Helical baffles are employed increasingly in shell-and-tube heat exchangers for their significant advantages in reducing pressure drop, vibration, and fouling while maintaining a higher heat transfer performance. In order to make good use of helical baffles, serial improvements have been made by many researchers. In this paper create model of optimal NN for prediction analysis in parameters compared to existing works. This hidden layer and neuron optimization process fish asked KHO technique used. For more verification, KHO is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is compared with that of various algorithms representative of the state-of-the-art in the area. The results of different algorithms are breaking down and stood out from comparative systems, and the finest results rising out of them are discovered by standing out the results from least MSE values. From the results our proposed method achieves minimum MSE compared to existing works and maximum prediction accuracy in optimization model.
heat transfer, optimization, fish, Neural Network, hidden layer and neuron, energy and efficiency
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