Direct Alloying of Steel with Manganese in Electrosmelting

Аннотация

The production efficiency in steelmaking may be increased by decreasing the consumption of scarce and expensive ferroalloys. One possibility here is direct alloying of steel with oxides. In the present work, the direct alloying of steel with manganese oxides (manganese ore) is thermodynamically analyzed and tested industrially. Two technologies are considered: alloying in oxidative conditions with smelting of the steel in a 100-t electrosmelting furnace; and alloying in reductive conditions with treatment of the steel in a ladle–furnace unit. Thermodynamic modeling of the oxidative technology by means of Astra software shows that the manganese content in the steel may be increased by introducing manganese ore. A determining factor here is the carbon content in the steel. For steel with moderate or high carbon content, the manganese content may be increased to 0.6% or more. For low-carbon steel, the residual manganese content depends on the carbon content at the end of oxygen injection. The corresponding graph is plotted. In the reductive technology, the main reaction in direct alloying is MnO + Si = Mn + SiO₂. Thermodynamic analysis yields relatively approximate data. Therefore, a semiempirical analysis is adopted, on the basis of data from experimental tests on the FeO/MnO ratio in the slag at the end of steel treatment in the ladle–furnace unit. This approach is possible since the metal–slag system approaches equilibrium on prolonged treatment in the ladle–furnace unit. On the basis of the FeO/MnO ratio, as well as the assumptions that the initial slag basicity is retained and that the FeO content in the slag is maintained at around 1%, a balance equation is written, describing the direct alloying of the steel by manganese ore in the ladle. The balance equation permits calculation of the primary technological parameters of direct alloying by manganese ore in production conditions. The agreement of the theoretical calculations and industrial data is good.