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Received June 2, 2020
Accepted August 17, 2020
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Maximization of the power production in LNG cold energy recovery plant via genetic algorithm

Department of Chemical Engineering, Hanyeong University, Yeosu-si, Jeonnam 59720, Korea 1School of Chemical Engineering, Chonnam National University, Gwangju 61186, Korea 2Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu-si, Jeonnam 59626, Korea
chkang@jnu.ac.kr
Korean Journal of Chemical Engineering, February 2021, 38(2), 380-385(6), 10.1007/s11814-020-0662-7
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Abstract

This paper presents an optimization model via genetic algorithm (GA) to maximize the power generation potential of a liquefied natural gas (LNG) cold energy recovery plant. LNG releases a large amount of cold energy during vaporization prior to transport for service, and this cold energy can be effectively utilized to generate power using a heat engine. We performed a thermodynamic analysis for a power generation system combining the organic rankine cycle (ORC) driven by LNG exergy and the direct expansion cycle. Both LNG and the working fluid in the combined ORC are light hydrocarbon mixtures, and their physical properties were estimated using the Peng-Robinson equation. We conducted a thorough investigation of the effects that the working fluid composition brought about on the thermal efficiency of the heat engine through an analysis using Aspen HYSYS interfaced with a GA-based Matlab solver. The results showed that optimization of the working fluid composition led to an increase of 58.4% in the performance of the combined ORC in terms of the net work.

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