Prediction of Heat Capacity of Ionic Liquids Based on COSMO-RS Sσ-profile

Zhao, Yongsheng; Zhang, Shuai; Zhang, Xiangping; Zhang, Suojiang

doi:10.1016/b978-0-444-63578-5.50037-2

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…The residual heat capacity, c p r , is an important thermochemical parameter of ILs for the design of processes involving heat transfer. c p r can only be estimated from PC-SAFT, as the COSMO-RS model does not allow direct estimation of this property unless, for example, an empirical QSPR is established that links c p and the σ -profiles . However, such approaches were not considered in this work.…”

Section: Resultsmentioning

confidence: 99%

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Predicting the Thermodynamics of Ionic Liquids: What to Expect from PC-SAFT and COSMO-RS?

Jiřiště

Klajmon

2022

J. Phys. Chem. B

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Two popular thermodynamic modeling frameworks, namely, the PC-SAFT equation of state and the COSMO-RS model, are benchmarked for their performance in predicting the thermodynamic properties of pure ionic liquids (ILs) and the solubility of CO2 in ILs. The ultimate goal is to provide an illustration of what to expect from these frameworks when applied to ILs in a purely predictive way with established parametrization approaches, since the literature generally lacks their mutual comparisons. Two different modeling approaches with respect to the description of the molecular structure of ILs are tested within both models: a cation–anion pair as (i) a single electroneutral supermolecule and (ii) a pair of separately modeled counterions (ion-based approach). In general, we illustrate that special attention should be paid when estimating unknown thermodynamic data of ILs even with these two progressive thermodynamic frameworks. For both PC-SAFT and COSMO-RS, the supermolecule approach generally yields better results for the vapor pressure and the vaporization enthalpy of pure ILs, while the ion-based approach is found to be more suitable for the solubility of CO2. In spite of some shortcomings, COSMO-RS with the supermolecule approach shows the best overall predictive capabilities for the studied properties. The ion-based strategy within both models has significant limitations in the case of the vaporization properties of ILs. In COSMO-RS, these limitations can, to a certain extent, be surpassed by additional quantum mechanical calculations of the ion pairing in the gas phase, while the ion-based PC-SAFT approach still needs a sophisticated improvement to be developed. As an initiating point, we explore one possible and simple route considering a high degree of cross associations between the counterions in the gas phase.

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Section: Resultsmentioning

confidence: 99%

“…c p r can only be estimated from PC-SAFT, as the COSMO-RS model does not allow direct estimation of this property unless, for example, an empirical QSPR is established that links c p and the σprofiles. 135 However, such approaches were not considered in this work.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Thermodynamics of Ionic Liquids: What to Expect from PC-SAFT and COSMO-RS?

Jiřiště

Klajmon

2022

J. Phys. Chem. B

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“…The application of QSPR models in predicting the physicochemical properties of ionic liquids has been reported extensively in the literature. Such examples are density, , viscosity, , surface tension, melting point, , thermal decomposition, − and heat capacity …”

Section: Introductionmentioning

confidence: 99%

“…Such examples are density, 53,54 viscosity, 55,56 surface tension, 57 melting point, 58,59 thermal decomposition, 60−63 and heat capacity. 64 The conductor-like screening model for real solvents (COSMO-RS) is a tool that has been used previously to design molecular descriptors by representing the molecule's surface with the surface polarity distribution (σ-profile) and their distribution areas (S σ-profile ). 65 The use of COSMO-RS-based molecular descriptors combined with the QSPR approach proved its applicability in predicting different properties.…”

Section: Introductionmentioning

confidence: 99%

Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Lemaoui

Hatab

Darwish

et al. 2021

ACS Sustainable Chem. Eng.

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The case of sustainable solvents is of great interest both academically and industrially. With research communities becoming more aware of the negative impacts of conventional organic solvents, a range of greener and more sustainable solvents have been developed to counter the harmful drawbacks associated with conventional solvents. Among these, eutectic solvents (ESs) attracted considerable attention for their "green" properties and have proven their usefulness as environmentally benign alternatives to classical solvents. Among the various desirable characteristics of ESs, pH is a key property with significant implications for the design and control of industrial-scale applications. However, selecting an ES with the required pH for a particular application is a challenging task, especially with extensive experimentally determined data being time consuming and expensive. Therefore, in this work, the pH of various ESs have been predicted via novel quantitative structure−property relationships (QSPR) models using two machine learning algorithms, a multiple linear regression (MLR) and an artificial neural network (ANN), with a set of molecular descriptors generated by COSMO-RS. A total of 648 experimental points for 41 chemically unique ESs prepared from 9 HBAs and 21 HBDs at different temperatures were utilized for sufficient data set representation. On the basis of the statistical analysis of the models, it can be concluded that both approaches can be utilized as powerful predictive tools in estimating the pH of new ESs with the ANN model having better predictive capabilities and the MLR model being more interpretable. These models inspire and stimulate the development of robust models to predict the properties of designer solvents from the drawn molecular structures, which will save time and resources.

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“…It also provides a measure of the uncertainty in its prediction. On the negative side, although in most cases the precision of MLR models is enough for engineering purposes, it is as a rule lower than that of the ANN models [32]. However, the main shortcoming of MLR models is that their quality depends on the validity of several restrictive assumptions, which rarely hold in their entirety in any practical application.…”

Section: Introductionmentioning

confidence: 99%

Regression Diagnostics with Predicted Residuals of Linear Model with Improved Singular Value Classification Applied to Forecast the Hydrodynamic Efficiency of Wave Energy Converters

Tenekedjiev

Abdussamie

et al. 2021

Applied Sciences

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In the preliminary stages of design of the oscillating water column (OWC) type of wave energy converters (WECs), we need a reliable cost- and time-effective method to predict the hydrodynamic efficiency as a function of the design parameters. One of the cheapest approaches is to create a multiple linear regression (MLR) model using an existing data set. The problem with this approach is that the reliability of the MLR predictions depend on the validity of the regression assumptions, which are either rarely tested or tested using sub-optimal procedures. We offer a series of novel methods for assumption diagnostics that we apply in our case study for MLR prediction of the hydrodynamics efficiency of OWC WECs. Namely, we propose: a novel procedure for reliable identification of the zero singular values of a matrix; a modified algorithm for stepwise regression; a modified algorithm to detect heteroskedasticity and identify statistically significant but practically insignificant heteroscedasticity in the original model; a novel test of the validity of the nullity assumption; a modified Jarque–Bera Monte Carlo error normality test. In our case study, the deviations from the assumptions of the classical normal linear regression model were fully diagnosed and dealt with. The newly proposed algorithms based on improved singular value decomposition (SVD) of the design matrix and on predicted residuals were successfully tested with a new family of goodness-of-fit measures. We empirically investigated the correct placement of an elaborate outlier detection procedure in the overall diagnostic sequence. As a result, we constructed a reliable MLR model to predict the hydrodynamic efficiency in the preliminary stages of design. MLR is a useful tool at the preliminary stages of design and can produce highly reliable and time-effective predictions of the OWC WEC performance provided that the constructing and diagnostic procedures are modified to reflect the latest advances in statistics. The main advantage of MLR models compared to other modern black box models is that their assumptions are known and can be tested in practice, which increases the reliability of the model predictions.

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Prediction of Heat Capacity of Ionic Liquids Based on COSMO-RS Sσ-profile

Cited by 10 publications

References 7 publications

Predicting the Thermodynamics of Ionic Liquids: What to Expect from PC-SAFT and COSMO-RS?

Predicting the Thermodynamics of Ionic Liquids: What to Expect from PC-SAFT and COSMO-RS?

Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Regression Diagnostics with Predicted Residuals of Linear Model with Improved Singular Value Classification Applied to Forecast the Hydrodynamic Efficiency of Wave Energy Converters

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