Steelmaking end point prediction method based on neural networks

Abstract

The invention relates to a steelmaking end point prediction method based on neural networks. The neural networks are used for replacing manual experience to predict the end point of steelmaking of a medium and small sized converter, a plurality of sets of production parameters are collected to serve as independent variables, flame temperature information and flame spectral information in the converter are collected through optical fibers to serve as independent variables, the multiple sets are used for training three layers of BP neural networks, MIV is adopted to screen the independent variables, the independent variable most affecting the end point is selected out to serve as an input parameter, an input parameter of the converter to be measured is selected to be inputted to the trained neural networks, and predicted converter end point temperature and the end point carbon content are obtained; and the shortcoming of manual experience prediction of the end point of steelmaking of the medium and small sized converter is overcome, optical fiber conduction is used for precisely measuring flame information inside the converter, and the accurate neutral networks are obtained.

Description

technical field [0001] The invention relates to the field of automatic control of steelmaking, and more specifically relates to a method for predicting the end point of converter steelmaking. Background technique [0002] Steelmaking end point control in converter steelmaking is one of the key technologies in converter steelmaking, and the steel production of converter steelmaking accounts for more than 80% of the total steel production. In large and medium-sized key iron and steel enterprises, the output of converter steel occupies a dominant position, so improving the production capacity and control level of converter steelmaking has always been valued by people. Converter steelmaking is a very complicated metallurgical reaction process with many influencing factors. In order to realize the automatic control of the converter smelting process, many detection technologies have been developed at home and abroad. The commonly used methods mainly include artificial experience ...

AI Technical Summary

Problems solved by technology

[0006] Patent Document 2004100568923 discloses a technical solution for using artificial neural network to predict the end temperature and carbon content of converter steelmaking. Forecast error of

There are many factors that affect the carbon content and temperature at the end of the converter. Patent document 2011103240380 discloses that the flame information at the furnace mouth is used as the main parameter, and the neural network is trained together with other parameters to predict the carbon content and temperature at the end of the converter. There is flame information in this document Obtaining inaccurate questions leads to large prediction errors

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