Mechanical design of plate-fin heat exchangers for industry using social learning chaotic based Kho-Kho optimization

Abstract

The success of a new mechanical device is very much dependent on its design. To attract users, product design should be better than the present by its design as well as performance within proper cost. Here, a mechanical model is carried out for optimal designing of the Plate-fin heat exchangers (PFHE) to minimize five objective functions such as total heat transfer rate, total weight, total mass flow rate, number of entropy generation unit, and total annual cost. In this paper, a novel Social Learning based Chaotic Kho-Kho (SL-C-KKO) optimization approach is proposed to design PFHE. Among social animals, social learning plays an important role in behaviour learning. It helps people to learn from other’s actions (behaviour) without incurring the price of specific trials and errors by avoiding the learning complexity. Chaos is simple to set up and avoids the need for a local trapping solution. The chaotic search initialization method is used in this research to initialize the population for the KKO. As a result, it takes significantly less time to converge and able to reach global optimality. Using the proposed SL-C-KKO, optimal value of total heat transfer rate, total weight, mass flow rates at the hot region, mass flow rate at the cold region, number of entropy generation and total annual cost are obtained as 1107.1 Watt, 22.7 kg, 1.84 Kg/s, 2.01 Kg/s, 0.1321, and 823 $ respectively. The obtained results are compared with some existing results.