Application of Genetic Algorithm Combined with BP Neural Network in Soft Sensor of Molten Steel Temperature

Author

Tian, Huixin ; Mao, Zhizhong ; Wang, Shu ; Li, Kun

Author_Institution

Inf. Sci. & Eng. Coll., Northeast Univ. Shenyang

Volume

2

fYear

0

fDate

0-0 0

Firstpage

7742

Lastpage

7745

Abstract

In this paper, a new soft sensor model of LF molten steel temperature is introduced after analyzing the process of LF refining and the factors of influencing molten steel temperature. A genetic algorithm combined with improved back-propagation (BP) neural network is used in this model. Genetic algorithm is used to optimize weight and bias values of BP network. The simulation studies show this new combined algorithm has global searching abilities and can improve the prediction model precision and convergence speed. Based on the results, the model is programmed by C language to use in real process. The heats that error between the temperature predicted by the combined algorithm model and real temperature dose not go beyond plusmn 5 degree is more than 85% of all

Keywords

backpropagation; furnaces; genetic algorithms; liquid metals; metal refining; process control; steel; temperature control; temperature sensors; C language; backpropagation neural network; convergence speed; genetic algorithm; ladle furnace molten steel temperature; ladle furnace refining; prediction model precision; soft sensor model; Automatic control; Convergence; Genetic algorithms; Intelligent networks; Neural networks; Predictive models; Refining; Smelting; Steel; Temperature sensors; BP neural network; LF refining; genetic algorithm; molten steel temperature; soft sensor;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713475

Filename

1713475