Application of improved local models of large scale databasebased online modeling to prediction of molten iron temperature of blast furnace

Norio Kaneko; Shinroku Matsuzaki; Masahiro Ito; Haruhisa Oogai; Kenko Uchida

doi:10.2355/isijinternational.50.939

Application of improved local models of large scale databasebased online modeling to prediction of molten iron temperature of blast furnace

Norio Kaneko^*, Shinroku Matsuzaki, Masahiro Ito, Haruhisa Oogai, Kenko Uchida

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

The large scale database-based online modeling called LOM is the one of local modeling method. This method has been developed to apply the just-in-time modeling for the blast furnace by us. In this paper, we propose two new types of local models in LOM to improve the prediction performance. One is used weighted multiple regression model as a linear local model of LOM. The other is used on-line Bayesian learning model as a nonlinear local model of LOM. In order to compare the prediction performance of the two types of local models in LOM, we evaluate the prediction performance by using the real process data of the blast furnace.

Original language	English
Pages (from-to)	939-945
Number of pages	7
Journal	ISIJ International
Volume	50
Issue number	7
DOIs	https://doi.org/10.2355/isijinternational.50.939
Publication status	Published - 2010

Keywords

Blast furnace
Just-in-time modeling
Mutual information
On-line bayesian learning
Prediction
Process control
Sequential monte carlo
Weighted multiple regression

ASJC Scopus subject areas

Mechanical Engineering
Mechanics of Materials
Materials Chemistry
Metals and Alloys

Access to Document

10.2355/isijinternational.50.939

Cite this

@article{013738c2628d4f079b2cdcdad75a66e9,

title = "Application of improved local models of large scale databasebased online modeling to prediction of molten iron temperature of blast furnace",

abstract = "The large scale database-based online modeling called LOM is the one of local modeling method. This method has been developed to apply the just-in-time modeling for the blast furnace by us. In this paper, we propose two new types of local models in LOM to improve the prediction performance. One is used weighted multiple regression model as a linear local model of LOM. The other is used on-line Bayesian learning model as a nonlinear local model of LOM. In order to compare the prediction performance of the two types of local models in LOM, we evaluate the prediction performance by using the real process data of the blast furnace.",

keywords = "Blast furnace, Just-in-time modeling, Mutual information, On-line bayesian learning, Prediction, Process control, Sequential monte carlo, Weighted multiple regression",

author = "Norio Kaneko and Shinroku Matsuzaki and Masahiro Ito and Haruhisa Oogai and Kenko Uchida",

year = "2010",

doi = "10.2355/isijinternational.50.939",

language = "English",

volume = "50",

pages = "939--945",

journal = "ISIJ International",

issn = "0915-1559",

publisher = "Iron and Steel Institute of Japan",

number = "7",

}

TY - JOUR

T1 - Application of improved local models of large scale databasebased online modeling to prediction of molten iron temperature of blast furnace

AU - Kaneko, Norio

AU - Matsuzaki, Shinroku

AU - Ito, Masahiro

AU - Oogai, Haruhisa

AU - Uchida, Kenko

PY - 2010

Y1 - 2010

N2 - The large scale database-based online modeling called LOM is the one of local modeling method. This method has been developed to apply the just-in-time modeling for the blast furnace by us. In this paper, we propose two new types of local models in LOM to improve the prediction performance. One is used weighted multiple regression model as a linear local model of LOM. The other is used on-line Bayesian learning model as a nonlinear local model of LOM. In order to compare the prediction performance of the two types of local models in LOM, we evaluate the prediction performance by using the real process data of the blast furnace.

AB - The large scale database-based online modeling called LOM is the one of local modeling method. This method has been developed to apply the just-in-time modeling for the blast furnace by us. In this paper, we propose two new types of local models in LOM to improve the prediction performance. One is used weighted multiple regression model as a linear local model of LOM. The other is used on-line Bayesian learning model as a nonlinear local model of LOM. In order to compare the prediction performance of the two types of local models in LOM, we evaluate the prediction performance by using the real process data of the blast furnace.

KW - Blast furnace

KW - Just-in-time modeling

KW - Mutual information

KW - On-line bayesian learning

KW - Prediction

KW - Process control

KW - Sequential monte carlo

KW - Weighted multiple regression

UR - http://www.scopus.com/inward/record.url?scp=77956789396&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956789396&partnerID=8YFLogxK

U2 - 10.2355/isijinternational.50.939

DO - 10.2355/isijinternational.50.939

M3 - Article

AN - SCOPUS:77956789396

SN - 0915-1559

VL - 50

SP - 939

EP - 945

JO - ISIJ International

JF - ISIJ International

IS - 7

ER -

Application of improved local models of large scale databasebased online modeling to prediction of molten iron temperature of blast furnace

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this