Development of Dehumidification Effectiveness Prediction Model for a Liquid Desiccant System based on Actual Operation Data

Abstract

In this study, through researches using simulation and experimental studies, dehumidification prediction model of the practical liquid desiccant system has been proposed. To propose the model, we designed and installed a pilot system of the actual liquid desiccant system, and conducted system performance test based on outdoor air conditions of Seoul in summer of 2013 and 2014. This research conducted statistical analysis based on the accumulated experiment data of 2 years, and proposed a linear equation that reflected contribution analysis between dehumidification effectiveness and operational parameters of operation and interactions of each parameter.

This study contains total of 5 steps of research approach and each step is as the following:

As step 1. Conducted literature analysis of desiccant system and literature searches related with the liquid desiccant system.

As step 2. Conducted the liquid desiccant system analysis based on simulation and studies related with dehumidification effectiveness prediction model.

As step 3. Built a pilot system of the liquid desiccant system and obtained system dehumidification effectiveness experimental data through a 2-year system operation.

As step 4. Analyzed accumulated experiment data using statistical analytic method, analyzed contribution between parameter and dehumidification effectiveness and proposed dehumidification prediction model of linear equation.

As step 5. Compared dehumidification effectiveness prediction values with prediction model as proposed in the existing research literatures in order to verify the validity of the proposed model.

The summary of this study is as follows.

1) This study identified the properties of liquid desiccant system through the simulation based on the theory ahead of empirical studies of the liquid desiccant system, and conducted studies related with the dehumidification effectiveness prediction model which is based on simulation. The proposed prediction model, in comparison with the existing literature study results, in the case of the liquid desiccant system that uses lithium chloride(LiCl) solution shows a maximum of 9% deviation and a 4% of deviation if lithium bromide(LiBr) was used.

2) According to the properties of the liquid desiccant system verified through theoretical studies, pilot system was installed and operated to experiment. Pilot system has a maximum dehumidification capability of 0.0123kg/kg and can process sensible cooling of 14Kw.

3) Various experiments were conducted based on outdoor air conditions in the summer of 2013 and 2014 in Korea after the system is installed. Korea’s summer climate is hot and humid and is an ideal condition to test the performance of system. In addition, statistical analysis was conducted for the experiment data accumulated for over 2 years.

4) A proposed 2FI linear equation model through statistical analysis has the advantage to analyze the performance of dehumidification relatively easy compare to the system model based on a limited theory proposed in the existing literature studies, and R2-value of the proposed prediction model is about 0.831, but is thought to be a bit low caused in the process of analyzing 8155 resulting values.

5) Prediction value comparison was conducted with the prediction models verified in the existing literature in order to verify the validity of the proposed model. The result confirmed similar trends mostly depending on the changes of system parameters, and verified that the contribution with the actual dehumidification effectiveness values by comparing with the existing literature models under actual system operation conditions.

This study identified the performance of dehumidification of liquid desiccant system through theoretical and empirical studies and proposed a dehumidification effectiveness prediction model that can predict the dehumidification effectiveness. Further experiments to obtain a wider application range for the dehumidification effectiveness prediction model proposed by the study will be required in the future. Model’s validity shall be verified under more diverse conditions through evaluating the dehumidification effectiveness under diverse ambient air conditions and to the changes according to additional parameters. The meaning of comparison between prediction values of the proposed model and prediction values of literature study models is significant, however, further research integrated experiments that can cover the range of parameters proposed in the existing studies, proposals with its verification will be required.