نوع مقاله : یادداشت فنی
نویسندگان
1 دانشکده مهندسی مکانیک، دانشگاه بوعلی سینا، همدان
2 گروه مکانیک، دانشگاه آزاد اسلامی واحد علوم و تحقیقات مرکزی، اراک
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
In the present study, a comprehensive thermodynamic modeling and optimization of CGAM problem is presented. The CGAM problem refers to a cogeneration plant which delivers 30 MW of electricity and 14kg/s of saturated steam at 20 bar. The installation consists of a gas turbine followed by an air preheater that uses part of the thermal energy of the gases leaving the turbine, and a heat-recovery steam generator in which the required steam is produced. This system is composed of air compressor, combustion chamber (CC), Air Preheater, Gas Turbine (GT), and a Heat Recovery Heat Exchanger. In this paper, at the first stage, each part of the system is modeled using thermodynamic laws; and next, with by applying economic functions, the optimization of this problem is performed. For optimization, objective functions includes the total cost rate of the system product. The environmental conditions are defined as T0 = 298.15K and P0 = 1.013 bar. The fuel for the total plant is natural gas (taken as methane) with a lower heating value (LHV) which is equal to 50000 kJ/kg. And then, solve the problem which is formulated as a set covering problem by Genetic algorithm and PSO algorithm is solved. In the end of this article, we compare these solving approaches (Genetic and PSO algorithms) to know which of them are working efficiently. The design parameters of this cycle are compressor pressure ratio $(r_{AC})$, compressor isentropic efficiency $(\eta_{AC})$, GT isentropic efficiency $(\eta_{GT})$ , CC inlet temperature $(T_3)$, and turbine inlet temperature $(T_4)$. After reviewing the results of the two algorithms, it is showed that both algorithms which to optimize the results are almost identical. However, the PSO algorithm is simple to implement and more flexible in programmingand convergence rate than compared to the capability of Genetic algorithm is capable. The results show that the PSO algorithm is more efficient than Genetic algorithm in solvinge CGAM problem.
کلیدواژهها [English]
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