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An exergy-based approach for hydrogen network integration

Author

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  • Wang, Yufei
  • Wu, Sidong
  • Feng, Xiao
  • Deng, Chun

Abstract

Increasingly strict environmental and product-quality regulations coupled with the change of crude oil to high-sulfur and heavier oil have increased refineries' hydrogen demand. Various HNI (hydrogen network integration) methods have been used to achieve efficient use of hydrogen by refineries, leading to reduced energy consumption and cost. However, to minimize the energy consumption of a hydrogen network, not only the hydrogen utility consumption but also the energy consumption of the whole network should be taken into account. In this paper, the superstructure and mathematical model for integration of a hydrogen network with purification are established, in which all purification processes are expressed by the same modular. The total exergy consumption is used as the objective function for optimization, which encompasses fresh hydrogen consumption, compression work and energy consumption of purification processes. The energy consumption of a purification process is expressed in terms of its minimum separation work. A case study is used to illustrate the exergy-based optimization approach.

Suggested Citation

  • Wang, Yufei & Wu, Sidong & Feng, Xiao & Deng, Chun, 2015. "An exergy-based approach for hydrogen network integration," Energy, Elsevier, vol. 86(C), pages 514-524.
    • Handle: RePEc:eee:energy:v:86:y:2015:i:c:p:514-524
    DOI: 10.1016/j.energy.2015.04.051
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    References listed on IDEAS

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    1. Umana, Blessing & Shoaib, Abeer & Zhang, Nan & Smith, Robin, 2014. "Integrating hydroprocessors in refinery hydrogen network optimisation," Applied Energy, Elsevier, vol. 133(C), pages 169-182.
    2. Wu, Sidong & Yu, Zemiao & Feng, Xiao & Liu, Guilian & Deng, Chun & Chu, Khim Hoong, 2013. "Optimization of refinery hydrogen distribution systems considering the number of compressors," Energy, Elsevier, vol. 62(C), pages 185-195.
    3. Jia, Nan & Zhang, Nan, 2011. "Multi-component optimisation for refinery hydrogen networks," Energy, Elsevier, vol. 36(8), pages 4663-4670.
    4. Kumar, A. & Gautami, G. & Khanam, S., 2010. "Hydrogen distribution in the refinery using mathematical modeling," Energy, Elsevier, vol. 35(9), pages 3763-3772.
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    Cited by:

    1. Liu, Xuepeng & Liu, Jian & Deng, Chun & Lee, Jui-Yuan & Tan, Raymond R., 2020. "Synthesis of refinery hydrogen network integrated with hydrogen turbines for power recovery," Energy, Elsevier, vol. 201(C).
    2. Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Walmsley, Timothy G. & Jia, Xuexiu, 2018. "New directions in the implementation of Pinch Methodology (PM)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 439-468.
    3. Hwangbo, Soonho & Lee, In-Beum & Han, Jeehoon, 2016. "Multi-period stochastic mathematical model for the optimal design of integrated utility and hydrogen supply network under uncertainty in raw material prices," Energy, Elsevier, vol. 114(C), pages 418-430.
    4. Dai, Wang & Shen, Renjie & Zhang, Di & Liu, Guilian, 2017. "The integration based method for identifying the variation trend of fresh hydrogen consumption and optimal purification feed," Energy, Elsevier, vol. 119(C), pages 732-743.

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