A statistical model for predicting the liquid steel temperature in ladle and tundish by bootstrap filter

Sho Sonoda, Noboru Murata, Hideitsu Hino, Hiroshi Kitada, Manabu Kano

研究成果: Article査読

16 被引用数 (Scopus)

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.

本文言語English
ページ(範囲)1086-1091
ページ数6
ジャーナルisij international
52
6
DOI
出版ステータスPublished - 2012

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys
  • Materials Chemistry

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