Spiral heat exchanger optimization using genetic algorithm

This paper investigates optimization methods based on Genetic Algorithms (GAs) for spiral heat exchangers. The purpose of designing heat exchanger depends on its application and could be total cost, heat transfer coecient or both of them. The current targeting methods identify optimum points from both economic and thermodynamic views, and capture a trade-o between two objectives. Optimizations, using single objective functions, are performed in order to investigate parameter behavior in two dierent applications of SHEs. Also, this work takes care of numerous geometric parameters in the presence of logical constraints. Multi-objective and weighted function optimizations, using genetic algorithm, are developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coecient. Optimized heat transfer coecient, compared to its rst value at basic design, had a 13% increase, and total cost in optimized case presents 50% reduction compared to the basic design. Also in trade-o cases, heat transfer coecient and total cost have been improved up to 60% increment and 20% reduction, respectively. Therefore, designing heat exchanger, using presented optimal methods in this research, are proposed as useful methods for designers, engineers and researchers.