Hybrid End-Point Static Control Model for 80 Tons BOF Steelmaking

被引：4

作者：

Wang, Miao ^{[1
]}

Gao, Chuang ^{[2
]}

Ai, Xingang ^{[1
]}

Zhai, Baopeng ^{[3
]}

Li, Shengli ^{[1
]}

机构：

[1] Univ Sci & Technol Liaoning, Sch Met & Mat, Anshan 114051, Liaoning, Peoples R China

[2] Univ Sci & Technol Liaoning, Sch Elect & Informat Engn, Anshan 114051, Liaoning, Peoples R China

[3] Angang Grp Automat Co Ltd, Anshan 114000, Peoples R China

来源：

TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS | 2022年 / 75卷 / 09期

关键词：

End-point control model; Twin support vector regression; Whale optimization algorithm; Basic oxygen furnace; PREDICTION MODEL;

D O I：

10.1007/s12666-022-02603-8

中图分类号：

TF [冶金工业];

学科分类号：

0806 ;

摘要：

Accurate control of the basic oxygen furnace (BOF) end-point can effectively improve the quality of steel. A hybrid end-point static control model is proposed to calculate the oxygen blowing volume and lime weight, and realizes the control of the end-point of BOF. Firstly, the prediction model is established based on twin support vector regression (TSVR). Secondly, the difference between the predicted value and actual value of the obtained model is used as the objective function, combined with the whale optimization algorithm (WOA) and incremental algorithm to optimize the objective function. Finally, the optimal value vector of oxygen blowing volume and lime weight is obtained. The simulation calculation is carried out by using the actual production sample of an 80 tons BOF. The results show that the proposed prediction model has high prediction accuracy, and the calculated oxygen blowing volume and lime weight can meet the actual field production.

引用

页码：2281 / 2288

页数：8

共 50 条

[21] End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression
Gao, C.
Shen, M.G.
Metalurgija, 2019, 58 (01): : 29 - 32
[22] END-POINT PREDICTION OF BASIC OXYGEN FURNACE (BOF) STEELMAKING BASED ON IMPROVED TWIN SUPPORT VECTOR REGRESSION
Gao, C.
Shen, M. G.
METALURGIJA, 2019, 58 (1-2): : 29 - 32
[23] Transfer Learning Deep EDC-Tabnet for Predicting End-Point of BOF Steelmaking Process with Small Samples
Dong, Shijian
Guo, Jiachen
Hu, Wenyue
Zhou, Jing
Jiang, Xiaoqing
Gong, Wei
METALLURGICAL AND MATERIALS TRANSACTIONS B-PROCESS METALLURGY AND MATERIALS PROCESSING SCIENCE, 2025, 56 (01): : 913 - 929
[24] End-Point Prediction of BOF Steelmaking Based on Flame Spectral Feature Selection Using WCARS-ISPA
Zhu Wen-qiong
Zhou Mu-chun
Zhao Qi
Liao Jun
SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41 (08) : 2332 - 2336
[25] The Control and Prediction of End- Point Phosphorus Content during BOF Steelmaking Process
Wang, Zhou
Xie, Feiming
Wang, Bin
Liu, Qing
Lu, Xinchun
Hu, Lining
Cai, Fei
STEEL RESEARCH INTERNATIONAL, 2014, 85 (04) : 599 - 606
[26] THE DEVELOPMENT OF A MATHEMATICAL-MODEL OF END-POINT CONTROL-SYSTEM FOR TOP AND BOTTOM BLOWING PROCESS IN BOF
TAKAWA, T
KATAYAMA, K
HOTEIYA, M
HIRAYAMA, N
TETSU TO HAGANE-JOURNAL OF THE IRON AND STEEL INSTITUTE OF JAPAN, 1987, 73 (07): : 836 - 843
[27] A Homotopic Algorithm of NN and its Application for the End-Point Prediction Model of BOF Process
Feng, Mingxia
Chen, Ren
Li, Qiang
APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY, 2014, 513-517 : 431 - 434
[28] Dynamic end-point control in BOF through a fast and simultaneous determination of the steel/slag composition
Wauters, M.
Knevels, J.
Meilland, R.
Bengston, A.
REVUE DE METALLURGIE-CAHIERS D INFORMATIONS TECHNIQUES, 2006, 103 (09): : 374 - 380
[29] MODEL-BASED ADAPTIVE HYBRID CONTROL FOR MANIPULATORS WITH GEOMETRIC END-POINT CONSTRAINT
ARIMOTO, S
NANIWA, T
LIU, YH
ADVANCED ROBOTICS, 1995, 9 (01) : 67 - 80
[30] Prediction model of End-Point Carbon Content for BOF Based on LM BP Neural Network
Li, Changrong
Zhao, Haowen
Yin, Qing
MANUFACTURING PROCESS TECHNOLOGY, PTS 1-5, 2011, 189-193 : 4446 - 4450

← 1 2 3 4 5 →