Statistical analysis of the liquid desiccant system operation parameters in hot and humid climatesopen access
- Authors
- Park, Joon-Young; Cho, Sang-Hyeon; Jeong, Jae Weon
- Issue Date
- May-2016
- Publisher
- REHVA
- Keywords
- Liquid desiccant system (LD); Statistical analysis; Response surface methodology (RSM); Prediction model
- Citation
- CLIMA 2016 - proceedings of the 12th REHVA World Congress, v.9, pp.1 - 10
- Indexed
- OTHER
- Journal Title
- CLIMA 2016 - proceedings of the 12th REHVA World Congress
- Volume
- 9
- Start Page
- 1
- End Page
- 10
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/154563
- Abstract
- The aim of this research is to propose a simplified prediction model for liquid desiccant system performance in hot and humid climates. The liquid desiccant pilot system used in this research was designed 2000m3/h process air and 35kw aircooled chiller provides cooling water at any given temperature. The lithiumchloride (LiCl) is used as the liquid desiccant solution. (The experiment data is collected from the liquid desiccant pilot system operation.) The dehumidification performance test is conducted during the summer in Seoul, South Korea when the outdoor air (i.e. process air) is hot and humid. The liquid desiccant system performance data was statistically analyzed using the response surface methodology (RSM). The impact of each operation parameters on the liquid desiccant system performance were estimated by analysis of variance provided from Design expert 9.0 tool. After analyzing process, the simplified linear prediction model was derived as a function of six operation parameters which have significant impact on the system performance (e.g. air mass flow rate, solution mass flow rate, ambient air dry-bulb temperature, ambient air humidity ratio, solution inlet temperature and solution concentration). Finally, to verify the reliability of the proposed model, a
comparison of the response values predicted by the proposed model and the experimental data is conducted. The proposed model predictions are within 10% of the experimental values.
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