基于具有遗忘机制的在线顺序极限学习机预测转炉炼钢石灰脱磷利用率

张润灏; 杨健; 孙晗; 杨文魁

doi:10.1007/s12613-023-2732-4

Volume 31 Issue 3

Mar. 2024

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文章导航 > 矿物冶金与材料学报（英文） > 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

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研究论文

基于具有遗忘机制的在线顺序极限学习机预测转炉炼钢石灰脱磷利用率

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) 提出一种基于时间顺序的训练集和测试集的划分方法，使其适用于具有遗忘机制的在线顺序极限学习机。

(2) 基于具有遗忘机制的在线顺序极限学习机，构建了适用于转炉炼钢石灰脱磷利用率预测的机器学习模型，得出了炉次数据有效期内的最佳样本数。

(3) 结合平均影响值分析，得出预测模型中各变量对石灰脱磷利用率的影响程度。

中文摘要

将多元线性回归（multiple linear regression，MLR）、支持向量回归（support vector regression，SVR）和极限学习机（extreme learning machine，ELM），以及在线顺序ELM（online sequential ELM，OS-ELM）和具有遗忘机制的OS-ELM（OS-ELM with forgetting mechanism，FOS-ELM）模型应用于转炉炼钢石灰脱磷利用率的预测。与MLR和SVR模型相比，ELM模型表现出最好的预测性能。OS-ELM和FOS-ELM被应用于顺序学习和模型更新。FOS-ELM模型有效期内的最佳样本数被确定为1500个，最小总体平均绝对相对误差为0.058226。变量重要性分析表明，石灰质量、铁水初始磷含量和铁水质量是石灰利用率的最重要的变量。石灰利用率随着石灰质量的减少，以及初始磷含量和铁水质量的增加而增加。基于FOS-ELM的预测系统在实际工业生产中应用了一个月。在±1%、±3%和±5%的误差范围内，预测石灰利用率的命中率分别为61.16%、90.63%和94.11%。决定系数、平均绝对相对误差和均方根误差分别为0.8670、0.06823和1.4265。该系统在实际工业生产中，表现出良好的性能。

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Research Article

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

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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

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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