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Multi-Objective Optimization of Gas Turbine Power Cycle
Authors: Mohsen Nikaein
Abstract:Because of importance of energy, optimization of power generation systems is necessary. Gas turbine cycles are suitable manner for fast power generation, but their efficiency is partly low. In order to achieving higher efficiencies, some propositions are preferred such as recovery of heat from exhaust gases in a regenerator, utilization of intercooler in a multistage compressor, steam injection to combustion chamber and etc. However thermodynamic optimization of gas turbine cycle, even with above components, is necessary. In this article multi-objective genetic algorithms are employed for Pareto approach optimization of Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective optimization a number of conflicting objective functions are to be optimized simultaneously. The important objective functions that have been considered for optimization are entropy generation of RIGT cycle (Ns) derives using Exergy Analysis and Gouy-Stodola theorem, thermal efficiency and the net output power of RIGT Cycle. These objectives are usually conflicting with each other. The design variables consist of thermodynamic parameters such as compressor pressure ratio (Rp), excess air in combustion (EA), turbine inlet temperature (TIT) and inlet air temperature (T0). At the first stage single objective optimization has been investigated and the method of Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used for multi-objective optimization. Optimization procedures are performed for two and three objective functions and the results are compared for RIGT Cycle. In order to investigate the optimal thermodynamic behavior of two objectives, different set, each including two objectives of output parameters, are considered individually. For each set Pareto front are depicted. The sets of selected decision variables based on this Pareto front, will cause the best possible combination of corresponding objective functions. There is no superiority for the points on the Pareto front figure, but they are superior to any other point. In the case of three objective optimization the results are given in tables.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062060Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2390
 S. Farahat, E. Khorasani Nejad, S. M. Hoseini Sarvari , Thermodynamic Optimization of Turboshaft Engine using Multi-Objective Genetic Algorithm, World Academy of Science, Engineering and Technology 56 2009, pp 782-788.
 N. Srinivas, K. Deb, "Multi-Objective Optimization using Nondominated Sorting in Genetic Algorithm," Evolutionary Computation, Vol. 2, No. 3, pp. 221-248, 1994.
 K. Atashkari, N. Nariman-zadeh, A. Pilchi, A. Jamali, "Thermodynamic Pareto Optimization of Turbojet Engine Using Multi-Objective Genetic Algorithm," International Journal of Thermal Sciences, 44, PP. 1061- 1071, 2005.
 A. Osyezka, "Multi criteria optimization for engineering design," in: J. S. Gero(Ed.), Design Optimization, Academic Press, New York, 1985, pp. 193-227.
 M.Nikaein,"Thermodynamic optimization of Brayton cycle using exergy analysis with genetic algorithm", IEHT Journal, Vol. 1, PP 63-68, 2009