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, 2022, 44(7): 1171-1182. 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, 2022, 44(7): 1171-1182. 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, 2022, 44(7): 1171-1182. 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, 2022, 44(7): 1171-1182. 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

Research progress and application status of deep learning in steelmaking process

  • 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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