抄録
A statistical model for predicting the liquid steel temperature in the ladle and in the tundish is developed. Given a large data set in a steelmaking process, the proposed model predicts the temperature in a seconds with a good accuracy. The data are divided into four phases at the mediation of five temperature measurements: before tapping from the converter (CV), after throwing ferroalloys into the ladle, before and after the Ruhrstahl-Heraeus (RH) processing, and after casting into the tundish in the continuous casting (CC) machine. Based on the general state space modeling, the bootstrap filter predicts the temperature phase by phase. The particle approximation technique enables to compute general-shaped probability distributions. The proposed model gives a prediction not as a point but as a probability distribution, or a predictive distribution. It evaluates both uncertainty of the prediction and ununiformity of the temperature. It is applicable to sensitivity analysis, process scheduling and temperature control.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 1086-1091 |
| ページ数 | 6 |
| ジャーナル | ISIJ International |
| 巻 | 52 |
| 発行部数 | 6 |
| DOI | |
| 出版物ステータス | Published - 2012 |
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ASJC Scopus subject areas
- Mechanical Engineering
- Mechanics of Materials
- Materials Chemistry
- Metals and Alloys
これを引用
A statistical model for predicting the liquid steel temperature in ladle and tundish by bootstrap filter. / Sonoda, Sho; Murata, Noboru; Hino, Hideitsu; Kitada, Hiroshi; Kano, Manabu.
：: ISIJ International, 巻 52, 番号 6, 2012, p. 1086-1091.研究成果: Article
}
TY - JOUR
T1 - A statistical model for predicting the liquid steel temperature in ladle and tundish by bootstrap filter
AU - Sonoda, Sho
AU - Murata, Noboru
AU - Hino, Hideitsu
AU - Kitada, Hiroshi
AU - Kano, Manabu
PY - 2012
Y1 - 2012
N2 - A statistical model for predicting the liquid steel temperature in the ladle and in the tundish is developed. Given a large data set in a steelmaking process, the proposed model predicts the temperature in a seconds with a good accuracy. The data are divided into four phases at the mediation of five temperature measurements: before tapping from the converter (CV), after throwing ferroalloys into the ladle, before and after the Ruhrstahl-Heraeus (RH) processing, and after casting into the tundish in the continuous casting (CC) machine. Based on the general state space modeling, the bootstrap filter predicts the temperature phase by phase. The particle approximation technique enables to compute general-shaped probability distributions. The proposed model gives a prediction not as a point but as a probability distribution, or a predictive distribution. It evaluates both uncertainty of the prediction and ununiformity of the temperature. It is applicable to sensitivity analysis, process scheduling and temperature control.
AB - A statistical model for predicting the liquid steel temperature in the ladle and in the tundish is developed. Given a large data set in a steelmaking process, the proposed model predicts the temperature in a seconds with a good accuracy. The data are divided into four phases at the mediation of five temperature measurements: before tapping from the converter (CV), after throwing ferroalloys into the ladle, before and after the Ruhrstahl-Heraeus (RH) processing, and after casting into the tundish in the continuous casting (CC) machine. Based on the general state space modeling, the bootstrap filter predicts the temperature phase by phase. The particle approximation technique enables to compute general-shaped probability distributions. The proposed model gives a prediction not as a point but as a probability distribution, or a predictive distribution. It evaluates both uncertainty of the prediction and ununiformity of the temperature. It is applicable to sensitivity analysis, process scheduling and temperature control.
KW - Bootstrap filter
KW - General state space model
KW - Liquid steel temperature control
KW - Statistical modeling and simulation
KW - Steelmaking
UR - http://www.scopus.com/inward/record.url?scp=84863088966&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863088966&partnerID=8YFLogxK
U2 - 10.2355/isijinternational.52.1086
DO - 10.2355/isijinternational.52.1086
M3 - Article
AN - SCOPUS:84863088966
VL - 52
SP - 1086
EP - 1091
JO - ISIJ International
JF - ISIJ International
SN - 0915-1559
IS - 6
ER -