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

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