Octane numbers (ONs) of hydrocarbons: a QSPR study using optimal topological indices for the topological equivalents of the ONs

Ryzhov, A. N.; Bavykin, V. M.; Myshenkova, T. N.; Лапидус, А. Л.

doi:10.1007/s11172-007-0262-2

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…The neural network inputs can be molecular descriptors [20][21][22], functional groups [23,24], NMR spectroscopy [25]. Topological indices are developed to deal with complex physicochemical properties which incorporate the branching degree, shape and size of molecules [26][27][28]…”

Section: Introductionmentioning

confidence: 99%

Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures

Herreros

Tsolakis

et al. 2020

Fuel

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Section: Introductionmentioning

confidence: 99%

Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures

Herreros

Tsolakis

et al. 2020

Fuel

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“…Experimentally determined octane numbers of the selected hydrocarbons were quoted from Russian Chemical Bulletin [18]. The data sets contain 65 molecules which are divided into two training and validation sets.…”

Section: Methodsmentioning

confidence: 99%

QSPR Models for Octane Number Prediction

Al‐Fahemi

Albis

Gad

2014

Journal of Theoretical Chemistry

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Quantitative structure-property relationship (QSPR) is performed as a means to predict octane number of hydrocarbons via correlating properties to parameters calculated from molecular structure; such parameters are molecular mass , hydration energy , boiling point , octanol/water distribution coefficient log , molar refractivity , critical pressure , critical volume , and critical temperature . Principal component analysis (PCA) and multiple linear regression technique (MLR) were performed to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The results of PCA explain the interrelationships between octane number and different variables. Correlation coefficients were calculated using M.S. Excel to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The data set was split into training of 40 hydrocarbons and validation set of 25 hydrocarbons. The linear relationship between the selected descriptors and the octane number has coefficient of determination ( 2 = 0.932), statistical significance ( = 53.21), and standard errors ( = 7.7). The obtained QSPR model was applied on the validation set of octane number for hydrocarbons giving 2 = 0.942 and = 6.328.

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“…Ryzhov et al [30] used optimal topological indices for studying the octane numbers corresponding to 68 alkanes that have not been characterised experimentally, thus improving the results obtained by Smolenskii et al [33] that introduced a new QSPR method based on transition numerical values to topological equivalents of physicochemical properties of alkanes.…”

Section: Other Related Developments On Octane Numbermentioning

confidence: 99%

Development of a QSPR model for predicting octane number of alkanes using SD topological index

BERINDE¹,

Butean²,

Dippong³

2016

CMI

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The octane number (ON, MON and PON) for the molecular structures of 18 octane isomers have been correlated using the quantitative structureproperty relationship (QSPR) method, with topological index SD. For single parameter correlation the index SD shows poor results (RON, r = 0.406; MON, r = 0.490; PON, r = 0.448), whereas for two-parameter correlation almost any combination among the above DC was found to give relatively high r value. The best correlation coefficients are as follows: for RON, r = 0.993; MON, r = 0.968; PON, r = 0.985. For RON, the best model obtained by our regression analysis is RON = −227.218 + 7.63 ∗ SD − 37.111 ∗ DC , with r = 0.993, s = 4.8, F = 534

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Octane numbers (ONs) of hydrocarbons: a QSPR study using optimal topological indices for the topological equivalents of the ONs

Cited by 12 publications

References 7 publications

Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures

Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures

QSPR Models for Octane Number Prediction

Development of a QSPR model for predicting octane number of alkanes using SD topological index

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