A model for temperature prediction of melted steel in the electric arc furnace (EAF)

Authors:
Marcin Blachnik;Krystian Mączka;Tadeusz Wieczorek
Affiliations:
Silesian University of Technology, Department of Management and Computer Science, Katowice, Poland;Silesian University of Technology, Department of Management and Computer Science, Katowice, Poland;Silesian University of Technology, Department of Management and Computer Science, Katowice, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Year:
2010

Citing 2
Cited 3

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Building a Model for Time Reduction of Steel Scrap Meltdown in the Electric Arc Furnace (EAF): General Strategy with a Comparison of Feature Selection Methods

ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing

Identification of liquid state of scrap in electric arc furnace by the use of computational intelligence methods

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Evolutionary optimized forest of regression trees: application in metallurgy

HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Combining the advantages of neural networks and decision trees for regression problems in a steel temperature prediction system

HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

A constant aspiration to optimize electric arc steelmaking process causes an increase of the use of advanced analytical methods for the process support. The goal of the paper is to present the way to predict temperature of melted steel in the electric arc furnace and consequently, to reduce the number of temperature measurements during the process. Reducing the number of temperature measurements shortens the time of the whole process and allows increasing production.