Multi-objective optimization tool of shell-and-tube heat exchangers using a modified teaching-learning-based optimization algorithm and a compact Bell-Delaware method

The multi-objective optimization of heat exchangers can provide a reliable solution for both improved efficiency and reduction in cost. A computer program for the thermal and hydraulic design optimization of shell-and-tube heat exchangers is developed. Its user-friendly graphic-user interface provides an excellent feature for the teaching, learning, and preliminary design of shell-and-tube heat exchangers. In the present work, teaching-learning-based optimization (TLBO) algorithm is updated and implemented for better feasibility of optimum design. To improve the accuracy of the thermal and fluid analysis, the compact Bell-Delaware method (BDM) is newly implemented. Also, the effect of fouling is considered. The developed program using the updated TLBO and the compact BDM is validated against practical heat exchanger cases. The impacts of input parameters on the performance prediction of BDM and number of designs on finding optimum design of TLBO are also tested.