Paper Info
Title
Multiple neural network modeling method for carbon and temperature estimation in basic oxygen furnace
Abstract
In this paper, a novel multiple Neural Network (NN) models including forecasting model, presetting model, adjusting model and judgment model for Basic Oxygen Furnace (BOF) steelmaking dynamic process is introduced. The control system is composed of the preset model of the dynamic requirement for oxygen blowing and coolant adding, bath [C] and temperature prediction model, and judgment model for blowing-stop. In this method, NN technology is used to construct these models above; Fuzzy Inference (FI) is adopted to derive the control law. The control method of BOF steelmaking process has been successfully applied in some steelmaking plants to improve the bath Hit Ratio (HR) significantly.
Year
DOI
Venue
2006
10.1007/11760191_127
ISNN (2)
Keywords
Field
DocType
bof steelmaking process,preset model,control method,control law,control system,temperature prediction model,judgment model,forecasting model,multiple neural network modeling,temperature estimation,basic oxygen furnace,steelmaking plant,presetting model,neural network,neural network model,prediction model
Radial basis function,Control theory,Computer science,Steelmaking,Artificial intelligence,Control system,Artificial neural network,Basic oxygen steelmaking,Pattern recognition,Simulation,Fuzzy logic,Temperature measurement,Coolant
Conference
Volume
ISSN
ISBN
3973
0302-9743
3-540-34482-9
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Xin Wang153.76
Zhong-Jie Wang235664.60
Jun Tao3129.47