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Optimization of co-production air separation unit based on MILP under multi-product deterministic demand

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  • Kong, Fulin
  • Liu, Yuxin
  • Tong, Lige
  • Guo, Wei
  • Qiu, Yinan
  • Wang, Li

Abstract

Optimizing gas distribution is one of the most important energy management issues in process industries such as metallurgy and the chemical industry. The air separation unit (ASU) is characterized by the simultaneous production of multiple products (oxygen, nitrogen, liquid oxygen, liquid nitrogen and liquid argon) and the mutual restriction of each product. The existing single-product scheduling models are not applicable to the actual process and the multi-product scheduling models are only for production processes and usually do not consider constraints such as ASU start/stop. Therefore, for the production as well as the transmission and storage processes of the co-production air separation system, the paper developed a MILP model, which uses a proxy model to express the production space of ASUs. This model minimizes oxygen emission based on avoiding frequent startup and shutdown of ASU, which uses the start-stop and load regulation of ASU, the start-stop of liquefier, and vaporizer as means, and considers device operating constraints. We verified the feasibility of the method by combining actual cases application and analyzing the result of the influence of single product optimization results on the balance of other products.

Suggested Citation

  • Kong, Fulin & Liu, Yuxin & Tong, Lige & Guo, Wei & Qiu, Yinan & Wang, Li, 2022. "Optimization of co-production air separation unit based on MILP under multi-product deterministic demand," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s0306261922011187
    DOI: 10.1016/j.apenergy.2022.119850
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    References listed on IDEAS

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    1. 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.
    2. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    3. Fernández, David & Pozo, Carlos & Folgado, Rubén & Guillén-Gosálbez, Gonzalo & Jiménez, Laureano, 2017. "Multiperiod model for the optimal production planning in the industrial gases sector," Applied Energy, Elsevier, vol. 206(C), pages 667-682.
    4. Jiang, Sheng-Long & Peng, Gongzhuang & Bogle, I. David L. & Zheng, Zhong, 2022. "Two-stage robust optimization approach for flexible oxygen distribution under uncertainty in integrated iron and steel plants," Applied Energy, Elsevier, vol. 306(PB).
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    Cited by:

    1. Kong, Fulin & Liu, Yuxin & Shen, Minghai & Tong, Lige & Yin, Shaowu & Wang, Li & Ding, Yulong, 2023. "A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage," Renewable Energy, Elsevier, vol. 209(C), pages 533-545.
    2. Che, Gelegen & Zhang, Yanyan & Tang, Lixin & Zhao, Shengnan, 2023. "A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants," Applied Energy, Elsevier, vol. 345(C).
    3. 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).

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