基于氧平衡机理与深度神经网络的转炉吹氧时间混合预测模型

邵鑫; 刘青; 信自成; 张江山; 周涛; 李少帅

doi:10.1007/s12613-023-2670-1

Volume 31 Issue 1

Jan. 2024

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文章导航 > 矿物冶金与材料学报（英文） > 2024 > 31(1): 106-117

Xin Shao, Qing Liu, Zicheng Xin, Jiangshan Zhang, Tao Zhou, and Shaoshuai Li, Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network, Int. J. Miner. Metall. Mater., 31(2024), No. 1, pp. 106-117. https://doi.org/10.1007/s12613-023-2670-1

Cite this article as:

Xin Shao, Qing Liu, Zicheng Xin, Jiangshan Zhang, Tao Zhou, and Shaoshuai Li, Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network, Int. J. Miner. Metall. Mater., 31(2024), No. 1, pp. 106-117. https://doi.org/10.1007/s12613-023-2670-1

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研究论文

基于氧平衡机理与深度神经网络的转炉吹氧时间混合预测模型

1)
北京科技大学绿色低碳钢铁冶金全国重点实验室，北京 100083，中国
2)
钢铁生产制造执行系统技术教育部工程研究中心，北京 100083，中国
3)
莱芜钢铁集团银山型钢有限公司，济南 271104，中国

通讯作者:
刘青 E-mail: qliu@ustb.edu.cn

文章亮点

(1) 系统分析了转炉冶炼过程氧平衡的影响因素并研究了转炉氧平衡机理;

(2) 构建并优化了深度神经网络模型，提高了转炉吹氧量预测精度;

(3) 融合氧平衡机理模型与深度神经网络模型，开发了转炉吹氧时间混合预测模型。

中文摘要

转炉工序作为炼钢–连铸区段的起始工序，其冶炼周期的大幅波动将严重干扰炼钢–连铸区段的高效调度，阻碍生产的稳定运行。转炉吹氧量是转炉冶炼过程重要的工艺参数之一，对转炉冶炼周期具有直接影响。本文建立了一种基于氧平衡机理（OBM）与深度神经网络（DNN）的转炉吹氧时间混合预测模型。混合模型通过三步法来实现转炉吹氧时间预测。首先，分别优化OBM模型与DNN模型，并应用OBM模型与DNN模型来预测转炉氧气消耗体积；其次，针对OBM模型与DNN模型的预测结果，通过优化求解得到更精准的氧气消耗体积；最后，根据各炉次的转炉供氧强度获得转炉吹氧时间。通过收集自中国某中大型炼钢厂的实际数据对所提混合模型进行了验证，并与多元线性回归模型、氧平衡机理模型以及包括极限学习机、反向传播神经网络和深度神经网络在内的神经网络模型进行了比较。测试结果表明，当采用3个隐含层，各隐含层神经元数为32-16-8，学习率为0.1的网络结构时，所提混合模型具有优于其他模型的性能表现，具备最佳的预测精度与更强的泛化能力。混合模型在±300 m³误差范围内的命中率为96.67%，判定系数（R²）与均方根误差（RMSE）分别为0.6984，150.03 m³；吹氧时间在±0.6 min误差范围内的预测命中率为89.50%，R²与RMSE分别为0.9486，0.3592 min。

关键词:

Research Article

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

+ Author Affiliations

Abstract

Abstract

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process, which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error ±300 m³ is 96.67%; determination coefficient (R²) and root mean square error (RMSE) are 0.6984 and 150.03 m³, respectively. The oxygen blow time prediction hit ratio within the error ±0.6 min is 89.50%; R² and RMSE are 0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.
- basic oxygen furnace;
- oxygen consumption;
- oxygen blowing time;
- oxygen balance mechanism;
- deep neural network;
- hybrid model

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