High-throughput computational screening on potential adsorbents for thermal compression CO2 Brayton cycle

By employing heat rather than mechanical work to compress the working fluid, the thermal compression CO2 Brayton cycle (TC-CBC) has been considered as a promising pathway to the efficient utilization of low-grade thermal energy. However, finding reasonable adsorbents to efficiently realize the thermal compression process via the CO2 adsorption–desorption loop has become a significant challenge to the development of such an innovative system. To solve the dilemma, high-throughput computational screening based on grand canonical Monte Carlo (GCMC) simulations and machine learning (ML) have been conducted to identify promising adsorbents from 1625 metal–organic frameworks (MOFs) for the TC-CBC. Results demonstrate that the thermodynamic efficiency and output per unit mass adsorbent of the system with a low-temperature heat source at 393 K can reach up to 9.34% and 21.84 kJ kg−1, respectively. MOFs with large surface area, pore volume, porosity, and moderate pore size have exhibited high thermodynamic performances. In addition to the low-temperature heat source, a high-temperature heat source is also considered in the analysis. The elevation of the thermodynamic performance is observed to be dependent on the structural properties of MOFs. With a random forest algorithm, a rapid and accurate prediction of thermodynamic performances for the innovative cycle is achieved.