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Multiperiod model for the optimal production planning in the industrial gases sector

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  • Fernández, David
  • Pozo, Carlos
  • Folgado, Rubén
  • Guillén-Gosálbez, Gonzalo
  • Jiménez, Laureano

Abstract

Cryogenic air separation to produce nitrogen, oxygen and argon with high quality requirements is an energy-intensive industrial process that requires large quantities of electricity. The complexity in operating these networks stems from the volatile conditions, namely electricity prices and products demands, which vary every hour, creating a clear need for computer-aided tools to attain economic and energy savings. In this article, we present a multiperiod mixed-integer linear programming (MILP) model to determine the optimal production schedule of an industrial cryogenic air separation process so as to maximize the net profit by minimizing energy consumption (which is the main contributor to the operating costs). The capabilities of the model are demonstrated by means of its application to an existing industrial process, where significant improvements are attained through the implementation of the MILP.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:667-682
    DOI: 10.1016/j.apenergy.2017.08.064
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    2. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    3. 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).
    4. 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.
    5. 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).

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