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Cycle analysis and economic evaluation for seawater-LNG Organic Rankine Cycles

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  • Lee, Su Won
  • Kwon, Jin Gyu
  • Kim, Moo Hwan
  • Jo, HangJin

Abstract

An Organic Rankine Cycle (ORC) has a variety of environmental and operating conditions, which result in different efficiency and net work of cycle. In this regard, it is important to determine the optimal operating conditions with the consideration of various operating variables since the outcomes of the low-grade waste heat recovery systems are low as well as highly dependent on the conditions. In this study, the ORC having seawater as a heat source and LNG cold energy as a heat sink is analyzed with cycle parameters as well as the environmental conditions such as seawater temperature. An ORC Economic evaluation Code (OEC) is developed to consider the effects of numerous parameters such as the pump outlet pressure, mass flow rate, pinch point, materials of the heat exchangers, the type of working fluid, cycle configuration, and seawater temperature simultaneously. Based on the analysis, an economic assessment is conducted, so we could find the optimal conditions for the designed ORC systems. Also, it is found that the seawater temperature variation could affect about 23.8% in terms of net work compared to fixed heat source temperature conditions.

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  • Lee, Su Won & Kwon, Jin Gyu & Kim, Moo Hwan & Jo, HangJin, 2021. "Cycle analysis and economic evaluation for seawater-LNG Organic Rankine Cycles," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221015073
    DOI: 10.1016/j.energy.2021.121259
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    Cited by:

    1. Tang, Changlong & Hu, Fan & Zhou, Xiaoguang & Li, Yajun, 2022. "Optimization methods for flexibility and stability related to the operation of LNG receiving terminals," Energy, Elsevier, vol. 250(C).
    2. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Zhang, Wujie & Wang, Yan, 2022. "Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection," Applied Energy, Elsevier, vol. 311(C).
    3. Aryanfar, Yashar & Mohtaram, Soheil & García Alcaraz, Jorge Luis & Sun, HongGuang, 2023. "Energy and exergy assessment and a competitive study of a two-stage ORC for recovering SFGC waste heat and LNG cold energy," Energy, Elsevier, vol. 264(C).
    4. Joy, Jubil & Kochunni, Sarun Kumar & Chowdhury, Kanchan, 2022. "Size reduction and enhanced power generation in ORC by vaporizing LNG at high supercritical pressure irrespective of delivery pressure," Energy, Elsevier, vol. 260(C).

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