Large-scale Evaluation of Cascaded Adsorption Heat Pumps Based on Metal/Covalent-Organic Frameworks
Rising demand for living environment and the increasing building energy consumption are creating intense pressure on the develoment of sustainable climate control systems. Cascaded adsorption heat pumps (AHPs) consisting of low-temperature stage (LS) and high-temperature stage (HS) driven by industrial waste heat or renewable energy provide promising solutions. However, their applications are restricted by the low coefficient of performance (COP) mainly due to the unsatisfcatory adsorption perfromance of adsorbents. Here we demonstrated a multiscale computational approach to assess the cooling performance of over three million cascaded AHPs based on novel nanporous metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs). This study demonstrated that MOFs and COFs are favorable for HS and LS of cascasded AHPs, respectively, due to their unique adsorption characteristics. Strutcure-property analysis revealed that large-pore adsorbents (mostly COFs) exhibiting stepwise adsorption isotherms are more suitable for the COPC of LS, and small-pore adsorbents (mostly MOFs) exhibiting type I isotherms are beneficial for the COPC of HS, thus leading to the best performers consisting of COFs in LS and MOFs in HS. Such findings were also validated by experiments. Furthermore, decision tree (DT) analysis hilighted the dominant role of the overall working capacity in determining the cooling performance. We finally demonstrated the succesful implementation of machine learning in speeding up the assessment of a vast number of cascaded AHPs by predicting the COPC of any adsorbent pairs.