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Multi-component optimisation for refinery hydrogen networks

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  • Jia, Nan
  • Zhang, Nan

Abstract

Heavier crude oil, tighter environmental regulations and increased heavy-end upgrading in the petroleum industry are leading to the increased demand for hydrogen in oil refineries. Hence, hydrotreating and hydrocracking processes now play increasingly important roles in modern refineries. Refinery hydrogen networks are becoming more and more complicated as well. Therefore, optimisation of overall hydrogen networks is required to improve the hydrogen utilisation in oil refineries. Previous work over hydrogen management has developed methodologies for H2 network optimisation, with a very simplistic assumption that all H2 rich streams consist of H2 and CH4 only, which leads to a serious doubt of solution’s feasibility. To overcome the drawbacks in previous work, an improved modelling and optimisation approach has been developed. Light hydrocarbon production and integrated flash calculation are incorporated into a hydrogen consumer model. An optimisation framework is developed to solve the resulting NLP problem. A case study is carried out to demonstrate the effectiveness of the developed approach.

Suggested Citation

  • Jia, Nan & Zhang, Nan, 2011. "Multi-component optimisation for refinery hydrogen networks," Energy, Elsevier, vol. 36(8), pages 4663-4670.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:8:p:4663-4670
    DOI: 10.1016/j.energy.2011.03.040
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    References listed on IDEAS

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    1. 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. 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.
    2. Yang, Minbo & Feng, Xiao & Chu, Khim Hoong & Liu, Guilian, 2014. "Graphical method for identifying the optimal purification process of hydrogen systems," Energy, Elsevier, vol. 73(C), pages 829-837.
    3. Napp, T.A. & Gambhir, A. & Hills, T.P. & Florin, N. & Fennell, P.S, 2014. "A review of the technologies, economics and policy instruments for decarbonising energy-intensive manufacturing industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 616-640.
    4. 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.
    5. 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.
    6. 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.
    7. Ratkiewicz, Artur & Truong, Thanh N., 2012. "A canonical form of the complex reaction mechanism," Energy, Elsevier, vol. 43(1), pages 64-72.
    8. Johansson, Daniella & Franck, Per-Åke & Berntsson, Thore, 2012. "Hydrogen production from biomass gasification in the oil refining industry – A system analysis," Energy, Elsevier, vol. 38(1), pages 212-227.
    9. Ozalp, Nesrin & Ibrik, Karim & Al-Meer, Mariam, 2013. "Kinetics and heat transfer analysis of carbon catalyzed solar cracking process," Energy, Elsevier, vol. 55(C), pages 74-81.
    10. 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.
    11. Deng, Chun & Zhou, Yuhang & Chen, Cheng-Liang & Feng, Xiao, 2015. "Systematic approach for targeting interplant hydrogen networks," Energy, Elsevier, vol. 90(P1), pages 68-88.

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