A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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

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

doi: 10.1109/JAS.2018.7511090
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
  • 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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