Experience of Using the Integral Indicator of the Domain Charge in Selecting the Basic Mode of the Domain Melt

Prediction of Composition and Properties of Final Smelting Products Based on Integral Indices of the Blast Furnace Burden and Temperature Blasting Mode

“…In accordance with the procedure of physicochemical simulation of reduction smelting using the sequence: burden + technology = smelting products [3,4], the composition of smelting products was calculated as a composition function of initial burden and parameters of technological mode based on prediction models of coefficients of element distribution L e (sulfur, silicon, manganese, titanium, and iron) between the smelting products, where the fraction of element transfer to slag are considered as variables depending on specific burden and process conditions: L e = f(F b , F T ), where F b are the burden parameters, F T are the technological parameters. The experimental results [5,6] demonstrated reasonability of application of integral criteria of burden K b and thermal blasting mode K bl for prediction of composition of smelting products.…”

“…Integral indicator of blast furnace burden K b includes combination of ratios of burden oxides and parameters of primary melts characterizing aggregate transformations and reduction of materials in furnace [5,6]: Fe rich /SiO 2 is the indicator of burden enrichment; CaO/SiO 2 is the burden basicity; MgO/SiO 2 , Al 2 O 3 /SiO 2 , R 2 O/CaO are the magnesium, alumina, and alkaline modules of the burden; T filt , T df are the temperature of filtration through coke packing and droplet flow of primary molten slag, °C; FeO ps is the FeO content in primary molten slag, wt %; Δe is the chemical equivalent of slag forming portion of the burden e; and ρ is the indicator of stoichiometry of slag forming portion of the burden.…”

“…The simulation subsystem of the Expert system regrading the selection of an optimum composition of multicomponent blast furnace burden includes the model of Metal-Slag System [1], which predicts the composition and properties of smelting products based on physicochemical simulation of structure and properties of metallurgical melts and their interactions [3][4][5][6]. The chemical composition of final molten products is determined by predicting coefficients of distribution of burden elements between cast iron and slag as a function of integral indicators of loaded burden and process mode [4].…”

Model Decision-Making System in the Task of Choosing the Optimal Composition of the Blast Furnace Burden Under Specific Operating Conditions of Bf

“…The experience of studying the mineralogy of final blast furnace slags has shown that the properties of slag melts, in particular, the melting point and viscosity, are a consequence not only of the composition and thermodynamic conditions, but also of its specific structure, which reflects the mineralogical composition. The Kb indicator includes a combination of the ratios of oxides in the burden and the parameters of primary melts, which characterize the aggregate transformation and reduction of materials in the furnace [18]: Ferich /SiO2an indicator of the richness of the burden; CaO/SiO2basicity of the burden; MgO/SiO2, Al2O3/SiO2, R2O/CaOmagnesium, alumina and alkaline modules of the burden; Tfilt, Tdf, -temperature of filtration through the coke filling and droplet flow of the primary slag melt, 0 C; FeOps -FeO content in the primary slag melt, %; Δe is the chemical equivalent of the slag-forming part of the burden, e; ρ is the stoichiometry indicator of the slag-forming part of the burden. Characteristics used to assess the influence of technological operating conditions of the blast furnace are: the theoretical temperature of coke combustion Tt, the degree of gas utilization СО, the length of the tuyere zone Ltz, as well as the furnace temperature index FTI (which is determined on the basis of the temperatures of the coke combustion Tt, blast furnace gas Tbg, pig iron The method of constructing of the burden integral indicators based on the use of the generalized Harrington desirability function allows "assembling" various indicators into a single generalized indicator and thus increase the information power of the optimization criterion.…”