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Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant

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  • Adamson, Richard
  • Hobbs, Martin
  • Silcock, Andy
  • Willis, Mark J.

Abstract

The development and on-line application of a steady-state optimisation strategy for a multiple cryogenic air separation unit and compressor plant is discussed. Implemented using mixed integer linear programming (MILP), it is demonstrated that the optimiser improves site efficiency at steady state by reduction of power consumption by up to 5% (a significant saving for such an energy intensive process) while meeting customer demand specifications. This is achieved through determination of the production distribution of the air separation units and optimal load distribution of the compression network, while simultaneously ensuring network material balance and network component operating constraints are met. In addition, the work demonstrates achievable benefits of demand side load management during peak power pricing periods, using liquid oxygen as an effective energy storage device. A key constituent of the optimisation strategy is linear modelling to predict individual unit power consumption. Piece-wise linear data-based models of compressor and air separation unit power are shown to provide accurate models which improve existing on-site power prediction by up to 80% for compressors and 60% for the air separation units.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:189:y:2017:i:c:p:221-232
    DOI: 10.1016/j.apenergy.2016.12.061
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Cummings, Thomas & Adamson, Richard & Sugden, Andrew & Willis, Mark J., 2017. "Retrospective and predictive optimal scheduling of nitrogen liquefier units and the effect of renewable generation," Applied Energy, Elsevier, vol. 208(C), pages 158-170.
    4. Ghaib, Karim & Ben-Fares, Fatima-Zahrae, 2018. "Power-to-Methane: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 433-446.
    5. 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).
    6. Otashu, Joannah I. & Baldea, Michael, 2018. "Grid-level “battery” operation of chemical processes and demand-side participation in short-term electricity markets," Applied Energy, Elsevier, vol. 220(C), pages 562-575.
    7. 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).
    8. Miroslav Variny & Dominika Jediná & Miroslav Rimár & Ján Kizek & Marianna Kšiňanová, 2021. "Cutting Oxygen Production-Related Greenhouse Gas Emissions by Improved Compression Heat Management in a Cryogenic Air Separation Unit," IJERPH, MDPI, vol. 18(19), pages 1-32, October.
    9. Miroslav Variny & Dominika Jediná & Patrik Furda, 2021. "Comment on Hamayun et al. Evaluation of Two-Column Air Separation Processes Based on Exergy Analysis. Energies 2020, 13 , 6361," Energies, MDPI, vol. 14(20), pages 1-8, October.
    10. Laing, Harry & O'Malley, Chris & Browne, Anthony & Rutherford, Tony & Baines, Tony & Moore, Andrew & Black, Ken & Willis, Mark J., 2022. "Optimisation of energy usage and carbon emissions monitoring using MILP for an advanced anaerobic digester plant," Energy, Elsevier, vol. 256(C).

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