Project title: Research Area: Motivation of the project: Goals: Planned experimental strategy to reach the proposed goal:

Transcription

1 Project title: Analysis of thermodynamic models of steelmaking refining slags Research Area: Extractive Metallurgy: Control of Metallurgical Processes Motivation of the project: Thermodynamic models for prediction of CaO and MgO saturation in refining slags and corresponding mass balance calculations are important tools for optimization of refining process, (e.g. desulfurization) and for reduction of refractory wear. Computational thermodynamics is already a well developed field and can address the thermodynamic modeling of slags. Nevertheless, to apply it, it is required a rather expert knowledge and careful analysis of its results. Building a model that is based on experimental data and computational thermodynamics but that does not require direct interaction of metallurgist at the shop floor with the intricacies of modeling is a desired outcome. These models provide the calculated amount of fluxes, deoxidizers and alloys to produce an optimum slag at the ladle furnace. Goals: To obtain accurate and updated slag models from experimental phase diagrams and computational thermodynamics to perform mass balance calculations in order to get optimum slags in ladle furnace stations, both in terms of CaO and MgO saturation, that provide good metallurgical outcomes and refractory compatibility. Planned experimental strategy to reach the proposed goal: Review of existing slag models available in literature and perform computational thermodynamics simulations to calculate important parameters in ladle slags as function of slag temperature, such as CaO and MgO saturation point and sulfide capacity. Using the data gathered in the approach described above, correlations will be built that are amenable to use in a computer program or a spreadsheet to perform mass balance calculations to get optimum ladle slags. Main references: ASTH, H. G. Desenvolvimento de escórias de refino secundário para o forno panela da V&M do Brasil f. Dissertação (Mestrado em Engenharia Metalúrgica, Materiais e de Minas Metalurgia Extrativa) PPGEM, Universidade Federal de Minas Gerais, Belo Horizonte, COSTA e SILVA, A. L. V. Objetivos e algumas limitações na descrição termodinâmica de fases e sistemas através do método CALPHAD. Tecnologia em Metalurgia e Materiais, v. 3, n.3, jan.- mar. 2007, p OERTEL, L. C.; COSTA e SILVA, A. L. V. Application of thermodynamic modeling to slag-metal equilibria in steelmaking. Calphad, v. 23, n. 3-4, p , PRETORIUS, E. Slags and the relationship with refractory life and steel production. Short duration course presented at XXXIII ABM Steelmaking Seminar p. PRETORIUS, E.; MARR, R. Computer modeling of refractory/slag/metal interactions. In: INTERNATIONAL CONFERENCE ON MOLTEN SLAGS, FLUXES AND SALTS, 5, 1997, Sydney, Australia. Proceedings Warrendale: Iron and Steel Society p SOMMERVILLE, I. D.; YANG, Y. Basicity of metallurgical slags. The AusIMM Proceedings, v. 306, n. 1, 2001, p VDEh. Slag Atlas. 2nd. ed. Düsseldorf: Verlag Stahleisen GmbH,

2 1. Introduction Slags are essential components in every pyrometallurgical process (VDEh, 1995): more than only a by-product as they are sometimes considered, they are active in metal refining in operations like desulfurization and dephosphorization in steelmaking. Other functions performed by slags are the protection of refractories and water panels from arc flare in electric arc furnaces (EAF) and ladle furnaces (LF), capture of inclusions formed by the deoxidation reactions and formation of an insulation layer to prevent steel reoxidation and temperature losses (ASTH, 2011, p. 4). An old adage saying take care of the slag and the steel will take care of itself is well known in slag literature (OERTEL, COSTA e SILVA, 1999). The chemical and physical properties of slags are relevant when considering their interaction with furnace or ladle refractories (PRETORIUS, 2002, p. 4,5,9,12 and 14). Slags that are too fluid can easily penetrate in refractories pores and cracks, leading to fast degradation of the lining. On the other hand, a crusty slag brings difficulties in the process, such as accumulation of alloys and fluxes on their top, slow kinetics in metal-slag reactions and a blockage for penetration of injection lances and electrodes. As slaglines of EAF and LF are mostly made of MgO-based or dolomite-based refractories, another important factor is the slag dissolution potential of CaO and/or MgO present in refractories (PRETORIUS, 2002, p. 14). An isothermal section of CaO-MgO-SiO 2 at 1600 C, shown in Figure 1, gives an idea of chemical requirements of a good slag for a ladle refining. All compositions in blue region are fully liquid. A point inside this region is not saturated in CaO and/or MgO and, besides being too fluid, can dissolve CaO and/or MgO from refractories until saturation line is reached. To be compatible with MgO-C refractories, the MgO saturation line must be the target for an optimum slag (PRETORIUS, 2002, p. 14, 68). The CaO saturation, on the other hand, is important in two situations: 1) to be compatible with dolomite refractories, and 2) to provide slag with a strong tendency to capture S from steel. This second situation is applicable even when the lining is made of MgO-C refractories. So, even in this case, a dual saturation point must be the optimum target for a good ladle slag. Figure 1: Isothermal section for the CaO-MgO-SiO 2 system at 1600 C Source: PRETORIUS, MARR, Adapted. Although the diagram presented in Figure 1 is very useful and informative, actual ladle refining slags are not limited to this three-component slag system. Systems usually found in secondary refining includes CaO, MgO, Al 2 O 3, SiO 2, FeO, MnO and CaF 2, so an approach should be selected to describe appropriately a starting slag so that calculations to reach an optimum composition are possible. Experimental available phase diagrams generally cover three to four components at some important temperatures, such as 1600 C and 1700 C. Both intermediate temperatures and more complex compositions (5 or more components in slags) should be handled in a different way: either simplifying the system to a 3 or 4 component slag system, usually the closest system to the real slag, and making use of derived correlations 2

3 from these experimental diagrams or, if available, from computational thermodynamic calculations (e.g. using Thermo-Calc and FactSage) to investigate higher-order systems, without simplifying assumptions. An example of an approach based on published phase diagrams is shown by Pretorius and Marr (1997, p. 285), where an expression for evaluation of MgO solubilities in CaO-MgO- Al 2 O 3 -SiO 2 slags at 1600 C is presented: % MgO = Λ 2092 Λ Equation 1 In Equation 1, Λ is the optical basicity, defined elsewhere (SOMMERVILLE, YANG, 2001). This equation is valid for Λ greater than 0.69 and is valid until the slag reaches saturation in CaO. Figure 2 shows MgO saturation levels as a function of optical basicity for 4 different contents of alumina in slag. Note that the upper boundaries for each alumina content are indicated. Another interesting point here is the superposition of curves for high optical basicity that is what explain just one formula to represent this interval. Figure 2: MgO saturation levels in slags of the system CaO-MgO-Al 2O 3-SiO 2 at 1600 C. Source: PRETORIUS, MARR, According to Oertel and Costa e Silva (1999), slags are a high complexity system in metallurgy and this complexity is reflected in the number of models developed to faithfully describe some portion of their behavior. Ionic two-sublattice model, Ban-ya s regular solution model, Gayes s model (a.k.a as IRSID model), quasichemical model and the associated solution model are amongst the various models developed during the last decades. It is also mentioned that the level of agreement between model and experimental data is not the same among the available models. Therefore, computational thermodynamic packages require an expert knowledge to take advantage of their powerful features and also require an availability of licenses and databases (usually expensive) to perform calculations (usually time consuming). This limit somehow the direct application of their results at the steelmaking shop. So an approach to allow offline usage of computational thermodynamic calculations should be devised. One example of expert knowledge required to make effective use of computational thermodynamics is shown in Figure 3. Two different models were selected to describe the Gibbs free energy of liquid present in system MgO-Al 2 O 3 -SiO 2 and some differences in calculated results are noted. For example, the area and shape of mullite and spinel s primary phase fields are different in both diagrams. The primary phase field of MgO is quite similar in both approaches, however. 3

4 Legend Cr: Cristobalite SiO 2 Tr: Trydimite SiO 2 Cord: Cordierite 2MgO.2Al 2O 3.5SiO 2 Ol: Olivinine 2MgO.SiO 2 Sp: Spinel MgO.Al 2O 3 Mul: Mullite 3Al 2O 3.2SiO 2 Ppx: Protoenstatite MgO.SiO 2 Sap: Sapphirine 4MgO.5Al 2O 3.2SiO 2 Liq2: SiO 2-rich liquid (a) (b) Figure 3: Liquidus surface of the system MgO-Al 2O 3-SiO 2 calculated according to two different models for the Gibbs free energy of the liquid. (a) Kapoor-Frohberg-Gaye model (b) Ionic liquid model. Source: COSTA e SILVA, 2007, p. 37. Irrespective of the approach employed (1 - correlations built from experimental phase diagrams, 2 - calculations from computational thermodynamics package or 3 - a mixed approach), a target optimum slag in terms of chemical and physical properties should be calculated. The principles of CaO and MgO saturation described earlier apply here as well. In the 1990 s, Eugene Pretorius and Robert C. Nunnington developed a series of slag models for application in steelmaking processes. For ladle slags, they analyzed experimental phase diagrams of the system CaO-MgO-Al 2 O 3 -SiO 2 (CAMS) to develop correlations to be used in a procedure that takes the chemistry of starting slag and calculates the chemistry of the optimum slag, according to an optimization criteria: dual saturation, CaO saturation, MgO saturation or fixed MgO percentage. Each criterion is suitable for different situation in ladles, as briefly explained in the text for Figure 1. Table 1 gives two examples of slag optimization calculation: starting slag #1 is a fictious one from CAMS system and starting slag #2 is a real starting ladle slag from an aluminium-killed steel. Both optimum slags are dual saturated in terms of CaO and MgO. The effect of CaO addition in optimum slag #1 as calculated in Thermo-Calc 2015a is illustrated in Figure 4: as the MgO precipitation does not start immediately, this slag can be not exactly dual saturated. 7 g of CaO are added before slag becomes MgO saturated. Table 1: Dual Saturated Ladle Slags at 1600 C - Examples MgO CaO FeO Al 2O 3 SiO 2 MnO Cr 2O 3 Starting Slag # Optimum Slag # Starting Slag # Optimum Slag # CaF 2 TiO Figure 4: Effect of CaO addition in optimum slag #1 using Thermo-Calc results: after circa of 7 g of CaO added to an initial 100 g of optimum slag #1, MgO starts precipitating from slag. 4

5 The calculation of the composition of an optimum slag is only one system s building block. Another one, as important as the first, is the mass balance of all inputs that participates in slag formation in ladle (carryover slag, fluxes and deoxidation products). With these components working together, one can obtain an optimum slag through the careful calculation of flux additions, slag deoxidizers and alloys. This optimum slag will serve a two-fold purpose: be suitable for refining steel and also compatible with slag line refractories. The goal of this project is to review the existing slag model (developed by Eugene Pretorius and Robert Nunnington as described earlier) using a full review of available literature on slag modeling and performing thermodynamic simulations using Thermo-Calc software. The specific goal is to review calculation of dual saturation (CaO and MgO) composition of slags (like the ones presented in Table 1) using new experimental data (available in literature) and thermodynamic simulations (like the one shown in Figure 4). 2. Materials and Methods The development of a revised mathematical slag model to be applied in ladle metallurgy will be carried out through two paths: 1. A complete review of experimental measurements of phase diagrams and related parameters available in literature (sulphide capacity, liquidus temperatures, activity of oxides, etc.) as well as of mathematical models also available in literature and construction of mathematical correlations amenable to software coding; 2. Thermodynamic simulations using Thermo-Calc 2015a (license owned by Magnesita Refractories) and FactSage (through Magnesita partnership with UFSCar) and tabulation of data for construction of mathematical correlations. This path involves validation of calculated results against the experimental evidences in lower-order systems (3 to 4 components). The first path will require the careful investigation of literature from the last 25 years using the resources provided by the university, through its access to Periódicos Capes consortium. A wealthy literature is also provided by the website that hosts some of proceedings of Molten Slags, Fluxes and Salts Conferences and also by research groups involved in phase diagram assessment through CALPHAD methodology. Key research groups in this area are In-Ho Jung s group of McGill University and Computational Thermodynamics division from Royal Institute of Technology (KTH), Sweden. The second path will involve the use of SLAG2 database, provided by Thermo-Calc, which is based on IRSID activities in slag modeling in 1980 s and reviewed by Thermo-Calc along the years. The results of thermodynamic calculations will be used to build mathematical correlations from these data. The final slag model will based on a critical assessment of data obtained through the two research paths and the corresponding correlations will be constructed and implemented in a reviewed version of a ladle slag model. 3. Research resources As a mathematical modeling project, the resources needed are access to scientific literature as already provided by Periódicos Capes to the university and a license of Thermo- Calc and SLAG2 database, owned by Magnesita Refractories. It is also possible, through the 5

6 partnership between Magnesita Refractories and UFSCar s GEMM lab, the access to FactSage simulations. In the scope of this project, it is not expected to have physical experimentation on slag/metal reactions or slag characterization, so there is no need, a priori, to have laboratory resources available in order to get the established goals. 4. Proposed schedule Months Mar Apr May June July Aug Sept Oct Nov Dec Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Jan Feb 2019 Step Literature Review Mathematical correlations from experimental data Thermodynamic Simulations in Selected Systems Mathematical correlations from calculated data Construction of a routine to calculate optimum slags from initial slags Dissertation writing and review Dissertation Defense 5. Bibliographic references GAYE, H. et al. Slag modelling and industrial applications. High temperature materials and processes, v. 20, n. 3-4, Oct. 2001, p KANG, Y-B.; PELTON, A. D. Thermodynamic model and database for sulfides dissolved in molten oxide slags. Metallurgical and Materials Transactions B, v. 40B, n. 6, Dec. 2009, p LEHMAN, J.; JUNG, I-H.; ZHANG, L. The main thermo-statistical models of metallurgical slags: theory and applications. In: INTERNATIONAL CONFERENCE OF MOLTEN SLAGS, FLUXES AND SALTS, 9, 2012, Beijing, China. Proceedings Beijing: The Chinese Society for Metals, 2012 LUZ, A. P. et al. Slag conditioning effects on MgO-C refractory corrosion performance. Ceramics International, v. 39, n. 7, 2013, p MILLS, K. C. The estimation of slag properties. Short duration course presented as part of Southern African Pyrometallurgy, March 07th, p. PRETORIUS, E.; MARR, R. The effect of slag modeling to improve steelmaking processes. In: ELECTRIC FURNACE CONFERENCE, Proceedings Warrendale: AIST, SOUZA, D. A.; CONCEIÇÃO, P. V. S.; GONZALEZ, F. J. L.; LUZ, A. P. Use of slag modeling for increasing ladle refractory service life. In: IAS STEEL CONFERENCE, 21, September 13-15, 2016, Rosário, Argentina. Proceedings San Nicolás: Instituto Argentino de Síderurgia,