Multi- objective optimization for recuperator of microturbine using genetic algorithm

In this paper, multi objective genetic algorithm is applied to optimize one type of recuperator in a 200 kW microturbine by considering two key parameters such as recuperator efficiency and cost. ε-NTU method is selected for the recuperator efficiency and pressure drop calculation. The recuperator total cost consists of capital cost, operational cost and maintenance cost. A plate-fin heat exchanger with offset strip fin for counter and cross flow arrangements is chosen for optimization. Fin pitch, fin height, fin offset length, cold stream flow length, non-flow stream length and hot stream flow length are considered as six design parameters. NSGA-II (Non-dominated Sorting Genetic Algorithm) is conducted to maximize recuperator efficiency and minimize its total cost. Results of the optimization are presented as a set of designs, called ‘Pareto-optimal solutions’. The results reveal the confliction between the two objective functions. It can be concluded that any change in the geometry of the recuperator increasing the efficiency also increases the total cost and vice versa. Finally, the optimal designs are compared together based on non-dominated sorting concept and the final optimal designs are obtained.