Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks
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 se...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-05-01
|
| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785421002118 |
| _version_ | 1819137322730389504 |
|---|---|
| author | Kyungchan Son Jaegak Lee Haejin Hwang Wonseok Jeon Hyunseok Yang Il Sohn Younghwan Kim Hyungsic Um |
| author_facet | Kyungchan Son Jaegak Lee Haejin Hwang Wonseok Jeon Hyunseok Yang Il Sohn Younghwan Kim Hyungsic Um |
| author_sort | Kyungchan Son |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T10:49:02Z |
| format | Article |
| id | doaj.art-47c814e9ad3a46cebc559095cc2f78d2 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-12-22T10:49:02Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-47c814e9ad3a46cebc559095cc2f78d22022-12-21T18:28:49ZengElsevierJournal of Materials Research and Technology2238-78542021-05-0112555568Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networksKyungchan Son0Jaegak Lee1Haejin Hwang2Wonseok Jeon3Hyunseok Yang4Il Sohn5Younghwan Kim6Hyungsic Um7Department of Mechanical Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, South KoreaDepartment of Mechanical Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, South KoreaDepartment of Mechanical Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, South KoreaDepartment of Mechanical Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, South KoreaDepartment of Mechanical Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea; Corresponding author.Department of Materials Science & Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, South KoreaDongkuk Steel R&D Center, 70, Geonposaneop-ro 3214beon-gil, Daesong-Myun, Nam-Gu, Pohang, 37874, South KoreaDongkuk Steel R&D Center, 70, Geonposaneop-ro 3214beon-gil, Daesong-Myun, Nam-Gu, Pohang, 37874, South KoreaSlag 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.http://www.sciencedirect.com/science/article/pii/S2238785421002118Electric arc furnace (EAF)Slag foaming heightLong short-term memory (LSTM) networkSoft sensor modelMachine learning |
| spellingShingle | Kyungchan Son Jaegak Lee Haejin Hwang Wonseok Jeon Hyunseok Yang Il Sohn Younghwan Kim Hyungsic Um Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks Journal of Materials Research and Technology Electric arc furnace (EAF) Slag foaming height Long short-term memory (LSTM) network Soft sensor model Machine learning |
| title | Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks |
| title_full | Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks |
| title_fullStr | Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks |
| title_full_unstemmed | Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks |
| title_short | Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks |
| title_sort | slag foaming estimation in the electric arc furnace using machine learning based long short term memory networks |
| topic | Electric arc furnace (EAF) Slag foaming height Long short-term memory (LSTM) network Soft sensor model Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2238785421002118 |
| work_keys_str_mv | AT kyungchanson slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks AT jaegaklee slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks AT haejinhwang slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks AT wonseokjeon slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks AT hyunseokyang slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks AT ilsohn slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks AT younghwankim slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks AT hyungsicum slagfoamingestimationintheelectricarcfurnaceusingmachinelearningbasedlongshorttermmemorynetworks |