A comparative thermodynamic analysis of Kalina and organic Rankine cycles for hot dry rock: a prospect study in the Gonghe Basin

Xuelin ZHANG, Tong ZHANG, Xiaodai XUE, Yang SI, Xuemin ZHANG, Shengwei MEI

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Front. Energy ›› 2020, Vol. 14 ›› Issue (4) : 889-900. DOI: 10.1007/s11708-020-0704-1
RESEARCH ARTICLE
RESEARCH ARTICLE

A comparative thermodynamic analysis of Kalina and organic Rankine cycles for hot dry rock: a prospect study in the Gonghe Basin

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Abstract

Hot dry rock is a new type of geothermal resource which has a promising application prospect in China. This paper conducted a comparative research on performance evaluation of two eligible bottoming cycles for a hot dry rock power plant in the Gonghe Basin. Based on the given heat production conditions, a Kalina cycle and three organic Rankine cycles were tested respectively with different ammonia-water mixtures of seven ammonia mass fractions and nine eco-friendly working fluids. The results show that the optimal ammonia mass fraction is 82% for the proposed bottoming Kalina cycle in view of maximum net power output. Thermodynamic analysis suggests that wet fluids should be supercritical while dry fluids should be saturated at the inlet of turbine, respectively. The maximum net power output of the organic Rankine cycle with dry fluids expanding from saturated state is higher than that of the other organic Rankine cycle combinations, and is far higher than the maximum net power output in all tested Kalina cycle cases. Under the given heat production conditions of hot dry rock resource in the Gonghe Basin, the saturated organic Rankine cycle with the dry fluid butane as working fluid generates the largest amount of net power.

Keywords

hot dry rock / Kalina cycle / organic Rankine cycle / thermodynamic analysis

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Xuelin ZHANG, Tong ZHANG, Xiaodai XUE, Yang SI, Xuemin ZHANG, Shengwei MEI. A comparative thermodynamic analysis of Kalina and organic Rankine cycles for hot dry rock: a prospect study in the Gonghe Basin. Front. Energy, 2020, 14(4): 889‒900 https://doi.org/10.1007/s11708-020-0704-1

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Acknowledgments

This work was supported by the State Grid Technology Program (SGRI-DL-71-15-006), and the Scientific and Technological Project of Qinghai Province (2018-ZJ-726).

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2020 Higher Education Press
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