Method for predicting temperature of furnace core dead stock column of ironmaking blast furnace based on multi-element linear regression algorithm

Abstract

The invention discloses a method for predicting the temperature of a furnace core dead stock column of an ironmaking blast furnace based on a multi-element linear regression algorithm, and belongs tothe technical field of metallurgy information processing. According to the method, the temperature target value DMTgoal of the furnace core dead stock column is calculated, data is processed, Pearsoncorrelation analysis is carried out on the processed data sample, and condition variables are preliminarily selected according to the result of correlation analysis. Pearson correlation analysis is carried out on each condition variable, and mutually independent condition variables are selected as possible according to the correlation analysis result to establish a model. Condition variable is then screened by a least square method and an AIC-based variable screening criterion, the fitting degree and the regression coefficient of the primary multi-element linear regression equation are checked, and a multi-element linear regression model is obtained. According to the method, the multi-element linear regression algorithm is provided for the first time to predict the temperature of the furnace core dead stock column, the temperature of the furnace core dead stock column in the next five days can be predicted in a high-precision mode, and the early warning function of the temperature of the furnace core dead stock column can be realized.

Description

Technical field [0001] The invention relates to the technical field of metallurgical information processing, and more specifically, to a method for predicting the temperature of a dead material column in an iron-making blast furnace core based on a multiple linear regression algorithm. Background technique [0002] Managers and producers of modern iron and steel enterprises always strive to maintain the long-term stability of blast furnace production in order to obtain the maximum benefits of iron and steel enterprises. The hearth activity is like the "heart" of blast furnace production. Once there is a problem with the hearth activity, which destroys the blast furnace's stable and forward movement, the loss will be very huge. [0003] At present, the quantitative monitoring of hearth activity has become a hot, important and difficult point in blast furnace production. How to realize the quantitative calculation of hearth activity, how to realize online monitoring of hearth activit...

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Problems solved by technology

[0008] In order to overcome the technical problems of complex, low-efficiency, poor adaptability and strong dependence on experience in the calculation of the dead stock column temperature in the hearth hearth of a large blast furnace, the present invention proposes a method based on a multiple linear regression algorithm to predict the temperature of the dead stock column in the ironmaking blast furnace core method; the present invention can predict the temperature of the furnace core dead material column without accurately calculating the temperature of the furnace core dead material column, so that the prediction of the furnace core dead material column temperature is separated from the empirical formula for the first time, and solves the problem of furnace core dead material column temperature. Calculation of core dead material column temperature is inefficient, poor adaptability and strongly dependent on experience technical problems; for the problem of low temperature judgment of furnace core dead material column temperature, the multivariate linear regression model about furnace core dead material column temperature established by the present invention simultaneously With low temperature early warning function, it can realize low temperature early warning of furnace core dead material column temperature

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