IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i17p3235-d259922.html
   My bibliography  Save this article

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

Author

Listed:
  • José Díaz

    (Polytechnic School of Engineering, University of Oviedo, 33204 Gijón, Spain)

  • Francisco Javier Fernández

    (Polytechnic School of Engineering, University of Oviedo, 33204 Gijón, Spain)

  • Inés Suárez

    (Polytechnic School of Engineering, University of Oviedo, 33204 Gijón, Spain)

Abstract

In oxygen steelmaking, the charge calculation strongly depends on hot metal temperature prediction. Although a hot metal temperature drop from the blast furnace in a steel plant may be too complex to be accurately modeled in detail, the combined use of sensors and statistical models can improve temperature estimation and result in better cost, quality and productivity, as well as lower emissions. In order to develop a simple but robust method for hot metal temperature forecasting, the suitability of infrared thermometry and time series forecasting has been studied. Simultaneous infrared thermometer measurement and video recording was used for designing the processing of the thermometer signal. The resulting temperature estimations are in good agreement with disposable thermocouple measurements giving an error of 11 °C with 60% reliability (chances of obtaining a successful output). Conversely, the time series approach was based mainly on the AutoRegressive Integrated Moving Average (ARIMA) model in which five additional process variables were introduced as exogenous predictors, as well as using a moving window of past observations for continuous model training. The resulting error was 15 °C with more than 90% reliability. Combining measuring and modeling approaches reduced the error to 13 °C with 100% reliability, thereby providing a hybrid procedure that has long-term stability and is self-adaptive to varying production scenarios.

Suggested Citation

  • José Díaz & Francisco Javier Fernández & Inés Suárez, 2019. "Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques," Energies, MDPI, vol. 12(17), pages 1-18, August.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3235-:d:259922
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/17/3235/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/17/3235/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3235-:d:259922. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.