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

Research output: Contribution to journalArticle

13 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1086-1091
Number of pages6
JournalISIJ International
Issue number6
Publication statusPublished - 2012 Jul 4



  • Bootstrap filter
  • General state space model
  • Liquid steel temperature control
  • Statistical modeling and simulation
  • Steelmaking

ASJC Scopus subject areas

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

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