Performance Improvement of the LNG Regasification Process Based on Geothermal Energy Using a Thermoelectric Generator and Energy and Exergy Analyses
Amin Mohammadi and
Akbar Maleki ()
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Amin Mohammadi: Department of Renewable Energies and Environment, Faculty of New Science and Technologies, University of Tehran, Tehran 1439957131, Iran
Akbar Maleki: Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
Sustainability, 2024, vol. 16, issue 24, 1-22
Abstract:
In this paper, a new approach is proposed to improve the performance of the LNG regasification process in a geothermal-transcritical CO 2 –LNG cycle by using thermoelectric generators. Energy and exergy analyses were applied to the proposed system and the plant’s performance is compared with the conventional CO 2 –LNG cycle. To achieve the optimal solution for the system, a multi-objective optimization technique based on a genetic algorithm is used. This study’s findings revealed that in the conventional CO 2 –LNG cycle, the highest exergy destruction occurs in the preheater. However, integrating a thermoelectric generator allows a portion of this destroyed exergy to be converted into power. The proposed system demonstrated 2% less exergy destruction compared to the conventional system. Moreover, the TEG contributes additional power, increasing the net output power of the system by 24%. This improvement ultimately enhances the overall exergy efficiency of the system. The analysis also concluded that, although a lower LNG mass flow rate reduces the system’s net power output, it improves the exergy efficiency. Overall, the proposed system exhibits an 8.37% higher exergy efficiency and a 24.22% greater net output power compared to the conventional CO 2 –LNG cycle.
Keywords: LNG storage regasification; exergy; thermoelectric generator; transcritical carbon dioxide cycle; sustainability; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:24:p:10881-:d:1542110
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