Modelling of Slags in the Molten State

Transcription

1 Modelling of Slags in the Molten State Anton S. Lopis a,b, Quinn G. Reynolds a and Kabwika Bisaka a a Pyrometallurgy Division, Mintek, Randburg b CHPC, Rosebank, Cape Town South Africa

2 Smelting in DC Arc Furnace Pyrometallurgy Division of Mintek has Pilot scale DC Arc Furnaces up to 5.6MVA for smelting R&D Ores are smelted: metals, alloys and slag (generally a waste material) Process Improvement Desired: Increased Efficiency, Reduced Energy and Environmental Impact, etc

3 Atomistic Modelling of in DC Arc Furnace Variables: Electrical power, temperature, heating/cooling rates, composition of components, and smelting sequence A better understanding is invaluable Atomistic Modelling can provide Previously modelled metals and alloys in molten state Currently: Molten Slags especially vital property of electrical conductivity

4 Models Employed Two models have been employed in this work: a Polarizable model [1,2] and a Pair Potential model [3] The Porarizable model by Beck et al includes oxygen polarisability by using a Tangney-Scandolo (T-S) interatomic potential involving interactions between charges and induced dipoles Electrostatic summations are dealt with using the Wolf direct lattice summation Models from this work are available for MgO, SiO 2 and Al 2 O 3 so further model development would be required (such as using the Force Matching approach and QM data) T-S and Wolf are implemented properly in the IMD program, so we used IMD 1. P. Beck, P. Brommer, J. Roth and H.-R. Trebin, Ab initio based polarizable force field generation and application to liquid silica and magnesia, J. Chem. Phys., 135 (2011) S. Hocker, P. Beck, S. Schmauder, J. Roth and H.-R. Trebin, Simulation of crack propagation in alumina with ab initio based polarizable force field, J. Chem. Phys., (2012) B. Guillot and N. Sator, A computer simulation study of natural silicate melts. Part I: Low pressure properties, Geochimica et Cosmochimica Acta 71 (2007)

5 Models Employed Guillot and Sator (G-S) employ a model using partial charges and a Buckingham potential [3] The model is however applicable to a variety of silicate melts: SiO 2, TiO 2, Al 2 2O 3, Fe 2 O 3, FeO, MgO, CaO, Na 2 O and K 2 O They have used DL_POLY, and DL_POLY_2.20 is our standard MD platform in any case (although any MD code should be able implement this model) Preliminary calculations were performed for MgO and SiO 2 using both models However, we have insufficient results to compare the models properly. G-S was chosen for the work presented here - flexibility 3. B. Guillot and N. Sator, A computer simulation study of natural silicate melts. Part I: Low pressure properties, Geochimica et Cosmochimica Acta 71 (2007)

6 Electrical Conductivity from MD Trajectories The electrical conductivity for an ionic system is calculated in terms of ionic diffusion The diffusion coefficient D is computed from the mean square displacement of the ions: The exact conductivity is computed in terms of atom displacements: If one omits the cross terms one obtains the Nernst-Einstein approximation in terms of D: Implementing the exact calculation is underway, but is proving rather tricky

7 Validation: Peridotite Example One of the systems studied by Guillot-Sator (G-S) is peridotite and comprises the main components of interest Peridotite is useful for validation and was set up and simulated in a similar manner to our other slag systems using the G-S model 3.5 Oxygen: Diffusion Coeff 2.5 Si: Diffusion Coeff D (10-9 m2/s) O Guillot & Sator D (10-9 m2/s) Si Guillot & Sator Diffusion coefficients of the slower ions are shown

8 Validation: Peridotite Example The diffusion coefficients of fastest moving ions are shown Not shown are Al (slow) and Ca (medium speed) Agreement with G-S is clear for Mg and Fe (Ca & Al as well) The relative speed of the ion types is consistent between all the slag systems studied Mg: Diffusion Coeff Fe: Diffusion Coeff D (10-9 m2/s) Mg Guillot & Sator D (10-9 m2/s) Fe Guillot & Sator

9 Physical Properties for Slags Studied Density and enthalpy T dependence are shown for Peroditite and slag systems A, B, and C Peroditite density is also validated against G-S The values obtained are not unreasonable The density and enthalpy slopes are the useful properties of thermal expansion coefficient and heat capacity Density Enthalpy A C B Peridotite Guillot & Sator A C B Peridotite Density (g/cm3) Enthalpy (J / K / mol)

10 Electrical Properties for Slags Studied Electrical conductivity Nernst-Einstein results for peridotite are shown in terms of each ion's contribution to the total (LHS) There are major differences between NE and the exact calculation of G-G The resistivity of 3 slag systems of interest and Peridotite shown (RHS) Electrical Conductivity Electrical Resistivity Conductivity (s/m) O Mg Si Ca Fe Al Sum Guillot & Sator Resistivity (ohm.cm) Compos_A Compos_B Compos_C Perdotite Guillot & Sator Resitivity behaviour is reasonable

11 Electrical Resistivity Comparisons Mass % ρ ρ Composition CaO FeO MgO SiO2 Al2O3 (N-E) Empirical Correlation (Ω cm) (Ω cm) A B C Peridotite Resistivity is compared with an experimentally based correlation Haven Ratio (H) is correction between Nernst-Einstein (NE) and Exact conductivity (1/resistivity) values For Ionic Liquids H in range: 1.3 to 2 [4] For Glasses H in the range: 0.2 to 0.5 [5] 4. T. Fromling, M. Kunze, M. Schonhoff, J. Sundermeyer, and B. Roling, Enhanced Lithium Transference Numbers in Ionic Liquid Electrolytes, J. Phys. Chem. B, 112 (2008) J.O. Isard, The Haven ratio in glasses, J. Non-Cryst. Sol., 246 (1999)

12 Electrical Resistivity Comparisons Mass % ρ ρ Composition CaO FeO MgO SiO2 Al2O3 (N-E) Empirical Correlation (Ω cm) (Ω cm) A B C Peridotite Composition A with low SiO2 content is likely an ionic liquid (H = 1.3 to 2) Composition B, C and Peridote are glasses (H = 0.2 to 0.5) Hence dividing NE resistivity values by H yields agreement Analysis code for Exact conductivity is under development, but tricky

13 Conclusions The simulation results are all reasonable and have been validated against the literature Electrical conduct. calculated with Nernst-Einstein approx. are in the right ballpark as experiment Corrected values using Haven Ratio agreement Important - get exact conduct. analysis code working Overall the methodology is successful Model adjustment is possible using Force Matching Development of a database of slag properties (especially conductivity) is possible

14 Acknowledgements Department of Minerals and Energy: Funding PhD Student based at CHPC : Lehlohono Mongalo CHPC: Computational Infrastructure: Happy Sithole Jeff Y. Chen S. Eric Mbele Samuel M. Mabakane Sakhile Masoka

15 Thank you