Estimation of Heat Capacity of Ionic Liquids Using <i>S</i><sub>σ-profile</sub> Molecular Descriptors

Zhao, Yongsheng; Zeng, Shaojuan; Zhang, Shuai; Afzal, Raja Muhammad; Zhang, Xiangping

doi:10.1021/acs.iecr.5b03576

Cited by 35 publications

(27 citation statements)

References 31 publications

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“…The Meccano approach uses large functional groups (usually whole ions) while the Lego approach divides the structure of compounds into smaller functional groups, similar to those in traditional group contribution methods. Most of them are based on group contribution methods [12,14,17,19,23,24,33].…”

Section: Introductionmentioning

confidence: 99%

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Oster

Jacquemin

Hardacre

et al. 2018

The Journal of Chemical Thermodynamics

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Efficient, fast and accurate heat transfer units design is currently a 'hot topic' due to the demand for more approachable and high-performance-ability materials. This is usually performed by the prediction of physical properties coupled with sufficient structure searching (for example with genetic algorithm). Ionic liquids have been found to be prospective replacement materials in this case; however, the predictive capabilities of existing models still remain poor which affects their practical application significantly. It has also been observed that for some quaternary-phosphonium based carboxylate ILs, the models fail, particularly, for thermal conductivity and heat capacity predictions. The impact of electronic structure on the heat capacity was confirmed by DFT calculations, and this was also included in further refinement. The aim of this work is to assess the predictive capabilities of existing models for thermal conductivity and heat capacity, with further improvements based on more accurate investigated structure characterization (DFT) and reparameterization (group contribution methodology). The ILs chosen for the initial study are trihexyl(tetradecyl)phosphonium-based carboxylate derivatives.

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Section: Introductionmentioning

confidence: 99%

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Oster

Jacquemin

Hardacre

et al. 2018

The Journal of Chemical Thermodynamics

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“…The MLR method is by far the best-known BB model, with numerous applications to develop predictive models in various engineering tasks (see [29][30][31]). It uses approximation function of multiple variables which is linear according to the unknown parameters.…”

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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“…Literature review shows that there are few reports concerning prediction of heat capacity for ILs [26][27][28][29][30][31][32][33]. Preiss et al [28] proposed a two parameter model for estimation of heat capacity of ILs based on relation between molar volume and heat capacity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of heat capacities of ionic liquids using chemical structure based networks

Barati-Harooni

Najafi‐Marghmaleki

Mohammadi

2017

Journal of Molecular Liquids

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Estimation of Heat Capacity of Ionic Liquids Using S_σ-profile Molecular Descriptors

Cited by 35 publications

References 31 publications

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

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

Prediction of heat capacities of ionic liquids using chemical structure based networks

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Estimation of Heat Capacity of Ionic Liquids Using Sσ-profile Molecular Descriptors

Cited by 35 publications

References 31 publications

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

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

Prediction of heat capacities of ionic liquids using chemical structure based networks

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Estimation of Heat Capacity of Ionic Liquids Using S_σ-profile Molecular Descriptors