An Intelligent Optimization Strategy for Blast Furnace Charging Operation Considering Three-Dimensional Burden Surface Shape

Jicheng Zhu; Zhaohui Jiang; Dong Pan; Haoyang Yu; Chuan Xu; Ke Zhou; Weihua Gui

doi:10.1109/JAS.2025.125192

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

J. Zhu, Z. Jiang, D. Pan, H. Yu, C. Xu, K. Zhou, and W. Gui, “An intelligent optimization strategy for blast furnace charging operation considering three-dimensional burden surface shape,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125192

Citation:

J. Zhu, Z. Jiang, D. Pan, H. Yu, C. Xu, K. Zhou, and W. Gui, “An intelligent optimization strategy for blast furnace charging operation considering three-dimensional burden surface shape,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125192

Citation:

J. Zhu, Z. Jiang, D. Pan, H. Yu, C. Xu, K. Zhou, and W. Gui, “An intelligent optimization strategy for blast furnace charging operation considering three-dimensional burden surface shape,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125192

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An Intelligent Optimization Strategy for Blast Furnace Charging Operation Considering Three-Dimensional Burden Surface Shape

doi: 10.1109/JAS.2025.125192

Funds: This work was supported in part by the Science and Technology Innovation Program of Hunan Province (2024RC1007), the Young Scientists Fund of the National Natural Science Foundation of China (62303491), the Major Program of Xiangjiang Laboratory (22XJ01005), the Young Scientists Fund of the National Natural Science Foundation of China (62203473), and Central South University Post-Graduate Independent Exploration and Innovation Project (2024ZZTS0451)

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Author Bio:
Jicheng Zhu received the B.S. degree in automation from the Hunan University of Science and Technology, Xiangtan, China, in 2020. He is currently working toward the Ph.D. degree in control science and engineering with Central South University, Changsha, China.His research interests include complex industrial process modeling and control, evolutionary computation, and deep learning

Zhaohui Jiang received the M. Eng. degree in automatic control engineering and the Ph.D. degree in control science and engineering from Central South University, Changsha, China, in 2006 and 2011, respectively.He is currently a Professor with Central South University. His research interests include detection technology and automatic equipment, image processing, industrial VR, and modeling and optimal control of complex industrial processes

Dong Pan received his B.S. degree in automatic and the Ph.D. degree in control science and engineering from Central South University, Changsha, China, in 2015 and 2021, respectively.He is currently an Associate Professor with Central South University. His research interests include infrared thermography, vision-based measurement, image processing, and deep learning

Haoyang Yu received the B.S. and Ph.D. degrees in precision instrument from Tsinghua University, Beijing, China, in 2016 and 2022, respectively.Since February 2022, he has been with the Department of Automation, Central South University, Changsha, China, where he is currently a Lecturer. His research interests include optical measurement, laser spectroscopy, laser ranging, and signal processing

Chuan Xu received the M.S. degree in automatic control from Central South University, Changsha, China, in 2021, where he is currently pursuing the Ph.D. degree.His main research interests include image processing, data analysis, deep learning, and modeling of complex industrial process

Ke Zhou received the B.S. degree in automatic control from Chongqing University, Chongqing, China, in 2018, and the Ph.D. degree in control science and engineering from Central South University, Changsha, China, in 2023.He is currently a algorithm engineer of CISDI Information Technology (Chongqing) Co., Ltd. His research interests include complex industrial process modeling, data mining, artificial intelligence, and machine learning

Weihua Gui received the degree of the B.Eng. in Electrical Engineering and the M.Eng. in Automatic Control Engineering from Central South University, China in 1976 and 1981, respectively.Since 1991, he has been a Full Professor with Central South University. He is an academician of the Chinese Academy of Engineering. His research interests include modeling and optimal control of complex industrial process, distributed robust control, and fault diagnoses
Corresponding author: Zhaohui Jiang, e-mail: jzh0903@csu.edu.cn
Received Date: 2024-09-18
Revised Date: 2024-11-23
Accepted Date: 2025-01-11

Available Online: 2025-01-20

Abstract

Abstract

Today, a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace (BF). Previous research on charging operations always focused on the two-dimensional shape of the burden surface (i.e., a single radial profile) while neglecting the unique feature of global dissymmetry, severely restricting the development of precise charging. For this reason, this study proposes an innovative optimization strategy for the charging operation under the three-dimensional burden surface, which is the first attempt in this field. First, a practicable region partitioning scheme is introduced, and the partitioning results are then integrated with the charging mechanism to construct a three-dimensional burden surface prediction model. Next, the intrinsic relationship between the operational parameters and charging volume is revealed based on the law of mass conservation, which forms the basis for defining a novel operational parameter with variable-speed utility, referred to as the neotype charging matrix (NCM). To find the best NCM, a customized NCM optimization strategy, involving a dual constraint handling technique in conjunction with a two-stage hybrid variable differential evolution algorithm, is further developed. The industrial experiment results manifest that the partitioning scheme significantly enhances the accuracy of burden surface description. Moreover, the NCM optimization strategy offers greater flexibility and higher accuracy than current mainstream optimization strategies for the charging matrix (CM).
- Blast furnace ironmaking process,
- charging operation,
- intelligent optimization strategy,
- three-dimensional burden surface

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References(43)

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An Intelligent Optimization Strategy for Blast Furnace Charging Operation Considering Three-Dimensional Burden Surface Shape

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