An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry

Zhongyang Han; Jun Zhao; Wei Wang

doi:10.1109/JAS.2017.7510439

Volume 4 Issue 2

Apr. 2017

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(2): 216-222

Zhongyang Han, Jun Zhao and Wei Wang, "An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 216-222, Apr. 2017. doi: 10.1109/JAS.2017.7510439

Citation:

Zhongyang Han, Jun Zhao and Wei Wang, "An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 216-222, Apr. 2017. doi: 10.1109/JAS.2017.7510439

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An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry

doi: 10.1109/JAS.2017.7510439

Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China

Funds:

This work was supported by National Natural Science Foundation of China 61473056

This work was supported by National Natural Science Foundation of China 61533005

This work was supported by National Natural Science Foundation of China 61522304

This work was supported by National Natural Science Foundation of China U1560102

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Author Bio:
Jun Zhao (M10) graduated from Dalian Jiaotong University, China, in 2003. He received the Ph. D. degree in engineering from 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.(zhaoj@dlut.edu.cn)

Wei Wang (SM13) 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.(wangwei@dlut.edu.cn)
Corresponding author: Zhongyang Han graduated from City Institute, Dalian University of Technology (DLUT), China, in 2010. He received the Ph. D. degree from Dalian University of Technology, China, in 2016. He is currently engaging in the postdoctoral research with the School of Control Science and Engineering, Dalian University of Technology, Dalian, China. His research interests include computer integrated manufacturing systems, artificial intelligence, data mining, and machine learning. Corresponding author of this paper.(e-mail: hzy dut@mail.dlut.edu.cn)
Received Date: 2015-11-02
Accepted Date: 2016-08-18

Abstract

Abstract

As an essential energy resource in steel industry, oxygen is widely utilized in many production procedures. With different demands of the oxygen amount, a gap between the generation and consumption always occurs. Therefore, its related optimization and scheduling work along with the electricity cost to fill the gap has a great impact on daily production and efficient energy utilization in steel plant. Considering an oxygen system in a steel plant in China, a nonlinear programming model for oxygen system scheduling is proposed in this study, which concerns not only the practical characteristics of the energy pipeline network, but also the electricity cost acquired by a fitting regression modeling between the load of air separation units (ASU) and its corresponding electricity consumption. A set of constraints is formulated for restricting the practical adjusting capacity and filling the imbalance gap of oxygen. To solve the proposed scheduling model with electricity cost consideration, a particle swarm optimization (PSO) algorithm is then adopted. To verify the effectiveness of the proposed approach, a large number of experiments employing the real data from this plant are carried out, both for the fitting regression and the scheduling optimization phases. And the results demonstrate that such a practice-based solution successfully resolves the oxygen scheduling problem and simultaneously minimizes the electricity cost, which will be beneficial for the enterprise.
- Electricity consumption,
- nonlinear programming,
- oxygen scheduling,
- steel industry

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An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry

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