Blast furnace dynamics using multiple autoregressive models with exogenous inputs

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Antti Nurkkala, Frank Pettersson, Henrik Saxén
Publisher: IRON STEEL INST JAPAN KEIDANREN KAIKAN
Publication year: 2012
Journal: Isij International
Journal acronym: ISIJ INT
Volume number: 52
Issue number: 10
Start page: 1764
End page: 1771
Number of pages: 8
ISSN: 0915-1559
eISSN: 1347-5460


Abstract

Autoregressive models with exogenous inputs are useful tools for analyzing systems with unknown dynamics, but are limited by the assumption that the relations between inputs and output(s) are linear. For complex systems with nonlinear or abruptly changing dynamics it is possible to modify the technique by allowing for multiple local models and designing a strategy for switching between them. A method by which this can be realized is developed in the paper. The technique is applied on a complex problem in the metallurgical industry, i.e., the prediction of hot metal silicon content in the blast furnace. A set of local models is developed for different parts of a training set, using a statistical criterion for model selection. The resulting local models are then applied to predict future values of the silicon content. It is demonstrated that the method is capable to develop models, among which a proper choice can be made for prediction. The potential of multi-step predictions is also studied. Finally, some conclusions concerning the method and the results are drawn.


Keywords

blast furnace, hot metal, multiple models, prediction

Last updated on 2019-14-11 at 03:52