Abstract
Most thermal/chemical industries are equipped with heat exchangers to enhance thermal efficiency. The performance of heat exchangers highly depends on design modifications in the tube side, such as the cross-sectional area, orientation, and baffle cut of the tube. However, these parameters do not exhibit a specific relation to determining the optimum design condition for shell and tube heat exchangers with a maximum heat transfer rate and reduced pressure drops. Accordingly, experimental and numerical simulations are performed for a heat exchanger with varying tube geometries. The heat exchanger considered in this investigation is a single-shell, multiple-pass device. A Generalized regression neural network (GRNN) is applied to generate a relation among the input and output process parameters for the experimental data sets. Then, an Artificial immune system (AIS) is used with GRNN to obtain optimized input parameters. Lastly, results are presented for the developed hybrid GRNN-AIS approach.
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