Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

Runhao Zhang; Jian Yang; Han Sun; Wenkui Yang

doi:10.1007/s12613-023-2732-4

Volume 31 Issue 3

Mar. 2024

Turn off MathJax

Article Contents

Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2024 > 31(3): 508-517

Runhao Zhang, Jian Yang, Han Sun, and Wenkui Yang, Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism, Int. J. Miner. Metall. Mater., 31(2024), No. 3, pp. 508-517. https://doi.org/10.1007/s12613-023-2732-4

Cite this article as:

Runhao Zhang, Jian Yang, Han Sun, and Wenkui Yang, Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism, Int. J. Miner. Metall. Mater., 31(2024), No. 3, pp. 508-517. https://doi.org/10.1007/s12613-023-2732-4

Citation:

PDF( 2997 KB)

Research Article

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

+ Author Affiliations

Corresponding author:
Jian Yang E-mail: yang_jian@t.shu.edu.cn
Received: 2 May 2023; Revised: 26 July 2023; Accepted: 22 August 2023; Available online: 25 August 2023

Abstract

Abstract

The machine learning models of multiple linear regression (MLR), support vector regression (SVR), and extreme learning machine (ELM) and the proposed ELM models of online sequential ELM (OS-ELM) and OS-ELM with forgetting mechanism (FOS-ELM) are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process. The ELM model exhibites the best performance compared with the models of MLR and SVR. OS-ELM and FOS-ELM are applied for sequential learning and model updating. The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500, with the smallest population mean absolute relative error (MARE) value of 0.058226 for the population. The variable importance analysis reveals lime weight, initial P content, and hot metal weight as the most important variables for the lime utilization ratio. The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight. A prediction system based on FOS-ELM is applied in actual industrial production for one month. The hit ratios of the predicted lime utilization ratio in the error ranges of ±1%, ±3%, and ±5% are 61.16%, 90.63%, and 94.11%, respectively. The coefficient of determination, MARE, and root mean square error are 0.8670, 0.06823, and 1.4265, respectively. The system exhibits desirable performance for applications in actual industrial production.
- basic oxygen furnace steelmaking;
- machine learning;
- lime utilization ratio;
- dephosphorization;
- online sequential extreme learning machine;
- forgetting mechanism

FullText(HTML)

Supplements(0)

References(38)

References

[1]	X.H. Huang, Principles of Iron and Steel Metallurgy, 4th ed., Publishing house of Metallurgical Industry, Beijing, 2013.
[2]	S.Y. Kitamura, H. Shibata, K.I. Shimauchi, and S.Y. Saito, The importance of dicalcium-silicate on hot metal dephosphorization reaction, Rev. Met. Paris, 105(2008), No. 5, p. 263. doi: 10.1051/metal:2008040
[3]	W.K. Yang, J. Yang, Y.Q. Shi, et al., Effect of temperature on dephosphorization of hot metal in double-slag converter steelmaking process by high-temperature laboratorial experiments, Steel Res. Int., 92(2021), No. 3, art. No. 2000438. doi: 10.1002/srin.202000438
[4]	H. Sun, J. Yang, X.W. Lu, et al., Dephosphorization in double slag converter steelmaking process at different temperatures by industrial experiments, Metals, 11(2021), No. 7, art. No. 1030. doi: 10.3390/met11071030
[5]	J. Yang, M. Kuwabara, T. Asano, A. Chuma, and J. Du, Effect of lime particle size on melting behavior of lime-containing flux, ISIJ Int., 47(2007), No. 10, p. 1401. doi: 10.2355/isijinternational.47.1401
[6]	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
[7]	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
[8]	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), p. 799.
[9]	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
[10]	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
[11]	S.L. Jiang, X.Y. Shen, and Z. Zheng, Gaussian process-based hybrid model for predicting oxygen consumption in the converter steelmaking process, Processes, 7(2019), No. 6, art. No. 352. doi: 10.3390/pr7060352
[12]	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
[13]	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, Springer International Publishing, Switzerland, 2018, p. 211.
[14]	L.Z. Yang, B. Li, Y.F. Guo, S. 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
[15]	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
[16]	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
[17]	X.J. Wang, M.S. You, Z.Z. Mao, and P. Yuan, Tree-structure ensemble general regression neural networks applied to predict the molten steel temperature in ladle furnace, Adv. Eng. Inform., 30(2016), No. 3, p. 368. doi: 10.1016/j.aei.2016.05.001
[18]	Y.H. Liu, H.B. Lu, H.Q. Zhang, X. Wu, Y.B. Zhong, and Z.S. Lei, Quality prediction of continuous casting slabs based on weighted extreme learning machine, IEEE Access, 10(2022), p. 78231. doi: 10.1109/ACCESS.2022.3192541
[19]	D. Cemernek, S. Cemernek, H. Gursch, et al., Machine learning in continuous casting of steel: A state-of-the-art survey, J. Intell. Manuf., 33(2022), No. 6, p. 1561. doi: 10.1007/s10845-021-01754-7
[20]	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.
[21]	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
[22]	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
[23]	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. Contr., 66(2018), p. 51. doi: 10.1016/j.jprocont.2018.03.005
[24]	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
[25]	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
[26]	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
[27]	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
[28]	R. Zhang and J. Yang, State of the art in applications of machine learning in steelmaking process modeling, Int. J. Miner. Metall. Mater., 30(2023), No. 11, p. 2055. doi: 10.1007/s12613-023-2646-1
[29]	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
[30]	G.B. Huang, Q.Y. Zhu, and C.K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, 70(2006), No. 1-3, p. 489. doi: 10.1016/j.neucom.2005.12.126
[31]	G.B. Huang, Q.Y. Zhu, and C.K. Siew, Extreme learning machine: A new learning scheme of feedforward neural networks, [in] 2004 IEEE International Joint Conference on Neural Networks, Budapest, 2005, p. 985.
[32]	N.Y. Liang, G.B. Huang, P. Saratchandran, and N. Sundararajan, A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Trans. Neural Netw., 17(2006), No. 6, p. 1411. doi: 10.1109/TNN.2006.880583
[33]	J.W. Zhao, Z.H. Wang, and D.S. Park, Online sequential extreme learning machine with forgetting mechanism, Neurocomputing, 87(2012), p. 79. doi: 10.1016/j.neucom.2012.02.003
[34]	F. Pedregosa, G. Varoquaux, A. Gramfort, et al., Scikit-learn: Machine learning in python, J. Mach. Learn. Res., 12(2011), p. 2825.
[35]	L. Ferrado, Pyoselm. A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for Online Machine Learning, 2021 [2023–03–02]. https://github.com/leferrad/pyoselm
[36]	L.J. Feng, C.H. Zhao, Y.L. Li, M. Zhou, H.L. Qiao, and C. Fu, 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.
[37]	M. Iwasaki and M. Matsuo, Change and development of steel-making technology, Nippon Steel Tech. Rep., 391(2011), p. 88.
[38]	H. Sun, J. Yang, W.K. Yang, and R.H. Zhang, Comprehensive evaluation of phosphorus enrichment capacity for decarburization slag at different temperatures based on industrial experiments, mineral phase analysis and ion–molecule coexistence theory, Metall. Mater. Trans. B, 54(2023), No. 1, p. 115. doi: 10.1007/s11663-022-02674-4