Journal article
Estimating the density of deep eutectic solvents applying supervised machine learning techniques
SCIENTIFIC REPORTS, v 12(1), 4954
23 Mar 2022
PMID: 35322084
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R-2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).
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Details
- Title
- Estimating the density of deep eutectic solvents applying supervised machine learning techniques
- Publication Details
- SCIENTIFIC REPORTS, v 12(1), 4954
- Publisher
- NATURE PORTFOLIO; BERLIN
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000772605500047
- Scopus ID
- 2-s2.0-85126879358
- Other Identifier
- 991021861307904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Chemical