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 4 Issue 2
Apr.  2017

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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

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

doi: 10.1109/JAS.2017.7510439
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

More Information
  • 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.

     

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