Optimization of Double Pipe Heat Exchanger Using Genetic Algorithm

Abstract

Swarm Intelligence algorithms like PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), Glow-worm swarm Optimization, etc. have been utilized by researchers for solving optimization problems. As compared to traditional mathematical techniques the evolutionary algorithms require less-specific parameters. Current researchers utilize the explorations capabilities of such algorithms in the respective fields of their research. Heat exchangers are widely used in process industries for transfer of heat between process fluids. However, being of complicated geometry and being controlled by a variety of geometric parameter, it becomes essential for the designer to choose amongst the best possible combination of geometric parameter by selecting a proper optimization technique. Traditional optimization techniques are time consuming and does not offer expected outcomes. The nature inspired algorithms are quite time tested and are used by several researchers for optimizing the problems of their respective field. Optimization of double pipe heat exchanger is done using genetic algorithm in the present work. As usual, the overall cost of any heat exchanger equipment is governed by the surface area. The lower the area lower is the manufacturing cost, whereas if the area if higher the cost is also high. Moreover, calculations regarding the pressure drop should also be considered. The inner and outer diameter and length of the pipe are the governing geometric parameters in this study. The final surface area is found to be 3.5 m 2 .