Rising summertime outdoor air humidity is leading to an increase in the dehumidification load required to maintain acceptable indoor environments. The objective of this study is to predict the dehumidification performance of a window-type liquid desiccant ventilation system by using a regression model which can be implemented in EnergyPlus. A prototype of the dehumidification module of the ventilation system proposed in a previous study was developed, and experiments were conducted under varying outdoor air temperature and humidity conditions to derive regression models. Based on the experimental results obtained under controlled operating conditions representative of residential ventilation applications, empirical regression models were developed to predict the outlet air conditions as functions of the inlet air temperature and humidity, in a form compatible with implementation in EnergyPlus. The developed models achieved high predictive accuracy, with coefficients of determination of R2 = 0.98 for outlet air humidity and R2 = 0.99 for outlet air temperature. Furthermore, the daily dehumidification performance of the system was evaluated for a residential building during the summer season in Seoul(122days). The results show that the system satisfied 100% or more of the daily required dehumidification load on approximately 49% of the total days, while providing an average of approximately 60% of the required dehumidification load on the days when the requirement was not fully met. These results indicate that the proposed system can make a practical contribution to mitigating ventilation-induced latent cooling loads in hot and humid environments.
Hong et al. (Tue,) studied this question.