<p> In this work, we proposed multi-scale screening, which employs both molecular and process-level models, to identify high-performing MOFs for energy-efficient separation of SF$_6$ from SF$_6$ and N$_2$ mixture. Grand canonical Monte Carlo (GCMC) simulations were combined with ideal adsorption process simulation to computationally screen 14,000 metal-organic frameworks (MOFs) for adsorptive separation of SF$_6$ \/ N$_2$. More than 150 high-performing MOFs were identified based on the results from GCMC simulations at the pressure and vacuum swing conditions, and subsequently evaluated using the ideal adsorption process simulation. High-performing MOFs selected for the VSA conditions are able to achieve the 90 \% target purity level of SF$_6$ but none of the selected MOFs for PSA conditions could. Cascade PSA configuration was proposed and adopted to improve the purity level of the separated SF$_6$. Cascade PSA configuration was also adopted to improve the purity. In the pump efficiency scenarios of 80, 20, and 10 \%, the VSA and cascade PSA cases were compared, which concluded 10 \% scenario prefers the PSA case whereas the VSA case is favored in the others. Top-performing MOFs identified from the multi-scale computational approach were found to be able to produce 90\% purity SF$_6$ with 0.10 - 0.4 and 0.5 - 1.4 MJ per kg of SF$_6$ for VSA and PSA, respectively.<br></p>
New correlations for predicting two-phase electrical submersible pump performance under downhole conditions using field data
