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ORIGINAL RESEARCH article

Front. Chem.
Sec. Theoretical and Computational Chemistry
Volume 12 - 2024 | doi: 10.3389/fchem.2024.1480468

Predicting the solubility of CO 2 and N 2 in ionic liquids based on COMSO-RS and machine learning

Provisionally accepted
Hongling Qin Hongling Qin 1,2,3,4*Ke Wang Ke Wang 2,3,4,5,6*Xifei Ma Xifei Ma 2,3,4,7Fangfang Li Fangfang Li 1*Yanrong Liu Yanrong Liu 2,3,4,5,6,7*Xiaoyan Ji Xiaoyan Ji 1*
  • 1 Luleå University of Technology, Luleå, Sweden
  • 2 Key Laboratory of Green Process and Engineering, Institute of Process Engineering (CAS), Beijing, Beijing Municipality, China
  • 3 State Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering (CAS), Beijing, China
  • 4 Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering (CAS), Beijing, China
  • 5 Longzihu New Energy Laboratory, Zhengzhou Institute of Emerging Industrial Technology, Zhengzhou, Henan Province, China
  • 6 Henan University, Kaifeng, Henan Province, China
  • 7 School of Chemical Engineering, University of the Chinese Academy of Sciences, Beijing, China

The final, formatted version of the article will be published soon.

    As ionic liquids (ILs) continue to be prepared, there is a growing need to develop theoretical methods for predicting the properties of ILs, such as gas solubility. In this work, different strategies were employed to obtain the solubility of CO2 and N2, where a conductor-like screening model for real solvents (COSMO-RS) was used as the basis. First, experimental data on the solubility of CO2 and N2 in ILs were collected. Then, the solubility of CO2 and N2 in ILs was predicted using COSMO-RS based on the structures of cations, anions, and gases. To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. The results show that the COSMO-RS with correction can significantly improve the prediction of CO2 solubility, and the corresponding average absolute relative deviation (AARD) is decreased from 43.4% to 11.9%. In contrast, such an option cannot improve that of the N2 dataset. Instead, the results obtained from coupling machine learning algorithms with the COSMO-RS model agree well with the experimental results, with an AARD of 0.94% for the solubility of CO2 and an average absolute deviation (AAD) of 0.15% for the solubility of N2.

    Keywords: Ionic Liquid, CO2 solubility, N2 solubility, COSMO-RS, machine learning

    Received: 16 Aug 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Qin, Wang, Ma, Li, Liu and Ji. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hongling Qin, Luleå University of Technology, Luleå, Sweden
    Ke Wang, Key Laboratory of Green Process and Engineering, Institute of Process Engineering (CAS), Beijing, 100190, Beijing Municipality, China
    Fangfang Li, Luleå University of Technology, Luleå, Sweden
    Yanrong Liu, Key Laboratory of Green Process and Engineering, Institute of Process Engineering (CAS), Beijing, 100190, Beijing Municipality, China
    Xiaoyan Ji, Luleå University of Technology, Luleå, Sweden

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.