High-pressure vapor–liquid and vapor–liquid–liquid equilibria in the carbon dioxide+1-heptanol system

Secuianu, Catinca; Feroiu, Viorel; Geană, Dan

doi:10.1016/j.fluid.2008.06.020

Cited by 24 publications

(22 citation statements)

References 15 publications

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“…The procedure is the same as in our previous papers [3][4][5][6]12,17]. The entire internal loop of the apparatus including the equilibrium cell was rinsed several times with carbon dioxide.…”

Section: Central European Journal Of Chemistrymentioning

confidence: 99%

“…A semi-predictive approach for calculating phase equilibria based on minimum experimental data is presented. The Soave-Redlich-Kwong (SRK) cubic equation of state coupled with a G E -EOS mixing rule was used to model the complex phase behavior of carbon dioxide and alcohols series [3,12]. The Huron-Vidal (HV) mixing rules coupled with the reduced UNIQUAC excess Gibbs energy model [14,15] was chosen.…”

Section: Introductionmentioning

confidence: 99%

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Phase equilibria experiments and calculations for carbon dioxide + methanol binary system

2009

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Abstract:Vapor-liquid equilibria (VLE) data for the carbon dioxide + methanol system was measured at 293.15, 298.15, 310.15, and 323.15 K. Phase behavior measurements were made in a high-pressure visual cell with variable volume, based on the static-analytic method. The pressure range under investigation was between 4.8 and 95.1 bar. The Soave-Redlich-Kwong (SRK)-EOS coupled with Huron-Vidal (HV) mixing rules and a reduced UNIQUAC model, was used in a semi-predictive approach, in order to represent the phase behavior (critical curve, isothermal VLE) of the system. The topology of the phase behavior of the carbon dioxide + methanol system is satisfactory predicted with the SRK/HV-residual UNIQUAC model. © Versita Warsaw and Springer-Verlag Berlin Heidelberg.

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Section: Central European Journal Of Chemistrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phase equilibria experiments and calculations for carbon dioxide + methanol binary system

2009

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“…To predict the VLE of CO 2 -alkanols mixtures by the proposed ANN model, the input data to the network were taken from different sources [6,9,10,[14][15][16]. The equilibrium temperature, the CO 2 mole fraction in the liquid phase, the physical properties of each pure alkanols (critical pressure, critical temperature and acentric factor) were selected as input variables, while the equilibrium pressure and the CO 2 mole fraction in the vapor phase were selected as output variables.…”

Section: Proposed Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…Experimental data sets were chosen with temperature range from 313.15 to 443.46 K and the pressure range from 1.14 to 21.391 MPa respectively. The experimental data used for training and generalization of ANN model are those reported by Elizalde-Solis et al [6] for CO 2 +1-hexanol and CO 2 +1-heptanol systems and by Secuianu et al [9,10] for CO 2 +1-heptanol system. Silva-Oliver et al [15] reported the VLE of for the CO 2 +1-pentanol and CO 2 +2-pentanol systems.…”

Section: Proposed Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…Secuianu et al [9,10] used the Soave-RedlichKwong (SRK) EOS [11] coupled with Huron-Vidal (HV) [12] mixing rules and a reduced UNIQUAC model [13] to represent the complex phase behavior of CO 2 +1-heptanol system at different temperatures. According to their reported results, at the high temperatures of 411.99 and 431.54 K and low temperatures of 292.0 and 316 K the predicted equilibrium pressure and vapor phase CO 2 mole fraction are not accurate enough for process design.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the vapor-liquid equilibrium of carbon dioxide+alkanol systems by using an artificial neural network

ZareNezhad

Aminian

2011

Korean J. Chem. Eng.

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A multi-layer feed-forward artificial neural network has been presented for accurate prediction of the vapor liquid equilibrium (VLE) of CO 2 +alkanol mixtures. Different types of alkanols namely, 1-propaol, 2-propanol, 1-butanol, 1-pentanol, 2-pentanol, 1-hexanol and 1-heptanol, are used in this study. The proposed network is trained using the Levenberg-Marquardt back propagation algorithm, and the tan-sigmoid activation function is applied to calculate the output values of the neurons of the hidden layers. According to the network's training, validation and testing results, a six layer neural network is selected as the best architecture. The presented model is very accurate over wide ranges of experimental pressure and temperatures. Comparison of the suggested neural network model with the most important thermodynamic correlations shows that the proposed neuromorphic model outperforms the other available alternatives. The predicted equilibrium pressure and vapor phase CO 2 mole fraction are in good agreement with experimental data suggesting the accuracy of the proposed neural network model for process design.

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Beyond Henry’s law in the gas–liquid equilibrium

Carrero-Mantilla

2021

ChemTexts

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High-pressure vapor–liquid and vapor–liquid–liquid equilibria in the carbon dioxide+1-heptanol system

Cited by 24 publications

References 15 publications

Phase equilibria experiments and calculations for carbon dioxide + methanol binary system

Phase equilibria experiments and calculations for carbon dioxide + methanol binary system

Predicting the vapor-liquid equilibrium of carbon dioxide+alkanol systems by using an artificial neural network

Beyond Henry’s law in the gas–liquid equilibrium

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