Toward learning steelmaking—A review on machine learning for basic oxygen furnace process
Abstract Basic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods a...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2023-09-01
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| Series: | Materials Genome Engineering Advances |
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| Online Access: | https://doi.org/10.1002/mgea.6 |
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| author | Maryam Khaksar Ghalati Jianbo Zhang G. M. A. M. El‐Fallah Bogdan Nenchev Hongbiao Dong |
| author_facet | Maryam Khaksar Ghalati Jianbo Zhang G. M. A. M. El‐Fallah Bogdan Nenchev Hongbiao Dong |
| author_sort | Maryam Khaksar Ghalati |
| collection | DOAJ |
| description | Abstract Basic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, Machine Learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions. |
| format | Article |
| id | doaj-art-22233dfe843a4c0792af8a645a51d29e |
| institution | OA Journals |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-22233dfe843a4c0792af8a645a51d29e2025-08-20T02:11:35ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972023-09-0111n/an/a10.1002/mgea.6Toward learning steelmaking—A review on machine learning for basic oxygen furnace processMaryam Khaksar Ghalati0Jianbo Zhang1G. M. A. M. El‐Fallah2Bogdan Nenchev3Hongbiao Dong4School of Engineering University of Leicester Leicester UKSchool of Engineering University of Leicester Leicester UKSchool of Engineering University of Leicester Leicester UKIntellegens Limited Cambridge UKSchool of Engineering University of Leicester Leicester UKAbstract Basic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, Machine Learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions.https://doi.org/10.1002/mgea.6BOF steelmakingdata‐driven modelingindustry 4.0machine learning |
| spellingShingle | Maryam Khaksar Ghalati Jianbo Zhang G. M. A. M. El‐Fallah Bogdan Nenchev Hongbiao Dong Toward learning steelmaking—A review on machine learning for basic oxygen furnace process Materials Genome Engineering Advances BOF steelmaking data‐driven modeling industry 4.0 machine learning |
| title | Toward learning steelmaking—A review on machine learning for basic oxygen furnace process |
| title_full | Toward learning steelmaking—A review on machine learning for basic oxygen furnace process |
| title_fullStr | Toward learning steelmaking—A review on machine learning for basic oxygen furnace process |
| title_full_unstemmed | Toward learning steelmaking—A review on machine learning for basic oxygen furnace process |
| title_short | Toward learning steelmaking—A review on machine learning for basic oxygen furnace process |
| title_sort | toward learning steelmaking a review on machine learning for basic oxygen furnace process |
| topic | BOF steelmaking data‐driven modeling industry 4.0 machine learning |
| url | https://doi.org/10.1002/mgea.6 |
| work_keys_str_mv | AT maryamkhaksarghalati towardlearningsteelmakingareviewonmachinelearningforbasicoxygenfurnaceprocess AT jianbozhang towardlearningsteelmakingareviewonmachinelearningforbasicoxygenfurnaceprocess AT gmamelfallah towardlearningsteelmakingareviewonmachinelearningforbasicoxygenfurnaceprocess AT bogdannenchev towardlearningsteelmakingareviewonmachinelearningforbasicoxygenfurnaceprocess AT hongbiaodong towardlearningsteelmakingareviewonmachinelearningforbasicoxygenfurnaceprocess |