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Khajeh, A.; Modarress, H. Quantitative Structure–Property Relationship for Flash Points of Alcohols. Ind. Eng. Chem. Res. 2011, 50, 11337-11342.

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Khajeh, A.; Modarress, H. Quantitative Structure–Property Relationship for Flash Points of Alcohols. Ind. Eng. Chem. Res. 2011, 50, 11337-11342.

QDB archive DOI: 10.15152/QDB.160   DOWNLOAD

QsarDB content

Property FP: Flash point [K]

Tab3Tab4: ANFIS model for alcohols i

Neural network (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 120 0.959 12.844
Validation set external validation 31 0.951 11.122
Eq12: GFA-MLR model for alcohols i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 120 0.935 16.208
Validation set external validation 31 0.911 14.936

Citing

When using this QDB archive, please cite (see details) it together with the original article:

  • Ahte, P. Data for: Quantitative Structure–Property Relationship for Flash Points of Alcohols. QsarDB repository, QDB.160. 2015. http://dx.doi.org/10.15152/QDB.160

  • Khajeh, A.; Modarress, H. Quantitative Structure–Property Relationship for Flash Points of Alcohols. Ind. Eng. Chem. Res. 2011, 50, 11337-11342. http://dx.doi.org/10.1021/ie2004708

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dc.date.accessioned 2015-05-28T20:20:37Z
dc.date.available 2015-05-28T20:20:37Z
dc.date.issued 2015-05-28
dc.identifier.uri http://hdl.handle.net/10967/160
dc.identifier.uri http://dx.doi.org/10.15152/QDB.160
dc.description.abstract In this paper, quantitative structure property relationship (QSPR) models have been developed to predict flash points for some common alcohols based on a data set of 151 components. With the use of the genetic function approximation (GFA) approach, four descriptors have been selected from a set of more than 1000 molecular descriptors. These selected descriptors were used as inputs for the adaptive neuro-fuzzy inference system (ANFIS) model. The GFA model resulted in squared correlation coefficient values of 0.935 and 0.91 respectively for the training and test sets, whereas ANFIS resulted in the values of 0.959 and 0.951 for the training and test sets, respectively. However, the linear and nonlinear models can give satisfactory prediction results, but the ANFIS model is somewhat superior.
dc.publisher Priit Ahte
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Khajeh, A.; Modarress, H. Quantitative Structure–Property Relationship for Flash Points of Alcohols. Ind. Eng. Chem. Res. 2011, 50, 11337-11342.
qdb.property.endpoint 1. Physical Chemical Properties 1.19. Flash point en_US
qdb.descriptor.application DRAGON en_US
qdb.prediction.application MATLAB en_US
qdb.prediction.application Materials Studio en_US
bibtex.entry article en_US
bibtex.entry.author Khajeh, A.
bibtex.entry.author Modarress, H.
bibtex.entry.doi 10.1021/ie2004708 en_US
bibtex.entry.journal Ind. Eng. Chem. Res. en_US
bibtex.entry.month Oct
bibtex.entry.number 19 en_US
bibtex.entry.pages 11337-11342 en_US
bibtex.entry.title Quantitative Structure–Property Relationship for Flash Points of Alcohols en_US
bibtex.entry.volume 50 en_US
bibtex.entry.year 2011
qdb.model.type Neural network (regression) en_US
qdb.model.type Regression model (regression) en_US


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