Regression Models for Performance Prediction of Internally-Cooled Liquid Desiccant Dehumidifiers

Abstract

In this study, using response surface methodology and central composite design, regression models were developed relating 12 input factors to the supply air outlet humidity ratio and temperature of 4-fluid internally-cooled liquid desiccant dehumidifiers. The selected factors are supply air inlet temperature, supply air inlet humidity ratio, exhaust air inlet temperature, exhaust air inlet humidity ratio, liquid desiccant inlet temperature, liquid desiccant concentration, liquid desiccant flow rate, supply air mass flow rate, the ratio of exhaust to supply air mass flow rate, the thickness of the channel, the channel length, and the channel width of the dehumidifier. The designed experiments were performed using a numerical two-dimensional heat and mass transfer model of the liquid desiccant dehumidifier. The numerical model predicted the measured values of the supply air outlet humidity ratio within 6.7%. The regression model's predictions of the supply air outlet humidity ratio matched the numerical model's predictions and measured values within 4.5% and 7.9%, respectively. The results showed that the input factors with the most significant effect on the dehumidifying process in order of significance from high to low are as follows: supply air inlet humidity ratio, liquid desiccant concertation, length of channels, and width of channels. The developed regression models provide a straightforward means for performance prediction and optimization of internally-cooled liquid desiccant dehumidifiers.