遗传算法
多目标优化
固体氧化物燃料电池
可用能
火用
数学优化
最优化问题
海水淡化
总成本
功率(物理)
工程类
工艺工程
数学
化学
物理
微观经济学
经济
物理化学
阳极
生物
量子力学
遗传学
膜
电极
作者
Javid Beyrami,Ata Chitsaz,Kiyan Parham,Øystein Arild
出处
期刊:Energy
[Elsevier BV]
日期:2019-11-01
卷期号:186: 115811-115811
被引量:30
标识
DOI:10.1016/j.energy.2019.07.141
摘要
A solid oxide fuel cell (SOFC) integrated with single effect desalination (SED) unit is modeled and optimized from exergoeconomic, enviroeconomic and exergoenvironmental points of view. A multi-criterion optimization technique based on genetic algorithm in the form of three various scenarios is accomplished. In scenario I (exergoeconomic optimization), the total exergy efficiency (ηe,t) and total cost rate (Z˙t) of the hybrid system are assumed as two objective function, while in scenario II (enviroeconomic optimization), the CO2 emission rate (EMI) and total cost rate (Z˙t) of the hybrid system are considered as the two objectives. Eventually, in scenario III (exergoenvironmental optimization), the total exergy efficiency (ηe,t) and the CO2 emission rate (EMI) are presumed as the optimization functions. In various scenarios, optimization technique is carried out with the aim of maximizing the ηe,t and minimizing the EMI andZ˙t. The multi-objective optimization results show that among different scenarios, the minimum total cost rate and CO2 emission rate occurs in the Scenario II, while the maximum total exergy efficiency occur in the Scenario I. In addition, it is revealed that, at the optimal conditions, the maximum net electrical power is achieved by Scenario I.
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