2008
DOI: 10.1007/s11172-008-0073-0
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Cetane numbers of hydrocarbons: calculations using optimal topological indices

Abstract: The QSPR problem for cetane numbers of hydrocarbons, a key property of oil fuels, was considered for the first time. Based on the approach developed, calculations have been carried out and relations derived for prediction of the cetane numbers of alkanes and cycloalkanes. Results of cetane number calculations for 180 unknown and non studied hydrocarbons are presented.Relationships between certain physicochemical prop erties of organic compounds and the structures of their molecules can successfully be modeled … Show more

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Cited by 28 publications
(33 citation statements)
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“…For these molecules predictive models are required that enable rapid estimation of fuel ignition quality. 4,13 In the past decades, several models have been developed to predict (D)CN [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and RON/MON 12,16,[29][30][31][32] of (oxygenated) hydrocarbons by utilizing quantitative structureproperty relationship (QSPR) modeling. In QSPR, the modeling process can be broken down into two steps: First, QSPR models introduce molecular descriptors D = [d 1 , d 2 , ..., d n ] T that depend on the structure of a molecule m. Second, a regression model F (D) : D →p is fitted that predicts a propertyp as a function of D. 33 The regression model is either linear or nonlinear, depending on the QSPR.…”
Section: Introductionmentioning
confidence: 99%
“…For these molecules predictive models are required that enable rapid estimation of fuel ignition quality. 4,13 In the past decades, several models have been developed to predict (D)CN [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and RON/MON 12,16,[29][30][31][32] of (oxygenated) hydrocarbons by utilizing quantitative structureproperty relationship (QSPR) modeling. In QSPR, the modeling process can be broken down into two steps: First, QSPR models introduce molecular descriptors D = [d 1 , d 2 , ..., d n ] T that depend on the structure of a molecule m. Second, a regression model F (D) : D →p is fitted that predicts a propertyp as a function of D. 33 The regression model is either linear or nonlinear, depending on the QSPR.…”
Section: Introductionmentioning
confidence: 99%
“…Such methods include consensus modeling, where linear and nonlinear models are employed in parallel to obtain an averaged predicted CN value -these models can predict CN for a variety of molecular classes with a blind prediction root-mean-squared error of 6.5 [20], however multiple linear models are surpassed by the accuracy of ANNs when fatty acid methyl esters are used to predict CN [21]. Many methods rely on quantitative structure-property relation-Pre-Print Figure 1: Workflow diagram illustrating the experimental procedure utilized by the present work ship (QSPR) descriptors, which are numerical measurements of an assortment of physical and chemical properties relating to molecules, and have proven to be successful when applied to predicting the CN of pure hydrocarbons [22] and branched paraffins [23]. Additionally, ANNs have been applied to predicting YSI using QSPR descriptors as training data and obtained 95% confidence in test set prediction accuracy (r-squared value of predictions for compounds not used in ANN training) [24].…”
Section: Predicting Cetane Number and Yield Sooting Indexmentioning
confidence: 99%
“…Such methods include consensus modeling, where linear and nonlinear models are employed in parallel to obtain an averaged predicted CN value -these models can predict CN for a variety of molecular classes with a blind prediction root-mean-squared error of 6.5 [20], however multiple linear models are surpassed by the accuracy of ANNs when fatty acid methyl esters are used to predict CN [21]. Many methods rely on quantitative structure-property relation-Pre-Print ship (QSPR) descriptors, which are numerical measurements of an assortment of physical and chemical properties relating to molecules, and have proven to be successful when applied to predicting the CN of pure hydrocarbons [22] and branched paraffins [23]. Additionally, ANNs have been applied to predicting YSI using QSPR descriptors as training data and obtained 95% confidence in test set prediction accuracy (r-squared value of predictions for compounds not used in ANN training) [24].…”
Section: Predicting Cetane Number and Yield Sooting Indexmentioning
confidence: 99%