Microchannel Membrane-based IoNanofluid Reactor with Machine-learning Optimization for High-density and Low-temperature Absorption Thermal Energy Storage

Description

The building sector accounts for 20–40% of the total energy consumption in different regions, and this proportion exceeds 90% in terms of electricity in Hong Kong. Energy saving and emission mitigation by utilizing renewable/waste energy have been widely encouraged, but it is difficult to match the timing and intensity of these intermittent energy sources with time-variable building loads. Thermal energy storage plays a significant role in building energy saving by balancing the supply-demand mismatch. Among the various thermal energy storage technologies, the absorption thermal energy storage (ATES) stands out due to its sound performance considering energy storage density, energy storage efficiency, and charging temperature. In addition, the stored energy can be discharged in the form of cooling, heating, or dehumidification, offering wider flexibility. All these merits can facilitate more effective utilization of renewable/waste energy for buildings.  However, the existing ATES systems have three major issues to be addressed for wider applications: (1) improve the reactor compactness for higher energy storage density, (2) lower the charging temperature to utilize the low-grade energy that otherwise is unusable, and (3) avoid the H2O/salt crystallization for higher reliability. Therefore, there is a growing need to develop novel ATES with minimized space requirement, reduced capital cost, increased energy efficiency, and eliminated crystallization risk. This proposal seeks to achieve these goals by developing a microchannel membrane-based reactor using ionanofluid (nanoparticle-dispersed ionic liquid) towards high-density, low-temperature, and crystallization-free ATES. The microchannel membrane-based reactor offers a high specific surface area and integrates the solution/refrigerant flows, which allows for very high compactness. The direct diffusion of water molecules through the membrane makes it possible to lower charging temperatures. The ionic liquids are promising absorbents to avoid crystallization due to their low melting temperatures. Finally, use of ionanofluids and geometry optimization further enhances the heat/mass transfer and energy storage performance. Based on numerical and experimental methods, the project objectives include: (1) model and develop prototype of the compact ATES that uses the novel reactor; (2) characterize and correlate the bidirectional desorption/condensation and absorption/evaporation processes in the novel reactor; (3) characterize the dynamic charging/discharging behaviors and energy storage performance of the novel ATES; and (4) use machine-learning multi-objective optimization to select reactor geometry that maximizes the energy storage performance. The proposed research is significant for the development of small-size, high-efficiency, and crystallization-free ATES, facilitating efficient and affordable low-carbon energy storage technologies in Hong Kong and all over the world.