same time,the steel industry is also a major energy consumer and polluter.In the current national coordination to do a good job of "carbon peak""carbon neutral"background,the traditional steelmaking process urgently needs to be transformed into intelligent and green.In recent years,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.Steelmaking process is an extremely complex industrial scenario with many influencing factors and high security requirements,which is one of the areas where deep learning has not been applied on a large scale yet.This paper compares the principles and types of deep learning.and summarizes the development history and research status of deep learning in steelmaking process with domestic and foreign application examples.It is pointed out that the application of deep learning in steelmaking process mainly has the advantages of simp extraction,strong generalization ability and high model plasticity,but also faces the challenges of hi ta dependency, difficult pre- processing and production safety to be verified.It is proposed that in the future,with the application of high-precision sensors,the popularization of the Internet of Things,the iteration of computing hardware,and the innovation of algorithms, deep learning models can be more effectively applied to more s sce making,which will promote the intelligent development of metallurgical industry. KEY WORDS Steelmaking process;deep learning;neural network,application scenarios 钢铁工业是国家生产力的重要体现，在国民经济发展与国防建设中起到物质基础的作用山。近 年来我国粗钢产量稳居全球首位，2020年更是首次突破10亿吨，达到10.65亿吨。同时，庞大的 产能背后，钢铁行业也是能耗和污染太户在当前全国统筹做好“碳达峰”“碳中和”背景下，传 统炼钢工艺亟待向智慧化和绿色化转型。炼钢整体流程可划分为初炼、精炼和连铸三个阶段依次进行， 每个阶段又有多道处理又序 我国粗钢产品约90%来自转炉炼钢，其余来自电炉)，但无论采用何 种初炼方式，炼钢硫程均具有工序高度复杂、控制过程非线性的特点，难以建立准确的数学模型 进行描述，这使得智慧化之路面临挑战。为应对治炼过程的上述特性，作为机器学习近年来兴起 的重要分支，深度学匀被引入到炼钢领域且已获得广泛应用，这是一种以人工神经网络为基本架构， 对数据进行特征提取并实现知识学习的非线性建模算法)，其已成为钢铁行业智慧化的重要手段之 一。本文对深度学习原理进行了介绍，并将前人在深度学习应用于炼钢的工序过程所做的工作以及 优缺点等进行了综合阐述。 1.深度学习原理及类型 深度学习目的在于模拟生物体内的神经网络处理外界信息的过程。与生物神经细胞可接受外界same time, the steel industry is also a major energy consumer and polluter. In the current national coordination to do a good job of "carbon peak" "carbon neutral" background, the traditional steelmaking process urgently needs to be transformed into intelligent and green. In recent years, 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. Steelmaking process is an extremely complex industrial scenario with many influencing factors and high security requirements, which is one of the areas where deep learning has not been applied on a large scale yet. This paper compares the principles and types of deep learning, and summarizes the development history and research status of deep learning in steelmaking process with domestic and foreign application examples. It is pointed out that the application of deep learning in steelmaking process mainly has the advantages of simple feature extraction, strong generalization ability and high model plasticity, but also faces the challenges of high data dependency, difficult pre￾processing and production safety to be verified. It is proposed that in the future, with the application of high-precision sensors, the popularization of the Internet of Things, the iteration of computing hardware, and the innovation of algorithms, deep learning models can be more effectively applied to more scenarios in steelmaking, which will promote the intelligent development of metallurgical industry. KEY WORDS Steelmaking process; deep learning; neural network; application scenarios 钢铁工业是国家生产力的重要体现，在国民经济发展与国防建设中起到物质基础的作用[1]。近 年来我国粗钢产量稳居全球首位，2020 年更是首次突破 10 亿吨，达到 10.65 亿吨[2]。同时，庞大的 产能背后，钢铁行业也是能耗和污染大户，在当前全国统筹做好“碳达峰”“碳中和”背景下，传 统炼钢工艺亟待向智慧化和绿色化转型。炼钢整体流程可划分为初炼、精炼和连铸三个阶段依次进行， 每个阶段又有多道处理工序。我国粗钢产品约 90%来自转炉炼钢，其余来自电炉[3]，但无论采用何 种初炼方式，炼钢全流程均具有工序高度复杂、控制过程非线性的特点，难以建立准确的数学模型 进行描述，这使得智慧化之路面临挑战[4]。为应对冶炼过程的上述特性，作为机器学习近年来兴起 的重要分支，深度学习被引入到炼钢领域且已获得广泛应用，这是一种以人工神经网络为基本架构 ， 对数据进行特征提取并实现知识学习的非线性建模算法[5-7]，其已成为钢铁行业智慧化的重要手段之 一。本文对深度学习原理进行了介绍，并将前人在深度学习应用于炼钢的工序过程所做的工作以及 优缺点等进行了综合阐述。 1. 深度学习原理及类型 深度学习目的在于模拟生物体内的神经网络处理外界信息的过程。与生物神经细胞可接受外界 录用稿件，非最终出版稿