1. Department of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran 1417466191, Iran 2. Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz 61963165, Iran 3. Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran 1417466191, Iran 4. University of Paris Ouest Nanterre La Defense, 50 rue Sevres, 92 410 Ville dAvray, France 5. Faculty of Mechanical & Energy Engineering, Shahid Beheshti University, A.C., Tehran 1417466191, Iran
Exergetic sustainability evaluation and optimization of an irreversible Brayton cycle performance
Mohammad H. AHMADI1(), Mohammad-Ali AHMADI2, Esmaeil ABOUKAZEMPOUR3, Lavinia GROSU4, Fathollah POURFAYAZ1, Mokhtar BIDI5
1. Department of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran 1417466191, Iran 2. Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz 61963165, Iran 3. Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran 1417466191, Iran 4. University of Paris Ouest Nanterre La Defense, 50 rue Sevres, 92 410 Ville dAvray, France 5. Faculty of Mechanical & Energy Engineering, Shahid Beheshti University, A.C., Tehran 1417466191, Iran
Owing to the energy demands and global warming issue, employing more effective power cycles has become a responsibility. This paper presents a thermodynamical study of an irreversible Brayton cycle with the aim of optimizing the performance of the Brayton cycle. Moreover, four different schemes in the process of multi-objective optimization were suggested, and the outcomes of each scheme are assessed separately. The power output, the concepts of entropy generation, the energy, the exergy output, and the exergy efficiencies for the irreversible Brayton cycle are considered in the analysis. In the first scheme, in order to maximize the exergy output, the ecological function and the ecological coefficient of performance, a multi-objective optimization algorithm (MOEA) is used. In the second scheme, three objective functions including the exergetic performance criteria, the ecological coefficient of performance, and the ecological function are maximized at the same time by employing MOEA. In the third scenario, in order to maximize the exergy output, the exergetic performance criteria and the ecological coefficient of performance, a MOEA is performed. In the last scheme, three objective functions containing the exergetic performance criteria, the ecological coefficient of performance, and the exergy-based ecological function are maximized at the same time by employing multi-objective optimization algorithms. All the strategies are implemented via multi-objective evolutionary algorithms based on the NSGAII method. Finally, to govern the final outcome in each scheme, three well-known decision makers were employed.
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