Dynamic Multi-objective Operation Optimization of Blast Furnace Based on Evolutionary Algorithm

被引：0
|
作者：
Zhao, Yumeng [1 ,2 ]
Zhang, Jingchuan [1 ,2 ]
Jiang, Meng [1 ,2 ]
Fu, Kai [1 ,2 ]
Deng, Qiyuan [1 ,2 ]
Wang, Xianpeng [3 ,4 ]
机构：
[1] Liaoning Engn Lab Data Analyt & Optimizat Smart I, Shenyang 110819, Peoples R China
[2] Liaoning Key Lab Mfg Syst & Logist Optimizat, Shenyang 110819, Peoples R China
[3] Natl Frontiers Sci Ctr Ind Intelligence & Syst Op, Shenyang, Peoples R China
[4] Northeastern Univ, Minist Educ, Key Lab Data Analyt & Optimizat Smart Ind, Shenyang 110819, Peoples R China
来源：
BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS, PT 1, BIC-TA 2023 | 2024年 / 2061卷
基金：
中国国家自然科学基金;
关键词：
Blast furnace ironmaking; Operation optimisation; Dynamic multiobjective optimisation; Evolutionary algorithm;
D O I：
10.1007/978-981-97-2272-3_19
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Blast furnaces play a critical role in the steel industry, and their operational optimization is crucial for energy conservation and emissions reduction. This paper examines the impact of changes in operational conditions on blast furnace performance. We propose a dynamic multi-objective optimization algorithm based on multiple short time series (MT-DC-RVEA) to solve the constructed dynamic multi-objective operational optimization model for blast furnaces. Experimental results validate the effectiveness of the proposed algorithm in solving the operational optimization model for blast furnace operations.
引用
收藏
页码：254 / 261
页数：8
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