| 초록 |
The separation of ethane and ethylene is considered industrially important, but the physical properties of them are very similar, making it difficult and expensive to separate. Recently, Metal Organic Frameworks (MOFs) are attracting attention because they can be used for the separation through adsorption under ambient conditions. In this study, about 5,000 virtual MOFs were generated to calculate selectivity, and machine learning and generic algorithms were accompanied to create MOFs with high ethane selectivity. Using our algorithm, MOFs with the selectivity value higher than 4 were obtained by Henry constant using GCMC simulation. Non-accessible Pore blocking was performed on the top structures and IAST selectivity higher than 3.5 is obtained among them through GCMC simulation. We have demonstrated that our algorithm is helpful for designing MOFs with specific properties. |
