梁颖宗

个人信息Personal Information

副教授

硕士生导师

教师拼音名称:Liang Yingzong

入职时间:2018-12-27

所在单位:材料与能源学院

职务:Associate Professor

联系方式:yliang@gdut.edu.cn

在职信息:在职

主要任职:Associate Professor

其他任职:Associate Professor

毕业院校:The Hong Kong University of Technology

学科:工程热物理

论文成果

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Multi-objective optimization of supercritical CO2 Brayton cycles for coal-fired power generation with two waste heat recovery schemes

点击次数:

影响因子:10.4

DOI码:10.1016/j.enconman.2023.117962

发表刊物:Energy Conversion and Management

关键字:The efficiency of supercritical CO2 (sCO2) Brayton cycle (SCBC) based coal-fired power generation can be enhanced by harnessing the waste heat from sCO2 cooling and flue gas, which currently remains largely untapped. In this paper, we propose two types of design roadmap for utilizing this waste heat. The first method involves using an organic Rankine cycle (ORC) to generate additional power, while the second method utilizes LiBr/H2O absorption refrigeration cycle (ARC) to further cool down compressor inlet sCO2, and thereby reduces its compression power consumption. An energy-economic-environmental multi-criteria models are formulated to access performance of the aforementioned designs and compare them with a standalone sCO2 Brayton recompression cycle system (Standalone). The non-dominated sorting genetic algorithm II is applied to carry out the multi-objective optimization of the three systems. The results show that the SCBC-ARC system achieves the maximum thermal efficiency (ηth) and minimum environmental impact load (EIL), while SCBC-ORC system achieves the minimum levelized cost of electricity (LCOE). We also find that minimizing LCOE conflicts with maximizing ηth and minimizing EIL, respectively. The relationship between maximizing ηth and minimizing EIL is consistent, suggesting that increasing efficiency will alleviate environmental impact of the systems. We also identify and discuss balanced designs for the systems, and our results show that the ηth of SCBC-ORC and SCBC-ARC is 1.40% and 1.70% higher than the Standalone, respectively, the LCOE is 0.56% lower and 3.66% higher than Standalone, respectively, and EIL is 1.16% and 1.59% lower than the Standalone, respectively.

合写作者:Wei Chen,Jianyong Chen,Zhi Yang,Ying Chen

第一作者:Yingzong Liang

论文类型:期刊论文

通讯作者:Xianglong Luo

卷号:300

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收录刊物:SCI