Abstract
Precise control of inclusion and molten steel compositions during ladle-furnace refining is important to obtain high-quality steel. Mass-transfer behavior affects these compositions. A model was developed to investigate the mass transfer occurring between molten steel, slag, inclusions, and the refractory during ladle-furnace refining, using two-film theory to describe the reactions. A coupled-reaction model based on the CaO–Al2O3–MgO–SiO2–FeO–P2O5 slag and Mn–Si–Al–Ca–Mg–P–S–O steel systems was applied to describe the reactions between molten steel and slag; the reactions between the refractory lining and slag or steel were described using average industrial erosion rate data. The model was used to calculate changes in the compositions of molten steel and slag, oxygen activity at the slag–molten steel interface, and composition of the inclusions. The calculated results agreed with operational results for a 100 t ladle furnace at the Tangsteel plant in China.
Introduction
Ladle furnaces (LF) are commonly used for high-quality steelmaking to obtain molten steel with an appropriate composition and to control the composition of inclusions. A series of complex reactions occurs between steel, slag, and inclusions during ladle refining. Many researchers have investigated LF refining processes from the perspective of thermodynamics [1, 2]. Although the reactions occurring between steel, slag, and inclusions may reach equilibrium in the laboratory, this does not always occur during industrial production because of inadequate refining time. Mass-transfer behavior affects the compositions of molten steel, slag, and inclusions; however, its mechanism is not completely understood. It is therefore necessary to analyze mass-transfer behavior under industrial conditions during LF treatment.
Graham et al. [3] analyzed the operational results of a 165 t ladle-refining process and predicted the compositional changes in metal and slag using a coupled-reaction model. Following this work, Harada [3, 4, 5, 6] developed a kinetic model that considered the reactions between metal, slag, and inclusions in the CaO–Al2O3–MgO–SiO2–FeO slag and Mn–Si–Al–Ca–Mg steel systems. Jia et al. [7] developed a mass-transfer model for Mg transfer between metal, slag, and inclusions in the Tangshan Iron and Steel Group Company Limited (Tangsteel) plant (Hebei, China). No other elements in the molten steel were investigated. To date, changes in [P] in molten steel and the oxygen activity at the steel–slag interface during ladle refining have not been investigated.
In this study, a kinetic model was built to predict the compositions of molten steel, slag, and inclusions in the CaO–Al2O3–MgO–SiO2–FeO–P2O5 slag and Mn–Si–Al–Ca–Mg–P–S steel systems for the first time. The kinetic model was based on and compared with operational conditions in the Tangsteel plant. The oxygen activity at the steel–slag interface was calculated and a parametric study conducted. A series of samples was collected during production to examine the precision of the model.
Kinetics model
To analyze the compositions of slag, steel, and inclusions, a kinetics model based on two-film theory and the coupled-reaction model was developed. The reactions considered are shown in Figure 1. Slag–steel reactions, inclusion–steel reactions, and refractory erosion (including the slag line and wall) were considered.

Schematic illustration of the model.
Slag–steel kinetic model
A coupled-reaction model, frequently applied to describe the dephosphorization and desulfurization reactions in molten iron and the reoxidation reactions in molten steel by slag, was adopted [8, 9, 10]. The coupled-reaction kinetic model was based on the CaO–Al2O3–MgO–SiO2–FeO–P2O5 slag and the Ca–Al–Mg–Si–Fe–P–O steel systems. Equation (1) describes the reactions between slag and steel:
The oxidation of elements including Mg, Al, Ca, Mn, Si, P, Fe in the molten steel can be expressed by eq. (1). Equation (2) describes the equilibrium constant KM:
The desulphurization reaction was described by eq. (3), and the equilibrium constant was defined by eq. (4):
The effective equilibrium constants Em for elements including M=Mg, Al, Ca, Mn, Si, P in the molten steel were expressed by eq. (5):
Equations (6) and (7) describe the effective equilibrium constants for the oxidation of Fe and the desulphurization reaction:
During the transfer of component M from molten steel to slag, the flux density should be constant; this was expressed by eq. (8):
where JM is the molar flux density (mol·mm−2·s−1), km is the conventional mass-transfer coefficient defined by mole/volume concentrations (mm·s−1), and ρ is the density of the metal or slag phase (kg·m−3).
The flux densities of Fe, S, and dissolved oxygen were given by eqs (9), (10), and (11), respectively:
The concentrations of the species at the interface of molten steel and slag were calculated using eqs (12)–(17).
Equation (18) expressed the electrical neutrality and suggested that the total molar flux densities of cations and anions were equal. The oxygen activity at the interface of molten steel and slag was calculated using eq. (18):
The mass transfer of each component in the slag and molten steel was then calculated at a time step (Δt) using eqs (19) and (20), respectively. The cross-sectional area of the bath was used as the interfacial area between the metal and slag phases.
where Δ[%M] is the concentration change of a component in the metal phase (mass %) and ΔWx is the mass change of each component (g).
Kinetic model for inclusions and steel
The inclusion and steel model was based on two-film theory. The diffusions of Mg and Ca in molten steel were assumed to be rate-controlled steps, and the shapes of the inclusions were considered to be spherical with an outer diameter (Ro) that remained constant with time.
Based on eq. (21) and the balance of Mg and Ca in the metal and inclusions, the compositions of the inclusions were given by eqs (22) and (23):
Refractory erosion
Refractory erosion affects the chemical composition of molten steel. A model was therefore developed for evaluating erosion of the refractory lining, including at the slag line and ladle wall. The reaction between slag and the slag line was modeled by the dissolution of MgO in the ladle wall into the slag. The reaction between the ladle wall and molten steel was modeled using eqs (24) and (25):
A flow chart for the computations is shown in Figure 2.

Flow chart of the kinetic model.
Parameters
Equilibrium constant
The equilibrium constants of the reactions [11, 12] in the model are shown in Table 1.
Equilibrium constants of reactions.
| Reactions | logKM-O |
|---|---|
| [P] + 2.5[O]=(PO2.5) | 18425/T−14.53 |
| [Si] + 2[O]=(SiO2) | 32000/T−12.29 |
| [Fe] + [O]=(FeO) | 6340/T−2.745 |
| [S] + (CaO)=(CaS) + [O] | 1166/T−2.103 |
| [Ca] + [O]=(CaO) | 48521/T−4.028521 |
| [Mn] + [O]=(MnO) | 28950/T−12.52 |
| [Al] + 1.5[O]=(AlO1.5) | 29465/T−8.75 |
| [Mg] + [O]=(MgO) | 81217/T−23.05 |
Activities of molten steel component
The activities of the molten steel components were calculated using the interaction parameter model proposed by Wagner [13], as given by eqs (26) and (27). The interaction coefficients [14] of the molten steel components are shown in Table 2.
Interaction coefficients of molten steel components at 1873 K.
| j i | C | Si | Mn | S | P | Al | Mg | Ca | O |
|---|---|---|---|---|---|---|---|---|---|
| Al | 0.091 | 0.0056 | 0.03 | 0.045 | −0.05 | −6.6 | |||
| O | −0.45 | −0.131 | −0.021 | −0.133 | 0.07 | −3.9 | −280 | 0 | −0.2 |
| Si | 0.18 | 0.11 | 0.002 | 0.056 | 0.11 | 0.058 | −0.07 | −0.23 | |
| Mn | −0.07 | −0.048 | −0.004 | −0.02 | −0.08 | ||||
| S | 0.11 | 0.063 | −0.026 | −0.028 | 0.029 | 0.035 | −1.82 | −259 | −0.27 |
| P | 0.13 | 0.12 | 0 | 0.028 | 0.062 | 0.037 | 0.13 | ||
| Ca | −0.34 | −0.01 | −0.016 | −140 | −0.07 | −780 |
Activities of slag component
The activities of the slag components were conveniently calculated using the regular solution model proposed by Landsmen [15]. The Lumsden model is given by eqs (28) and (29). The interaction energies [16] between cations of components in the slag
Interaction energies between cations of components in slag,
| j i | Fe2+ | Fe3+ | Mn2+ | Ca2+ | Mg2+ | Si2+ | P5+ | Al3+ |
|---|---|---|---|---|---|---|---|---|
| Fe2+ | −18.66 | 7.11 | −31.38 | 33.47 | −41.84 | −31.38 | −41.00 | |
| Fe3+ | −18.66 | −56.48 | −95.81 | −2.93 | 32.64 | 16.64 | −161.08 | |
| Mn2+ | 7.11 | −56.48 | −92.05 | 61.92 | −75.31 | −84.94 | −83.68 | |
| Ca2+ | −31.33 | −95.81 | −92.05 | −100.42 | −133.89 | −251.04 | −151.81 | |
| Mg2+ | 33.47 | −2.93 | 61.92 | −100.42 | −66.94 | −37.66 | −71.13 | |
| Si2+ | −41.84 | 32.64 | −75.31 | −133.89 | −66.94 | 83.68 | −127.61 | |
| P5+ | 31.38 | 14.64 | −84.94 | −251.04 | −37.66 | 83.68 | −261.50 | |
| Al3+ | −41.00 | −161.08 | −83.68 | −154.81 | −71.13 | −127.61 | −261.50 | |
| Na+ | 19.25 | 74.89 | 80.43 | −111.29 | −50.21 |
Conversion factors for activities.
| Reaction | Free energy change/J |
|---|---|
| FetO(l) + (1 − t)Fe(s or l)=FeO(r,s) | −8540 + 7.142 T |
| SiO2(β-tr)=SiO2(r,s) | 27150 − 2.054 T |
| SiO2(β-cr)=SiO2(r,s) | 27030 − 1.983 T |
| Al2O3=2[Al] + 3(O) | 867500 − 222.5 T |
| MnO(s)=MnO(r,s) | −32470 + 26.143 T |
| MnO(l)=MnO(r,s) | −86860 + 51.465 T |
| CaO(s)=CaO(r,s) | 18160 − 23.309 T |
| CaO(l)=CaO(r,s) | −40880 – 40703 T |
| MgO(s)=MgO(r,s) | 34350 − 16.736 T |
| MgO(1)=MgO(r,s) | −23300 + 1.833 T |
| P2O5(l)=2PO2.5(r,s) | 52720 − 230.706 T |
Mass-transfer coefficient
The argon blowing rate was also considered in the model. The mass-transfer coefficient of the metal phase was calculated using the empirical eqs (30), (31), and (32), given by Kitamura et al. [4]
where km and ks are the mass-transfer coefficients (m/s) in the film layer of liquid metal and slag phase, respectively, ε(w/t) is the stirring energy, Qg is the Ar gas flow rate (Nm3/min), TL and Tn are the temperatures (K) of molten steel and the gas, respectively, W is the mass of molten steel (kg), h0 (m) and hv (m) are the injection and bath depths, respectively, Pa is the atmospheric pressure (Pa), and dv (m) is the diameter of the bath. The values of the parameters are given in Table 5.
Parameters for model calculation.
| Parameter | Value |
|---|---|
| TL (K) | 1873 |
| hv (m) | 4 |
| h0 (m) | 1.7 |
| dv (m) | 3.42 |
| Q (Nm3/min) | 0.05, 0.4, 0.8 |
| ρm (kg/m3) | 7000 |
| Wm (kg) | 101400 |
| Ws (kg) | 15000 |
Initial conditions
The initial chemical compositions of the molten steel (0.027%C–0.25%Mn–0.013%S–0.012%P–0.007%Si–0.0474%Al–0.0005%O) and slag (1.44%FeO–4.35%SiO2–54.16%CaO–8.03%MgO–30.92%Al2O3–0.05%P2O5–0.235%MnO–0.837%CaS) were based on actual production data at the beginning of LF treatment.
Erosion rate of refractory lining
The erosion rate of the refractory lining was calculated using data from 120 heats. The erosion rate of the slag line was 0.025 mm/min for a refractory composition of 80% MgO and 20% C. The erosion rate of the ladle wall was 0.0125 mm/min for a 80%Al2O3–10%MgO–10%C composition.
Process parameters
The process to produce MR-T2.5 steel comprises the sequence: basic oxygen furnace (BOF) → LF → 1015×200 mm2 continuous casting (CC) → hot rolling → cold rolling. The steel-making process is performed in a 100 t BOF. After tapping the ladle, slag-skimming is performed and new premelted slag then added as a deoxidizer. Samples were taken during LF refining. The operating conditions and sampling scheme are shown in Figure 3. Aluminum was added as a deoxidizer. When the temperature and composition of molten steel satisfied the target requirements, the ladle was moved to the LF station. High-basicity refining slag was used for desulphurization; Fe–Mn alloy and Ca–Al wire were added to satisfy the compositional and performance requirements. After 35 min of LF refining, Ar gas was allowed to flow at 0.05 Nm3/min for 4 min.

Operating conditions in the Tangsteel plant.
Industrial practice
To verify the model results, low-carbon Al-killed steel of MR grade was sampled from the plant. The sampling points (labeled S) are shown in Figure 3. The composition of molten steel was analyzed via optical emission spectroscopy. The concentrations of Mg and Ca in the metal were analyzed via inductively coupled plasma atomic emission spectroscopy (ICP-AES). The slag composition was analyzed via X-ray fluorescence. The compositions of inclusions were determined using a Zeiss ULTRA55 scanning electron microscope with an Oxford Instruments Inca X-MAX50 energy-dispersive spectrometer.
Results and discussion
Composition of molten steel and slag
Figures 4, 5, and 6 show the compositional variations in the molten steel for the various elements. The calculated results are in agreement with the industrial data. The increase in the concentration of Mg is attributed to the reaction between slag and steel (Figure 4(a)). The increase in the concentration of Ca is attributed to the reaction between slag and steel at the start of ladle refining, and sharply increased on addition of Ca; the decrease is attributed to the reaction between inclusions and steel after Ca addition (Figure 4(a)). The decrease in the concentration of Al in the molten steel is attributed to its reaction with slag; however, this sharply increased on the addition of Al, as shown in Figure 4(b). Si and Mn concentrations increased because of deoxidation of slag, as shown in Figure 4(b). The calculated results for Ca, Mg, Al, Si, and Mn were in good agreement with the industrial results. The concentration of P in the molten steel increased slightly, while that in the slag decreased due to its deoxidation, as shown in Figure 5. The high basicity and low oxidizability suppressed the rephosphorization reaction. The predicted sulfur distribution between molten steel and slag increased with refining time (Figure 6). The concentrations of SiO2 and MnO decreased, whereas Si and Mn concentrations in the molten steel increased (Figures 4 and 7, respectively).

Comparison of calculated results with industrial data as a function of time for (a) Mg and Ca and (b) Al, Si, and Mn.
![Figure 5: Comparison of calculated results and industrial data as a function of time for [P] and (P2O5).](/document/doi/10.1515/htmp-2017-0011/asset/graphic/j_htmp-2017-0011_figure5.jpg)
Comparison of calculated results and industrial data as a function of time for [P] and (P2O5).
![Figure 6: Comparison of calculated results and industrial data as a function of time for [S] and (S).](/document/doi/10.1515/htmp-2017-0011/asset/graphic/j_htmp-2017-0011_figure6.jpg)
Comparison of calculated results and industrial data as a function of time for [S] and (S).

Comparison of calculated results and industrial data as a function of time for (a) FeO and MnO and (b) Al2O3, SiO2, MgO, and CaO.
With respect to the slag (Figure 7), the decrease in the concentration of FeO is attributed to deoxidation of the slag, and the calculated results agreed well with industrial results, as shown in Figure 7(a). The concentrations of CaO and MgO in slag hardly changed, as shown in Figure 7(b). The increase in the Al2O3 concentration of the slag is attributed to oxidation of Al in the molten steel.
Oxygen activity at the slag–molten steel interface
Figure 8 shows the predicted oxygen activity at the slag–molten steel interface and that calculated, ao, from the equilibrium of the reaction [Fe] + [O]=(FeO). ao* immediately dropped following the addition of Al. ao was larger than ao* at the beginning of the process, but their difference decreased during LF treatment. The reaction [Fe] + [O]=(FeO) at the interface was therefore not at equilibrium. The industrial result gave an activity of dissolved oxygen in molten steel of less than 5 ppm, while ao* at the slag–molten steel interface was 30–45 ppm. The oxygen activity at the slag–molten steel interface was higher than that in the molten steel; therefore, oxygen is expected to move from slag to molten steel during LF refining, even though the concentration of FeO in slag was approximately 1%.

Oxygen activity as a function of time: (a) ao* at the slag–steel interface and (b) ao in molten steel from the equilibrium of the reaction Fe + O=FeO.
It should be noted that ao* depends on the slag and steel compositions and affects the coupled reactions. The amount of the slag is an important factor: first, the amount of the slag fluctuates according to different amounts of top slag and final sulfur contents in the hot metal; second, the amount of the slag differs according to the steel grade. Figure 9(a) shows the effect of Si in molten steel on ao*. Obviously, ao* decreased with increasing Si in molten steel because such Si consumes interfacial oxygen. Figure 9(b) shows the effect of slag basicity on ao*, which decreased with increasing basicity because of the corresponding decreased activity of SiO2 in the slag. The effect of FeO in slag on ao* is shown in Figure 9(c). ao* increased with increasing concentration of FeO in the slag, presumably because of the reaction [Fe] + [O]=(FeO). Finally, the effect of slag mass on ao* is inferred from Figure 9(d): ao* increased as the slag mass increased because the FeO and SiO2 concentrations in the slag increased.

Oxygen activity at the slag–molten steel interface as a function of time: (a) Si in molten steel, (b) slag basicity, (c) slag FeO, and (d) slag mass.
Composition of inclusions
The changes in the average concentrations of MgO, CaO, and Al2O3 in an inclusion over time were also calculated by the model. Inclusion size is critical because it affects the metal–inclusion interfacial area. The size of the inclusions in the industrial samples was in the range of 2–5 μm, so, in the calculations, the inclusion size was set as 5 μm. Figure 10 shows the average change in the mass percent of the inclusions over time. The concentration of Al2O3 decreased, whereas the MgO and CaO concentrations increased because of reactions between the inclusions and steel. Particularly, the concentration of CaO in the inclusions increased rapidly after addition of Ca. The calculated changes in the Al2O3, MgO, and CaO concentrations were in agreement with the operational results.

Inclusion mass percent as a function of time and calculated concentrations of CaO, Al2O3, and MgO in the inclusions.
Figure 11 shows the calculated results and observed average compositions of the inclusions in the MgO–Al2O3–CaO ternary system. Operation in Region A, with wCaO of 40–60%, wAl2O3 of 35–65%, and wMgO of 0–20%, is most suitable for avoiding clogging of the nozzles with liquid inclusions at 1873 K. The calculated results and observed average compositions suggested that the average composition of the inclusions does not fall in the low-melting region at the end of LF treatment, as shown in Figure 11. Figure 12 shows that some of the inclusions move to the low-melting region, but most are not in the low-melting region by the end of LF treatment. Moving all inclusions into the low-melting region would require longer Ar blowing times; this is a topic for future study.

Comparison of calculated results and industrial data for the average CaO, Al2O3, and MgO concentrations in the inclusions in the CaO–MgO–Al2O3 ternary system.

Industrial data for the distribution of inclusions in the CaO–MgO–Al2O3 ternary system.
The average compositions of molten steel, slag, and inclusions can be predicted by using the proposed model.
Conclusions
A predictive model was established to investigate the mass transfer between slag, molten steel, and inclusions during LF treatment. The model predictions were compared with industrial results from the Tangsteel plant in China.
The proposed mass-transfer model can predict the average compositional changes in molten steel, slag, and inclusions during LF refining, as well as the distributions of sulfur and phosphorus.
Oxygen activity at the slag–molten steel interface is higher than the oxygen activity in molten steel; therefore, oxygen moved from slag to molten steel even though the FeO concentration in the slag was approximately 1%. A low oxygen activity at the slag–molten steel interface can be achieved by controlling the concentration of Si in the molten steel, slag basicity, concentration of FeO in slag, and slag mass.
Most inclusions found at the end of the LF treatment were not in the low-melting region. The Ar blowing time needs to be increased to move all inclusions into the low-melting region.
Funding statement: National Natural fund, (Grant / Award Number: ‘51404088’) the Natural Science Foundation of Hebei, (Grant / Award Number: ‘E2015209207’) The National Natural Science Fund, (Grant / Award Number: ‘51474089’).
References
[1] C. Zijian, G. Jing and C. Shusen, ISIJ Int., 20 (2013) 14–20.Search in Google Scholar
[2] H. Suito and R. Inoue, ISIJ Int., 36 (1996) 528–536.10.2355/isijinternational.36.528Search in Google Scholar
[3] A. Harada, N. Maruoka and H. Shibata, ISIJ Int., 53 (2013) 2110–2117.10.2355/isijinternational.53.2110Search in Google Scholar
[4] A. Harada, N. Maruoka and H. Shibata, ISIJ Int., 53 (2013) 2118–2125.10.2355/isijinternational.53.2118Search in Google Scholar
[5] A. Harada, N. Maruoka and H. Shibata, ISIJ Int., 54 (2014) 2569–2577.10.2355/isijinternational.54.2569Search in Google Scholar
[6] A. Harada, G. Miyano, N. Maruka and H. Shibata, ISIJ Int., 54 (2014) 2230–2238.10.2355/isijinternational.54.2230Search in Google Scholar
[7] Y.N. Jia, L.G. Zhu, C.J. Zhang et al., Ironmak. Steelmak., DOI:10.1080/03019233.2016.1240848.10.1080/03019233.2016.1240848Search in Google Scholar
[8] D.G.C. Robertson, B. Deo and S. Ohguchi, Ironmak. Steelmak., 11 (1984) 45–55.Search in Google Scholar
[9] S. Ohguchi, D.G.C. Roberson and B. Deo, Ironmak. Steelmak., 11 (1984) 202–213.Search in Google Scholar
[10] X. Zhang, B. Xie, H.Y. Li et al., Ironmak. Steelmak., 40 (2013) 282–288.10.1179/1743281212Y.0000000036Search in Google Scholar
[11] J.X. Chen, Handbook of Commonly used Diagrams and Data in Steelmaking Process, Metallurgical Industry Press, Beijing (1984).Search in Google Scholar
[12] X.H. Huang, Principles of Iron and Steel Metallurgy, Metallurgical Industry Press, Beijing (2002).Search in Google Scholar
[13] C. Wagner, Thermodynamics of Alloys, Addison-Wesley, Reading, MA (1952).Search in Google Scholar
[14] H. Suito and R. Inoue, ISIJ Int., 36 (1996) 528–536.10.2355/isijinternational.36.528Search in Google Scholar
[15] J. Lumsden, Physical Chemistry of Process Metallurgy, Interscience, New York, NY (1961).Search in Google Scholar
[16] S. Ban-Ya, ISIJ Int., 33 (1993) 2–11.10.2355/isijinternational.33.2Search in Google Scholar
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Articles in the same Issue
- Frontmatter
- Research Article
- Effect of Multidirectional Forging and Heat Treatment on Mechanical Properties of In Situ ZrB2p/6061Al Composites
- Short Communication
- Obtaining Ceramic Materials from Hydroxyapatite Using Spark-Plasma Sintering
- Research Articles
- An Investigation of Wear Behaviors of AA7075 Al Hybrid Composites
- Dephosphorization by Double-Slag Process in Converter Steelmaking
- Simulation of Dynamic Recrystallization Behavior under Hot Isothermal Compressions for as-extruded 3Cr20Ni10W2 Heat-Resistant Alloy by Cellular Automaton Model
- Post-weld Heat Treatment and Groove Angles Affect the Mechanical Properties of T92/Super 304H Dissimilar Steel Weld Joints
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- Mass-Transfer Model for Steel, Slag, and Inclusions during Ladle-Furnace Refining
- Repair Welding of the Tunnel Defect in Friction Stir Weld
- The Interface Reaction between Titanium and Iron-Nickel alloys
- Microstructure and Mechanical Properties of Friction Stir Processed A356 Cast Al under Air Cooling and Water Cooling