2011
DOI: 10.1021/ie102246v
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Empirical Method for Representing the Flash-Point Temperature of Pure Compounds

Abstract: Publication Date (Web): March 25, 2011International audienceFlash point temperature is one of the most-widely used physical properties for the evaluation of the flammability hazard of combustible liquids. In this communication, an empirical method involving normal boiling-point temperature and number of carbon atoms of the pure compounds is presented for accurate representation of the flash-point temperature of pure substances. A total of 1471 pure compounds belonging to 77 chemical families were used to devel… Show more

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Cited by 52 publications
(61 citation statements)
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“…Gharagheizi F. et al 13 utilized ANN to predict the solubility of solid complexes in supercritical carbon dioxide. Eslamimanesh A. et al…”
Section: Ann Solubility Modelmentioning
confidence: 99%
“…Gharagheizi F. et al 13 utilized ANN to predict the solubility of solid complexes in supercritical carbon dioxide. Eslamimanesh A. et al…”
Section: Ann Solubility Modelmentioning
confidence: 99%
“…In addition, an initial knowledge about the flash point of mixtures can be regarded as an effective tool in designing the target blends. Several studies have been performed to find the relationship between structure and flash points of organic compounds [5][6][7][8][9][10][11][12] and fuels [13,14]. However, to the best of our knowledge, there is little investigation in the case of blends of lubricating oil [15,16].…”
Section: Introductionmentioning
confidence: 99%
“…Besides, the most traditional prediction methods have some shortcomings such as being highly inaccurate and inefficiency. [1,11,12] So far as solubility of CO 2 in polymers is concerned, it is affected by lots of factors such as temperature, pressure, and the interactions with the groups of the macromolecular chains and so on. [13][14][15] Due to the nonlinear relationship among these factors, traditional methods of prediction of CO 2 solubility in polymers are insufficient to meet the requirements.…”
Section: Introductionmentioning
confidence: 99%
“…The results indicate that the proposed model provides a better prediction capability. Mehdizadeh et al [18] and Gharagheizi et al [11] proposed ANN methods to predict the solubility of different compounds in supercritical CO 2 and indicated that ANN methods show better performance by comparing with the other methods. Currently, Radial basis function artificial neural networks (RBF ANN) have received considerable attention due to their potential to approximate nonlinear behavior.…”
Section: Introductionmentioning
confidence: 99%