Inductive modeling of physico-chemical properties: Flash point of alkanes

Mathieu, Didier

doi:10.1016/j.jhazmat.2010.03.081

Cited by 27 publications

(29 citation statements)

References 22 publications

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“…However, decreasing α below the alkane value α = 1/2 does not yield any significant improvement and would prevent the use of the previously optimized parameters for sp 3 carbon atoms. 17 Therefore, we keep α = 1/2 in the present study. Equation 3 predicts that FP increases with molecular size, as usually observed experimentally and anticipated from the fact that larger compounds require higher temperatures to vaporize.…”

Section: Present Model Equationmentioning

confidence: 99%

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Flash Points of Organosilicon Compounds: How Data for Alkanes Combined with Custom Additive Fragments Can Expedite the Development of Predictive Models

Mathieu

2012

Ind. Eng. Chem. Res.

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An especially simple approach to the evaluation of flash point (FP) from additive fragment contributions is outlined. Being based on a square root expression derived from the examination of n-alkanes data, it avoids the need for nonlinear fitting procedures and for trial-and-error optimization of the analytical relationship between FP and molecular descriptors. Furthermore, in spite of a specially small number of additive contributions, the method can be applied to most organic molecules. For organosilicon compounds, it exhibits some advantages compared to previously available procedures, while providing very similar performances, with an average absolute deviation from experiment close to 12 K, a determination coefficient R 2 = 0.89 and only one error >40 K.

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Section: Present Model Equationmentioning

confidence: 99%

“…19 First, the relationship between FP and the additive contributions is extrapolated from a previous model for n-alkanes that proved especially successful, 17 rather than optimized on a trialand-error basis. Second, molecules are split into additive fragments specifically tailored to account for intermolecular interactions.…”

Section: Introductionmentioning

confidence: 99%

Flash Points of Organosilicon Compounds: How Data for Alkanes Combined with Custom Additive Fragments Can Expedite the Development of Predictive Models

Mathieu

2012

Ind. Eng. Chem. Res.

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“…In addition, our model shows greater consistency than the group contribution model. In particular, the model in [31] is far less accurate for the several smaller molecules such as ethane and propane, as well as octacosane and triancontane, whose flash point is mispredicted by over 30 • C, while the accuracy of our model roughly consistent for all the data entries ( Figure 13).…”

Section: Flash Pointmentioning

confidence: 77%

“…Parity plot for the flash point. Our neural network model is compared to a model based on the group contribution method[31].…”

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confidence: 99%

Predicting physical properties of alkanes with neural networks

Santak

Conduit

2019

Fluid Phase Equilibria

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We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods.

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“…The application of this approach is limited and a considerable amount of work is needed to determine the structural group contributions for compounds other than hydrocarbons 3. Keshavarz17 and Matthieu18 used an even simpler approach to predict the flash point of alkanes by employing only the number of carbon atoms as a descriptor. Although these group contribution models gave good flash point prediction for alkanes, they cannot be used for other classes of compounds.…”

Section: Introductionmentioning

confidence: 99%

Illuminating Flash Point: Comprehensive Prediction Models

Ballard

Casey

et al. 2014

Molecular Informatics

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Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure - property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically diverse compounds, with flash point spanning from less than -130 °C to over 900 °C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials.

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Inductive modeling of physico-chemical properties: Flash point of alkanes

Cited by 27 publications

References 22 publications

Flash Points of Organosilicon Compounds: How Data for Alkanes Combined with Custom Additive Fragments Can Expedite the Development of Predictive Models

Flash Points of Organosilicon Compounds: How Data for Alkanes Combined with Custom Additive Fragments Can Expedite the Development of Predictive Models

Predicting physical properties of alkanes with neural networks

Illuminating Flash Point: Comprehensive Prediction Models

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