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Process-integrated design of a sub-ambient membrane process for CO2 removal from natural gas power plants

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  • Lee, Sunghoon
  • Kim, Jin-Kuk

Abstract

This paper proposes an advanced sub-ambient membrane process that applies an improved CO2/N2 selectivity under cold temperatures for CO2 removal from a natural gas combined-cycle power plant. The use of a large excess of combustion air in a natural gas power plant results in a relatively diluted CO2 exhaust gas, typically by 3–4%, which makes it difficult to achieve an energy-efficient CO2 capture. The sub-ambient membrane process using exhaust gas recirculation and/or selective exhaust gas recirculation is designed to concentrate the CO2 from 4 to 6–10%. In addition, the heat integration of liquefied natural gas (LNG) regasification not only provides a nearly free cold energy source for producing a cold processing environment, it also maximizes the full potential of the heat recovery. Among the different membrane designs proposed, an integrated process using all design options demonstrates the lowest CO2 capture cost at $ 57/tCO2, which is a 55.1% reduction in capture costs, and a 70.1% decrease in parasitic load compared to an early configuration. A sensitivity analysis was conducted to understand the impact of the membrane performance, namely, the CO2 permeance and CO2/N2 selectivity, on the process design and economics of the CO2 capture process considered herein, through which guidelines for the development of membrane materials and a conceptual insight into the design and optimization of a membrane-based capture process can be obtained.

Suggested Citation

  • Lee, Sunghoon & Kim, Jin-Kuk, 2020. "Process-integrated design of a sub-ambient membrane process for CO2 removal from natural gas power plants," Applied Energy, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919319427
    DOI: 10.1016/j.apenergy.2019.114255
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    Cited by:

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    5. Vo, Nguyen Dat & Oh, Dong Hoon & Kang, Jun-Ho & Oh, Min & Lee, Chang-Ha, 2020. "Dynamic-model-based artificial neural network for H2 recovery and CO2 capture from hydrogen tail gas," Applied Energy, Elsevier, vol. 273(C).
    6. Hong, Bingyuan & Cui, Xuemeng & Wang, Bohong & Fan, Di & Li, Xiaoping & Gong, Jing, 2022. "Long-term dynamic allocation and maintenance planning of modular equipment to enhance gas field production flexibility," Energy, Elsevier, vol. 252(C).
    7. Ouyang, Tiancheng & Tan, Jiaqi & Wu, Wencong & Xie, Shutao & Li, Difan, 2022. "Energy, exergy and economic benefits deriving from LNG-fired power plant: Cold energy power generation combined with carbon dioxide capture," Renewable Energy, Elsevier, vol. 195(C), pages 214-229.
    8. Cheng, Jun & Wang, Yali & Liu, Niu & Hou, Wen & Zhou, Junhu, 2020. "Enhanced CO2 selectivity of mixed matrix membranes with carbonized Zn/Co zeolitic imidazolate frameworks," Applied Energy, Elsevier, vol. 272(C).

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