Design of shell-and-tube heat exchangers using multiobjective optimization

In this paper, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is presented to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. Nine decision variables were considered: tube layout pattern, number of tube passes, baffle spacing, baffle cut, tube-to-baffle diametrical clearance, shell-to-baffle diametrical clearance, tube length, tube outer diameter, and tube wall thickness. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) available in the multiobjective genetic algorithm module of MATLAB®. In order to verify the improvements in design that the method offers, two case studies from the open literature are presented. The results show that for both case studies, better values of the two objective functions can be obtained than the ones previously published. In addition, NSGA-II provides a Pareto front with a wider range of optimal decision variables. Ranking the Pareto-optimal solutions using a simple cost function shows that the costs for optimal design are lower than those reported in the literature for both case studies. The algorithm was also used to determine the impact of using continuous values of the tube length, diameter and thickness rather than using discrete standard industrial values to obtain the optimal heat transfer area and pumping power. Results show that using continuous values of these three decision variables only leads to marginally improved performance compared to discrete values.