Review of Intelligent Modeling for Sintering Process Under Variable Operating Conditions

被引：0
|
作者：
Hu, Jie [1 ,2 ,3 ]
Li, Hongxiang [1 ,2 ,3 ]
Liu, Junyong [1 ,2 ,3 ]
Du, Sheng [1 ,2 ,3 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat Co, Wuhan 430074, Peoples R China
[3] Minist Educ, Engn Res Ctr Intelligent Technol Geoexplorat, Wuhan 430074, Peoples R China
来源：
PROCESSES | 2025年 / 13卷 / 01期
基金：
中国国家自然科学基金;
关键词：
sintering process; mechanism modeling; data-driven modeling; operating conditions; prediction; BURN-THROUGH POINT; CARBON EFFICIENCY; PREDICTION MODEL; NEURAL-NETWORK; TIME-SERIES; FEO CONTENT; STRENGTH; FEATURES; QUALITY; SYSTEM;
D O I：
10.3390/pr13010180
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
The steel industry serves as a cornerstone of a nation's industrial system, with sintering playing a pivotal role in the steelmaking process. In an effort to enhance the intelligence of the sintering process and improve production efficiency, numerous scholars have carried out extensive research on data analysis and intelligent modeling techniques. These studies have made significant contributions to expanding production capacity, optimizing cost efficiency, and enhancing the quality of products, and supporting the sustainable development of the steel industry. This paper begins with an analysis of the sintering production process, explores the distinctive characteristics of the sintering process, and discusses the methods for identifying the operating conditions of sintering. It also provides an overview of the current state of research on both mechanism modeling and data-driven modeling approaches for the sintering process. Finally, the paper summarizes the existing challenges in sintering process modeling and offers perspectives on the future direction of research in this field.
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收藏
页数：18
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