Precise Burden Charging Operation During Iron-Making Process in Blast Furnace

被引:6
作者
Zhang, Haigang [1 ]
Sun, Shaolun [2 ]
Zhang, Sen [2 ]
机构
[1] Shenzhen Polytech, Inst Appl Artificial Intelligence Guangdong Hong, Shenzhen 518055, Peoples R China
[2] Univ Sci & Technol, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Surface charging; Shape; Production; Surface treatment; Optimization; Blast furnaces; Training; Bell-less top blast furnace; burden optimal model; precise charging strategy; multi-objective optimization; BELL-LESS TOP; EXTREME LEARNING-MACHINE; DIFFERENTIAL EVOLUTION; OPTIMIZATION; PREDICTION; MODEL;
D O I
10.1109/ACCESS.2021.3064885
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The burden charging operation in blast furnace is one of the most important operations during iron-making process. In this paper, we focus on the study of precise burden charging operation, which involves two aspects: How to obtain and form the optimal burden surface shape. For the first problem, we construct a mapping model between the burden surface characteristic parameters and the comprehensive operational performance indicators of the blast furnace, and transform the search of optimal burden surface shape into the target optimization problem. The second problem refers to establishing a suitable burden charging strategy based on the basic burden surface and the optimal burden surface. In our work, by adaptively adjusting the opening degree of the throttle valve, it is possible to control accurate burden volume during the rotation of the charging chute, which can make sure to spill the appropriate burden volume on the suitable charging units. In the simulation of experiments, we collected the real industrial data during iron-making process and demonstrated the efficiency of the proposed model.
引用
收藏
页码:45655 / 45667
页数:13
相关论文
共 34 条
[1]   Decoupling Control Method With Fuzzy Theory for Top Pressure of Blast Furnace [J].
An, Jianqi ;
Yang, Junyu ;
Wu, Min ;
She, Jinhua ;
Terano, Takao .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2019, 27 (06) :2735-2742
[2]   Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems [J].
Brest, Janez ;
Greiner, Saso ;
Boskovic, Borko ;
Mernik, Marjan ;
Zumer, Vijern .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (06) :646-657
[3]   Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm [J].
Buhari, Mohammed D. ;
Tian, Gui Yun ;
Tiwari, Rajesh .
IEEE SENSORS JOURNAL, 2019, 19 (05) :1777-1787
[4]   Hyperplane Assisted Evolutionary Algorithm for Many-Objective Optimization Problems [J].
Chen, Huangke ;
Tian, Ye ;
Pedrycz, Witold ;
Wu, Guohua ;
Wang, Rui ;
Wang, Ling .
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (07) :3367-3380
[5]  
Chen Xianzhong, 2010, 2010 2nd International Conference on Industrial and Information Systems (IIS 2010), P334, DOI 10.1109/INDUSIS.2010.5565841
[6]   Gas-powder flow in blast furnace with different shapes of cohesive zone [J].
Dong, X. F. ;
Pinson, D. ;
Zhang, S. J. ;
Yu, A. B. ;
Zulli, P. .
APPLIED MATHEMATICAL MODELLING, 2006, 30 (11) :1293-1309
[7]  
Fletcher R, 2013, PRACTICAL METHODS OP, V33, P675
[8]   Predicting aerodynamic instabilities in a blast furnace [J].
Gamero, FI ;
Colomer, J ;
Meléndez, J ;
Warren, P .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2006, 19 (01) :103-111
[9]  
Hao Xiao-jing, 2005, Journal of Northeastern University (Natural Science), V26, P363
[10]   Influence of Transient Blast Furnace Conditions on the Temperature in the Cooling System [J].
Horupakha V.V. ;
Semenov Y.S. ;
Shumelchik E.I. ;
Vashchenko S.V. .
Steel in Translation, 2019, 49 (6) :397-401