Endpoint carbon content prediction of VOD using RBF neural network

Author

Li Jianwen ; Liang Chengzhuang

Author_Institution

Sch. of Electro-Mech. Eng., Xidian Univ., Xian, China

fYear

2013

fDate

23-24 Dec. 2013

Firstpage

588

Lastpage

590

Abstract

VOD(Vacuum Oxygen Decarburization) is a special furnace steel refining process. In the process, the endpoint carbon content prediction is a very important criteria for smelting products. The mathematical modeling of VOD often does not reflect the actual situation adequately. Using RBF neural network, the model of the VOD process is established to predict endpoint carbon content. Main factors affecting the endpoint carbon content are chosen as the input of the RBF network. The simulation results show the prediction data can be used in the VOD process to is predict the endpoint carbon content.

Keywords

carbon; furnaces; metal refining; production engineering computing; radial basis function networks; smelting; steel; RBF neural network; VOD; endpoint carbon content prediction; furnace; smelting product; steel refining process; vacuum oxygen decarburization; Carbon; Mathematical model; Neural networks; Process control; Refining; Smelting; Steel; Endpoint carbon content; Mathematical model; Prediction; RBF neural network; VOD;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement, Sensor Network and Automation (IMSNA), 2013 2nd International Symposium on

Conference_Location

Toronto, ON

Type

conf

DOI

10.1109/IMSNA.2013.6743345

Filename

6743345