Metal-organic frameworks for atmospheric water extraction: Kinetic analysis and stochastic programming under climate variability

Abstract

Increasing demands for sustainable and distributed freshwater sources drive the exploration of water extraction from ambient air. This study presents a comprehensive computational approach for optimizing unit harvesting cost of the adsorption-based atmospheric water extraction (AWE) systems. There are three objectives (i) assessing the impact of climate variability: utilizing k-means clustering, utilizing climate data in different regions to explore the effects of ambient conditions in dry-hot (California), humid-hot (Florida), and dry-cold (Wyoming) regions, resulting in a preference for harvesting under humid-hot conditions. (ii) performing kinetic analysis: The derived kinetic model connects climate variability to operational time and regeneration temperature, critical process design variables. (iii) assessing adsorption materials: three materials (MIL-100 (Fe), MOF-303, and ZJNU-30) were assessed revealing the impact of variations in maximum capacity and isotherm shape on performance and cost. The optimization algorithm uses a two stage stochastic programming approach to account for climate variability and enables an optimization that balances the capital and operating costs across a range of temperature and humidity conditions.