Thermodynamic analysis and optimization of adsorption-based atmospheric water harvesting

Abstract Adsorption-based atmospheric water harvesting (AWH) technologies can enable decentralized and distributed water supplies in arid and water scarce regions with limited infrastructure. Recent advances in novel adsorbents, such as metal-organic frameworks (MOFs) and advanced zeolites, with high sorption capacity at low humidity and facile regeneration, promise the development of efficient AWH technologies. However, a comprehensive thermodynamic analysis based on fundamental material properties to predict optimal operating parameters and system-level efficiency has not been pursued. In this work, we present a generalized theoretical framework to optimize the energetic performance of thermally-driven adsorption-based AWH systems using fundamental material properties, such as adsorption isotherms. Using example characteristics of recently reported MOFs (MOF-801, MOF-303, and Ni2Cl2BTDD) with step-wise adsorption isotherms, we present AWH system-level theoretical efficiencies of each MOF based on the First and Second Law of Thermodynamics. We show the impact of heat source temperature from realistically achievable low-grade heat sources (up to 100 °C) on the overall efficiency. We also present the concept of a cascaded system which operates two adsorbent beds in series, and by capturing the condensation heat of the first bed, an increase in the overall efficiency can be achieved. At ambient conditions with relative humidities (RHs) below 40%, which is typical of arid climates, we show theoretical thermal (thermal energy to water conversion) and Second Law efficiencies of 0.33 and 0.18 with MOF-801 and MOF-303, and 0.56 and 0.19 with Ni2Cl2BTDD, respectively. For the cascaded system, a thermal efficiency of 0.7 and Second Law efficiency of 0.23 can be achieved with Ni2Cl2BTDD, over an order of magnitude greater than state-of-the-art refrigeration systems. Our framework presented can identify optimal operating parameters, and enable system-level predictions using materials properties for AWH and other related applications, including thermal energy storage, dehumidification, and desalination.