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 coefficient or both of them. The current targeting methods identify optimum points from both economic and thermodynamic views and capture a trade-off between two objectives. Optimizations using single objective functions are performed in order to investigate parameter behavior in two different 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 coefficient. Optimized heat transfer coefficient compared to its first 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-off cases, heat transfer coefficient 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.