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Estimation of biodiesel physical properties using local composition based models / Hamed Abedini Najafabadi in Industrial & engineering chemistry research, Vol. 51 N° 41 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 41 (Octobre 2012) . - pp. 13518-13526 Titre : Estimation of biodiesel physical properties using local composition based models Type de document : texte imprimé Auteurs : Hamed Abedini Najafabadi, Auteur ; Gholamreza Pazuki, Auteur ; Manouchehr Vossoughi, Auteur Année de publication : 2012 Article en page(s) : pp. 13518-13526 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Modeling  Physical  properties Résumé : In this study, the local composition based models such as the Wilson, the nonrandom two-liquid (NRTL), and the Wilson-NRF have been applied in correlation and estimation of density, viscosity, and surface tension of biodiesels. The thermodynamic models have been used in correlating the thermophysical properties for 215 experimental data points. These models have the interaction energy between each pair that is considered as adjustable parameters. To decrease the number of these adjustable parameters, it is assumed that the biodiesels are composed of two hypothetical components. The average absolute deviation (AADs) of the correlated density of biodiesels for the Wilson, the NRTL, and the Wilson-NRF models are 0.0141, 0.0136, and 0.0148, respectively. The AADs of the correlated viscosity of biodiesels for the Wilson, the NRTL, and the Wilson-NRF models are 0.638, 0.547, and 0.621, respectively. Also, the AAD of the correlated surface tension of biodiesels for the Wilson, the NRTL, and the Wilson-NRF models are 0.402, 0.392, and 0.479, respectively. Comparisons between the results of the models previously proposed in the literature with those obtained in the present study confirm the effectiveness of the local composition based models in estimating the physical properties of biodiesels. Among these models, the NRTL model can estimate physical properties of biodiesels the most accurately. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26493645 [article] Estimation of biodiesel physical properties using local composition based models [texte imprimé] / Hamed Abedini Najafabadi, Auteur ; Gholamreza Pazuki, Auteur ; Manouchehr Vossoughi, Auteur . - 2012 . - pp. 13518-13526. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 41 (Octobre 2012) . - pp. 13518-13526 Mots-clés : Modeling  Physical  properties Résumé : In this study, the local composition based models such as the Wilson, the nonrandom two-liquid (NRTL), and the Wilson-NRF have been applied in correlation and estimation of density, viscosity, and surface tension of biodiesels. The thermodynamic models have been used in correlating the thermophysical properties for 215 experimental data points. These models have the interaction energy between each pair that is considered as adjustable parameters. To decrease the number of these adjustable parameters, it is assumed that the biodiesels are composed of two hypothetical components. The average absolute deviation (AADs) of the correlated density of biodiesels for the Wilson, the NRTL, and the Wilson-NRF models are 0.0141, 0.0136, and 0.0148, respectively. The AADs of the correlated viscosity of biodiesels for the Wilson, the NRTL, and the Wilson-NRF models are 0.638, 0.547, and 0.621, respectively. Also, the AAD of the correlated surface tension of biodiesels for the Wilson, the NRTL, and the Wilson-NRF models are 0.402, 0.392, and 0.479, respectively. Comparisons between the results of the models previously proposed in the literature with those obtained in the present study confirm the effectiveness of the local composition based models in estimating the physical properties of biodiesels. Among these models, the NRTL model can estimate physical properties of biodiesels the most accurately. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26493645