A predictive vapor-pressure equation

Velasco, S.; Román, F. L.; White, J. A.; Mulero, A.

doi:10.1016/j.jct.2008.01.011

Cited by 16 publications

(9 citation statements)

References 39 publications

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“…Several modifications of the equation have been proposed (Wagner, 1973;Reid et al, 1987;Poling et al, 2001;Xiang, 2001;Velasco et al, 2008). However, all the previous equations are a function of temperature.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vaporization Rate of Aqueous Chemical Gas Hydrate Inhibitors Using a Modeling Tool

Bahadori¹

2015

Petroleum Science and Technology

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Gas hydrate is a longstanding problem in natural gas industry. Hydrate inhibition using chemical inhibitors is the most widely used method to prevent gas hydrate formation, and they are costly chemicals that are introduced into the natural gas production flow streams. Hydrate inhibition utilizes injection of glycol or methanol into a process stream, where it combines with the condensed aqueous phase to lower the hydrate formation temperature at a given pressure. In this work, easy-to-use mathematical predictive tools are developed for the prediction of vapor pressure of aqueous chemical hydrate inhibitors (methanol, ethylene glycol, diethylene glycol, and triethylene glycol) as a function of temperature and hydrate inhibitor mass fraction in condensed aqueous phase. The proposed tools showed promising results with average absolute deviation being around 1.5%.

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Section: Introductionmentioning

confidence: 99%

Estimation of Vaporization Rate of Aqueous Chemical Gas Hydrate Inhibitors Using a Modeling Tool

Bahadori¹

2015

Petroleum Science and Technology

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“…Vapor-pressure equations try to provide the temperature dependence of the saturated pressure of a fluid along the (liquid + vapor) coexistence curve. Since the Clapeyron equation was proposed in 1834, there has been a plethora of vapor-pressure equations described for both correlating and predicting experimental data of pure fluids [5].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the absence and the limited range of vapor pressure experimental data in the literature, some researchers have used different correlations to estimate enthalpies and saturated vapor pressure [5][6][7][8][9][10][11][12][13].…”

mentioning

confidence: 99%

Prediction Of Saturated Vapor Pressures Using Non-linear Equations And Artificial Neural Network Approach

Honarmand¹,

Sanjari²,

Badihi³

2014

J. Math. Computer Sci.

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A new method to estimate vapor pressures for pure compounds using an artificial neural network (ANN) is presented. A reliable database including more than 12000 data point of vapor pressure for testing, training and validation of ANN is used. The designed neural network can predict the vapor pressure using temperature, critical temperature, and acentric factor as input, and reduced pressure as output with 0.211% average absolute relative deviation. 8450 data points for training, 1810 data points for validation, and 1810 data points for testing have been used to the network design and then results compared to data source from NIST Chemistry Web Book. The study shows that the proposed method represents an excellent alternative for the estimation of pure substance vapor pressures and can be used with confidence for any substances.

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“…Current methods used to predict temperature-dependent properties can be classified into "group contribution" methods, methods based on the "corresponding-states principle" (for reviews of these methods see, for example, Poling et al, [3], Godavarty et al, [4] and Velasco et al, [5]) and "asymptotic behavior" correlations (see, for example, Marano and Holder [6]). These methods rely on several other property values, such as normal boiling temperature (T b ), critical temperature (T C ), critical pressure (P C ), and acentric factor ( ).…”

Section: Introductionmentioning

confidence: 99%

Analysis and Refinement of the TRC-QSPR Method for Vapor Pressure Prediction

Paster¹

2011

TOTHERJ

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Abstract:Various aspects associated with the use of the TRC-QSPR method (Shacham et al., Ind. Eng. Chem. Res. 49, 900-912, 2010, Ref. [1]) for the prediction of vapor pressure are investigated using a test set of 12 compounds from the nalkane series. This test set is used to check the consistency of the parameter values of the Wagner and Riedel equations and the resulting vapor pressure values in the full range between the triple point and critical point. Inconsistency has been detected in the parameters of the commonly used version of the Riedel equation as well as the calculated vapor pressure values near the critical point, T R >0.9.Vapor pressures prediction studies are carried out for the cases of interpolation, short and long range extrapolation and using either the acentric factor ( ), or number of C atoms (n C ), or the VEA1 descriptor in the TRC-QSPR equation. It is concluded that the prediction error is the lowest and within the experimental error limits over the entire temperature range, using the Wagner's equation and within the TRC-QSPR framework. Replacing by n C or by the descriptor VEA1 increases the prediction error, however good prediction accuracy is retained in the regions where experimental data are available for the predictive compounds. It is demonstrated that reliable vapor pressure predictions can be obtained using only n C for characterization of the target compound.

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A predictive vapor-pressure equation

Cited by 16 publications

References 39 publications

Estimation of Vaporization Rate of Aqueous Chemical Gas Hydrate Inhibitors Using a Modeling Tool

Estimation of Vaporization Rate of Aqueous Chemical Gas Hydrate Inhibitors Using a Modeling Tool

Prediction Of Saturated Vapor Pressures Using Non-linear Equations And Artificial Neural Network Approach

Analysis and Refinement of the TRC-QSPR Method for Vapor Pressure Prediction

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