Solubility of Nonanoic (Pelargonic) Acid in Supercritical Carbon Dioxide

Sparks, Darrell; Estévez, L. Antonio; Hernández, Rafael; Barlow, Keith; French, Todd

doi:10.1021/je700465u

Cited by 39 publications

(28 citation statements)

References 12 publications

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“…In del Valle and Aguilera equation the variation of enthalpy of vaporization with temperature is H(T ) = R(a + 2d/T ). Adachi and Lu [35,36] proposed that the association number (referred to as Ä in the Chrastil equation) is the exponent of the density and shows the dependence of the solubility on the density. According to Adachi and Lu, if Chrastil equation fitting is not as Table 3 Solubility of (+)-catechin in SC-CO2 with 5 mol.% of co-solvent and the SC-CO2 density.…”

Section: Solubility Of Catechin In Sc-comentioning

confidence: 99%

Catechin sublimation pressure and solubility in supercritical carbon dioxide

Soetaredjo¹,

Ismadji²,

Yuliana³

et al. 2013

Fluid Phase Equilibria

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a b s t r a c tSolubility of (+)-catechin in SC-CO 2 was measured at several temperatures (313.15, 323.15, 333.15 and 343.15 K) and pressure in the range of 12-26 MPa using ethanol as the co-solvent (5 mol.%). The experimental data were used for the correlation of density dependent solute solubility parameters. The equations of Chrastil, del Valle and Aguilera, Adachi and Lu, González, and Méndez-Santiago and Tejawere fitted very well for the solubility of (+)-catechin. The rate of mass loss of (+)-catechin showed that its sublimation pressure is very low, an indication of the stability of the compound. Thermogravimetric analysis was used to determine the sublimation pressure.

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Section: Solubility Of Catechin In Sc-comentioning

confidence: 99%

Catechin sublimation pressure and solubility in supercritical carbon dioxide

Soetaredjo¹,

Ismadji²,

Yuliana³

et al. 2013

Fluid Phase Equilibria

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“…Adachi-Lu y = ρ A+Bρ+C(ρ 2 ) exp ( a T + b) [41] Table 3-3: List of the semi-empirical models investigated for dye solubility modeling.…”

Section: Analysis Of Density-based Semi-empirical Modelsmentioning

confidence: 99%

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Srinidhi

2021

Preprint

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The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

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“…The dominant purpose of present paper is development of accurate and simple models to forecast solubility of different acids in supercritical carbon dioxide. Due to this, the required actual data for training and testing phases of models were assembled from the reliable source existed in literature [22,28,29,[32][33][34][35]. This collection of data contains the 180 acid solubility data points in terms of pressure, temperature and different acid structure.…”

Section: Experimental Data Gatheringmentioning

confidence: 99%

Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

Bemani¹,

Baghban²,

Shamshirband³

et al. 2019

Preprint

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In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

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Solubility of Nonanoic (Pelargonic) Acid in Supercritical Carbon Dioxide

Cited by 39 publications

References 12 publications

Catechin sublimation pressure and solubility in supercritical carbon dioxide

Catechin sublimation pressure and solubility in supercritical carbon dioxide

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

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