Electric arc furnace terminal temperature prediction system based on SVM

Abstract

A prediction system for electric arc furnace terminal temperature based on SVM belongs to the technical field of steel enterprise steelmaking automation. The system includes a static temperature prediction model and a dynamic temperature prediction model. The static temperature prediction model is composed of two inverse models and a positive model, wherein, the inverse model comprises an oxygen blowing quantity inverse model and an electric consumption inverse model, and the output of the inverse models is taken as the input of the positive model; and the output of the static temperature prediction model is taken as the input of the dynamic temperature model. The dynamic temperature prediction model is used for predicting temperature of molten steel at real time, and the static temperature prediction model is used for providing original temperature for the dynamic temperature prediction model. The precision of the static temperature prediction model is improved by establishing the inverse models through the two important control variable quantity of oxygen blowing quantity and electric consumption; moreover, the data used for establishing the models is actual data coming from the site. The prediction system has the advantages of predicting current temperature according to the positive model, and the temperature is taken as the original temperature of the dynamic temperature prediction model for predicting temperature in process at real time.

Description

Technical field [0001] The invention belongs to the technical field of steelmaking automation in iron and steel enterprises, and in particular provides a SVM-based electric arc furnace endpoint temperature prediction system, which uses a method of combining a static temperature prediction model and a dynamic temperature prediction model to predict the endpoint temperature of molten steel. This model is helpful to guide steelmaking production, increase the hit rate of molten steel endpoint temperature, improve control effect, shorten smelting cycle, improve steelmaking production efficiency, and reduce steelmaking production costs. Background technique [0002] Temperature is one of the important technical indicators of the electric arc furnace steelmaking process. If it is too high, electric energy will be wasted; if it is too low, it is difficult to guarantee the production of the next station. Therefore, accurate control of the molten steel end temperature is important for ener...

AI Technical Summary

Problems solved by technology

However, due to the inherent defects of neural networks such as over-learning, local minimum points, and structure and type design relying on expert experience, this limits its application in end-point temperature prediction to a certain extent.

Images