Investigation of dehumidifier performance parameters using ANN-PSO algorithm

ABSTRACT In this investigation, a performance analysis of a liquid desiccant-based dehumidification system was conducted by integrating a particle swarm optimization (PSO) algorithm and an artificial neural network. Experimental data are collected through past studies on falling film towers for flat plate and cylindrical surfaces, covering a wide range of liquid desiccant and air operating conditions. The neural network is fine-tuned through the use of the PSO algorithm. This optimization aims to enhance the accuracy of predicting the moisture absorption rate, change of specific humidity and dehumidification effectiveness of a liquid LiCl (Lithium chlorite) desiccant system. The effectiveness is contingent on various working parameters, including mass flow rate of moist air, mass flow rate of liquid LiCl solution desiccant, inlet air temperature, and relative humidity of inlet air, inlet temperature LiCl solution desiccant and desiccant concentration. The present ANN-PSO algorithm predicts the changes of absolute specific humidity, moisture absorption rate of water vapour from air and effectiveness of the dehumidification system. The present model precisely predict the performance parameters with R2 = 0.9989.