Design And Performance Analysis of Novel Desiccant Coated Heat Exchanger for Indoor Air Quality Enhancement

Abstract

In the intensive care unit of hospitals, retaining the relative humidity at a comfortable level, removing air-borne pollutants or microbial bacteria, filtering ambient air, and eradicating the condensed water vapor is crucial. Therefore, a novel desiccant coated heat exchanger (DCHE) is designed using an artificial neural network-based artificial intelligence (ANN–AI) tool for fulfilling this purpose. In the present investigation, a quick prediction of the exit parameters of the DCHE is done using the ANN-AI tool. It has been found out that the ANN-AI tool predicts the exit parameters with the least error. The design parameters chosen for designing the DCHE are tube diameter, fin depth, desiccant layer thickness, and flow channel length. Silica gel is used as a desiccant-coated material. By choosing thermal effectiveness and moisture effectiveness as performance parameters, and air inlet specific humidity, air inlet temperature, cooling water temperature, liquid to air mass flow rate, and cycle time as inlet parameters, and air outlet temperature, air outlet specific humidity, cooling water outlet temperature as the exit parameters, the developed model has been validated with the experimental data reported in the literature and found reasonable agreement with a maximum possible error of ± 13 %. Further, the performance of the model is compared using four different error criteria. Moreover, optimal and design operating parameters of the DCHE are predicted for a particular operating/design range utilizing the ANN–AI tool.