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基于动态注意力深度迁移网络的高炉铁水硅含量在线预测方法

蒋珂 蒋朝辉 谢永芳 潘冬 桂卫华

蒋珂, 蒋朝辉, 谢永芳, 潘冬, 桂卫华. 基于动态注意力深度迁移网络的高炉铁水硅含量在线预测方法. 自动化学报, 2021, 48(x): 1−15 doi: 10.16383/j.aas.c210524
引用本文: 蒋珂, 蒋朝辉, 谢永芳, 潘冬, 桂卫华. 基于动态注意力深度迁移网络的高炉铁水硅含量在线预测方法. 自动化学报, 2021, 48(x): 1−15 doi: 10.16383/j.aas.c210524
Jinag Ke, Jiang Zhao-Hui, Xie Yong-Fang, Pan Dong, Gui Wei-Hua. Online prediction method for silicon content of molten iron in blast furnace based on dynamic attention deep transfer network. Acta Automatica Sinica, 2021, 48(x): 1−15 doi: 10.16383/j.aas.c210524
Citation: Jinag Ke, Jiang Zhao-Hui, Xie Yong-Fang, Pan Dong, Gui Wei-Hua. Online prediction method for silicon content of molten iron in blast furnace based on dynamic attention deep transfer network. Acta Automatica Sinica, 2021, 48(x): 1−15 doi: 10.16383/j.aas.c210524

基于动态注意力深度迁移网络的高炉铁水硅含量在线预测方法

doi: 10.16383/j.aas.c210524
基金项目: 国家自然科学基金(61773406, 61725306, 61290325), 国家重大科研仪器研制项目(61927803), 中南大学研究生自主探索创新项目(2021zzts0183), 湖南省研究生科研创新项目(CX20210242)资助
详细信息
    作者简介:

    蒋珂:中南大学博士研究生. 2019年获得中南大学硕士学位. 主要研究方向为数据驱动的工业过程建模和控制, 过程数据分析和机器学习. E-mail: jiangke@csu.edu.cn

    蒋朝辉:中南大学自动化学院教授、博士生导师, 自动化科学与技术系主任. 2011年获得中南大学博士学位. 主要研究方向为智能传感与检测技术, 图像处理与智能识别, 人工智能与机器学习. 本文通讯作者. E-mail: jzh0903@csu.edu.cn

    谢永芳:中南大学自动化学院教授、博士生导师. 1993 年获得中南工业大学学士学位. 主要研究方向为分散控制和鲁棒控制, 过程控制, 工业大数据和知识自动化. E-mail: yfxie@csu.edu.cn

    潘冬:中南大学自动化学院讲师, 分别于2015年和2021年获得中南大学自动化学士学位和控制科学与工程博士学位. 2019年至2021年, 在加拿大拉瓦尔大学电子与计算工程系联合培养. 主要研究方向包括红外热成像, 视觉检测, 图像处理,深度学习. E-mail: pandong@csu.edu.cn

    桂卫华:中国工程院院士, 中南大学自动化学院教授. 1981年获得中南矿冶学院硕士学位. 主要研究方向为复杂工业过程建模, 优化与控制应用, 故障诊断与分布式鲁棒控制. E-mail: gwh@csu.edu.cn

Online Prediction Method for Silicon Content of Molten Iron in Blast Furnace Based on Dynamic Attention Deep Transfer Network

Funds: Supported by National Natural Science Foundation of China (61773406, 61725306, 61290325), National Major Scientific Research Equipment of China (61927803), Central South University Post-Graduate Independent Exploration and Innovation Project (2021zzts0183), and Hunan Provincial Innovation Foundation for Postgraduate (CX20210242)
More Information
    Author Bio:

    JINAG Ke Ph.D. candidate at the School of Automation, Central South University. She received a M.S. degree from Central South University in 2019. Her main research interests include data-based modeling and control of industrial process, process data analysis and machine learning

    JINAG Zhao-Hui Professor at the School of Automation, Central South University. Doctoral supervisor, and director of department of automation science and technology. He received his PhD degree from Central South University in 2011. His research interests include intelligent sensing and detection technology, image processing and intelligent recognition, artificial intelligence and machine learning. Corresponding author of this paper

    XIE Yong-Fang Professor and doctoral supervisor at the School of Automation, Central South University. He received his bachelor degree from Central South University of Technology in 1993. His research interest covers decentralized control and robust control, process control, industrial big data, and knowledge automation

    PAN Dong Lecturer at the School of Automation, Central South University. He received the B.S. degree in Automatic and the Ph. D degree in control science and engineering from Central South University in 2015 and 2021, respectively. From 2019 to 2021, he was a joint training Ph.D. student with the Department of Electrical and Computing Engineering of Université Laval, Quebec City, Canada. His main research interests include infrared thermography, vision-based measurement, image processing, and deep learning

    GUI Wei-Hua Academician of Chinese Academy of Engineering, and professor at the School of Automation, Central South University. He received his master degree from Central South Institute of Mining and Metallurgy in 1981. His main research interests include complex industrial process modeling, optimization and control applications, fault diagnosis and distributed robust control

  • 摘要: 铁水硅含量是反映高炉冶炼过程中热状态变化的灵敏指示剂, 但无法实时在线检测, 造成铁水质量调控盲目. 为此, 本文提出一种基于动态注意力深度迁移网络的高炉铁水硅含量在线预测方法. 首先, 针对传统深度网络静态建模思路无法准确描述过程变量与铁水硅含量之间的关系, 提出了一种基于注意力机制模块的输入过程变量与输出硅含量之间的动态关系描述方法; 其次, 为降低硅含量预测模型训练时对标签数据的依赖, 考虑到铁水温度跟硅含量数据之间的正相关性, 利用小时级硅含量标签数据微调基于分钟级铁水温度数据预训练好的深度模型的结构, 进而提高基于动态注意力深度迁移网络的硅含量预测精度; 同时, 为了增强预测网络的可解释性, 实时地给出了基于动态注意力机制模块计算的每个样本各过程变量对铁水硅含量的贡献度. 最后, 基于某钢铁厂2#高炉的工业实验验证了本文所提方法的准确性、有效性和先进性.
    1)  收稿日期 2021-06-10 录用日期 2021-11-02 Manuscript received June 10, 2021; accepted November 2, 2021 国家自然科学基金 (61773406, 61725306, 61290325), 国家重大科研仪器研制项目 (61927803), 中南大学研究生自主探索创新项目(2021zzts0183), 湖南省研究生科研创新项目 (CX20210242) 资助 Supported by National Natural Science Foundation of China (61773406, 61725306, 61290325), National Major Scientific Research Equipment of China (61927803), Central South University Post-Graduate Independent Exploration and Innovation Project (2021zzts0183), and Hunan Provincial Innovation Foundation for Postgraduate (CX20210242) 本文责任编委 Recommended by Associate Editor
    2)  1. 中南大学自动化学院 长沙 410000 2. 鹏城实验室 深圳 518000 1. Central South University, School of Automation, Changsha 410000 2. Peng Cheng Laboratory, Shenzhen 518000
  • 图  1  高炉三维仿真模拟图

    Fig.  1  Three-dimensional simulation diagram of the BF cast field

    图  2  去噪自编码机基本结构

    Fig.  2  Architecture of a denoising autoencoder

    图  3  堆叠去噪自编码机训练过程

    Fig.  3  The training process of stacking denoising autoencoders

    图  4  动态注意力机制模块

    Fig.  4  The dynamic attention mechanism module

    图  5  基于动态注意力机制模块的深度去噪自编码机网络

    Fig.  5  Deep denoising autoencoder network based on dynamic attention mechanism module

    图  6  铁水温度与铁水硅含量的散点图

    Fig.  6  The scatter plot of temperature and silicon content of molten iron

    图  7  高炉铁水测温系统. (a) 工业红外热像仪. (b) 一号出铁口. (c) 二号出铁口. (d) 三号出铁口.

    Fig.  7  Molten iron temperature measuring system in a blast furnace. (a) Industrial infrared thermal imager. (b) No.1 taphole. (c) No.2 taphole. (d) No.3 taphole.

    图  8  基于深度迁移网络的铁水硅含量在线预报模型

    Fig.  8  Online prediction model of silicon content in molten iron based on deep transfer network

    图  9  基于支持向量回归机的铁水硅含量预报结果

    Fig.  9  Prediction results of silicon content based on SVR

    图  10  基于堆叠去噪自编码机的铁水硅含量预报结果

    Fig.  10  Prediction results of silicon content based on S-DAE

    图  11  基于动态注意力机制深度网络的铁水硅含量预报结果

    Fig.  11  Prediction results of silicon content based on ADNet

    图  12  基于动态注意力深度迁移网络的铁水硅含量预报结果

    Fig.  12  Prediction results of silicon content based on ADTNet

    图  13  基于不同模型的铁水硅含量误差分布图

    Fig.  13  Prediction errors of silicon content in molten iron based on different models

    图  14  基于不同模型的预测和实际铁水硅含量分布散点图

    Fig.  14  The scatter plot of predictive and observed silicon content based on different models

    图  15  过程变量注意力得分热力图

    Fig.  15  The heat map of process variables attention scores

    表  1  过程变量MIC相关性系数

    Table  1  Maximal information coefficient of Process Variables

    过程变量 MICMIT MICSi 过程变量 MICMIT MICSi
    富氧率 0.104 0.115 冷风压力 0.104 0.094
    透气性指数 0.104 0.111 全压差 0.104 0.130
    CO 0.103 0.104 热风压力 0.103 0.116
    CO2 0.111 0.145 实际风速 0.100 0.113
    标准风速 0.117 0.111 冷风温度 0.102 0.109
    富氧流量 0.120 0.129 热风温度 0.101 0.115
    冷风流量 0.117 0.111 顶温 0.120 0.162
    鼓风动能 0.101 0.107 顶温下降管 0.111 0.155
    炉腹煤气量 0.108 0.127 阻力系数 0.103 0.110
    炉腹煤气指数 0.109 0.128 鼓风湿度 0.135 0.140
    顶压 0.128 0.156 富氧压力 0.103 0.096
    本小时实际喷煤量 0.100 0.136 上小时实际喷煤量 0.110 0.165
    下载: 导出CSV

    表  2  基于不同模型的预测性能

    Table  2  Prediction performance based on different models

    模型 RMSE MAE HR(%)
    SVR 0.0832 0.0635 77.5
    S-DAE 0.0794 0.0616 84.6
    ADNet 0.0772 0.0583 86.4
    ADTNet 0.0649 0.0509 90.0
    下载: 导出CSV
  • [1] 周平, 张丽, 李温鹏, 戴鹏, 柴天佑. 集成自编码与PCA的高炉多元铁水质量随机权神经网络建模. 自动化学报, 2018, 44(10): 1799-1811

    Zhou Ping, Zhang Li, Li Wen-Peng, Dai Peng, Chai Tian-You. Modeling of blast furnace multi-element molten iron quality with random weight neural network based on self-encoding and PCA. Acta Automatica Sinica, 2018, 44(10): 1799-1811
    [2] Zhou H, Zhang H F, and Yang C J. Hybrid model based intelligent optimization of ironmaking process. IEEE Transaction on Industrial Electronics, 2020, 67(3): 2469-247 doi: 10.1109/TIE.2019.2903770
    [3] Jiang K, Jiang Z H, Xie Y F, Pan D, Gui W H. Abnormality monitoring in the blast furnace ironmaking process based on stacked dynamic target-driven denoising autoencoders. IEEE Transactions on Industrial Informatics, DOI 10.1109/TII.2021.3084911
    [4] 郜传厚, 渐令, 陈积明, 孙优贤. 复杂高炉炼铁过程的数据驱动建模及预测算法. 自动化学报, 2009, 35(06): 725-730 doi: 10.3724/SP.J.1004.2009.00725

    Gao Chuan-Hou, Jian Ling, Chen Jia-Ming, Sun You-Xian. Data-driven modeling and prediction algorithm for complex blast furnace ironmaking process. Acta Automatica Sinica, 2009, 35(6): 725-730 doi: 10.3724/SP.J.1004.2009.00725
    [5] Chen S H, Gao C H. Linear priors mined and integrated for transparency of blast furnace black-Box SVM model. IEEE Transactions on Industrial Informatics, 2020, 16(6): 3862-3870 doi: 10.1109/TII.2019.2940475
    [6] Zhou P, Lv Y B, Wang H, and Chai T Y. Data-driven robust RVFLNs modeling of a blast furnace iron-making process using Cauchy distribution weighted M-Estimation. IEEE Transaction on Industrial Electronics, 2017, 64(9): 7141–7151 doi: 10.1109/TIE.2017.2686369
    [7] 宋贺达, 周平, 王宏, 柴天佑. 高炉炼铁过程多元铁水质量非线性子空间建模及应用. 自动化学报, 2016, 42(11): 1664-1679

    Song He-Da, Zhou Ping, Wang Hong, Chai Tian-You. Nonlinear subspace modeling of multivariate molten iron quality in blast furnace ironmaking and its application. Acta Automatica Sinica, 2016, 42(11): 1664-1679
    [8] Spirin N A, Onorin O P, Istomin A S. Study of transition processes of blast-furnace smelting by the mathematical model method. In: Proceedings of IOP Conference Series, Materials Science and Engineering, SuZhou, China: Institute of Physics Publishing, 2018. 012−073
    [9] Spirin N, Onorin O, Alexander I. Prediction of blast furnace thermal state in real-time operation. Solid State Phenomena, 2020, 299: 518-523 doi: 10.4028/www.scientific.net/SSP.299.518
    [10] Spirin N. A, Polinov A A, Gurin I A, Pishnograev SN. Information system for real-time prediction of the silicon content of iron in a blast furnace. Metallurgist, 2020, 63(9): 898-905
    [11] Saxen H, Gao C H, and Gao Z W. Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—A review. IEEE Transactions on Industrial Informatics, 2013, 9(4): 2213-2225 doi: 10.1109/TII.2012.2226897
    [12] 李温鹏, 周平. 高炉铁水质量鲁棒正则化随机权神经网络建模. 自动化学报, 2020, 46(04): 721-733

    Li Wen-Peng, Zhou Ping. Blast furnace hot metal quality robust regularization random weight neural network modeling. Acta Automatica Sinica, 2020, 46(04): 721-733
    [13] 蒋朝辉, 许川, 桂卫华, 蒋珂. 基于最优工况迁移的高炉铁水硅含量预测方法. 自动化学报

    Jiang Zhao-Hui, Xu Chuang, Gui Wei-Hua, Jiang Ke. Prediction method of hot metal silicon content in blast furnace based on optimal smelting condition migration. Acta Automatica Sinica, to be published.
    [14] Zhou P, Guo D W, Wang H, and Chai T Y. Data-driven robust M-LS-SVR-based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9): 4007-4021 doi: 10.1109/TNNLS.2017.2749412
    [15] 蒋朝辉, 董梦林, 桂卫华, 阳春华, 谢永芳. 基于Bootstrap的高炉铁水硅含量二维预报. 自动化学报, 2016, 42(05): 715-723

    Jiang Zhao-Hui, Dong Meng-Lin, Gui Wei-Hua, Yang Chun-Hua, Xie Yong-Fang. Two-dimensional prediction for silicon content of hot metal of blast furnace based on bootstrap. Acta Automatica Sinica, 2016, 42(5): 715-723
    [16] Li J P, Hua C C, Yang Y N, Guan X P. Bayesian block structure sparse based T–S fuzzy modeling for dynamic prediction of hot metal silicon content in the blast furnace. IEEE Transactions on Industrial Electronics, 2017, 65(6): 4933-4942
    [17] Hinton G E, Osindero S, and Teh Y W. A fast learning algorithm for deep belief nets. Neural Computing, 2006, 18(7): 1527-1554 doi: 10.1162/neco.2006.18.7.1527
    [18] Hinton G E, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process, 2012, 29(6): 82-97 doi: 10.1109/MSP.2012.2205597
    [19] Ma J, Wu F, Zhu J, Xu D, and Kong D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics, 2017, 73: 221 doi: 10.1016/j.ultras.2016.09.011
    [20] Krizhevsky A, Sutskever I, and Hinton G E. Imagenet classification with deep convolutional neural networks. in Process Advance Neural Information Process System, 2012, 1097-1105
    [21] Jiang K, Jiang Z H, Xie Y F, Chen Z P, Pan D, Gui W H. Classification of silicon content variation trend based on fusion of multilevel features in blast furnace ironmaking. Information Sciences, 2020, 521: 32-45 doi: 10.1016/j.ins.2020.02.039
    [22] Wang Y L, Pan Z F, Yuan X F, Yang C H, and Gui W H. A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network. ISA Transactions, 2020, 96: 457-467 doi: 10.1016/j.isatra.2019.07.001
    [23] Pan D, Jiang Z H, Chen Z P, Jiang K, Gui W H. Compensation method for molten iron temperature measurement based on heterogeneous features of infrared thermal images. IEEE Transactions on Industrial Informatics, 2020, 16(11): 7056-7066. doi: 10.1109/TII.2020.2972332
    [24] Pan D, Jiang Z H, Chen Z P, Gui W H, Xie Y F, Yang C H. Temperature measurement and compensation method of blast furnace molten iron based on infrared computer vision. IEEE Transactions on Instrumentation and Measurement, 2018, 68 (10): 3576-3588.
    [25] Vincent P, Larochelle H, Lajoie I, Bengio Y, and Manzagol P. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11: 3371–3408
    [26] Vincent P, Larochelle H, Bengio Y and Manzagol P A. Extracting and composing robust features with denoising autoencoders. In: Process of 25th International Conference Machine Learning, 2008, 1096-1103
    [27] Reshef D N, Reshef Y A. Detecting novel associations in large data sets. Science, 2011, 334(6062): 1518-1524 doi: 10.1126/science.1205438
    [28] Agarap A F. Deep learning using rectified linear units (ReLU). arXiv: 1803.08375, 2018
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  • 收稿日期:  2021-06-10
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