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深度学习在炼钢过程中的研究进展及应用现状

王仲亮 顾超 王敏 包燕平

王仲亮, 顾超, 王敏, 包燕平. 深度学习在炼钢过程中的研究进展及应用现状[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.08.17.001
引用本文: 王仲亮, 顾超, 王敏, 包燕平. 深度学习在炼钢过程中的研究进展及应用现状[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.08.17.001
WANG Zhong-liang, GU Chao, WANG Min, BAO Yan-ping. Research progress and application status of deep learning in steelmaking process[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.08.17.001
Citation: WANG Zhong-liang, GU Chao, WANG Min, BAO Yan-ping. Research progress and application status of deep learning in steelmaking process[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.08.17.001

深度学习在炼钢过程中的研究进展及应用现状

doi: 10.13374/j.issn2095-9389.2021.08.17.001
基金项目: 中央高校基本科研业务费资助项目 (FRF-TP-20-026A1);中国博士后科学基金特别资助项目 (2021T140050);钢铁冶金新技术国家重点实验室自主课题资助项目 (41621014)
详细信息
    通讯作者:

    顾超,E-mail: guchao@ustb.edu.cn; 包燕平,E-mail: baoyp@ustb.edu.cn

  • 中图分类号: TF34

Research progress and application status of deep learning in steelmaking process

More Information
    Corresponding author: GU Chao, E-mail: guchao@ustb.edu.cn; BAO Yan-ping, E-mail: baoyp@ustb.edu.cn
  • 摘要: 炼钢过程是极其复杂的工业场景,影响因素多且安全性要求极高,是当前深度学习尚未大规模应用的领域之一。在对深度学习的原理和类型进行梳理的基础之上,结合国内外应用实例,总结了深度学习在炼钢过程中的发展历程与研究现状。指出了深度学习在炼钢过程中应用主要有特征提取简单、泛化能力强、模型可塑性高的优势,同时也面临数据依赖性高、预处理难度大、生产安全性有待验证的挑战。提出了未来随着高精度传感器的应用、物联网的普及、计算硬件的迭代、以及算法的创新,深度学习模型可以更加有效地应用于炼钢的更多场景中,将推动冶金工业智能化发展。

     

  • 图  1  人工神经网络神经元结构及工作原理

    Figure  1.  Structure and working principle of artificial neural network neurons

    图  2  深度学习模型基本结构

    Figure  2.  Basic structure of the deep learning model

    图  3  神经网络分类. (a) 前馈神经网络; (b) 反馈神经网络; (c) 自组织神经网络

    Figure  3.  Neural network classification: (a) feed-forward neural network; (b) feedback neural network; (c) self-organizing neural network

    图  4  深度学习模型在炼钢过程中的部分应用

    Figure  4.  Partial applications of the deep learning model in the steelmaking process

    图  5  炼钢过程信息采集、传输和运算的要求

    Figure  5.  Requirements for information collection, transmission and operation in steelmaking process

    表  1  几种深度学习主流方法特征对比

    Table  1.   Comparison of the features of several mainstream methods of deep learning

    Deep learning methodsAdvantagesDisadvantages
    BP(1) Strong nonlinear mapping capability
    (2) High self-learning and self-adaptive capabilities
    (3) Some fault tolerance
    (1) Slow convergence speed
    (2) Easy to fall into local minima
    CNN(1) Partial connection
    (2) Value sharing
    (3) Hierarchical expression
    (1) Need to normalize the dataset
    (2) No memory function
    (3) Poor natural language processing skills
    WNN(1) Fast network convergence
    (2) Avoid getting stuck in a local optimum
    (3) High precision
    (1) Difficult to determine the nodes in the hidden layer
    (2) No adaptive selection of functions
    SOM(1) Self-organization changes network parameters
    (2) Only one neuron becomes the competition winner
    (1) Need to predetermine the number of neurons
    (2) Randomly generate the initial value of the weight vector
    下载: 导出CSV

    表  2  深度学习模型探索使用案例

    Table  2.   Deep learning model exploration use cases

    No.YearCountryApplication companiesProcessApplication
    11990USACopperweld Steel MillElectric arc furnaceProcess control
    21990USANorth Star Steel MillElectric arc furnaceProcess control
    31991FinlandRahhe Steel MillContinuous castingPourability forecast
    41991JapanYawata Steel MillContinuous castingSteel leakage forecast
    51994ChinaGuangzhou Steel MillElectric arc furnaceElectrode control
    61995ChinaBaoshan SteelConvertersDynamic model
    71997ChinaWuhan SteelConvertersEndpoint control
    82001ChinaXingcheng Special SteelElectric arc furnaceTemperature forecast
    下载: 导出CSV

    表  3  国内外炼钢企业智能化发展布局

    Table  3.   Intelligent development layout of domestic and foreign steelmaking enterprises

    No.YearCountryApplication companiesCooperation unitProject content
    12017KoreaPOSCOPOSCO Technical Research LaboratoriesDeep learning projects
    22017USABig River SteelNoodle AIArtificial intelligence platform
    32018ChinaBaowu SteelBaidu Online Network TechnologyAI + steel quality inspection
    42018ChinaAnshan Iron and SteelKingsoft Corporation LimitedPrecision steel cloud platform
    52018ChinaXiangtan Iron and SteelHuawei TechnologiesSmart factory project
    62018IndiaTata SteelTata Steel Digie-Shala DepartmentProcess optimization solutions
    72019ChinaJinnan Iron and SteelAlibaba GroupSteel scrap AI grading system
    82019ChinaMagang Holding CompanyTencentIntelligent decision-making and control platform
    92019JapanNippon SteelNS Solutions CorporationNS-DIG intelligent platform
    102019GermanyThyssenkruppMicrosoft“Alfred” artificial intelligence solution
    112020ChinaLuli GroupRamon Science and TechnologyRemote intelligent grading system for steel scrap
    122020ChinaBaowu SteelShanghai Baosight SoftwareBaowu ecotechnology platform
    下载: 导出CSV

    表  4  炼钢企业不同应用要求的最佳解决方案

    Table  4.   Best solution for different applications required by the industry

    No.Occurrence frequencyImpactApplication examplesSolutions
    1HighSeriousEndpoint prediction and defect detectionBuilding deep learning models
    2LowSeriousSecondary oxidation of steel and continuous casting leakageImprovement from the process route and
    operation system
    3HighMinorSmall fluctuations in the amount of raw and auxiliary
    materials added
    Solving through lean management
    4LowMinorTemperature measurement on the gun failure and spare
    part overdue
    Solving through routine inspection
    下载: 导出CSV
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  • 收稿日期:  2021-08-17
  • 网络出版日期:  2021-11-10

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