Developing a global approach for determining the molar heat capacity of deep eutectic solvents. (January 2022)

• Record Type: Journal Article
• Authors: Bagherzadeh, Ali; Shahini, Nahal; Saber, Danial; Yousefi, Pouya; Seyed Alizadeh, Seyed Mehdi; Ahmadi, Sina; Tat Shahdost, Farzad
• Is Part Of: Measurement. Volume 188(2022)
• Publication Date: 2022-01
• Subjects: Molar heat capacity -- Deep eutectic solvent -- Laboratory-measurement -- Machine learning -- Least-squares support vector regression; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110630
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: Thermodynamic knowledge is included in thermal characterizing deep eutectic solvent. A universal model is developed for estimating molar Cp of deep eutectic solvents. The model predicts 503 experimental data from eight references with the AARD = 0.27% The LSSVR accuracy is much better than the existing correlations in the literature. Temperature linearly increase the molar heat capacity of all deep eutectic solvents. Abstract: Deep eutectic solvents (DES) are a new class of green solvents. Reliable characterization of DESs is a prerequisite for their successful applications. The molar heat capacity (Cp) is likely an essential thermal property often measured through expensive and time-consuming experimentations. Hence, it is necessary to derive an accurate model for Cp calculation from readily available features. This study introduces a universal computational approach for calculating the Cp of 26 different DESs as a function of temperature, acentric factor, and critical properties. Ranking investigations over four accuracy indices approve that the least-squares support vector regression with the Gaussian kernel function (LSSVR-G) is more reliable than thirteen intelligent and two regression-based models. The LSSVR-G estimates 503 experimental data points of DES molar heat capacity with an absolute average relative deviation (AARD%) of 0.27%. The results show that the LSSVR accuracy is better than the existing empirical correlations in the literature.