@inproceedings{d1fdc9cdb0a8483e8caca9dba32220f8,
title = "Machine Learning-based Hybrid Process Design for the Recovery of Ionic Liquids",
abstract = "Recycling ionic liquids (ILs) from dilute aqueous solutions is essential for their applications in both labs and industries. In this work, an efficient hybrid process scheme that combines aqueous two-phase extraction (ATPE) and distillation operating at their highest efficiencies is proposed for the recovery of ILs from dilute aqueous solutions. To find high performance salting-out agents for ATPE, an optimal IL-based aqueous biphasic systems (ABS) design method is employed. In this optimal design method, a machine learning (ML)-based model, i.e., artificial neural network (ANN)-group contribution (GC) model, is applied to predict the phase equilibrium behaviours of IL-based ABS. As a proof of the concept, results of the recovery of two hydrophilic ILs from their aqueous solutions are presented.",
keywords = "IL recovery, Hybrid process schemes, ATPE, Machine learning, ABS",
author = "Yuqiu Chen and Xiaodong Liang and Georgios Kontogeorgis",
year = "2022",
doi = "10.1016/B978-0-323-85159-6.50024-5",
language = "English",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier",
pages = "145--150",
editor = "Yamashita, \{Yoshiyuki \} and Kano, \{Manabu \}",
booktitle = "Proceedings of the 14th International Symposium on Process Systems Engineering",
address = "United Kingdom",
note = "14th International Symposium on Process Systems Engineering (PSE 2021+) ; Conference date: 19-06-2022 Through 23-06-2022",
}