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ASU model with multiple adjustment types for oxygen scheduling concerning pipe pressure safety in steel enterprises

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Listed:
  • Zhang, Liu
  • Zheng, Zhong
  • Chai, Yi
  • Xu, Zhaojun
  • Zhang, Kaitian
  • Liu, Yu
  • Chen, Sujun
  • Zhao, Liuqiang

Abstract

Steel production requires enough oxygen, which is produced by cryogenic air separation (ASU) with a large load, high power, and slow adjustment. Facing complex oxygen demands, effective ASU scheduling can reduce energy waste and has great potential for low-carbon development. Current ASU scheduling models have only a sole load-adjustment type. To address the limitation, a novel scheduling framework model is researched for ASUs with various load-adjustment types, and is applied to an oxygen system scheduling mode1 for steel enterprises. In addition, current multi-period oxygen scheduling models have an issue that time discretization leads to oxygen pipe pressure deviation between model calculation and practical operation and impacts on system safety. Therefore, a novel method to compensate for pressure boundaries is studied to precisely handle the deviation. Some contrast tests concerning various load-adjustment types of ASU are conducted with simulative and real data. The simulative tests indicate that the framework can both unify multiple adjustment types (MAT) and flexibly migrate among various ASUs. In the real scenario, the oxygen scheduling model with the MAT-ASU avoids oxygen emission and reduces comprehensive cost (including electricity consumption and oxygen release loss) of the manual scheduling by 24%. The MAT enhances the response ability to complicated demands, thereby performing better to optimize the comprehensive cost. Though smaller pipe volume or larger discrete period size increases the pressure deviation, the compensation method perfectly ensures safe system operation.

Suggested Citation

  • Zhang, Liu & Zheng, Zhong & Chai, Yi & Xu, Zhaojun & Zhang, Kaitian & Liu, Yu & Chen, Sujun & Zhao, Liuqiang, 2023. "ASU model with multiple adjustment types for oxygen scheduling concerning pipe pressure safety in steel enterprises," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923003501
    DOI: 10.1016/j.apenergy.2023.120986
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    References listed on IDEAS

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    1. Zhao, Xiancong & Bai, Hao & Lu, Xin & Shi, Qi & Han, Jiehai, 2015. "A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process," Applied Energy, Elsevier, vol. 148(C), pages 142-158.
    2. Adamson, Richard & Hobbs, Martin & Silcock, Andy & Willis, Mark J., 2017. "Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant," Applied Energy, Elsevier, vol. 189(C), pages 221-232.
    3. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    4. Davis, Steven J & Lewis, Nathan S. & Shaner, Matthew & Aggarwal, Sonia & Arent, Doug & Azevedo, Inês & Benson, Sally & Bradley, Thomas & Brouwer, Jack & Chiang, Yet-Ming & Clack, Christopher T.M. & Co, 2018. "Net-Zero Emissions Energy Systems," Institute of Transportation Studies, Working Paper Series qt7qv6q35r, Institute of Transportation Studies, UC Davis.
    5. Kelley, Morgan T. & Pattison, Richard C. & Baldick, Ross & Baldea, Michael, 2018. "An MILP framework for optimizing demand response operation of air separation units," Applied Energy, Elsevier, vol. 222(C), pages 951-966.
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    1. Zhang, Liu & Zhang, Kaitian & Zheng, Zhong & Chai, Yi & Lian, Xiaoyuan & Zhang, Kai & Xu, Zhaojun & Chen, Sujun, 2023. "Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises," Applied Energy, Elsevier, vol. 351(C).
    2. Zhang, Liu & Zheng, Zhong & Chai, Yi & Zhang, Kaitian & Lian, Xiaoyuan & Zhang, Kai & Zhao, Liuqiang, 2024. "Enhancing robustness: Multi-stage adaptive robust scheduling of oxygen systems in steel enterprises under demand uncertainty," Applied Energy, Elsevier, vol. 359(C).

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