Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.engappai.2005.05.006 | Eng. Appl. of AI |
Keywords | Field | DocType |
sufficient warning,efficient approach,aerodynamic instability,qualitative trend analysis,principal component analysis,european chem project,differential pressure signal,blast furnace,blast volume,expert knowledge,signal analysis,reasoning,trend analysis | Signal processing,Computer science,Instability,Blast furnace,Slip (materials science),Differential pressure,Artificial intelligence,Machine learning,Principal component analysis,Aerodynamics | Journal |
Volume | Issue | ISSN |
19 | 1 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
5 | 0.61 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fco. I. Gamero | 1 | 5 | 0.61 |
| Joan Colomer | 2 | 13 | 5.21 |
| Joaquim Meléndez | 3 | 15 | 3.89 |
| Peter Warren | 4 | 5 | 0.61 |