Molecular-based artificial neural networks for selecting deep eutectic solvents for the removal of contaminants from aqueous media
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Chemical Engineering Journal, 2023, 476, pp. 146429
- Issue Date:
- 2023-11-15
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S1385894723051604-main.pdf | Published version | 3.67 MB |
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Computational methods for predicting a solvent's performance in a given application, and for selecting an adequate solvent for that application, are becoming increasingly essential for separation processes. In this context, this article presents a first-of-a-kind machine learning tool based on guided molecular design of solvents to predict the performance of various solvent systems in the solvent extraction process. The tool was demonstrated herein for the extraction of aqueous boron, through the selection of neoteric solvents from a dataset that spans different types of solvents (molecular solvents, deep eutectic solvents (DESs) and ionic liquids). The model was developed by first obtaining the COSMO-RS-based molecular descriptors (σ-profiles) for each solvent system and using them as input parameters to an Artificial Neural Network (ANN), in addition to other operational parameters (e.g., pH, temperature, ion concentration, and A/O ratio), while extraction efficiency as the output. The results showed that the optimal ANN configuration (59–20-15–1) exhibited remarkable predictability for boron extraction with an R2 of 0.988 and 0.977 for the training and testing sets, respectively. The model was used to investigate different solvent systems of which six new DESs were successfully synthesized, experimentally tested, and characterized across different properties such as density, viscosity, and leachability. The combination of Decanol and 2,2,4- trimethyl-1,3-pentanediol exhibited appreciable properties and high experimental extraction efficiency of 97.22%. The experimentally validated model demonstrates the effectiveness of molecular-based descriptors and machine learning for predicting the extraction capabilities of solvents in aqueous media and allows further exploration of new solvent systems based on their extraction performance at different operational conditions. This proof-of-concept approach can be effectively adopted to predict the extraction behavior of different solvent systems towards target contaminants in aqueous environments, thereby supporting both the design of separation processes and solvent screening for future applications.
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