Breakout Prediction Based on Twin Support Vector Machine of Improved Whale Optimization Algorithm
Breakout is one of the hazardous industrial accidents in continuous casting production, which has adverse effects on operational stability, product quality, personal safety and equipment life. Therefore, the study of the breakout prediction model is of great significance. A hybrid intelligent algori...
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| Veröffentlicht in: | ISIJ International Jg. 63; H. 5; S. 880 - 888 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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The Iron and Steel Institute of Japan
15.05.2023
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| ISSN: | 0915-1559, 1347-5460 |
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| Abstract | Breakout is one of the hazardous industrial accidents in continuous casting production, which has adverse effects on operational stability, product quality, personal safety and equipment life. Therefore, the study of the breakout prediction model is of great significance. A hybrid intelligent algorithm is proposed based on twin support vector machine of improved whale optimization (LWOA-TSVR) by introducing the Levy flight algorithm. It uses the LWOA algorithm to solve the optimization problem of the objective function in the TSVR algorithm, so as to obtain a new breakout prediction model. The simulation and industrial trials results show that the model has higher recognition accuracy than the traditional detection method, and can predict all the breakout signals accurately and timely. Five advanced algorithms are compared to further verify the performance of the model. The results prove that LWOA-TSVR has the advantages of fast convergence speed and high prediction accuracy compared with the other four algorithms. The model is applied to the practical production of a steel mill. Finally, the reported ratio of the model is 100% and the prediction accuracy is 98.2%, which is found to be more effective than the practical application system used in continuous casting production. Hence, this model provides a theoretical basis for breakout detection technology. |
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| AbstractList | Breakout is one of the hazardous industrial accidents in continuous casting production, which has adverse effects on operational stability, product quality, personal safety and equipment life. Therefore, the study of the breakout prediction model is of great significance. A hybrid intelligent algorithm is proposed based on twin support vector machine of improved whale optimization (LWOA-TSVR) by introducing the Levy flight algorithm. It uses the LWOA algorithm to solve the optimization problem of the objective function in the TSVR algorithm, so as to obtain a new breakout prediction model. The simulation and industrial trials results show that the model has higher recognition accuracy than the traditional detection method, and can predict all the breakout signals accurately and timely. Five advanced algorithms are compared to further verify the performance of the model. The results prove that LWOA-TSVR has the advantages of fast convergence speed and high prediction accuracy compared with the other four algorithms. The model is applied to the practical production of a steel mill. Finally, the reported ratio of the model is 100% and the prediction accuracy is 98.2%, which is found to be more effective than the practical application system used in continuous casting production. Hence, this model provides a theoretical basis for breakout detection technology. |
| ArticleNumber | ISIJINT-2022-372 |
| Author | Zhong, Ruxin Shi, Chunyang Chen, Jin Ma, Zhicai Wang, Baoshuai Sun, Peng Guo, Shiyu |
| Author_xml | – sequence: 1 fullname: Zhong, Ruxin organization: Institute of Global 100-100, Liaoning Institute of Science and Technology – sequence: 1 fullname: Ma, Zhicai organization: Institute of Global 100-100, Liaoning Institute of Science and Technology – sequence: 1 fullname: Guo, Shiyu organization: Institute of Global 100-100, Liaoning Institute of Science and Technology – sequence: 1 fullname: Wang, Baoshuai organization: Institute of Global 100-100, Liaoning Institute of Science and Technology – sequence: 1 fullname: Chen, Jin organization: Key Laboratory of Electromagnetic Processing of Materials (Ministry of Education), Northeastern University – sequence: 1 fullname: Sun, Peng organization: Institute of Global 100-100, Liaoning Institute of Science and Technology – sequence: 1 orcidid: 0000-0002-4846-4707 fullname: Shi, Chunyang organization: Institute of Global 100-100, Liaoning Institute of Science and Technology |
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Wang: ISIJ Int., 54 (2014), 119. https://doi.org/10.2355/isijinternational.54.119 – reference: 14) F. He and L. Zhang: Int. J. Adv. Manuf. Technol., 95 (2018), 4081. https://doi.org/10.1007/s00170-017-1517-1 – reference: 16) W. Zhang, F. Wang and N. Li: ISIJ Int., 61 (2021), 1915. https://doi.org/10.2355/isijinternational.ISIJINT-2020-637 – reference: 2) K. E. Blazek and I. G. Saucedo: ISIJ Int., 30 (1990), 435. https://doi.org/10.2355/isijinternational.30.435 – reference: 9) W. Xudong, Y. Man and C. Xingfu: ISIJ Int., 47 (2006), 1047. https://doi.org/10.2355/isijinternational.46.1047 – reference: 15) B. Zhang, X. Zhang and L. Fan: MATEC Web Conf., 61 (2016), 05020. https://doi.org/10.1051/matecconf/20166105020 – reference: 13) B. G. Thomas: Steel Res. Int., 89 (2018), 1700312. https://doi.org/10.1002/srin.201700312 – reference: 18) C. Cortes and V. Vapnik: Mach. Learn., 20 (1995), 273. https://doi.org/10.1007/BF00994018 – reference: 6) M. Sadat, A. Honarvar Gheysari and S. Sadat: Heat Mass Transf., 47 (2011), 1601. https://doi.org/10.1007/s00231-011-0822-8 – reference: 20) P. J. García Nieto, E. García-Gonzalo, J. C. Á. Antón, V. M. González Suárez, R. M. Bayón and F. M. Martín: J. Comput. Appl. Math., 330 (2018), 877. https://doi.org/10.1016/j.cam.2017.02.031 – reference: 1) S. I. Luk’yanov, E. S. Suspitsyn, S. S. Krasilnikov and D. V. Shvidchenko: Int. J. Adv. Manuf. Technol., 79 (2015), 1861. https://doi.org/10.1007/s00170-015-6945-1 – reference: 17) J. Yang, J. Zhang, W. Guo, S. Gao and Q. Liu: ISIJ Int., 61 (2021), 2100. https://doi.org/10.2355/isijinternational.ISIJINT-2020-540 – reference: 26) C. Gao, M. Shen, X. Liu, L. Wang and M. Chen: Trans. Indian Inst. Met., 72 (2019), 257. https://doi.org/10.1007/s12666-018-1479-5 – reference: 11) S. Hore, S. K. Das, M. M. Humane and A. K. Peethala: Trans. Indian Inst. Met., 72 (2019), 3015. https://doi.org/10.1007/s12666-019-01767-0 – reference: 23) J. Nasiri and F. M. Khiyabani: Cogent Math. 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| Title | Breakout Prediction Based on Twin Support Vector Machine of Improved Whale Optimization Algorithm |
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