Multi-Model Based PSO Method for Burden Distribution Matrix Optimization With Expected Burden Distribution Output Behaviors

Yong Zhang; Ping Zhou; Guimei Cui

doi:10.1109/JAS.2018.7511090

Volume 6 Issue 6

Nov. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(6): 1506-1512

Yong Zhang, Ping Zhou and Guimei Cui, "Multi-Model Based PSO Method for Burden Distribution Matrix Optimization With Expected Burden Distribution Output Behaviors," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1506-1512, Nov. 2019. doi: 10.1109/JAS.2018.7511090

Citation:

Yong Zhang, Ping Zhou and Guimei Cui, "Multi-Model Based PSO Method for Burden Distribution Matrix Optimization With Expected Burden Distribution Output Behaviors," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1506-1512, Nov. 2019. doi: 10.1109/JAS.2018.7511090

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Multi-Model Based PSO Method for Burden Distribution Matrix Optimization With Expected Burden Distribution Output Behaviors

doi: 10.1109/JAS.2018.7511090

Yong Zhang^{1, 2
,},
Ping Zhou^1
,,
Guimei Cui^2
,

1.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819
2.
School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

Funds:

the National Natural Science Foundation of China 61763038

the National Natural Science Foundation of China 61763039

the National Natural Science Foundation of China 61621004

the National Natural Science Foundation of China 61790572

the National Natural Science Foundation of China 61890934

the National Natural Science Foundation of China 61973137

the Fundamental Research Funds for the Central Universities N180802003

More Information

Author Bio:

Yong Zhang received the B.Eng. and the M.Sc. degrees from Daqing Petroleum Institute, Daqing, China, and Jiangnan University, Wuxi, China, in 2004 and 2007, respectively. He has been with the School of Information Engineering, Inner Mongolia University of Science and Technology since 2008, where he became an Associate Professor in 2016. He is currently working toward the Ph.D. degree in control theory and control engineering at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. The work was inspired by full-time fieldwork in Baotou Steel Group and observing pancakes in daily life. His current research interests include system identification, modeling and control of stochastic distribution system, distributed parameter system modeling and control, and their applications in complex blast furnace iron-making process and other industrial processes. He has published two ESI highly cited papers, and has received more than 200 citations in these areas. (e-mail: yinghuzhangyong@163.com)

Ping Zhou (M'12-SM'17) received the B.S., the M.S. and the Ph.D. degrees in control theory and engineering from Northeastern University, Shenyang, China, in 2003, 2006, and 2013, respectively. He is currently an Associate Professor with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He has authored over 80 papers and one monograph in these areas. His current research interests include development of feedback control for optimal process operation, and data-driven modeling and control. Dr. Zhou was a recipient of the Rockwell Automation Award for the Outstanding Application Paper with the Six World Congress on Intelligent Control and Automation, and the Keynote Paper Award for the Outstanding Application Paper in the 26th Chinese Process Control Conference. (e-mail: zhouping@mail.neu.edu.cn)

Guimei Cui received the B.S., the M.S. and the Ph.D. degrees at Baotou University of Iron and Steel Engineering, Batou, China, Harbin Shipbuilding Institute, Harbin, China, and University of Science and Technology Beijing, Beijing, China, in 1985, 1988, and 2006, respectively. Since 1985, she has been with the School of Information Engineering, Inner Mongolia University of Science and Technology, where she became a Full Professor in 2006. She has authored over 80 papers and one monograph in these areas. Her current research interests include network control, feedback control for optimal process operation, complex system modeling and control, and data-driven modeling and control. (e-mail: cguimei1@163.com)
Corresponding author:
Yong Zhang, e-mail: yinghuzhangyong@163.com
Received Date: 2017-03-30
Revised Date: 2017-08-11
Accepted Date: 2017-09-29

Abstract

Abstract

Burden distribution is one of the most important operations, and also an important upper regulation in blast furnace (BF) iron-making process. Burden distribution output behaviors (BDOB) at the throat of BF is a 3-dimensional spatial distribution produced by burden distribution matrix (BDM), including burden surface output shape (BSOS) and material layer initial thickness distribution (MLITD). Due to the lack of effective model to describe the complex input-output relations, BDM optimization and adjustment is carried out by experienced foremen. Focusing on this practical challenge, this work studies complex burden distribution input-output relations, and gives a description of expected MLITD under specific integral constraint on the basis of engineering practice. Furthermore, according to the decision variables in different number fields, this work studies optimization of BDM with expected MLITD, and proposes a multi-mode based particle swarm optimization (PSO) procedure for optimization of decision variables. Finally, experiments using industrial data show that the proposed model is effective, and optimized BDM calculated by this multi-model based PSO method can be used for expected distribution tracking.

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References

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Multi-Model Based PSO Method for Burden Distribution Matrix Optimization With Expected Burden Distribution Output Behaviors

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