Predicting of Covalent Organic Frameworks for Membrane-based Isobutene/1,3-Butadiene Separation: Combining Molecular Simulation and Machine Learning

Abstract

Efficient separation of C4 olefins is of critical importance and a challenging task in petrochemical industry. Covalent organic frameworks(COFs) could be used as promising candidates for membrane-based isobutene/1,3-butadiene(i-C₄H₈/C₄H₆) separation. Owing to large amounts of COFs appearing, however, the rapid prediction of optimal COFs is imperative before experimental efforts. In this work, we combine molecular simulation and machine learning to study COF membranes for efficient isolation of i-C₄H₈ over C₄H₆. Using molecular simulation, four potential COF membranes, which possess both high membrane performance score (MPS) value and moderate membrane selectivity were screened out and the mechanism of membrane separation further revealed is an adsorption dominated process. Further, random forest(RF) model with high prediction accuracy(R ²>0.84) was obtained and used for elucidating key factors in controlling the membrane selectivity and i-C₄H₈ permeability. Ultimately, the optimal COF features were obtained through structure-performance relationship study. Our results may trigger experimental efforts to accelerate the design of novel COFs with better i-C₄H₈/C₄H₆ separation performance.

Keywords

Covalent organic framework / Isobutene/1,3-butadiene separation / Molecular simulation / Machine learning

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