Causality Diagram-based Scheduling Approach for Blast Furnace Gas System

Feng Jin; Jun Zhao; Chunyang Sheng; Wei Wang

doi:10.1109/JAS.2017.7510715

Volume 5 Issue 2

Mar. 2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(2): 587-594

Feng Jin, Jun Zhao, Chunyang Sheng and Wei Wang, "Causality Diagram-based Scheduling Approach for Blast Furnace Gas System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 587-594, Mar. 2018. doi: 10.1109/JAS.2017.7510715

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Feng Jin, Jun Zhao, Chunyang Sheng and Wei Wang, "Causality Diagram-based Scheduling Approach for Blast Furnace Gas System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 587-594, Mar. 2018. doi: 10.1109/JAS.2017.7510715

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Causality Diagram-based Scheduling Approach for Blast Furnace Gas System

doi: 10.1109/JAS.2017.7510715

Feng Jin^1
,,
Jun Zhao^{1
,
,},
Chunyang Sheng^2
,,
Wei Wang^1
,

1.
Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2.
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China

Funds:

the National Natural Sciences Foundation of China 61473056

the National Natural Sciences Foundation of China 61533005

the National Natural Sciences Foundation of China 61522304

the National Natural Sciences Foundation of China 61603068

the National Natural Sciences Foundation of China U1560102

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Author Bio:
Feng Jin graduated from Northeasten University at Qinhuangdao, China, in 2012. He is currently pursuing the Ph.D. degree with the School of Control Science and Engineering, Dalian University of Technology, Dalian, China. His research interests include data based prediction and scheduling (e-mail: jinfeng 1126@126.com)

Jun Zhao (M'10) graduated from Dalian Jiaotong University, China, in 2003. He received the Ph.D. degree in engineering from the Dalian University of Technology, in 2008. He is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology. His current research interests include industrial production scheduling, computer integrated manufacturing, intelligent optimization, and machine learning(email: zhaoj@dlut.edu.cn)

Chunyang Sheng received the Ph.D. degree in control theory and control engineering, Dalian University of Technology, in 2014. He worked as a post-doctoral at the School of Computer Science and Technology, Dalian University of Technology from 2015 to 2016. He currently works as a Lecturer in Shandong University of Science and Technology. His research interests include machine learning, data based prediction scheduling and optimization(email: chunyang860124@163.com)

Wei Wang (SM'13) received the B.S., M.S., and Ph.D. degrees from Northeastern University, Shenyang, China, in 1982, 1986, and 1988 respectively, all in industrial automation. He was a PostDoctoral Fellow with the Division of Engineering Cybernetics, Norwegian Science and Technology University, Trondheim, Norway, from 1990 to 1992, a Professor and Vice Director of the National Engineering Research Center of Metallurgical Automation of China, Beijing, China, from 1995 to 1999, and a Research Fellow with the Department of Engineering Science, University of Oxford, Oxford, U.K., from 1998 to 1999. He is currently a Professor with the School of Control Sciences and Engineering, Dalian University of Technology, China. His current research interests include adaptive controls, computer integrated manufacturing, and computer controls of industrial processes(email: wangwei@dlut.edu.cn)
Corresponding author: Jun Zhao, email: zhaoj@dlut.edu.cn
Received Date: 2016-08-23
Accepted Date: 2017-05-25

Abstract

Abstract

Rational use of blast furnace gas (BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means (CFCM) clustering algorithm. In this algorithm, not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered. The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.
- Blast furnace gas system,
- causal fuzzy C-means (CFCM) clustering,
- causality diagram,
- scheduling

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References(15)

References

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Causality Diagram-based Scheduling Approach for Blast Furnace Gas System

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