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Ironmaking & Steelmaking
Processes, Products and Applications
Volume 42, 2015 - Issue 3
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Research Papers

Development and application of mould breakout prediction system with online thermal map for steel continuous casting

F. HeSchool of Metallurgical EngineeringAnhui University of Technology, Ma’anshan, 234002, China;School of Metallurgical and Ecological EngineeringUniversity of Science and Technology Beijing, Beijing100083, China
,
D.-F. HeSchool of Metallurgical and Ecological EngineeringUniversity of Science and Technology Beijing, Beijing100083, ChinaCorrespondence[email protected]
,
Z.-H. DengSchool of Metallurgical EngineeringAnhui University of Technology, Ma’anshan, 234002, China;School of Metallurgical and Ecological EngineeringUniversity of Science and Technology Beijing, Beijing100083, China
,
A.-J. XuSchool of Metallurgical and Ecological EngineeringUniversity of Science and Technology Beijing, Beijing100083, China
&
N.-Y. TianSchool of Metallurgical and Ecological EngineeringUniversity of Science and Technology Beijing, Beijing100083, China
Pages 194-208 | Received 06 Apr 2014, Accepted 10 Jul 2014, Published online: 24 Jul 2014

Citations (10)

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Read on this site (3)

Katarzyna Miłkowska-Piszczek, Marcin Rywotycki, Jan Falkus & Gniewko Kwinta. (2021) Experimental determination of the shell thickness within the primary cooling zone in the continuous casting process. Ironmaking & Steelmaking 48:9, pages 1102-1109.
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Yu Liu, Zhiqiang Xu, Xudong Wang & Dongdong Zhang. (2021) Space–time characteristics of true and false sticker breakouts in mould during continuous casting. Ironmaking & Steelmaking 48:8, pages 901-908.
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Haitao Qian, Fei He, Xi’an Xie, Zhenghai Zhu, Lingying Zhang, Linlin Zhang & Jiashun Shi. (2019) Recovery mechanism of the sticking-type breakout during continuous casting of steel. Ironmaking & Steelmaking 46:3, pages 259-268.
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Articles from other publishers (7)

Benguo Zhang, Wanbao Sheng, Di Wu & Ruizhong Zhang. (2022) Application of GA-ACO Algorithm in Thin Slab Continuous Casting Breakout Prediction. Transactions of the Indian Institute of Metals 76:1, pages 145-155.
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Deva Prasaad Neelakandan, Dinesh Reddy Alla, Jie Huang & Ronald J. O’Malley. (2022) Liquid Core Detection and Strand Condition Monitoring in a Continuous Caster Using Optical Fiber. Sensors 22:24, pages 9816.
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Md Obaidullah Ansari, Joyjeet Ghose, Somnath Chattopadhyaya, Debasree Ghosh, Shubham Sharma, Prashant Sharma, Abhinav Kumar, Changhe Li, Rajesh Singh & Sayed M. Eldin. (2022) An Intelligent Logic-Based Mold Breakout Prediction System Algorithm for the Continuous Casting Process of Steel: A Novel Study. Micromachines 13:12, pages 2148.
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Md Obaidullah Ansari, Somnath Chattopadhyaya, Joyjeet Ghose, Shubham Sharma, Drazan Kozak, Changhe Li, Szymon Wojciechowski, Shashi Prakash Dwivedi, Huseyin Cagan Kilinc, Jolanta B. Królczyk & Dominik Walczak. (2022) Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms. Materials 15:2, pages 670.
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Haiyang Duan, Xudong Wang, Yu Bai, Man Yao & Qingtao Guo. (2019) Integrated Approach to Density-Based Spatial Clustering of Applications with Noise and Dynamic Time Warping for Breakout Prediction in Slab Continuous Casting. Metallurgical and Materials Transactions B 50:5, pages 2343-2353.
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Artur Faizullin, Mikhail Zymbler, Dirk Lieftucht & Felix Fanghanel. (2018) Use of Deep Learning for Sticker Detection During Continuous Casting. Use of Deep Learning for Sticker Detection During Continuous Casting.
Fei He & Lingying Zhang. (2018) Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules. The International Journal of Advanced Manufacturing Technology 95:9-12, pages 4081-4089.
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