Zahra Hajabdollahi - Department of Energy and Power Engineering, HUST University, Wuhan, China
Hassan Hajabdollahi - Department of Energy and Power Engineering, HUST University, Wuhan, China

In this paper a gas turbine power plant including air preheater (recuperator), heat recovery steam generator and air cooler system was modeled. Eight parameters were selected as the design variables. Fast and elitist non-dominated sorting genetic algorithm (NSGA-II) was applied (to maximize the exergy efficiency and to minimize the total cost rate) for the mentioned cogeneration system. The total cost rate is included the investment cost, operational cost and environmental impact penalty cost. The presented work included Energy, Exergy, Economy and Environmental (4E) analysis in which all system design parameters were optimally estimated. The optimization problem was developed for variable ambient temperature (VAT) during a year and their results were compared with constant ambient temperature (CAT) during a year. The results for a simple gas turbine showed that at the optimum point, the exergy efficiency reduced about 5.6 percent and total cost rate increased about 4.4 percent when the results for VAT was compared with CAT situation. When the system included a gas turbine with preheater, the total cost decreased and exergy efficiency increased for 39% and 30% respectively (in comparison with a simple gas turbine system). The above percentages were 39.5% and 29.8% respectively for variable ambient temperature. Furthermore when the system included a gas turbine with both preheater and inlet cooling, the total cost decreased and exergy efficiency increased 41% and 34% respectively (in comparison with a simple gas turbine system)..

Gas Turbine, CFD, Optimization, Genetic Algorithm, Thermodynamic Analysis