Emergy-based economic and environmental analysis and multi-objective optimization of a two-cascade solar gas turbine power plant

被引:29
|
作者
Babaelahi, Mojtaba [1 ]
Rafat, Ehsan [1 ]
Mofidipour, Ehsan [1 ]
机构
[1] Univ Qom, Dept Mech Engn, POB 3716146611, Qom, Iran
关键词
Solar; Gas turbine; Exergy; Emergy; Environment; Optimization; EXERGOENVIRONMENTAL ANALYSES; THERMOECONOMIC ANALYSIS; CYCLE; HYBRIDIZATION; EFFICIENCY; SYSTEMS; DESIGN; ENERGY; HEAT;
D O I
10.1016/j.spc.2019.06.002
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
One of the critical issues in the design and optimization of the power generation systems are the economic and environmental characteristics of these systems. Accordingly, in this paper, the new multi-step Emergo-Exergo-Economic and Emergo-Exergo-Environmental approach has been selected to examine the economic and environmental aspects and optimization of a new two-step solar-powered gas turbine cycle. In the first step, the power generation system is analyzed based on the first and second thermodynamic rules. And the corresponding exergy and energy parameters are determined at different points in the power cycle. In the next step, the weight and the price of all components are determined. And the amounts of emergy related to different equipment and mass flows (based on the conversion of the exergy unit to the solar energy joule (sej)) are calculated, and the monetary and ecological performance of the cycle are examined. Based on the governed results, the two most irreversible equipment with maximum exergy destruction and minimum emergy-based exergoenvironmental factor are selected for retrofit. Based on the sensitivity analysis, the effect of decision variables on the exergy and emergy parameters has been investigated. To achieve an optimum design, the multi-objective optimizations have been performed to maximize the exergetic efficiency, monetary and ecological performance using the particle swarm optimization algorithm. The Optimization results show that the exergetic, monetary and ecological performance of the cycle can be improved significantly with change in decision variables. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:165 / 177
页数:13
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