Ionic liquid binary mixtures: Machine learning-assisted modeling, solvent tailoring, process design, and optimization

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Author(s)Chen, Yuqiu
Author(s)Ma, Sulei
Author(s)Lei, Yang
Author(s)Liang, Xiaodong
Author(s)Liu, Xinyan
Author(s)Kontogeorgis, Georgios M.
Author(s)Gani, Rafiqul
Date Accessioned2024-05-07T14:38:22Z
Date Available2024-05-07T14:38:22Z
Publication Date2024-04-15
DescriptionThis is the peer reviewed version of the following article: Chen Y, Ma S, Lei Y, et al. Ionic liquid binary mixtures: Machine learning-assisted modeling, solvent tailoring, process design, and optimization. AIChE J. 2024; 70(5):e18392. doi:10.1002/aic.18392, which has been published in final form at https://doi.org/10.1002/aic.18392. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.. © 2024 American Institute of Chemical Engineers. This article will be embargoed until 04/15/2025.
AbstractThis work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)-IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed ML-based GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML-based GC models are sequentially integrated into computer-aided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of IL-IL binary mixtures in practical applications.
SponsorThe authors gratefully acknowledge the financial support from the University of Delaware, the Technical University of Denmark, and the National Natural Science Foundation of China (21706198, 22208253).
CitationChen Y, Ma S, Lei Y, et al. Ionic liquid binary mixtures: Machine learning-assisted modeling, solvent tailoring, process design, and optimization. AIChE J. 2024; 70(5):e18392. doi:10.1002/aic.18392
ISSN1547-5905
URLhttps://udspace.udel.edu/handle/19716/34343
Languageen_US
PublisherAIChE Journal
KeywordsH2 recovery
Keywordsionic liquid mixtures
Keywordsmachine learning
Keywordsproperty modeling
Keywordssolvent tailoring
TitleIonic liquid binary mixtures: Machine learning-assisted modeling, solvent tailoring, process design, and optimization
TypeArticle
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