 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. 15

16 Modelling assumptions 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 Alumina distribution requires transient approach 16

17 Modelling assumptions 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 17

18 Modelling assumptions 4) Full cell bath flow Due to stationary MHD and bubble draft steady state approach sufficient Detailed geometry of ledge profile required Single anode shape relevant for bubble release and current pick-up Current pick-up 110% 105% 100% 95% 90% 85% 80% 75% 70% 65% 60% Operation days of anode 18

19 Steady state metal pad profile and MHD prediction Cooperation with IFE (19)

20 Steady state metal pad 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 twofluid 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: see J. Hua et al, Light Metals 2014, 691 Standard k-ε turbulence model is solved for the turbulent viscosity that is included in the effective viscosity of fluids. (20)

21 Steady state metal pad profile and MHD prediction Modelling approach: Flow field Two phase flow model approach for bath layer and metal pad see J. Hua et al, Light Metals 2014, 691 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 (21)

22 Steady state metal pad profile and MHD prediction Magnetic field: Electric current density J is calculated as see J. Hua et al, Light Metals 2014, 691 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: The Lorentz force: (22)

23 Steady state metal pad profile and MHD prediction Turbulence model: see J. Hua et al, Light Metals 2014, 691 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 (23)

24 Steady state metal pad profile and MHD prediction Implementation Mesh adjustments: see J. Hua et al, Light Metals 2014, 691 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 Spring smooth is used to improve volume mesh quality (24)

25 Steady state metal pad profile and MHD prediction 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 see J. Hua et al, Light Metals 2014, 691 Mesh: 35 mm wide gaps between anodes resolved for 4 x 12 m cell geometry (25)

26 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 see J. Hua et al, Light Metals 2014, 691 (26)

27 Steady state metal pad profile and MHD prediction Velocity field: Metal surface speed Metal pad surface speed transferred to full cell bath flow model see J. Hua et al, Light Metals 2014, 691 (27)

28 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 see J. Hua et al, Light Metals 2014, 691 Measurement approach with steel rods Rod dissolution profile flow direction Dissolved mass / time flow speed Very rough integral method More precise approach needed (28)

29 Transient bubble and chemical reaction flow model Cooperation with SINTEF, HIST (29)

30 Transient bubble and chemical reaction flow model 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 CO 2 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 see K.E. Einarsrud et al, Light Metals 2015, 649 (30)

31 Transient bubble and chemical reaction flow model 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 see K.E. Einarsrud et al, Light Metals 2015, 649 (31)

32 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. CO 2 gas generation is defined to the current density as of Faraday s equations. see K.E. Einarsrud et al, Light Metals 2015, 649 (32)

33 Transient bubble and chemical reaction flow model 2) Dissolved fields Seven species are considered; Al 2 O 3, NaF, AlF 3, Na 2 Al 2 O 2 F 6, Na 2 Al 2 OF 4, Na + and CO 2. The behavior of all species is governed by a generic advection-diffusion equation: see K.E. Einarsrud et al, Light Metals 2015, 649 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 CO 2 enables nucleation of CO 2 bubbles on pores. (not fully understood) (33)

34 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. see K.E. Einarsrud et al, Light Metals 2015, 649 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 (34)

35 Transient bubble and chemical reaction flow model 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. see K.E. Einarsrud et al, Light Metals 2015, 649 (35)

36 Transient bubble and chemical reaction flow model see K.E. Einarsrud et al, Light Metals 2015, 649 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. (36)

37 Transient bubble and chemical reaction flow model 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 CO 2 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 (37)

38 Transient bubble and chemical reaction flow model Simulation results Typical simulation on 30 seconds of anode bubble flow with variation of: Current density (CD = 8000/11000 A/m 2 ) Anode inclination ( = 2 / 4 ) Anode cathode distance Investigation of process parameter outside today operation conditions possible... 2 CD = 8000 A/m 2 CD = A/m 2... but huge CPU time of 300 h *) with fix time steps of 0.001s per simulation needed. *) 12 cores on SGI Altix 8600 cluster 4 Gas phase distribution after 30 sec of flow time (38)

39 Transient bubble and chemical reaction flow model 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 see K.E. Einarsrud et al, Light Metals 2015, 649 (39)

40 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 see K.E. Einarsrud et al, Light Metals 2015, 649 (40)

41 Transient bubble and chemical reaction flow model Lab cell experiment: Voltage signal with similar behavior e.g. at CD=0.8 A/m 2, 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 (coalescence and adhesion). see K.E. Einarsrud et al, Light Metals 2015, 649 (41)

42 Steady state full cell bath flow model Cooperation with CSIRO (42)

43 Steady state full cell bath flow model Modelling approach: see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway 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 (43)

44 Steady state full cell bath flow model Modelling implementation: Realistic geometry required: Anodes of different age considered Ledge profile of sides and ends see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway Anode with deep, flat and no slots Metal pad profile Symmetry Plane Free Surface Treated as a degassing boundary Alumina Feed Area Anode Base Gas inlet through red surfaces (44)

45 Steady state full cell bath flow model Simulation results: see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway 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 (45)

46 Steady state full cell bath flow model Simulation results: see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway 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 (46)

47 Steady state full cell bath flow model 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 see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway More effort needed on measurement technique (47)

48 Transient alumina reaction and distribution model Cooperation with CSIRO (48)

49 Transient alumina reaction and distribution model see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway 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 (49)

50 Transient alumina reaction and distribution model see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway 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 Initial mass fraction of species needs several iteration to stabiles Explicit alumina feeding curve can be applied at every feeding spots (50)

51 Transient alumina reaction and distribution model see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway Simulation results: 1) High undissolved alumina in feeding area 2) Well distribution of dissolved alumina 1 3) NaF concentration indicating good performance 3 2 (51)

52 Transient alumina reaction and distribution model see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway 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 Mass Fraction Alumina Point 1 Point 2 Point 3 Point 4 Point 5 Point 6 Point 7 Point 8 Point 9 Mass Fraction Na2Al2OF6 Point 1 Point 2 Point 3 Point 4 Point 5 Point 6 Point 7 Point 8 Point Time [s] Time [s] (52)

53 Transient alumina reaction and distribution model see P. Witt et al, 10 th Int Conf on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway 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 (53)

54 Conclusions 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 (54)

55 Acknowledgement This work was supported by RCN project ES Thanks you for your attention (55)