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International Journal of Energy Research
Article . 2004 . Peer-reviewed
License: Wiley TDM
Data sources: Crossref
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Exergy analysis of liquefied natural gas cold energy recovering cycles

Authors: Wang Qiang; Li Yanzhong; Chen Xi;

Exergy analysis of liquefied natural gas cold energy recovering cycles

Abstract

Liquefied natural gas (LNG) is known as ‘green fuel’ used in power plant, automobile and so forth due to its higher energy density and environmentally friendly advantages. LNG, besides its high quality chemical exergy, has plenty of physical exergy such as cold exergy and pressure exergy, which could be utilized further. Analysis of physical exergy and its affected factors has been conducted. Based on the analysis, several cycles used for recovering and applying the physical exergy of LNG, such as combined power cycle, gas turbine power generation cycle and automobile air-conditioning system have been proposed. The parameters affecting the performance of the cycles are discussed. The recovery and utilization of physical exergy of LNG are the important measures to save energy and protect the environment. Copyright © 2005 John Wiley & Sons, Ltd.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Average
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