Multi-objective optimization of an irreversible regenerative Brayton cycle using genetic algorithm

An irreversible regenerative Brayton cycle is thermodynamically optimized in the view of finite time thermodynamic (FTT) and multi-objective genetic algorithm (MOGA) approaches. Power output and thermal efficiency of the model are considered as dual objective functions. These two objectives for a given model are derivedusing FTT approach. Maximization of two objectives is done at the same time using MOGA. Five decision variables such as effectiveness of source-side heat exchanger, effectiveness of sink-side heat exchanger, effectiveness of regenerator-side heat exchanger, source temperature, and temperature of the working fluid are considered. Pareto optimal frontier between power output and thermal efficiency is obtained in MATLAB environment. The best optimal values of power output and thermal efficiency are selected from Pareto frontier using TOPSIS, LINMAP and Shannon Entropy decision making methods. Moreover, results obtained from three decision making methods are compared and best amongst them are selected. Finally, effect of various performance parameters on dual objectives are discussed and presented on graphs.