Converter tapping silicon-manganese alloy addition amount determination method based on yield prediction

Abstract

The invention relates to the technical field of ferrous metallurgy, and provides a converter tapping silicon-manganese alloy addition amount determination method based on yield prediction. The methodcomprises the following steps of: S1, collecting converter multi-heat production data and carrying out normalization processing; S2, determining a model input variable; S3, determining factors influencing the alloy yield in a converter tapping process and adopting the same as the input variable of a model; S4, establishing a monotonicity constrained BP artificial neural network Mn element yield prediction model; S5, adjusting model parameters to obtain an optimized prediction result; and S6, determining the predicted addition amount of converter tapping silicon-manganese alloy. According to the method, the BP artificial neural network is improved by adopting a monotonicity constraint mode, so that the BP artificial neural network can be combined with a metallurgical reaction mechanism to predict the yield of the Mn element at the smelting end point of a converter, and a prediction effect better than that of a common BP artificial neural network can be obtained; The method has good accuracy and economic benefits, and can provide beneficial guidance for the addition of alloys in the field production process.

Description

technical field [0001] The invention relates to the technical field of iron and steel metallurgy, in particular to a method for determining the addition amount of silicon-manganese alloy for converter tapping based on yield prediction. Background technique [0002] In recent years, the intelligent manufacturing technology in the field of iron and steel metallurgy has continued to develop, and the control technology of converter steelmaking has gradually developed from manual experience and static control to intelligent control based on models. The tapping and alloying operation in the converter steelmaking process is an important part of the whole smelting process. During the tapping process, operators need to add corresponding alloys according to the technological requirements of smelting steel types. While removing excess oxygen from molten steel, the content of alloy elements such as silicon and manganese in molten steel can meet the composition requirements of steel type...

AI Technical Summary

Problems solved by technology

[0005] At present, intelligent algorithms are widely used in the field of forecasting. It can be considered to train through a large number of existing historical data to establish a prediction model of yield rate. BP artificial neural network is the most commonly used one, and general intelligent algorithm training only depends on Historical data cannot take into account the relevant metallurgical mechanism of the alloying reaction process, the generalization effect is poor, and it is difficult to obtain a model with better prediction effect

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