Revealing acetylene separation performances of anion-pillared MOFs by combining molecular simulations and machine learning

Abstract

Acetylene is a crucial chemical feedstock that can be efficiently purified from CH4 and CO2 through adsorption-based separation methods. Combining advantages of organic and inorganic chemistry, metal-organic frame-works (MOFs) provide high separation performances in adsorption processes. In this work, anion-pillared (AP) MOFs were computationally investigated for C2H2/CH4 and C2H2/CO2 separations using Grand Canonical Monte Carlo (GCMC) simulations. Results of molecular simulations were used to compute selectivity, working capacity and regenerability, which were then combined to identify the top adsorbents and their structural features for C2H2/CH4 and C2H2/CO2 separations. The best adsorbents were computed to have C2H2 selectivities, working capacities, regenerabilities of 25.5-30.6 (6.1-7.3), 5.6-6 (4.9-5.8) mol/kg, 81.3-83 % (81.2-85.5 %) for C2H2/ CH4 (C2H2/CO2) separation, respectively. We then developed machine learning (ML) models to accurately predict C2H2, CH4, and CO2 adsorption amounts in AP-MOFs for equimolar C2H2/CH4 and C2H2/CO2 mixtures by using pore-limiting diameter, surface area, isosteric heat of adsorption as the input features. ML-predicted gas adsorption amounts, separation performance metrics and adsorbent rankings were found to be in good agree-ment with those directly obtained from GCMC simulations. Therefore, ML models that we developed can be used to accurately and quickly screen large number of AP-MOFs and related materials to identify the top performing materials for C2H2/CH4 and C2H2/CO2 separations.