Modeling the solubility of Alkyl Ketene Dimer in supercritical carbon dioxide: Peng-Robinson, group contribution methods, and effect of critical density on solubility predictions

Adenekan, Kolawole; Hutton-Prager, Brenda

doi:10.1016/j.fluid.2019.112415

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…Therefore, knowledge of critical temperature ( T c ), pressure ( P c ), density (ρ c ), and the acentric factor (ω) is required for many widely used EOSs, such as Peng-Robinson and Soave-Redlich-Kwong . Other than EOSs, various models use critical properties as inputs to predict physiochemical properties, such as diffusion coefficients, − surface tension, , and solubilities. − In addition, they are used to predict Lennard-Jones parameters, which are required to model transport and collisions in a reaction rate calculation. ,− It is clear that critical properties and acentric factors are widely used in a multitude of fields, and thus it is important to have accurate values for the same. However, obtaining these molecular properties through experiments is time-intensive and expensive and therefore, the development of computational prediction tools is necessary.…”

Section: Introductionmentioning

confidence: 99%

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning

Biswas

Chung

Ramírez

et al. 2023

J. Chem. Inf. Model.

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Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric factors are expensive and time intensive; therefore, we developed a machine learning (ML) model that can predict these molecular properties given the SMILES representation of a chemical species. We explored directed message passing neural network (D-MPNN) and graph attention network as ML architecture choices. Additionally, we investigated featurization with additional atomic and molecular features, multitask training, and pretraining using estimated data to optimize model performance. Our final model utilizes a D-MPNN layer to learn the molecular representation and is supplemented by Abraham parameters. A multitask training scheme was used to train a single model to predict all the critical properties and acentric factors along with boiling point, melting point, enthalpy of vaporization, and enthalpy of fusion. The model was evaluated on both random and scaffold splits where it shows state-of-the-art accuracies. The extensive data set of critical properties and acentric factors contains 1144 chemical compounds and is made available in the public domain together with the source code that can be used for further exploration.

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Section: Introductionmentioning

confidence: 99%

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning

Biswas

Chung

Ramírez

et al. 2023

J. Chem. Inf. Model.

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“…Therefore, the obvious and straightforward approach of modelling solubility of a solid solute in a SCF is the use of a thermodynamic approach based on equations of state (EoS). [23][24][25][26][27][28][29][30] This can be divided into three main classes: cubic EoS, complex non-cubic EoS and conductor-like screening model (COSMO) based on quantum mechanical calculation results to determine the interaction between molecules in a mixture, which can be used to predict the solubility of a solid solute in SCÀ CO 2 . The second approach is empirical or semi-empirical correlations [31][32][33][34][35][36] and the third is intelligent computer techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The solubility of a solid solute in SCF is a phase equilibrium problem. Therefore, the obvious and straightforward approach of modelling solubility of a solid solute in a SCF is the use of a thermodynamic approach based on equations of state (EoS) [23–30] . This can be divided into three main classes: cubic EoS, complex non‐cubic EoS and conductor‐like screening model (COSMO) based on quantum mechanical calculation results to determine the interaction between molecules in a mixture, which can be used to predict the solubility of a solid solute in SC−CO 2 .…”

Section: Introductionmentioning

confidence: 99%

QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide

Euldji

Si‐Moussa

Hamadache

et al. 2022

Molecular Informatics

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Quantitative structure–property relationship (QSPR) modeling was investigated to predict drug and drug‐like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug‐like compounds, accounting for 3971 experimental data points (EDPs), was collected and used for modelling the relationship between selected molecular descriptors and solubility fraction data achieved by a nonlinear approach (Artificial neural network, ANN) based on molecular descriptors. Experimental solubility data for a given drug were published as a function of temperature and pressure. In the present study, 11 significant PaDEL descriptors (AATS3v, MATS2e, GATS4c, GATS3v, GATS4e, GATS3 s, nBondsM, AVP‐0, SHBd, MLogP, and MLFER_S), the temperature and the pressure were statistically proved to be sufficient inputs. The architecture of the optimized model was found to be {13,10,1}. Several statistical metrics, including average absolute relative deviation (AARD=3.7748 %), root mean square error (RMSE=0.5162), coefficient of correlation (r=0.9761), coefficient of determination (R2=0.9528), and robustise (Q2=0.9528) were used to validate the obtained model. The model was also subjected to an external test by using 143 EDPs. Sensitivity analysis and domain of application were examined. The overall results confirmed that the optimized ANN‐QSPR model is suitable for the correlation and prediction of this property.

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“…Alkyl ketene dimer (AKD) is a sizing agent widely used to change the hydrophilic structure of cellulose in the paper manufacturing industry. [ 21 ] With the fact that use of wood‐based materials increased in recent years, the fields of application are gradually expanding. [ 25 ] The use of AKD as an internal bonding agent results in fiber surfaces being more hydrophobic by increasing the angle of connection between the penetration of fibers and fluids (matrix).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the control of the interface between the NF and the matrix is an important aspect in determination of the physical properties of the final composite material. [21] The high specific strength and stiffness of some natural fiber (NF) types have approximate equivalent properties that can be compared to the properties of GF. [20,22] In the literature, it has been reported that the specific modules of GF/PP composites and bast fiber/PP composites are in the same range.…”

Section: Introductionmentioning

confidence: 99%

Mechanical characteristics and hydrophobicity of alkyl ketene dimer compatibilized hybrid biopolymer composites

et al. 2021

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In this study, the effect of alkyl ketene dimer (AKD) on the mechanical and physical properties of natural fiber (NF) and glass fiber (GF) reinforced poly(lactic acid) (PLA) composites were investigated in detail. The hybrid PLA composites were manufactured by extrusion method followed by hot press molding. AKD was used to improve the hydrophobicity of hybrid PLA composites and compatibility of NF and PLA in the polymer matrix. The tensile and flexural tests, thickness swelling, water absorption, and contact angle measurements were accomplished for determining the performance of the hybrid PLA composites. The surface morphology of the hybrid PLA composites was also performed by scanning electron microscopy. The incorporation of AKD and NF into PLA matrix improved the mechanical strengths by 40% and above compared to those of samples without AKD. But, tensile and flexural moduli decreased as 7% and 14% of the samples with NF and AKD, respectively. Contact angle was higher for the hybrid PLA composites with AKD compared with the composites without AKD. However, the results revealed that in the presence of GF, AKD had no significant effect on overall properties of the hybrid PLA composites. In the light of the findings, AKD may have a potential usage as coupling agent in the NF filled PLA composites.

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Modeling the solubility of Alkyl Ketene Dimer in supercritical carbon dioxide: Peng-Robinson, group contribution methods, and effect of critical density on solubility predictions

Cited by 9 publications

References 48 publications

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning

QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide

Mechanical characteristics and hydrophobicity of alkyl ketene dimer compatibilized hybrid biopolymer composites

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