Vol 48, No 1 (2018)

In practice, the concept of slag capacity is used to assess the distribution of elements between condensed phases. In particular, researchers determine the sulfide, phosphate, chromate, and nitride capacity of slags. In the present work, a mathematical model of the manganese capacity is derived. To that end, two equivalent forms of the manganese capacity are derived from the equilibrium constants of the redox reaction of manganese [Mn] + (1/2)O₂ = (MnO). These indices reflect the manganese distribution between the metal and the slag and do not depend on the composition of the metal and the gas phase. One version takes the form C^Mn = K_Mn/γ_(MnO). If we take logarithms and use the known equilibrium constant K_Mn of the redox reaction, we may write logC^Mn = 21122/T–logγ_(MnO)–4.5509. To find the activity coefficient of manganese oxide, equilibrium between hot metal, cast iron, ferrosilicon, ferromanganese, and the corresponding slags is studied experimentally at various temperatures, on circulatory apparatus permitting the study of heterogeneous equilibria involving the gas phase. Using the apparatus, the change in gas volume in the reactions is monitored and automatically recorded and constant pressure is automatically maintained in the system. The attainment of equilibrium is also judged from the constancy of chemical composition of the condensed phases over time. If numerical values of γ_(MnO) are available, they may be used to calculate the manganese capacity of all the slags from the equation already given. For the sake of practical convenience, the manganese capacity is written in terms of the temperature and the optical basicity λ_ed calculated from the electron density known for elements in the periodic table: logC^Mn =–1.866λ_ed + 21049/T–3.131 (R² = 0.997). According to this equation, the manganese capacity depends only on λ_ed and the temperature and may be used for metals and slags of practically any composition.

High-lime synthetic slags for refining steels in the ladle–furnace unit are investigated. The content of the slag mixtures is as follows: 60 wt % CaO, 7 and 8 wt % MgO, 7–23 wt % Al₂O₃, and 9–18 wt % SiO₂, with additions of 8 wt % CaF₃ and 5–15 wt % Na₂O. Polymer theory is used to calculate the composition of the anionic subsystem in the slag melts. The log-mean polymerization constants K_p^* for multicomponent melts are calculated from the known polymerization constants in binary systems. It is found that K_p^* ≈ 10^–3–10^–2 in the range 1500–1600°C. In that range, the melt’s degree of polymerization is 3 × 10^–4–8 × 10^–3. In the most polymerized melt, the ionic content of the dimers Si₂O₇^6- and Al₂O₇^8- is no more than 0.1 and 1.5% of the values for the corresponding monomers. Therefore, we assume, with an error of about 2%, that the structural units of the anionic subsystem are monomers AlO₄^5- and SiO₄^4- simple O^2– and F^– ions (slag 7). The cationic subsystem consists of Ca²⁺, Mg²⁺, Na⁺, and Al³⁺ ions in octahedral coordination with oxygen (less than 3% of all the Al atoms). In all the melts, the concentrations of free oxygen ions O^2– and Ca²⁺ ions are similar. In half the cases, the content of O^2– ions is greater than the content of Ca²⁺ ions. The mean mobility U and self-diffusion coefficient D for all the cations are calculated from data for the electrical conductivity and the density. With increase in temperature from 1500 to 1600°C, U and D increase by 50 and 60%, respectively, in all the slags. With increase in the mutual substitution of the components in the slag mixtures M = n(Na₂O, CaF₂)/n(Al₂O₃ + SiO₂), mol/mol, at 1600°C, U increases from 1.14 × 10^–8 to 1.46 × 10^–8 m²/(V s) for slags 1–6 (0 ≤ M ≤ 1.1) and from 1.01 × 10^–8 to 1.66 × 10^–8 m²/(V s) for slags 7–10 (0.25 ≤ M ≤ 0.65). Correspondingly, D increases from 9.2 × 10^–10 to 12.8 × 10^–10 m²/s for slags 1–6 and from 8.2 × 10^–10 to 14.3 × 10^–10 m²/s for slags 7–10. The temperature dependence of U and D may be approximated by an Arrhenius equation with activation energies E_U and E_D. With increase in M in the given ranges, E_U declines from 146 to 100 kJ/mol (slags 1–6) and from 124.5 to 109 kJ/mol (slags 7–10). Likewise, E_D declines from 159 to 116.5 kJ/mol (slags 1–6) and from 139.5 to 124 kJ/mol (slags 7–10). The mean values of E_U and E_D correlate with the mean distance between the cations in the melts. On the basis of the proposed alternative model of the conductivity, the O^2– ions may also transfer electric charge. Preliminary estimates show that the oxygen transport number at 1600°C may exceed 0.1 in some slags.

The experimental production of sinter with the addition of a byproduct of magnesite production— serpentinite–magnesite—is considered. This additive improves the strength of the sinter produced. Recommendations are made regarding the best use of serpentinite–magnesite in the sintering shop at Ural Steel.

Ladle-treatment slags are primary wastes from steel production. Slag utilization is prevented by its spontaneous decomposition as a result of polymorphic transformation of dicalcium silicate with decrease in slag temperature. At OOO NLMK-Kaluga, the slag is stabilized by the introduction of boron oxide in the high–magnesia flux during ladle treatment. Industrial tests indicate stabilization of the slag and the formation of a protective layer on the lining, preventing slag corrosion.

The introduction of pipe rolling from continuous-cast bullet on the TPA 220 piercing mill, with an expanded range (up to a diameter of 245 mm, as against the previous limit of 219 mm) and increase in pipe length, is described. The mechanical parameters of the mill are measured and assessed. Options for further modernization of the mill and the production of thin-walled pipe with a diameter/wall ratio of more than 30 are outlined.

The operation of precision alloys in cybernetic circuits is considered. Principles that may be used to standardize the physical properties of precision alloys in complex operating conditions are outlined. Means are proposed for regulating the basic properties of alloys, including their reliability, their performance, and the structure of the life cycle.