Assessing Seasonal System Performance: Design and Simulation of a Heat Pump-Coupled Internally-Cooled Membrane-Based Liquid Desiccant Air-Conditioning System

The air conditioning (AC) market is dominated by vapor compression systems, which rely on inefficient overcooling for dehumidification. As a more efficient alternative, a heat pump-coupled, internally cooled, membrane-based liquid desiccant AC System has been proposed. However, detailed modeling of internally cooled membrane-based liquid desiccant dehumidifiers is computationally expensive and time-consuming, limiting the ability to evaluate system-level performance under long-term transient conditions. To overcome the barrier, this study developed simplified internally cooled dehumidifier models using Artificial Neural Networks (ANNs) and integrated them into a system simulation built in Modelica. The model was applied to evaluate system performance from May to September across multiple U.S. climate zones. The proposed configuration provides latent and sensible cooling simultaneously, with system performance compared against conventional vapor compression AC systems in terms of thermal comfort, system coefficient of performance (COP), and cooling capacity. Simulation results show that in Atlanta, GA, a representative hot and humid climate, the proposed system achieves a seasonal COP of 5.92, nearly 91 % higher than the conventional AC systems. Climate dependence was also evident: the system delivered up to 27.7 % higher COP in hot-humid versus hot-dry conditions. The work demonstrates that ANN-based dehumidifier models are a simple, accurate, and computationally efficient tool for seasonal, system-level transient simulations. The findings highlight the potential of heat pump–coupled liquid desiccant AC systems to significantly improve energy efficiency in diverse climates, offering a viable pathway toward next-generation sustainable cooling technologies. © © 2025. Published by Elsevier B.V.