Converter steelmaking endpoint intelligent control method

Abstract

The invention provides a converter steelmaking endpoint intelligent control method, which is realized by the following subsystems: 1) a data preprocessing subsystem: acquiring data from a database, performing data preprocessing, determining endpoint carbon content and an input variable of a temperature prediction subsystem model through independence and correlation analysis, and ensuring model precision; 2) a molten steel endpoint prediction subsystem: predicting the endpoint carbon content and the endpoint temperature of converter steelmaking by adopting a wavelet weight-based non-parallel support vector regression machine algorithm; 3) an oxygen blowing amount and auxiliary material calculation subsystem: calculating an optimization error according to the output feedback of the prediction model in combination with a cetacean swarm optimization algorithm and an incremental calculation method, and calculating the oxygen blowing amount, lime, light-burned dolomite and other auxiliary material addition amounts required in the blowing stage on the premise of ensuring the minimum optimization error; 4) a model updating subsystem: updating and upgrading the prediction subsystem regularly according to the actual production condition. One-key steelmaking of the converter can be realized.

Description

technical field [0001] The invention relates to the technical field of converter steelmaking, in particular to an intelligent control method for the end point of converter steelmaking. Background technique [0002] Converter steelmaking is the main steelmaking method in my country, and the control of the end point composition and temperature of molten steel directly affects the quality of products. Since the smelting process of steelmaking is an extremely complex physical and chemical reaction process, the end point control of converter steelmaking is a research focus and difficulty in the field of iron and steel metallurgy. The research on its control has gone through experience control, static control, dynamic control and Intelligent control several stages. [0003] With the rapid development of computer technology and Internet of Things technology, a good foundation has been laid for the realization of one-key steelmaking. Intelligent control is a current research hotsp...

AI Technical Summary

Problems solved by technology

The disadvantage of the above model is that the model is easy to fall into the local minimum, which will lead to the inability to obtain the global optimal solution in the modeling process, which will bring a lot of inconvenience to find the optimal parameters of the model, so the modeling efficiency is very low, which is not conducive to the realization of a key steelmaking

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