Online predicting method for silicon content of blast furnace molten iron

Abstract

The invention provides an online predicting method for silicon content of blast furnace molten iron. According to a blast furnace silicon element transferring mechanism, a parameter which affects of silicon content of the molten iron is selected from blast furnace operation parameters as an input variable of a predicting model. A Pearson correlation analysis method is used for determining a lag time length between the input variable and the silicon content of the molten iron. Then standardizing processing is performed on the sample data and the predicting data of the input variable. The influence of different dimensions to model predicting accuracy is eliminated. A kernel extreme learning machine is utilized for predicting the silicon content of the molten iron at a next time point. A sliding window updating method is used for performing online updating on training set data. A genetic algorithm is introduced for optimizing the key parameter of the kernel extreme learning machine model.The online predicting method according to the invention is suitable for long-time online predicting to the silicon content of the blast furnace molten iron. An actual testing result proves a fact that the predicting method according to the invention has relatively high predicting precision. The method facilitates advanced understanding of the silicon content of the blast furnace molten iron by blast furnace operators, thereby adjusting the operation parameters in time.

Description

technical field [0001] The invention relates to the fields of industrial process monitoring, modeling and simulation, in particular to an online prediction method for silicon content in blast furnace molten iron. Background technique [0002] The silicon content of blast furnace molten iron is one of the important indicators to reflect the thermal state of the lower part of the blast furnace. However, the blast furnace is a working system with a large time-lag characteristic. Only by predicting the silicon content of the molten iron can timely adjustments be made to the operating parameters of the blast furnace, so that the silicon content of the molten iron can be maintained at an ideal level. [0003] The current prediction models for silicon content in molten iron are mainly divided into three categories: 1) mechanism models, 2) expert experience models, and 3) data-driven models. The mechanism model is mainly based on the transport mechanism of silicon in the blast furn...

AI Technical Summary

Problems solved by technology

When establishing a silicon content prediction model, the generalization performance and training speed of the model should also be considered, and then an appropriate intelligent algorithm should be selected, and most studies have not considered the highly nonlinear and time-varying characteristics of the blast furnace system

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