A New Group-Contribution Method for the Estimation of Physical Properties of Hydrocarbons

Skander, N.; Chitour, C. E.

doi:10.2516/ogst:2002024

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…However, this is not the case in the literature where the estimation of acentric factor requires an estimation of critical properties. Hence, to carryout the comparison, we use relations (8) and (9) to compute the critical temperature and relations (10) and (11) to compute the critical pressure. More precisely, the obtained values of acentric factor from model (7) are compared against two approaches: the first ones use both Riazi-Daubert relations for computing critical temperature and pressure and the second one uses both Lee-Kesler relations.…”

Section: Acentric Factormentioning

confidence: 99%

“…Simmrock et al [1] surveyed 56 methods for estimating critical temperature, 55 for critical pressure and 54 for critical volume. Even if several accurate methods are reported, the characterization of the petroleum fractions still remains an active research area [2][3][4][5][6][7][8][9][10][11]. This is mainly due to the fact that a small error in critical property estimation can lead to a much higher error in thermo-physical property prediction, which can deteriorate the system operation efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Developing Correlations for Prediction of Petroleum Fraction Properties using Genetic Algorithms

Souahi

Kaabeche

2008

Oil & Gas Science and Technology - Rev. IFP

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Résumé -Développement de corrélations pour la prédiction des propriétés des fractions pétrolières par les algorithmes génétiques -Notre étude concerne la caractérisation des fractions pétrolières dont les propriétés thermodynamiques et physiques peuvent seulement être connues par une expérimentation lourde et coûteuse due à la multiplicité de leurs constituants. Après une introduction des éléments et des nouvelles tendances dans l'utilisation des techniques d'intelligence artificielle, cet article prouve que les algorithmes génétiques peuvent être appliqués à ce domaine du pétrole. Par conséquent, nous proposons une approche empirique pour estimer les propriétés critiques et le facteur acentrique des fractions pétrolières, basée sur leurs points d'ébullition et densité facilement accessibles. Les algorithmes génétiques nous fournissent aussi une forme appropriée de fonction pour la prédiction de ces propriétés. Des résultats très prometteurs sont obtenus et plusieurs perspectives méritant d'autres investigations sont soulignées. Abstract -Developing Correlations for Prediction of Petroleum Fraction Properties Using GeneticAlgorithms -This paper deals with the characterization of petroleum fractions whose thermo-physical behaviours can only be known through expensive measurement efforts due to the multiplicity of their constituents. After introducing the issue and new trends in the use of artificial intelligence techniques, this paper shows how genetic algorithms can be applied to this field. Hence, we propose an empirical approach for estimating petroleum fractions critical properties and acentric factor based on their boiling point and density that can be easily obtained. Genetic algorithms provide us with a proper function form for the prediction. Moreover, very promising results are obtained and several relevant issues that deserve further investigations are emphasized.

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Section: Acentric Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing Correlations for Prediction of Petroleum Fraction Properties using Genetic Algorithms

Souahi

Kaabeche

2008

Oil & Gas Science and Technology - Rev. IFP

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“…This limitation is observed when experiments are carried out at extreme conditions or when experimentation is very expensive and time consuming. For example, modeling of extraheavy oil fractions presents these circumstances especially in VLE simulation [12] because very few experimental data are available. So, the property estimation (e.g.…”

Section: Introductionmentioning

confidence: 99%

A QSPR strategy to select models with extrapolative ability – Critical temperatures of linear and aromatic hydrocarbons

Peraza

Rojas

Bolívar

et al. 2014

JCM

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This paper deals with the problem of evaluating the predictive and extrapolative ability of QSPR models by using the PaDEL-descriptor software. In this sense, the selections of training and external data sets were modified considering the molecular weight. Two criteria were used to evaluate the extapolative ability: high correlation factor (R 2 ) (criterion I) and positive ΔR 2 and high R 2 (criterion II). Based on internal and external validation, it is shown that criterion II has a better performance than criterion I. Other selection criteria were found by considering the maximum square correlation coefficient (Q 2 ext ) or the minimum standard deviation (σext) for the external set with high R 2 values. These facts are supported by a systematic variation of the correlation factor (VCF) and the variation of correlation coefficients (VCC), as analysis tools proposed in this article. The methodology was successfully applied to critical temperature (Tc) estimation of linear alkanes and aromatic compounds, considering extrapolation to the heaviest compounds. Descriptors obtained for studied cases, using the criterion II, are in some way in agreement with group contribution and QSPR methods from literature.

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“…Essentially, they proposed a general linear regression model that relates the contribution of the functional groups to several physical and thermodynamic properties such as: critical temperature, critical pressure, critical volume, melting point, normal boiling point, standard Gibbs energy, and standard enthalpy. Skander and Chitour (2002, 2003) proposed a different group contribution design to estimate some physical properties of hydrocarbons: boiling point, freezing point, and liquid density. They grouped experimental data into the following compound families: n ‐paraffins, i ‐paraffins, olefins, alcyns, naphthenes, and aromatics.…”

Section: Introductionmentioning

confidence: 99%

A neural network approach to predict activity coefficients

Ramirez-Beltran¹,

Vallés²,

Estévez³

et al. 2009

Can J Chem Eng

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Artificial neural networks (ANNs) and a group-contribution approach were used to develop an algorithm to predict activity coefficients for binary solutions. The Levenberg-Marquardt algorithm was used to train the ANN and to predict the parameters of the Margules equation. The ANN was trained using phase-equilibrium database from DECHEMA. The selected systems include alcohols, phenols, aldehydes, ketones, and ethers. The trim mean based on 20% data elimination was selected as the best representation of the Margules-equation parameters. The algorithm was validated with 121 VLE systems and results show that the ANN provides a relative improvement over the UNIFAC method.Des réseaux de neurones artificiels (RNA) et une méthode de contribution de groupe ontété utilisés pour créer un algorithme permettant de prédire les coefficients d'activité des solutions binaires. L'algorithme de Levenberg-Marquardt aété utilisé pour entraîner le RNA et pour prédire les paramètres de l'équation de Margules. Le RNA aété entraîné en utilisant la base de données du diagramme d'équilibre de DECHEMA. Les systèmes sélectionnés comprennent les alcools, les phénols, les acétaldéhydes, les cétones et leséthers. La moyenne basée sur l'élimination de 20% des données aété sélectionnée commeétant celle représentant le mieux les paramètres de l'équation de Margules. L'algorithme aété validé avec 121 systèmes d'ELV et les résultats indiquent que le RNA conduità une amélioration relative par rapport au modèle UNIFAC.

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A New Group-Contribution Method for the Estimation of Physical Properties of Hydrocarbons

Cited by 9 publications

References 3 publications

Developing Correlations for Prediction of Petroleum Fraction Properties using Genetic Algorithms

Developing Correlations for Prediction of Petroleum Fraction Properties using Genetic Algorithms

A QSPR strategy to select models with extrapolative ability – Critical temperatures of linear and aromatic hydrocarbons

A neural network approach to predict activity coefficients

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