Predicting aerodynamic instabilities in a blast furnace

Authors:
Fco. I. Gamero;Joan Colomer;Joaquim Meléndez;Peter Warren
Affiliations:
University of Girona, Catalonia, Av.Lluis Santalo s/n (Girona). E-17071, Spain;University of Girona, Catalonia, Av.Lluis Santalo s/n (Girona). E-17071, Spain;University of Girona, Catalonia, Av.Lluis Santalo s/n (Girona). E-17071, Spain;Corus Teesside Cast Products Redcar, Cleveland TS10 5QW, UK
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

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A framework for on-line trend extraction and fault diagnosis

Engineering Applications of Artificial Intelligence

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Abstract

This paper discusses the analysis of differential pressure signals in a blast furnace stack by using principal component analysis (PCA) and qualitative trend analysis (QTA) based on episodes. These methods can work jointly or separately and are applied using two toolboxes developed within the European CHEM project. The objective in this paper is to predict aerodynamic instability in a blast furnace with sufficient warning to enable the blast volume to be reduced in order to minimise that instability. Both methods based on signals and the expert knowledge provide an efficient approach to slip prediction. ^(C)xxx 2004. All rights reserved.