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

• Published in: AIChE journal Vol. 70; no. 5
• Main Authors: Chen, Yuqiu, Ma, Sulei, Lei, Yang, Liang, Xiaodong, Liu, Xinyan, Kontogeorgis, Georgios M., Gani, Rafiqul
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.05.2024; American Institute of Chemical Engineers
• Subjects: Algorithms; Artificial neural networks; Binary mixtures; Coke oven gas; Coke ovens; Design; Design optimization; H2 recovery; ionic liquid mixtures; Ionic liquids; Learning algorithms; Machine learning; Modelling; Neural networks; property modeling; solvent tailoring; Solvents; Surface tension
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.18392

This 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.