Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks

Kyungchan Son, Jaegak Lee, Haejin Hwang, Wonseok Jeon, Hyunseok Yang, Il Sohn, Younghwan Kim, Hyungsic Um

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Slag foaming is a key factor in terms of quality and productivity in the electric arc furnace (EAF) steelmaking process. Optimal control of slag foaming is required, but is difficult due to the absence of practical on-line measuring methods and the broad process variability. In this study, a soft sensor model, which correlates the influential process variables with the slag foaming height, was developed by using machine learning based long short-term memory (LSTM) networks for modeling sequential and nonlinear data. The developed model was validated using actual steelmaking dataset in terms of performance metrics such as the root mean square error (RMSE), coefficient of determination (R2), and correlation coefficient corresponding to a value for a SS400 carbon steel grade to be 42.3, 0.905, and 0.963, respectively. In order to evaluate the general applicability of the developed model for other steel grades, data for A615 and S355 steel grades were also applied and found to satisfy the benchmark standards indicating that the developed model can be applied to the broad range of other steel grades. Sensitivity-based Pruning (SBP) on the model shows that electricity, carbon and oxygen are the most influential process variables to the slag foaming height and could potentially be used to promote enhanced optimization in terms of energy saving and cost-efficiency for the EAF steelmaking process.

Original languageEnglish
Pages (from-to)555-568
Number of pages14
JournalJournal of Materials Research and Technology
Volume12
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20172010400170 ). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) ( 2018R1A4A1025986 ).

Publisher Copyright:
© 2021 The Authors.

All Science Journal Classification (ASJC) codes

  • Ceramics and Composites
  • Biomaterials
  • Surfaces, Coatings and Films
  • Metals and Alloys

Fingerprint

Dive into the research topics of 'Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks'. Together they form a unique fingerprint.

Cite this