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

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.

Keywords

machine learning / steelmaking process modeling / artificial neural network / support vector machine / case-based reasoning / data processing

References

[1] Mitchell TM. Machine Learning, 1997, New York, McGraw-Hill., 1.

[2] Pan GF, Wang FY, Shang CL, et al. Advances in machine learning- and artificial intelligence-assisted material design of steels. Int. J. Miner. Metall. Mater., 2023, 30(6): 1003.

[3] Xu ZJ, Zheng Z, Gao XQ. Operation optimization of the steel manufacturing process: A brief review. Int. J. Miner. Metall. Mater., 2021, 28(8): 1274.

[4] Xu T, Song G, Yang Y, Ge PX, Tang LX. Visualization and simulation of steel metallurgy processes. Int. J. Miner. Metall. Mater., 2021, 28(8): 1387.

[5] Lin L, Zeng JQ. Consideration of green intelligent steel processes and narrow window stability control technology on steel quality. Int. J. Miner. Metall. Mater., 2021, 28(8): 1264.

[6] Yin RY. Review on the study of metallurgical process engineering. Int. J. Miner. Metall. Mater., 2021, 28(8): 1253.

[7] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput., 2006, 18(7): 1527.

[8] Wang HB, Xu AJ, Ai LX, Tian NY, Du X. An integrated CBR model for predicting endpoint temperature of molten steel in AOD. ISIJ Int., 2012, 52(1): 80.

[9] Aamodt A, Plaza E. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun., 1994, 7(1): 39.

[10] Wang XZ, Han M, Wang J. Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction. Eng. Appl. Artif. Intell., 2010, 23(6): 1012.

[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] Xing J, Peng JJ, Yin YH. Combination model based on CBR and SVM for BOF oxygen volume calculation. Adv. Mater. Res., 2013, 634–638, 3741.

[14] Han M, Li Y, Cao ZJ. Hybrid intelligent control of BOF oxygen volume and coolant addition. Neurocomputing, 2014, 123, 415.

[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] Park TC, Kim BS, Kim TY, Jin IB, Yeo YK. Comparative study of estimation methods of the endpoint temperature in basic oxygen furnace steelmaking process with selection of input parameters. Korean J. Met. Mater., 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.

[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] Liu C, Tang LX, Liu JY. Least squares support vector machine with self-organizing multiple kernel learning and sparsity. Neurocomputing, 2019, 331, 493.

[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] Zhou MC, Zhao Q, Chen YR. Endpoint prediction of BOF by flame spectrum and furnace mouth image based on fuzzy support vector machine. Optik, 2019, 178, 575.

[23] Wang M, Li SL, Gao C, Fan Y. End-point prediction TSVR model accuracy of 80 t BOF steelmaking. Iron Steel, 2020, 55(7): 53.

[24] Acosta SM, Amoroso AL, Sant’Anna MO, Junior OC. Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression. Ann. Oper. Res., 2022, 316(2): 905.

[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.

[26] Wang MA, Gao CA, Ai XG, Zhai BP, Li SL. Whale optimization end-point control model for 260 tons BOF steelmaking. ISIJ Int., 2022, 62(8): 1684.

[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.

[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.

[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.

[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.

[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.

[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] Tian HX, Mao ZZ. An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace. IEEE Trans. Autom. Sci. Eng., 2010, 7(1): 73.

[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] Bae J, Li YR, Ståhl N, Mathiason G, Kojola N. Using machine learning for robust target prediction in a basic oxygen furnace system. Metall. Mater. Trans. B, 2020, 51(4): 1632.

[36] Yang JP, Zhang JS, Guo WD, Gao S, Liu Q. Endpoint temperature preset of molten steel in the final refining unit based on an integration of deep neural network and multiprocess operation simulation. ISIJ Int., 2021, 61(7): 2100.

[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.

[39] Liu C, Tang LX, Liu JY. A stacked autoencoder with sparse Bayesian regression for end-point prediction problems in steelmaking process. IEEE Trans. Automat. Sci. Eng., 2020, 17(2): 550.

[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.

[41] Duarte ICD, de Almeida GM, Cardoso M. Heat-loss cycle prediction in steelmaking plants through artificial neural network. J. Oper. Res. Soc., 2022, 73(2): 326.

[42] Wu SW, Yang J, Zhang RH, Ono H. Prediction of endpoint sulfur content in KR desulfurization based on the hybrid algorithm combining artificial neural network with SAPSO. IEEE Access, 2020, 8, 33778.

[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.

[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.

[45] Qin RS. Artificial neural network study of the electrical conductivity of mould flux. Mater. Sci. Technol., 2021, 37(18): 1476.

[46] Xin ZC, Zhang JS, Zheng J, Jin Y, Liu Q. A hybrid modeling method based on expert control and deep neural network for temperature prediction of molten steel in LF. ISIJ Int., 2022, 62(3): 532.

[47] Zebari R, Abdulazeez A, Zeebaree D, Zebari D, Saeed J. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J. Appl. Sci. Technol. Trends, 2020, 1(2): 56.

[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.

[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.

[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] García S, Ramírez-Gallego S, Luengo J, Benítez JM, Herrera F. Big data preprocessing: Methods and prospects. Big Data Anal., 2016, 1(1): 1.

[52] He F, Zhang LY. Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network. J. Process Control, 2018, 66, 51.

[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] Liu Z, Cheng SS, Liu PB. Prediction model of BOF end-point P and O contents based on PCA-GA-BP neural network. High Temp. Mater. Process., 2022, 41(1): 505.

[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.

[57] Rivera WA, Xanthopoulos P. A priori synthetic oversampling methods for increasing classification sensitivity in imbalanced data sets. Expert Syst. Appl., 2016, 66, 124.

[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.

[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 endpoint phosphorus content in EAF steelmaking based on BP neural network with periodical data optimization, Metals, 12(2022), No. 9, art. No. 1519.

[61] Son K, Lee J, Hwang H, et al. Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks. J. Mater. Res. Technol., 2021, 12, 555.

[62] Strąkowski R, Pacholski K, Więcek B, Olbrycht R, Wittchen W, Borecki M. Estimation of FeO content in the steel slag using infrared imaging and artificial neural network. Measurement, 2018, 117, 380.

[63] Zhang B, Xue ZL, Liu K, Xiao WB. Development and application of prediction model for end-point manganese content in converter based on data from sub-lance. Adv. Mater. Res., 2013, 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.

[65] Liu LM, Li P, Chu MX, Gao CA. End-point prediction of 260 tons basic oxygen furnace (BOF) steelmaking based on WNPSVR and WOA. J. Intell. Fuzzy Syst., 2021, 41(2): 2923.

[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.

[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] Manohar PA, Shivathaya SS, Ferry M. Design of an expert system for the optimization of steel compositions and process route. Expert Syst. Appl., 1999, 17(2): 129.

[69] Mishra PN, Kak SK, Srivastava SC. An expert system for LD steel making. IETE J. Res., 2001, 47(1–2): 85.

[70] Wang L, Ji XM, Liu J. Application of artificial intelligence in intelligent manufacturing in steel industry. Iron Steel, 2021, 56(4): 1.

[71] Liu J. Artificial intelligence drives changes in metallurgical industry. Iron Steel, 2020, 55(6): 1.

[72] Rezaee B. Desulfurization process using Takagi–Sugeno–Kang fuzzy modeling. Int. J. Adv. Manuf. Technol., 2010, 46(1): 191.

[73] Feng K, Xu AJ, He DF, Yang LZ. Case-based reasoning method based on mechanistic model correction for predicting endpoint sulphur content of molten iron in KR desulphurization. Ironmaking Steelmaking, 2020, 47(7): 799.

[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.

[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.

[76] Mesa Fernández JM, Cabal V, Montequin VR, Balsera JV. Online estimation of electric arc furnace tap temperature by using fuzzy neural networks. Eng. Appl. Artif. Intell., 2008, 21(7): 1001.

[77] Klimas M, Grabowski D. Application of shallow neural networks in electric arc furnace modeling. IEEE Trans. Ind. Appl., 2022, 58(5): 6814.

[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] Han M, Cao ZJ. An improved case-based reasoning method and its application in endpoint prediction of basic oxygen furnace. Neurocomputing, 2015, 149, 1245.

[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.

[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.

[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.

[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] Zhang HN, Xu AJ, Cui J, He DF, Tian NY. Establishment of neural network prediction model for terminative temperature based on grey theory in hot metal pretreatment. J. Iron Steel Res. Int., 2012, 19(6): 25.

[85] Chen P, Lu YZ, Chen YW. Extremal optimization combined with LM gradient search for MLP network learning. Int. J. Comput. Intell. Syst., 2010, 3(5): 622.

[86] Han Y, Zhang CJ, Wang L, Zhang YC. Industrial IoT for intelligent steelmaking with converter mouth flame spectrum information processed by deep learning. IEEE Trans. Ind. Inform., 2020, 16(4): 2640.

[87] Zhang YC, Zhang CJ, Zeng K, Zhu LG, Han Y. Research on terminal control model of intelligent mining of flame spectral information of converter mouth in late smelting stage. Ironmaking Steelmaking, 2021, 48(6): 677.

[88] Han M, Liu C. Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine. Appl. Soft Comput., 2014, 19, 430.

[89] Chen P, Lu YZ. Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction. J. Zhejiang Univ. Sci. A, 2010, 11(11): 841.

[90] Feng MX, Ll Q, Zou ZS. An outlier identification and judgment method for an improved neural-network BOF forecasting model. Steel Res. Int., 2008, 79(5): 323.

[91] Liu H, Yao S. End point prediction of basic oxygen furnace (BOF) steelmaking based on improved bat-neural network. Metalurgija, 2019, 58(3–4): 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.

[93] Wang HB, Cai J, Feng K. Predicting the endpoint phosphorus content of molten steel in BOF by two-stage hybrid method. J. Iron Steel Res. Int., 2014, 21, 65.

[94] Zhou KX, Lin WH, Sun JK, et al. Prediction model of end-point phosphorus content for BOF based on monotone-constrained BP neural network. J. Iron Steel Res. Int., 2022, 29(5): 751.

[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] Wang Z, Chang J, Ju QP, Xie FM, Wang B, Li HW, Wang B, Lu XC, Fu GQ, Liu Q. Prediction model of endpoint manganese content for BOF steelmaking process. ISIJ Int., 2012, 52(9): 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.

[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] He F, Chai XY, Zhu ZH. Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning. High Temp. Mater. Process., 2022, 41(1): 403.

[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] Han M, Zhao Y. Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine. Expert Syst. Appl., 2011, 38(12): 14786.

[104] Cox IJ, Lewis RW, Ransing RS, Laszczewski H, Berni G. Application of neural computing in basic oxygen steelmaking. J. Mater. Process. Technol., 2002, 120(1–3): 310.

[105] Frattini Fileti AM, Pacianotto TA, Cunha AP. Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel. Eng. Appl. Artif. Intell., 2006, 19(1): 9.

[106] Gao M, Gao JT, Zhang YL, Yang SF. Evaluation and modeling of scrap utilization in the steelmaking process. JOM, 2021, 73(2): 712.

[107] Shukla AK, Deo B, Robertson DGC. Scrap dissolution in molten iron containing carbon for the case of coupled heat and mass transfer control. Metall. Mater. Trans. B, 2013, 44(6): 1407.

[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] Tian HX, Mao ZZ, Wang AN. Hybrid modeling for soft sensing of molten steel temperature in LF. J. Iron Steel Res. Int., 2009, 16(4): 1.

[110] Lv W, Mao ZZ, Yuan P. Ladle furnace steel temperature prediction model based on partial linear regularization networks with sparse representation. Steel Res. Int., 2012, 83(3): 288.

[111] Lv W, Mao ZZ, Yuan P, Jia MX. Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace. Knowl. Based Syst., 2012, 36, 280.

[112] Lü W, Mao ZZ, Yuan P. Ladle furnace liquid steel temperature prediction model based on optimally pruned bagging. J. Iron Steel Res. Int., 2012, 19(12): 21.

[113] He F, Xu AJ, Wang HB, He DF, Tian NY. End temperature prediction of molten steel in LF based on CBR. Steel Res. Int., 2012, 83(11): 1079.

[114] Wang XJ. Ladle furnace temperature prediction model based on large-scale data with random forest. IEEE/CAA J. Autom. Sin., 2016, 4(4): 770.

[115] Tian HX, Liu YD, Li K, Yang RR, Meng B. A new AdaBoost.IR soft sensor method for robust operation optimization of ladle furnace refining. ISIJ Int., 2017, 57(5): 841.

[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.

[117] Yuan F, Xu AJ, Gu MQ. Development of an improved CBR model for predicting steel temperature in ladle furnace refining. Int. J. Miner. Metall. Mater., 2021, 28(8): 1321.

[118] Xin ZC, Zhang JS, Jin Y, Zheng J, Liu Q. Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network. Int. J. Miner. Metall. Mater., 2023, 30(2): 335.

[119] Feng K, Xu AJ, Wu PF, He DF, Wang HB. 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., 2019, 26(6): 585.

[120] Feng K, Wang HB, Xu AJ, He DF. Endpoint temperature prediction of molten steel in RH using improved case-based reasoning. Int. J. Miner. Metall. Mater., 2013, 20(12): 1148.

[121] Bao YP, Li X, Wang M. A novel method for endpoint temperature prediction in RH. Ironmaking Steelmaking, 2019, 46(4): 343.

[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.

[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.

[124] C.J. Guan, W. You, and X.M. Lin, Prediction model of endpoint 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] Wilson EL, Karr CL, Bennett JP. An adaptive, intelligent control system for slag foaming. Appl. Intell., 2004, 20(2): 165.

[129] Xin ZC, Zhang JS, Lin WH, et al. Sulphide capacity prediction of CaO–SiO_2–MgO–Al₂O₃ slag system by using regularized extreme learning machine. Ironmaking Steelmaking, 2021, 48(3): 275.

[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.

[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.

[132] Wang YJ, Sun FM, Li DJ. On-line modeling and monitoring for multi-operation batch processes with infinite data types. Cluster Comput., 2019, 22(6): 14855.

[133] Alshawarghi H, Elkamel A, Moshiri B, Hourfar F. Predictive models and detection methods applicable in water detection framework for industrial electric arc furnaces. Comput. Chem. Eng., 2019, 128, 285.

[134] Saci A, Al-Dweik A, Shami A. Autocorrelation integrated Gaussian based anomaly detection using sensory data in industrial manufacturing. IEEE Sens. J., 2021, 21(7): 9231.