Performance analysis and optimization of a PEMFC-CAORC system based on 3D construction method of thermodynamic cycle
Performance analysis and optimization of a PEMFC-CAORC system based on 3D construction method of thermodynamic cycle
复制标题
基于热力循环3D构建方法的PEMFC-CAORC系统性能分析与优化
DOI：
10.1016/j.enconman.2021.114730
复制
发表时间：
2021-11
影响因子：
10.4
通讯作者：
Yin Yan
中科院分区：
文献类型：
--
作者：
Liu Guokun;Qin Yanzhou;Li Mengyuan;Liu Yuhang;Zheng Jiayang;Liu Xin;Yin Yan
In this study, a novel proton exchange membrane fuel cell (PEMFC) system is proposed, in which a composition adjustment organic Rankine cycle (CAORC) system is integrated. The waste heat from both PEMFC stack and after-burner is recovered by CAORC system to realize the maximum output power conversion from system waste heat. Based on 3D construction method of thermodynamic cycle, the system thermodynamic model is established to evaluate the performance of the proposed PEMFC-CAORC system. The effects of PEMFC operating temperature, current density and high evaporation temperature are analyzed. The system energy and exergy efficiencies are 39.44% and 47.60% at the PEMFC operating temperature of 358 K and current density of 0.6 A cm−2. As the PEMFC operating temperature rises, its performance can be further improved, and the system energy and exergy efficiencies at 368 K reach 40.36% and 48.72%. As the PEMFC current density increases, the system net power increases while both efficiencies drop. To improve the CAORC performance specifically, the evaporation temperature in the after-burner is suggested to be high enough, and genetic algorithm is used to optimize key parameters of the CAORC. The mixture of Benzene and Toluene are the optimum zeotropic fluid type for all optimization cases investigated, while its proportion, separation dryness and evaporation temperature vary in different PEMFC operating conditions. Compared with the original results, the CAORC thermal efficiency increases by over 4% with genetic algorithm optimization.
登录
查看更多内容
影响因子：
--
作者：
Weicong Xu;Li Zhao;S. Deng;Shuangjun Li
通讯作者：
Weicong Xu;Li Zhao;S. Deng;Shuangjun Li
影响因子：
--
作者：
Lao Xingsheng;Ma Shihu;Zhao Juntao;Zeng Hong;Zhu Weilin;Z. Yangjun
通讯作者：
Lao Xingsheng;Ma Shihu;Zhao Juntao;Zeng Hong;Zhu Weilin;Z. Yangjun
影响因子：
7.2
作者：
Jae Yoon Lee;Jae Hong Lee;T. Kim
通讯作者：
Jae Yoon Lee;Jae Hong Lee;T. Kim
影响因子：
11.2
作者：
Weicong Xu;S. Deng;Li Zhao;Yue Zhang;Shuangjun Li
通讯作者：
Weicong Xu;S. Deng;Li Zhao;Yue Zhang;Shuangjun Li
影响因子：
6.4
作者：
H. Nami;S. Mahmoudi;A. Nemati
通讯作者：
H. Nami;S. Mahmoudi;A. Nemati