Hybrid ZOA-SNN technique heat transfer enhancement of the heat exchanger

Abstract

This paper proposes a hybrid ZOA-SNN technique for Heat Transfer Enhancement of the Heat Exchanger. The proposed hybrid technique is the combined performance of both the Zebra Optimization algorithm (ZOA) and Spiking Neural Network. Commonly it is named as ZOA-SNN method. The proposed method’s main goals are to control temperature and optimize heat transfer (HT). The proposed technique was analyzed using an optimization technique to get a minimal pressure drop and maximum possible heat transfer efficiency for the heat exchanger design. By then, the proposed model is executed on the MATLAB work stage and the performance is calculated using the present procedures. The pressure drop for the proposed strategy is 225 pa. The temperature rise for the proposed method is 3.5 °C. When the temperature drops by 3.5 °C, the heat transfer rate is 4.5 (L/min). Better outcomes are shown by the proposed method in all approaches like Nonprofit Organization (NPO) Obstructive Sleep Apnea (OSA), Global Outstanding Assessment (GOA). From the result, it is concluded that the proposed approach-based temperature is lower and the heat transfer is maximized in contrast to current methods.