The efficiency of a vapor compression system is strongly influenced by the performance of the finned-tube heat exchangers it employs. Heat exchanger performance is strongly influenced by the refrigerant circuitry, i.e., the connection sequence of the tubes. This paper describes an evolutionary computation-based approach to designing an optimized refrigerant circuitry used in Intelligent System for Heat Exchanger Design (ISHED).

The technique used in ISHED employs two separate approaches to generate designs, the knowledge-based evolutionary computation module and the symbolic learning-based evolutionary computation module. The optimization example presented in this paper employed each module independently and used the combined approach to demonstrate the benefits of each module and the power of the combined module approach. The best circuitry designs determined through these optimization runs yielded substantial improvements over the original design; the symbolic learning and knowledge based modules returned circuitry designs that improved the heat exchanger capacity by 2.6 % and 4.8 % respectively, while the combined module approach resulted in a circuitry design that improved the capacity by 6.5 %.