Molecular simulations and deep neural networks-based interpretable machine learning modelling of reverse adsorptive MOFs for ethane/ethylene separation

The thermal decomposition of ethane (C2H6) and the steam cracking of fossil fuels are the main sources of ethylene (C2H4). However, it usually contains 5%-9% of C2H6 residue, which must be reduced to ensure its utilization during polymerization. C2H6 and C2H4 have comparable kinetic diameters and boiling points (C2H6: 4.44, 184.55 K; C2H4: 4.16, 169.42 K), which makes the separation process very difficult. This contribution employs a methodology that integrates machine learning (ML) with Monte Carlo simulations to evaluate the ddmof database to develop a predictive model for separating ethane (C2H6) and ethylene (C2H4). The ML model's input is the metal-organic frameworks (MOFs) chemical and structural descriptors. The grand canonical Monte Carlo (GCMC) simulations in RASPA software were carried out to calculate the equilibrium adsorption of ethane and ethylene. Different ML models such as random forest, decision tree, and deep neural network models have been tested to estimate the selectivity and ethane uptake from the MOF data being generated. Interpretable ML model using SHapley Additive exPlanations (SHAP) is developed for the better understanding of the impact of the parameters on selectivity and ethane uptake. A user-friendly graphical user interface (GUI) is presented, allowing users to predict the ethane uptake and selectivity of MOFs simply by entering the values of chemical and structural descriptors.