Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
381692 | Engineering Applications of Artificial Intelligence | 2006 | 9 Pages |
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.1 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Fco. I. Gamero, Joan Colomer, Joaquim Meléndez, Peter Warren,