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

doi:10.1007/s40242-022-1452-z

高等学校化学研究 ›› 2022, Vol. 38 ›› Issue (2): 421-427.doi: 10.1007/s40242-022-1452-z

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

CAO Xiaohao^1,2, HE Yanjing^1,3, ZHANG Zhengqing^1,4, SUN Yuxiu^1,4, HAN Qi^1,4, GUO Yandong⁵, ZHONG Chongli^1,4

1. State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, P. R. China;
2. School of Material Science and Engineering, Tiangong University, Tianjin 300387, P. R. China;
3. School of Textile Science and Engineering, Tiangong University, Tianjin 300387, P. R. China;
4. School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, P. R. China;
5. College of Mathematics Science, Bohai University, Jinzhou 121013, P. R. China

收稿日期:2021-11-09 修回日期:2021-11-26 出版日期:2022-04-01 发布日期:2021-11-29
通讯作者: ZHANG Zhengqing, ZHONG Chongli E-mail:zhangzhengqing@tiangong.edu.cn;zhongchongli@tiangong.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (Nos.22038010, 21878229, 22108202， 22008179, 21978212, 22078024) and the Science and Technology Plans of Tianjin, China(Nos.19PTSYJC00020, 20ZYJDJC00110, 21ZYJDJC00040).

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

CAO Xiaohao^1,2, HE Yanjing^1,3, ZHANG Zhengqing^1,4, SUN Yuxiu^1,4, HAN Qi^1,4, GUO Yandong⁵, ZHONG Chongli^1,4

1. State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, P. R. China;
2. School of Material Science and Engineering, Tiangong University, Tianjin 300387, P. R. China;
3. School of Textile Science and Engineering, Tiangong University, Tianjin 300387, P. R. China;
4. School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, P. R. China;
5. College of Mathematics Science, Bohai University, Jinzhou 121013, P. R. China

Received:2021-11-09 Revised:2021-11-26 Online:2022-04-01 Published:2021-11-29
Contact: ZHANG Zhengqing, ZHONG Chongli E-mail:zhangzhengqing@tiangong.edu.cn;zhongchongli@tiangong.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Nos.22038010, 21878229, 22108202， 22008179, 21978212, 22078024) and the Science and Technology Plans of Tianjin, China(Nos.19PTSYJC00020, 20ZYJDJC00110, 21ZYJDJC00040).

摘要/Abstract

摘要： 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(R²>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/C₄H₆separation performance.

关键词: Covalent organic framework, Isobutene/1,3-butadiene separation, Molecular simulation, 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(R²>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/C₄H₆separation performance.

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

CAO Xiaohao, HE Yanjing, ZHANG Zhengqing, SUN Yuxiu, HAN Qi, GUO Yandong, ZHONG Chongli. Predicting of Covalent Organic Frameworks for Membrane-based Isobutene/1,3-Butadiene Separation: Combining Molecular Simulation and Machine Learning[J]. 高等学校化学研究, 2022, 38(2): 421-427.

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

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

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