Optimization Research on Converter Steelmaking Process Parameters Based on DOE

Bi Hong Lu; Yu Kai Li; Bao Zhang Qu

doi:10.4028/www.scientific.net/KEM.579-580.128

Paper Titles

Experimental Research on Ultrasonic Torsional Vibration-Assisted Grinding of Hard and Brittle Materials
p.101

Synthetic Analysis and Simulation of One-Side Processing Profile Error of Globoidal Cam
p.105

Study on the Manufacturing Service Trading Platform Based on Processing Behavior
p.113

Research on CNC Grinding Program of Overall End Milling Cutter
p.122

Optimization Research on Converter Steelmaking Process Parameters Based on DOE
p.128

Theoretical Research and Simulation of Grit’s Motion for Internal Ultrasonic Vibration Grinding
p.133

Technology on Ni-ZrO₂-CeO₂ Nanocomposites by Pulse Electroforming in an Ultrasonic Field
p.138

Study on the Surface Quality of Silicon Nitride Ceramics in Ultrasonic Vibration Grinding
p.144

Modification on TiO₂-a Photosensitive Material
p.148

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Optimization Research on Converter Steelmaking Process Parameters Based on DOE

Abstract:

The temperature and carbon content at blow end point should be controlled strictly during the BOF converter steelmaking process. Meanwhile many factors have impact on the temperature and carbon content at blow end point. These factors include initial weight of molten iron, initial weight of scrap steel, oxygen blow duration, the temperature and carbon content when lowering the sublance, as well as the weight of all kinds of addition reagents. In order to determine the optimized process parameters so as to reach the ideal temperature and carbon content at blow end point, this paper built a series of experiment programs based on DOE. According to the experiment programs, authors conducted these experiments with the help of RBF neural network and analyzed each parameters as well as some interactions impact on target. According to the statistical analysis results of experiment data (the SNR), authors extract significant factors and reached an optimized process parameters A₃B₃C₂D₁E₁F₃G₂H₃J₂. According to the RBF neural network, the prediction error of carbon content and temperature is only 0.0063 and 0.0159 respectively. The result proves that DOE is an effective method in optimizing process parameters, and worth promoting and applying in converter steelmaking process.