Large-scale evaluation of cascaded adsorption heat pumps based on metal/covalent–organic frameworks

Abstract

Rising demand for suitable living environments and increasing building energy consumption are creating intense pressure on the development of sustainable climate control systems. Cascaded adsorption heat pumps (AHPs) consisting of a 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 unsatisfactory adsorption performance of adsorbents. Here we demonstrated a multiscale computational approach to assess the cooling performance of over three million cascaded AHPs based on novel nanoporous metal–organic frameworks (MOFs) and covalent–organic frameworks (COFs). This study demonstrated that MOFs and COFs are favorable for the HS and LS of cascaded AHPs, respectively, due to their unique adsorption characteristics. Structure–property analysis revealed that large-pore adsorbents (mostly COFs) exhibiting stepwise adsorption isotherms are more suitable for the COP_C of the LS, and small-pore adsorbents (mostly MOFs) exhibiting type I isotherms are beneficial for the COP_C of the HS, thus leading to the best performers consisting of COFs in the LS and MOFs in the HS. Such findings were also validated by experiments. Furthermore, decision tree (DT) analysis highlighted the dominant role of the overall working capacity in determining the cooling performance. We finally demonstrated the successful implementation of machine learning in speeding up the assessment of a vast number of cascaded AHPs by predicting the COP_C of any adsorbent pairs.