High-pressure phase equilibria for the carbon dioxide–2-pentanol and carbon dioxide–water–2-pentanol systems

Lee, Hyun-Song; Lee, Huen

doi:10.1016/s0378-3812(98)00349-5

Cited by 18 publications

(17 citation statements)

References 14 publications

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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%

“…Silva-Oliver et al [15] reported the VLE of for the CO 2 +1-pentanol and CO 2 +2-pentanol systems. Also, Lee et al [14] reported the VLE data for the binary mixture of CO 2 +2-pentanol. ElizaldeSolis et al [16] presented the high-pressure vapor-liquid equilibria of CO 2 +1-propanol, CO 2 +2-propanol and CO 2 +1-butanol binary systems.…”

Section: Proposed Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…Lee [14] and Oliver et al [15] reported the VLE data for the CO 2 +1-pentanol and CO 2 +2-pentanol systems and Elizalde-Solis et al [16] presented the high-pressure VLE data for CO 2 +1-propanol, CO 2 +2-propanol and CO 2 +1-butanol binary mixtures. However, their modeling approach does not give reliable VLE predictions at all ranges of pressures and temperatures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Proposed Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Section: Proposed Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…P38.7 bar = , 0.5724 ω= ) are those adopted by Lee and Lee (1998). The value of the i 1 κ parameter in the PRSV EOS was fitted using the procedure developed by Orbey and Sandler (1998) using vapor pressure data compiled by Boublík et al (1973).…”

Section: Parameter Fitting and Critical Point Calculationsmentioning

confidence: 99%

“…Jennings et al (1993) measured phase equilibria of carbon dioxide separately with ethanol, 1 -butanol, and 1 -pentanol. Lee and Lee (1998) measured the phase equilibria of the carbon dioxide + 2-pentanol mixture. More recently, GaliciaLuna and coworkers obtained phase equilibrium and critical point data for binary m ixtures of carbon dioxide and different alkanols.…”

Section: Introductionmentioning

confidence: 99%

Modeling the high-pressure behavior of binary mixtures of carbon dioxide+alkanols using an excess free energy mixing rule

Castier

Galicia‐Luna

Sandler

2004

Braz. J. Chem. Eng.

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-The behavior of binary mixtures of carbon dioxide+alkanol (1-butanol, 2-butanol, 1-pentanol, or 2-pentanol) at high pressures was modeled using the Stryjek-Vera version of the Peng-Robinson equation of state. Two mixing rules were compared for this purpose: the classical one-fluid van der Waals and the Wong-Sandler mixing rules. In the case of the latter mixing rule, the NRTL model for the excess Gibbs free energy was used. We observed that both mixing rules can reproduce the qualitative aspects of the critical diagrams of these binary mixtures. A comparison of the results shows that there is no clear superiority of one mixing rule over the other in terms of the ability to predict the critical behavior of these mixtures.

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Fluid Phase Equilibria of Binary Mixtures with Supercritical Solvents with in-situ Concentration Measurements by Raman Spectroscopy

Stratmann

Schweiger

2004

Supercritical Fluids as Solvents and Reaction Media

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High-pressure phase equilibria for the carbon dioxide–2-pentanol and carbon dioxide–water–2-pentanol systems

Cited by 18 publications

References 14 publications

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

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

Modeling the high-pressure behavior of binary mixtures of carbon dioxide+alkanols using an excess free energy mixing rule

Fluid Phase Equilibria of Binary Mixtures with Supercritical Solvents with in-situ Concentration Measurements by Raman Spectroscopy

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