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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。Abstract: The steel industry is an important embodiment of national productivity and contributes to the development of the national economy and defense construction as a material foundation. Recently, China’s crude steel production ranked first in the world and in 2020, it exceeded 1 billion tons for the first time, reaching 1.065 billion tons. However, the steel industry is also a major energy consumer and polluter. In the current national coordination to do a good job of “carbon peak” and “carbon-neutral” background, the traditional steelmaking process urgently needs to be transformed into intelligent and green. Recently, as an important branch of machine learning, with artificial neural networks as the basic architecture, deep learning, a nonlinear modeling algorithm that can extract features from data and realize knowledge learning, has been applied in various industrial fields. The steelmaking process is an extremely complex industrial scenario with many influencing factors and high-security requirements. It is also an area where deep learning has not been applied on a large scale yet. Accordingly, in this study, the principles and types of deep learning were compared, and the development history and research status of deep learning in the steelmaking process with domestic and foreign application examples were summarized. The application of deep learning to the steelmaking process mainly has the advantages of simple feature extraction, strong generalization ability, and high model plasticity, but it also faces the challenges of high data dependency, difficult preprocessing, and verification of production safety. In the future, with the application of high-precision sensors, popularization of the Internet of Things, iteration of computing hardware, and innovation of algorithms, deep learning models can be effectively applied to more scenarios in steelmaking, which will promote the intelligent development of the metallurgical industry.
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Key words:
- steelmaking process /
- deep learning /
- neural network /
- application scenarios /
- data processing /
- nonlinear /
- intelligent
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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 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 methods Advantages Disadvantages 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 minimaCNN (1) Partial connection
(2) Value sharing
(3) Hierarchical expression(1) Need to normalize the dataset
(2) No memory function
(3) Poor natural language processing skillsWNN (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 functionsSOM (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
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表 2 深度学习模型探索使用案例
Table 2. Deep learning model exploration use cases
No. Year Country Application companies Process Application 1 1990 USA Copperweld Steel Mill Electric arc furnace Process control 2 1990 USA North Star Steel Mill Electric arc furnace Process control 3 1991 Finland Rahhe Steel Mill Continuous casting Pourability forecast 4 1991 Japan Yawata Steel Mill Continuous casting Steel leakage forecast 5 1994 China Guangzhou Steel Mill Electric arc furnace Electrode control 6 1995 China Baoshan Steel Converters Dynamic model 7 1997 China Wuhan Steel Converters Endpoint control 8 2001 China Xingcheng Special Steel Electric arc furnace Temperature forecast
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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. Year Country Application companies Cooperation unit Project content 1 2017 Korea POSCO POSCO Technical Research Laboratories Deep learning projects 2 2017 USA Big River Steel Noodle AI Artificial intelligence platform 3 2018 China Baowu Steel Baidu Online Network Technology AI + steel quality inspection 4 2018 China Anshan Iron and Steel Kingsoft Corporation Limited Precision steel cloud platform 5 2018 China Xiangtan Iron and Steel Huawei Technologies Smart factory project 6 2018 India Tata Steel Tata Steel Digie-Shala Department Process optimization solutions 7 2019 China Jinnan Iron and Steel Alibaba Group Steel scrap AI grading system 8 2019 China Magang Holding Company Tencent Intelligent decision-making and control platform 9 2019 Japan Nippon Steel NS Solutions Corporation NS-DIG intelligent platform 10 2019 Germany Thyssenkrupp Microsoft “Alfred” artificial intelligence solution 11 2020 China Luli Group Ramon Science and Technology Remote intelligent grading system for steel scrap 12 2020 China Baowu Steel Shanghai Baosight Software Baowu ecotechnology platform
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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 frequency Impact Application examples Solutions 1 High Serious Endpoint prediction and defect detection Building deep learning models 2 Low Serious Secondary oxidation of steel and continuous casting leakage Improvement from the process route and
operation system3 High Minor Small fluctuations in the amount of raw and auxiliary
materials addedSolving through lean management 4 Low Minor Temperature measurement on the gun failure and spare
part overdueSolving through routine inspection
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