Numerical Analysis of Optimising Liquid Desiccant Dehumidification for Sustainable Building Cooling: A Data-Driven Method Using Response Surface Methodology

Abstract

Leveraging data-driven methods such as Response Surface Methodology (RSM) has considerable potential for sustainable building cooling via mitigating energy consumption and environmental impacts. This research focuses on using the RSM to improve liquid desiccant dehumidification for sustainable building cooling performance using a D-optimal design. Specifically, the research intends to investigate the actual influence of the inlet air conditions and desiccant concentration on the performance of liquid desiccant dehumidification systems, i.e., the moisture removal rates and dehumidifier efficiency. To systematically conduct this research, a set of experimental data gathered from the open literature is utilised. This includes a specific set of inlet parameters of air temperature (27–34.5 °C), ratio of air humidity (20.5–25 g/kg), and solution temperature (27.5–38.5 °C) as the independent variables. Also, the feedback variables include the moisture removal rates (MRR) and efficacy (ϵ). The associated results of the analysis of variation indicate that the ratio of air humidity has the greatest influence on the moisture removal rate. However, the solution temperature and the ratio of air humidity have the most influence on efficacy. In the event of response optimisation, the result at MRR and (ϵ) are 0.54 g/s and 0.50, respectively, with a minimum desirability of 0.992 and 1.