机器学习在炼钢过程建模中的应用现状

张润灏; 杨健

doi:10.1007/s12613-023-2646-1

Volume 30 Issue 11

Nov. 2023

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Runhao Zhang and Jian Yang, State of the art in applications of machine learning in steelmaking process modeling, Int. J. Miner. Metall. Mater., 30(2023), No. 11, pp. 2055-2075. https://doi.org/10.1007/s12613-023-2646-1

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Runhao Zhang and Jian Yang, State of the art in applications of machine learning in steelmaking process modeling, Int. J. Miner. Metall. Mater., 30(2023), No. 11, pp. 2055-2075. https://doi.org/10.1007/s12613-023-2646-1

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特约综述

机器学习在炼钢过程建模中的应用现状

张润灏,
杨健

上海大学材料科学与工程学院，省部共建高品质特殊钢冶金与制备国家重点实验室，上海200444，中国

通讯作者:
杨健 E-mail: yang_jian@t.shu.edu.cn

文章亮点

(1) 系统地阐述了机器学习在处理炼钢任务时所使用的算法与数据处理方法。

(2) 系统地回顾了21世纪以来机器学习在炼钢过程建模中的应用。

(3) 总结并展望了机器学习技术在炼钢环节未来的发展应用方向。

中文摘要

随着钢铁工业自动化和信息化的发展，炼钢过程中产生了越来越多的数据。相比于传统的基于生产经验和冶金机理的方式，机器学习技术作为一种新的方法可以用于处理大数据，机器学习在炼钢过程中的应用已成为近年来的研究热点。本文概述了机器学习在炼钢过程建模中的应用，主要包括铁水预处理、一次炼钢、二次精炼等方面。在炼钢过程建模中，最常用的三种机器学习算法是人工神经网络、支持向量机和基于案例推理，分别占56%、14%和10%。炼钢厂收集到的工业大数据经常出现各种异常与错误。因此，数据的处理，尤其是数据清洗，对机器学习模型的性能表现至关重要。变量重要性也能用于优化工艺参数和指导工业生产。机器学习应用于铁水预处理建模，主要用于终点硫含量的预测。在一次炼钢中，包含了平炉、电炉和转炉炼钢，机器学习主要应用于元素终点和工艺参数的预测。对于二次精炼建模，机器学习在LF、RH、VD、AOD、VOD工艺均有应用。机器学习在炼钢过程建模中的进一步发展可以通过数据平台建设，研究成果向炼钢过程产业转化，以及提高机器学习模型的通用性等方面的努力来实现。

关键词:

Invited Review

State of the art in applications of machine learning in steelmaking process modeling

Runhao Zhang and
Jian Yang

+ Author Affiliations

Abstract

Abstract

With the development of automation and informatization in the steelmaking industry, the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process. Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data. The application of machine learning in the steelmaking process has become a research hotspot in recent years. This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment, primary steelmaking, secondary refining, and some other aspects. The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network, support vector machine, and case-based reasoning, demonstrating proportions of 56%, 14%, and 10%, respectively. Collected data in the steelmaking plants are frequently faulty. Thus, data processing, especially data cleaning, is crucially important to the performance of machine learning models. The detection of variable importance can be used to optimize the process parameters and guide production. Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction. The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking. Machine learning is used in secondary refining modeling mainly for ladle furnaces, Ruhrstahl–Heraeus, vacuum degassing, argon oxygen decarburization, and vacuum oxygen decarburization processes. Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform, the industrial transformation of the research achievements to the practical steelmaking process, and the improvement of the universality of the machine learning models.
- machine learning;
- steelmaking process modeling;
- artificial neural network;
- support vector machine;
- case-based reasoning;
- data processing

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References

[1]	T.M. Mitchell, Machine Learning, McGraw-Hill. New York, 1997, p. 1.
[2]	G.F. Pan, F.Y. Wang, C.L. Shang, et al., Advances in machine learning- and artificial intelligence-assisted material design of steels, Int. J. Miner. Metall. Mater., 30(2023), No. 6, p. 1003. doi: 10.1007/s12613-022-2595-0
[3]	Z.J. Xu, Z. Zheng, and X.Q. Gao, Operation optimization of the steel manufacturing process: A brief review, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1274. doi: 10.1007/s12613-021-2273-7
[4]	T. Xu, G. Song, Y. Yang, P.X. Ge, and L.X. Tang, Visualization and simulation of steel metallurgy processes, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1387. doi: 10.1007/s12613-021-2283-5
[5]	L. Lin and J.Q. Zeng, Consideration of green intelligent steel processes and narrow window stability control technology on steel quality, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1264. doi: 10.1007/s12613-020-2246-2
[6]	R.Y. Yin, Review on the study of metallurgical process engineering, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1253. doi: 10.1007/s12613-020-2220-z
[7]	G.E. Hinton, S. Osindero, and Y.W. Teh, A fast learning algorithm for deep belief nets, Neural Comput., 18(2006), No. 7, p. 1527. doi: 10.1162/neco.2006.18.7.1527
[8]	H.B. Wang, A.J. Xu, L.X. Ai, N.Y. Tian, and X. Du, An integrated CBR model for predicting endpoint temperature of molten steel in AOD, ISIJ Int., 52(2012), No. 1, p. 80. doi: 10.2355/isijinternational.52.80
[9]	A. Aamodt and E. Plaza, Case-based reasoning: Foundational issues, methodological variations, and system approaches, AI Commun., 7(1994), No. 1, p. 39. doi: 10.3233/AIC-1994-7104
[10]	X.Z. Wang, M. Han, and J. Wang, Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction, Eng. Appl. Artif. Intell., 23(2010), No. 6, p. 1012. doi: 10.1016/j.engappai.2009.12.007
[11]	Z. Xu and Z.Z. Mao, Comparisons of element yield rate prediction using feed-forward neural networks and support vector machine, [in] 2010 Chinese Control and Decision Conference, Xuzhou, 2010, p. 4163.
[12]	W. Yang, H.J. Meng, Y.J. Huang, and Z. Xie, Prediction on molten steel end temperature during tapping in BOF based on LS-SVM and PSO, [in] 9th International Conference on Measurement and Control of Granular Materials (MCGM 2011), Shanghai, 2012, p. 233.
[13]	J. Xing, J.J. Peng, and Y.H. Yin, Combination model based on CBR and SVM for BOF oxygen volume calculation, Adv. Mater. Res., 634-638(2013), p. 3741. doi: 10.4028/www.scientific.net/AMR.634-638.3741
[14]	M. Han, Y. Li, and Z.J. Cao, Hybrid intelligent control of BOF oxygen volume and coolant addition, Neurocomputing, 123(2014), p. 415. doi: 10.1016/j.neucom.2013.08.003
[15]	C. Liu, X.M. Song, T. Xu, and L.X. Tang, An operation optimization method based on improved EDA for BOF end-point control, [in] 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, 2016, p. 1077.
[16]	C. Gao, M.G. Shen, and L.D. Wang, End-point prediction of BOF steelmaking based on wavelet transform based weighted TSVR, [in] 2018 37th Chinese Control Conference (CCC), Wuhan, 2018, p. 3200.
[17]	T.C. Park, B.S. Kim, T.Y. Kim, I.B. Jin, and Y.K. Yeo, Comparative study of estimation methods of the endpoint temperature in basic oxygen furnace steelmaking process with selection of input parameters, Korean J. Met. Mater., 56(2018), No. 11, p. 813. doi: 10.3365/KJMM.2018.56.11.813
[18]	C.A. Gao, M.G. Shen, X.P. Liu, L.D. Wang, and M.X. Chu, End-point static control of basic oxygen furnace (BOF) steelmaking based on wavelet transform weighted twin support vector regression, Complexity, 2019(2019), art. No. 7408725. doi: 10.1155/2019/7408725
[19]	J. Kačur, M. Laciak, P. Flegner, J. Terpák, M. Durdán, and G. Tréfa, Application of support vector regression for data-driven modeling of melt temperature and carbon content in LD converter, [in] 2019 20th International Carpathian Control Conference (ICCC), Krakow-Wieliczka, 2019, p. 1.
[20]	C. Liu, L.X. Tang, and J.Y. Liu, Least squares support vector machine with self-organizing multiple kernel learning and sparsity, Neurocomputing, 331(2019), p. 493. doi: 10.1016/j.neucom.2018.11.067
[21]	P. Sismanis, Prediction of productivity and energy consumption in a consteel furnace using data-science models, [in] Business Information Systems, Seville, 2019, p. 85.
[22]	M.C. Zhou, Q. Zhao, and Y.R. Chen, Endpoint prediction of BOF by flame spectrum and furnace mouth image based on fuzzy support vector machine, Optik, 178(2019), p. 575. doi: 10.1016/j.ijleo.2018.10.041
[23]	M. Wang, S.L. Li, C. Gao, and Y. Fan, End-point prediction TSVR model accuracy of 80 t BOF steelmaking, Iron Steel, 55(2020), No. 7, p. 53. doi: 10.13228/j.boyuan.issn0449-749x.20190499
[24]	S.M. Acosta, A.L. Amoroso, Â.M.O. Sant’Anna, and O.C. Junior, Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression, Ann. Oper. Res., 316(2022), No. 2, p. 905. doi: 10.1007/s10479-021-04053-9
[25]	V. Manojlović, Ž. Kamberović, M. Korać, and M. Dotlić, Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters, Appl. Energy, 307(2022), art. No. 118209. doi: 10.1016/j.apenergy.2021.118209
[26]	M.A. Wang, C.A. Gao, X.G. Ai, B.P. Zhai, and S.L. Li, Whale optimization end-point control model for 260 tons BOF steelmaking, ISIJ Int., 62(2022), No. 8, p. 1684. doi: 10.2355/isijinternational.ISIJINT-2021-517
[27]	C.J. Zhang, Y.C. Zhang, and Y. Han, Industrial cyber-physical system driven intelligent prediction model for converter end carbon content in steelmaking plants, J. Ind. Inf. Integr., 28(2022), art. No. 100356. doi: 10.1016/j.jii.2022.100356
[28]	J. Phull, J. Egas, S. Barui, S. Mukherjee, and K. Chattopadhyay, An application of decision tree-based twin support vector machines to classify dephosphorization in BOF steelmaking, Metals, 10(2020), No. 1, art. No. 25. doi: 10.3390/met10010025
[29]	H. Li, S. Barui, S. Mukherjee, and K. Chattopadhyay, Least squares twin support vector machines to classify end-point phosphorus content in BOF steelmaking, Metals, 12(2022), No. 2, art. No. 268. doi: 10.3390/met12020268
[30]	Q.A. Li, C. Liu, and Q.X. Guo, Support vector machine with robust low-rank learning for multi-label classification problems in the steelmaking process, Mathematics, 10(2022), No. 15, art. No. 2659. doi: 10.3390/math10152659
[31]	J. Kačur, P. Flegner, M. Durdán, and M. Laciak, Prediction of temperature and carbon concentration in oxygen steelmaking by machine learning: A comparative study, Appl. Sci., 12(2022), No. 15, art. No. 7757. doi: 10.3390/app12157757
[32]	W. Li, X.C. Wang, X.S. Wang, and H. Wang, Endpoint prediction of BOF steelmaking based on bp neural network combined with improved PSO, [in] 3rd International Conference on Applied Engineering, Wuhan, 2016, p. 475.
[33]	H.X. Tian and Z.Z. Mao, An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace, IEEE Trans. Autom. Sci. Eng., 7(2010), No. 1, p. 73. doi: 10.1109/TASE.2008.2005640
[34]	G.S. Wei, R. Zhu, L.Z. Yang, and T.P. Tang, Hybrid modeling for endpoint carbon content prediction in EAF steelmaking, [in] Materials Processing Fundamentals 2018, Phoenix, 2018, p. 211.
[35]	J. Bae, Y.R. Li, N. Ståhl, G. Mathiason, and N. Kojola, Using machine learning for robust target prediction in a basic oxygen furnace system, Metall. Mater. Trans. B, 51(2020), No. 4, p. 1632. doi: 10.1007/s11663-020-01853-5
[36]	J.P. Yang, J.S. Zhang, W.D. Guo, S. Gao, and Q. Liu, End-point temperature preset of molten steel in the final refining unit based on an integration of deep neural network and multi-process operation simulation, ISIJ Int., 61(2021), No. 7, p. 2100. doi: 10.2355/isijinternational.ISIJINT-2020-540
[37]	D. Laha, ANN modeling of a steelmaking process, [in] International Conference on Swarm, Evolutionary, and Memetic Computing, Chennai, 2013, p. 308.
[38]	Z. Liu, S.S. Cheng, and P.B. Liu, Prediction model of BOF end-point temperature and carbon content based on PCA-GA-BP neural network, Metall. Res. Technol., 119(2022), No. 6, art. No. 605. doi: 10.1051/metal/2022091
[39]	C. Liu, L.X. Tang, and J.Y. Liu, A stacked autoencoder with sparse Bayesian regression for end-point prediction problems in steelmaking process, IEEE Trans. Automat. Sci. Eng., 17(2020), No. 2, p. 550. doi: 10.1109/TASE.2019.2935314
[40]	L.S. Carlsson, P.B. Samuelsson, and P.G. Jönsson, Interpretable machine learning—Tools to interpret the predictions of a machine learning model predicting the electrical energy consumption of an electric arc furnace, Steel Res. Int., 91(2020), No. 11, art. No. 2000053. doi: 10.1002/srin.202000053
[41]	I.C.D. Duarte, G.M. de Almeida, and M. Cardoso, Heat-loss cycle prediction in steelmaking plants through artificial neural network, J. Oper. Res. Soc., 73(2022), No. 2, p. 326. doi: 10.1080/01605682.2020.1824552
[42]	S.W. Wu, J. Yang, R.H. Zhang, and H. Ono, Prediction of endpoint sulfur content in KR desulfurization based on the hybrid algorithm combining artificial neural network with SAPSO, IEEE Access, 8(2020), p. 33778. doi: 10.1109/ACCESS.2020.2971517
[43]	R.H. Zhang, J. Yang, S.W. Wu, H. Sun, and W.K. Yang, Comparison of the prediction of BOF end-point phosphorus content among machine learning models and metallurgical mechanism model, Steel Res. Int., 94(2023), No. 5, art. No. 2200682. doi: 10.1002/srin.202200682
[44]	B. Nenchev, C. Panwisawas, X.A. Yang, et al., Metallurgical data science for steel industry: A case study on basic oxygen furnace, Steel Res. Int., 93(2022), No. 12, art. No. 2100813. doi: 10.1002/srin.202100813
[45]	R.S. Qin, Artificial neural network study of the electrical conductivity of mould flux, Mater. Sci. Technol., 37(2021), No. 18, p. 1476. doi: 10.1080/02670836.2021.2016269
[46]	Z.C. Xin, J.S. Zhang, J. Zheng, Y. Jin, and Q. Liu, A hybrid modeling method based on expert control and deep neural network for temperature prediction of molten steel in LF, ISIJ Int., 62(2022), No. 3, p. 532. doi: 10.2355/isijinternational.ISIJINT-2021-251
[47]	R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction, J. Appl. Sci. Technol. Trends, 1(2020), No. 2, p. 56. doi: 10.38094/jastt1224
[48]	L. Qi, H. Liu, Q. Xiong, and Z.X. Chen, Just-in-time-learning based prediction model of BOF endpoint carbon content and temperature via vMF mixture model and weighted extreme learning machine, Comput. Chem. Eng., 154(2021), art. No. 107488. doi: 10.1016/j.compchemeng.2021.107488
[49]	T. Vuolio, V.V. Visuri, A. Sorsa, S. Ollila, and T. Fabritius, Application of a genetic algorithm based model selection algorithm for identification of carbide-based hot metal desulfurization, Appl. Soft Comput., 92(2020), art. No. 106330. doi: 10.1016/j.asoc.2020.106330
[50]	Z. Chen, J.G. Wang, G.Q. Zhao, Y. Yao, and C. Xu, Endpoint temperature prediction of molten steel in VD furnace based on AdaBoost.RT-ELM, [in] 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), Liuzhou, 2020, p. 789.
[51]	S. García, S. Ramírez-Gallego, J. Luengo, J.M. Benítez, and F. Herrera, Big data preprocessing: Methods and prospects, Big Data Anal., 1(2016), No. 1, p. 1. doi: 10.1186/s41044-016-0001-5
[52]	F. He and L.Y. Zhang, Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network, J. Process Control, 66(2018), p. 51. doi: 10.1016/j.jprocont.2018.03.005
[53]	M.Q. Gu, A.J. Xu, D.F. He, H.B. Wang, and K. Feng, Prediction model of end-point molten steel temperature in RH refining based on PCA-CBR. [in] 11th International Symposium on High-Temperature Metallurgical Processing, San Diego, 2020, p. 741.
[54]	Z. Liu, S.S. Cheng, and P.B. Liu, Prediction model of BOF end-point P and O contents based on PCA–GA–BP neural network, High Temp. Mater. Process., 41(2022), No. 1, p. 505. doi: 10.1515/htmp-2022-0050
[55]	C. Spinola, C.J. Galvez-Fernandez, J. Munoz-Perez, J. Jerrer, J. Ma Bonelo, and J. Vizoso, An empirical model of the decarburization process in stainless steel production, [in] 2006 IEEE International Conference on Industrial Technology, Bombay, 2006, p. 2029.
[56]	L.Z. Yang, B. Li, Y.F. Guo, S.A. Wang, B.T. Xue, and S.Y. Hu, Influence factor analysis and prediction model of end-point carbon content based on artificial neural network in electric arc furnace steelmaking process, Coatings, 12(2022), No. 10, art. No. 1508. doi: 10.3390/coatings12101508
[57]	W.A. Rivera and P. Xanthopoulos, A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets, Expert Syst. Appl., 66(2016), p. 124. doi: 10.1016/j.eswa.2016.09.010
[58]	Z.L. Wang, Y.P. Bao, and C. Gu, Convolutional neural network-based method for predicting oxygen content at the end point of converter, Steel Res. Int., 94(2023), No. 1, art. No. 2200342. doi: 10.1002/srin.202200342
[59]	H.Y. Wen, Q. Zhao, Y.R. Chen, M.C. Zhou, M. Zhang, and L.F. Xu, Converter end-point prediction model using spectrum image analysis and improved neural network algorithm, Opt. Appl., 38(2008), No. 4, art. No. 693.
[60]	Y.C. Zou, L.Z. Yang, B. Li, et al., Prediction model of end-point phosphorus content in EAF steelmaking based on BP neural network with periodical data optimization, Metals, 12(2022), No. 9, art. No. 1519. doi: 10.3390/met12091519
[61]	K. Son, J. Lee, H. Hwang, et al., Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks, J. Mater. Res. Technol., 12(2021), p. 555. doi: 10.1016/j.jmrt.2021.02.085
[62]	R. Strąkowski, K. Pacholski, B. Więcek, R. Olbrycht, W. Wittchen, and M. Borecki, Estimation of FeO content in the steel slag using infrared imaging and artificial neural network, Measurement, 117(2018), p. 380. doi: 10.1016/j.measurement.2017.12.031
[63]	B. Zhang, Z.L. Xue, K. Liu, and W.B. Xiao, Development and application of prediction model for end-point manganese content in converter based on data from sub-lance, Adv. Mater. Res., 683(2013), p. 497. doi: 10.4028/www.scientific.net/AMR.683.497
[64]	K. Feng, A.J. Xu, D.F. He, and H.B. Wang, An improved CBR model based on mechanistic model similarity for predicting end phosphorus content in dephosphorization converter, Steel Res. Int., 89(2018), No. 6, art. No. 1800063. doi: 10.1002/srin.201800063
[65]	L.M. Liu, P. Li, M.X. Chu, and C.A. Gao, End-point prediction of 260 tons basic oxygen furnace (BOF) steelmaking based on WNPSVR and WOA, J. Intell. Fuzzy Syst., 41(2021), No. 2, p. 2923. doi: 10.3233/JIFS-210007
[66]	S.W. Wu and J. Yang, A convolutional neural network-based model for predicting lime utilization ratio in the KR desulfurization process, Metall. Res. Technol., 118(2021), No. 6, art. No. 603. doi: 10.1051/metal/2021074
[67]	M. Kordos, M. Blachnik, and T. Wieczorek, Evolutionary optimization of regression model ensembles in steel-making process, [in] International Conference on Intelligent Data Engineering and Automated Learning, Norwich, 2011, p. 369.
[68]	P.A. Manohar, S.S. Shivathaya, and M. Ferry, Design of an expert system for the optimization of steel compositions and process route, Expert Syst. Appl., 17(1999), No. 2, p. 129. doi: 10.1016/S0957-4174(99)00030-5
[69]	P.N. Mishra, S.K. Kak, and S.C. Srivastava, An expert system for LD steel making, IETE J. Res., 47(2001), No. 1-2, p. 85. doi: 10.1080/03772063.2001.11416207
[70]	L. Wang, X.M. Ji, and J. Liu, Application of artificial intelligence in intelligent manufacturing in steel industry, Iron Steel, 56(2021), No. 4, p. 1. doi: 10.13228/j.boyuan.issn0449-749x.20200503
[71]	J. Liu, Artificial intelligence drives changes in metallurgical industry, Iron Steel, 55(2020), No. 6, p. 1. doi: 10.13228/j.boyuan.issn0449-749x.20200191
[72]	B. Rezaee, Desulfurization process using Takagi−Sugeno−Kang fuzzy modeling, Int. J. Adv. Manuf. Technol., 46(2010), No. 1, p. 191. doi: 10.1007/s00170-009-2031-x
[73]	K. Feng, A.J. Xu, D.F. He, and L.Z. Yang, Case-based reasoning method based on mechanistic model correction for predicting endpoint sulphur content of molten iron in KR desulphurization, Ironmaking Steelmaking, 47(2020), No. 7, p. 799. doi: 10.1080/03019233.2019.1615307
[74]	S. Tomažič, G. Andonovski, I. Škrjanc, and V. Logar, Data-driven modelling and optimization of energy consumption in EAF, Metals, 12(2022), No. 5, art. No. 816. doi: 10.3390/met12050816
[75]	A. Reimann, T. Hay, T. Echterhof, M. Kirschen, and H. Pfeifer, Application and evaluation of mathematical models for prediction of the electric energy demand using plant data of five industrial-size EAFs, Metals, 11(2021), No. 9, art. No. 1348. doi: 10.3390/met11091348
[76]	J.M. Mesa Fernández, V.Á. Cabal, V.R. Montequin, and J.V. Balsera, Online estimation of electric arc furnace tap temperature by using fuzzy neural networks, Eng. Appl. Artif. Intell., 21(2008), No. 7, p. 1001. doi: 10.1016/j.engappai.2007.11.008
[77]	M. Klimas and D. Grabowski, Application of shallow neural networks in electric arc furnace modeling, IEEE Trans. Ind. Appl., 58(2022), No. 5, p. 6814. doi: 10.1109/TIA.2022.3180004
[78]	X.Z. Wang, J. Xing, J. Dong, and Z.S. Wang, Data driven based endpoint carbon content real time prediction for BOF steelmaking, [in] 2017 36th Chinese Control Conference (CCC), Dalian, 2017, p. 9708.
[79]	M. Han and Z.J. Cao, An improved case-based reasoning method and its application in endpoint prediction of basic oxygen furnace, Neurocomputing, 149(2015), p. 1245. doi: 10.1016/j.neucom.2014.09.003
[80]	J. Díaz and F.J. Fernández, Application of combined developments in processes and models to the determination of hot metal temperature in BOF steelmaking, Processes, 8(2020), No. 6, art. No. 732. doi: 10.3390/pr8060732
[81]	M. Laciak, J. Kačur, J. Terpák, M. Durdán, and P. Flegner, Comparison of different approaches to the creation of a mathematical model of melt temperature in an LD converter, Processes, 10(2022), No. 7, art. No. 1378. doi: 10.3390/pr10071378
[82]	K. Feng, L.Z. Yang, B.X. Su, W. Feng, and L.F. Wang, An integration model for converter molten steel end temperature prediction based on Bayesian formula, Steel Res. Int., 93(2022), No. 2, art. No. 2100433. doi: 10.1002/srin.202100433
[83]	H. Jo, H.J. Hwang, D. Phan, Y.M. Lee, and H. Jang, Endpoint temperature prediction model for LD converters using machine-learning techniques, [in] 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), Tokyo, 2019, p. 22.
[84]	H.N. Zhang, A.J. Xu, J. Cui, D.F. He, and N.Y. Tian, Establishment of neural network prediction model for terminative temperature based on grey theory in hot metal pretreatment, J. Iron Steel Res. Int., 19(2012), No. 6, p. 25. doi: 10.1016/S1006-706X(12)60122-8
[85]	P. Chen, Y.Z. Lu, and Y.W. Chen, Extremal optimization combined with LM gradient search for MLP network learning, Int. J. Comput. Intell. Syst., 3(2010), No. 5, p. 622. doi: 10.1080/18756891.2010.9727728
[86]	Y. Han, C.J. Zhang, L. Wang, and Y.C. Zhang, Industrial IoT for intelligent steelmaking with converter mouth flame spectrum information processed by deep learning, IEEE Trans. Ind. Inform., 16(2020), No. 4, p. 2640. doi: 10.1109/TII.2019.2948100
[87]	Y.C. Zhang, C.J. Zhang, K. Zeng, L.G. Zhu, and Y. Han, Research on terminal control model of intelligent mining of flame spectral information of converter mouth in late smelting stage, Ironmaking Steelmaking, 48(2021), No. 6, p. 677. doi: 10.1080/03019233.2021.1889907
[88]	M. Han and C. Liu, Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine, Appl. Soft Comput., 19(2014), p. 430. doi: 10.1016/j.asoc.2013.09.012
[89]	P. Chen and Y.Z. Lu, Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction, J. Zhejiang Univ. Sci. A, 11(2010), No. 11, p. 841. doi: 10.1631/jzus.A0900664
[90]	M.X. Feng, Q. Ll, and Z.S. Zou, An outlier identification and judgment method for an improved neural-network BOF forecasting model, Steel Res. Int., 79(2008), No. 5, p. 323. doi: 10.1002/srin.200806134
[91]	H. Liu and S. Yao, End point prediction of basic oxygen furnace (BOF) steelmaking based on improved bat-neural network, Metalurgija, 58(2019), No. 3-4, p. 207.
[92]	S. Pal and C. Halder, Optimization of phosphorous in steel produced by basic oxygen steel making process using multi-objective evolutionary and genetic algorithms, Steel Res. Int., 88(2017), No. 3, art. No. 1600193. doi: 10.1002/srin.201600193
[93]	H.B. Wang, J. Cai, and K. Feng, Predicting the endpoint phosphorus content of molten steel in BOF by two-stage hybrid method, J. Iron Steel Res. Int., 21(2014), p. 65. doi: 10.1016/S1006-706X(14)60123-0
[94]	K.X. Zhou, W.H. Lin, J.K. Sun, et al., Prediction model of end-point phosphorus content for BOF based on monotone-constrained BP neural network, J. Iron Steel Res. Int., 29(2022), No. 5, p. 751. doi: 10.1007/s42243-021-00655-6
[95]	J. Tao, S.S. Ouyang, and X. Wang, Intelligent method for BOF endpoint [P]&[Mn] estimation, [in] 2006 6th World Congress on Intelligent Control and Automation, Dalian, 2006, p. 7802.
[96]	X. Wang, S.Y. Li, Z.J. Wang, J. Tao, and J.X. Liu, A multiple RBF NN modeling approach to BOF endpoint estimation in steelmaking process. [in] International Symposium on Neural Networks (ISSN 2004), Dalian, 2004, p. 848.
[97]	J. Tao and W.D. Qian, Intelligent method for BOF endpoint vertical bar P vertical bar &vertical bar MN vertical bar estimation, [in] IFAC Workshop on New Technologies for Automation of Metallurgical Industry, Shanghai, 2004, p. 77.
[98]	Z. Wang, J. Chang, Q.P. Ju, F.M. Xie, B. Wang, H.W. Li, B. Wang, X.C. Lu, G.Q. Fu, and Q. Liu, Prediction model of end-point manganese content for BOF steelmaking process, ISIJ Int., 52(2012), No. 9, p. 1585. doi: 10.2355/isijinternational.52.1585
[99]	L.J. Feng, C.H. Zhao, Y.L. Li, et al., Multichannel diffusion graph convolutional network for the prediction of endpoint composition in the converter steelmaking process, IEEE Trans. Instrum. Meas., 70(2021), art. No. 3000413. doi: 10.1109/TIM.2020.3037953
[100]	X.Z. Wang and J. Dong, Fuzzy based similarity adjustment of case retrieval process in CBR system for BOF oxygen volume control, [in] 2013 Sixth International Conference on Advanced Computational Intelligence (ICACI), Hangzhou, 2014, p. 130.
[101]	F. He, X.Y. Chai, and Z.H. Zhu, Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning, High Temp. Mater. Process., 41(2022), No. 1, p. 403. doi: 10.1515/htmp-2022-0035
[102]	L.P. Qu, X.J. Zhang, and Y.Y. Qu, Research on BOF steelmaking endpoint control based on neural network, [in] 2012 24th Chinese Control and Decision Conference (CCDC), Taiyuan, 2012, p. 4110.
[103]	M. Han and Y. Zhao, Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine, Expert Syst. Appl., 38(2011), No. 12, p. 14786. doi: 10.1016/j.eswa.2011.05.071
[104]	I.J. Cox, R.W. Lewis, R.S. Ransing, H. Laszczewski, and G. Berni, Application of neural computing in basic oxygen steelmaking, J. Mater. Process. Technol., 120(2002), No. 1-3, p. 310. doi: 10.1016/S0924-0136(01)01136-0
[105]	A.M. Frattini Fileti, T.A. Pacianotto, and A.P. Cunha, Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel, Eng. Appl. Artif. Intell., 19(2006), No. 1, p. 9. doi: 10.1016/j.engappai.2005.06.002
[106]	M. Gao, J.T. Gao, Y.L. Zhang, and S.F. Yang, Evaluation and modeling of scrap utilization in the steelmaking process, JOM, 73(2021), No. 2, p. 712. doi: 10.1007/s11837-020-04529-2
[107]	A.K. Shukla, B. Deo, and D.G.C. Robertson, Scrap dissolution in molten iron containing carbon for the case of coupled heat and mass transfer control, Metall. Mater. Trans. B, 44(2013), No. 6, p. 1407. doi: 10.1007/s11663-013-9905-x
[108]	H.X. Tian, A.N. Wang, and Z.Z. Mao, A new soft sensor modeling method based on modified AdaBoost with incremental learning, [in] Joint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC), Shanghai, 2010, p. 8375.
[109]	H.X. Tian, Z.Z. Mao, and A.N. Wang, Hybrid modeling for soft sensing of molten steel temperature in LF, J. Iron Steel Res. Int., 16(2009), No. 4, p. 1. doi: 10.1016/S1006-706X(09)60051-0
[110]	W. Lv, Z.Z. Mao, and P. Yuan, Ladle furnace steel temperature prediction model based on partial linear regularization networks with sparse representation, Steel Res. Int., 83(2012), No. 3, p. 288. doi: 10.1002/srin.201100252
[111]	W. Lv, Z.Z. Mao, P. Yuan, and M.X. Jia, Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace, Knowl. Based Syst., 36(2012), p. 280. doi: 10.1016/j.knosys.2012.07.012
[112]	W. Lü, Z.Z. Mao, and P. Yuan, Ladle furnace liquid steel temperature prediction model based on optimally pruned bagging, J. Iron Steel Res. Int., 19(2012), No. 12, p. 21. doi: 10.1016/S1006-706X(13)60027-8
[113]	F. He, A.J. Xu, H.B. Wang, D.F. He, and N.Y. Tian, End temperature prediction of molten steel in LF based on CBR, Steel Res. Int., 83(2012), No. 11, p. 1079. doi: 10.1002/srin.201200028
[114]	X.J. Wang, Ladle furnace temperature prediction model based on large-scale data with random forest, IEEE/CAA J. Autom. Sin., 4(2016), No. 4, p. 770. doi: 10.1109/JAS.2016.7510247
[115]	H.X. Tian, Y.D. Liu, K. Li, R.R. Yang, and B. Meng, A new AdaBoost.IR soft sensor method for robust operation optimization of ladle furnace refining, ISIJ Int., 57(2017), No. 5, p. 841. doi: 10.2355/isijinternational.ISIJINT-2016-371
[116]	Q.D. Yang, J. Zhang, and Z. Yi, Predicting molten steel endpoint temperature using a feature-weighted model optimized by mutual learning cuckoo search, Appl. Soft Comput., 83(2019), art. No. 105675. doi: 10.1016/j.asoc.2019.105675
[117]	F. Yuan, A.J. Xu, and M.Q. Gu, Development of an improved CBR model for predicting steel temperature in ladle furnace refining, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1321. doi: 10.1007/s12613-020-2234-6
[118]	Z.C. Xin, J.S. Zhang, Y. Jin, J. Zheng, and Q. Liu, Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network, Int. J. Miner. Metall. Mater., 30(2023), No. 2, p. 335. doi: 10.1007/s12613-021-2409-9
[119]	K. Feng, A.J. Xu, P.F. Wu, D.F. He, and H.B. Wang, Case-based reasoning model based on attribute weights optimized by genetic algorithm for predicting end temperature of molten steel in RH, J. Iron Steel Res. Int., 26(2019), No. 6, p. 585. doi: 10.1007/s42243-019-00264-4
[120]	K. Feng, H.B. Wang, A.J. Xu, and D.F. He, Endpoint temperature prediction of molten steel in RH using improved case-based reasoning, Int. J. Miner. Metall. Mater., 20(2013), No. 12, p. 1148. doi: 10.1007/s12613-013-0848-7
[121]	Y.P. Bao, X. Li, and M. Wang, A novel method for endpoint temperature prediction in RH, Ironmaking Steelmaking, 46(2019), No. 4, p. 343. doi: 10.1080/03019233.2017.1392104
[122]	C. Gruber, B. Bückner, M. Schatzl, M. Thumfart, R. Eßbichl, and R. Rössler, Big data handling in process surveillance and quality control of secondary metallurgical processes, Steel Res. Int., 93(2022), No. 12, art. No. 2200060. doi: 10.1002/srin.202200060
[123]	S.H. Wang, H.F. Li, Y.J. Zhang, and Z.S. Zou, An integrated methodology for rule extraction from ELM-based vacuum tank degasser multiclassifier for decision-making, Energies, 12(2019), No. 18, art. No. 3535. doi: 10.3390/en12183535
[124]	C.J. Guan, W. You, and X.M. Lin, Prediction model of end-point for AOD furnace based on neural network, [in] 2009 IEEE International Conference on Mechatronics and Automation, Changchun, 2009, p. 2426.
[125]	Y.X. Hong, X. Jing, and Y.H. Tao, The endpoint forecast of AOD stove ferroalloy steel-making based on wavelet neural network, [in] 2010 Chinese Control and Decision Conference, Xuzhou, 2010, p. 2882.
[126]	J.W. Li and B.Y. Ma, Parameters adjustment for VOD endpoint carbon content and endpoint temperature prediction model, [in] 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA), Toronto, 2014, p. 595.
[127]	J.W. Li and C.Z. Liang, Endpoint carbon content prediction of VOD using RBF neural network, [in] 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA), Toronto, 2014, p. 588.
[128]	E.L. Wilson, C.L. Karr, and J.P. Bennett, An adaptive, intelligent control system for slag foaming, Appl. Intell., 20(2004), No. 2, p. 165. doi: 10.1023/B:APIN.0000013338.39348.46
[129]	Z.C. Xin, J.S. Zhang, W.H. Lin, et al., Sulphide capacity prediction of CaO–SiO₂–MgO–Al₂O₃ slag system by using regularized extreme learning machine, Ironmaking Steelmaking, 48(2021), No. 3, p. 275. doi: 10.1080/03019233.2020.1771892
[130]	S. Barui, S. Mukherjee, A. Srivastava, and K. Chattopadhyay, Understanding dephosphorization in basic oxygen furnaces (BOFs) using data driven modeling techniques, Metals, 9(2019), No. 9, art. No. 955. doi: 10.3390/met9090955
[131]	H. Saigo, K.C. Dukka B, and N. Saito, Einstein–Roscoe regression for the slag viscosity prediction problem in steelmaking, Sci. Rep., 12(2022), No. 1, art. No. 6541. doi: 10.1038/s41598-022-10278-w
[132]	Y.J. Wang, F.M. Sun, and D.J. Li, On-line modeling and monitoring for multi-operation batch processes with infinite data types, Cluster Comput., 22(2019), No. 6, p. 14855. doi: 10.1007/s10586-018-2426-2
[133]	H. Alshawarghi, A. Elkamel, B. Moshiri, and F. Hourfar, Predictive models and detection methods applicable in water detection framework for industrial electric arc furnaces, Comput. Chem. Eng., 128(2019), p. 285. doi: 10.1016/j.compchemeng.2019.06.005
[134]	A. Saci, A. Al-Dweik, and A. Shami, Autocorrelation integrated Gaussian based anomaly detection using sensory data in industrial manufacturing, IEEE Sens. J., 21(2021), No. 7, p. 9231. doi: 10.1109/JSEN.2021.3053039