Mechanics & Industry
Volume 17, Number 5, 2016
|Number of page(s)||15|
|Published online||10 June 2016|
Thermo-economic assessment of three-stage combined cycle power system using ammonia-water mixture
1 School of Mechanical Engineering,
College of Engineering, University of Tehran, Tehran, Iran
2 School of Engineering Science, School of Mechatronic System Engineering, Simon Fraser University, Vancouver, Canada
3 Scientific and Technological Research Council of Turkey, Marmara Research Center, Energy Institute, Kocaeli, Turkey
a Corresponding author:
Accepted: 22 October 2015
Thermo-economic modeling and multi-objective optimization studies are performed for a three-stage combined cycle system using ammonia water mixture as working fluid. This combined cycle plant is composed of three main subsystems, Brayton cycle, Rankine and Kalina cycles. Energy and exergy analyses and multi-objective optimization are included. In order to optimize the system, a multi-objective optimization method based on a fast and elitist non-dominated sorting genetic algorithm is applied to determine the best design parameters of the system. The two objective functions considered for the optimization purpose are the total cost rate of the system including equipment costs, and the second objective function is the system exergy efficiency. The total cost rate of the system is minimized while the cycle exergy efficiency is maximized using an evolutionary algorithm. In order to convey a deeper understanding and identify the necessary trade-offs within the optimized objectives in a multifaceted fashion, multi-objective optimizations are conducted in the study. Moreover, a closed form equation is derived to provide the relationship between the exergy efficiency and total cost rate. Finally, sensitivity analyses are performed to better understand the effects of various key design parameters on the total exergy destruction rate, exergy efficiency and total cost rate of the system.
Key words: Efficiency / exergy / three-stage cycle / energy / multi-objective optimization
© AFM, EDP Sciences 2016
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