Application of deep learning in iron ore sintering process:a review

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外文摘要：In the wake of the era of big data,the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry.The sintering process is an extremely complex industrial scene.As the main process of the blast furnace ironmaking industry,it has great economic value and envi-ronmental protection significance for iron and steel enterprises.It is also one of the fields where deep learning is still in the exploration stage.In order to explore the application prospects of deep learning techniques in iron ore sintering,a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures.Firstly,the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail,and then,the development of iron ore sintering simulation techniques was introduced.Secondly,deep learning techniques were introduced,including commonly used models of deep learning and their applications.Thirdly,the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction,controlling,and optimisation of key parameters.Generally speaking,deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.

外文关键词：

Deep learningSintering processModellingSimulation technologyIntelligent sintering

作者：

Yu-han Gong、Chong-hao Wang、Jie Li、Muhammad Nasiruddin Mahyuddin、Mohamad Tarmizi Abu Seman

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作者单位：

School of Electrical and Electronic Engineering,Universiti Sains Malaysia,14300 Nibong Tebal,Penang,Malaysia

College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China

Technology Transfer Center,North China University of Science and Technology,Tangshan 063210,Hebei,China

School of Metallurgy and Energy,North China University of Science and Technology,Tangshan 063210,Hebei,China

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基金：

Department of Education of Hebei Province,China

项目编号：

QN2019026

出版年：

2024

DOI：

10.1007/s42243-024-01197-3

钢铁研究学报(英文版)

钢铁研究总院

钢铁研究学报(英文版)

CSTPCD

影响因子：0.584

ISSN：1006-706X

年,卷(期)：2024.31(5)