Kinney Revisited:  An Improved Group Contribution Method for the Prediction of Boiling Points of Acyclic Alkanes

Palatinus, Joseph A.; Sams, Cassandra M.; Beeston, Christopher M.; Carroll, Felix A.; Argenton, and André B.; Quina, Frank H.

doi:10.1021/ie0604425

Cited by 16 publications

(23 citation statements)

References 18 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The group contribution based models which use ANNs have been widely used to predict various properties such as liquid viscosity [14][15] , thermal conductivity 16 , infinite dilution activity 17 , and density of ionic liquids 18 , normal boiling point (NBP) 19 , flash point (FP) 13 and melting point 20 .…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Strategies to develop robust neural network models: Prediction of flash point as a case study

Alibakshi

2018

Analytica Chimica Acta

View full text Add to dashboard Cite

Artificial neural network (ANN) is one of the most widely used methods to develop accurate predictive models based on artificial intelligence and machine learning. In the present study, the important practical aspects of developing a reliable ANN model e.g. appropriate assignment of the number of neurons, number of hidden layers, transfer function, training algorithm, dataset division and initialization of the network are discussed. As a case study, predictability of the flash point for a dataset of 740 organic compounds using ANNs was investigated via a total number of 484220ANNs to allow covering a wide range of parameters affecting the performance of an ANN. Among all studied parameters, the number of neurons or layers was found to be the most important parameters to develop a reliable ANN with low overfitting risk. To evaluate appropriate number of neurons and layers, a value of equal or greater than 10 for the ratio of the training samples to the ANN constants was suggested as a rule of thumb. More ever, a strategy for evaluation of the authentic performance of ANNs and deciding about the reliability of an ANN model was proposed which is applicable to other models developed by supervised learning. Based on the introduced considerations, an ANN model was proposed for predicting the flash point of pure organic compounds. According to the results, the new model was found to produce the lowest error compared to other available models.

show abstract

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Strategies to develop robust neural network models: Prediction of flash point as a case study

Alibakshi

2018

Analytica Chimica Acta

View full text Add to dashboard Cite

show abstract

“…3 As shown in Figure 1, there is an excellent correlation of Y BP values calculated in this way with the number of carbon atoms in the unbranched 1-alkynes.…”

Section: Introductionmentioning

confidence: 66%

“…However, we did make a slight change in the interpretation of one parameter. For steric reasons, a methyl substituent on C3 of a 1-alkyne was counted as M instead of M 3 . A similar interpretation of the M 3 parameter was made for the alkenes.…”

Section: Methodsmentioning

confidence: 99%

“…This approach gave a correlation of experimental and predicted boiling points having R 2 = 0.999 for 198 acyclic alkanes containing from six to 30 carbons. 3 Based on the success of this approach for alkanes, we have begun to explore the prediction of T B values of other organic compounds. Recently we reported excellent correlations for the boiling points of alkenes 5 and alkylbenzenes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A simple method to evaluate, correlate and predict boiling and flash points of alkynes

Godinho¹,

Carroll²,

Quina³

2012

J. Braz. Chem. Soc.

Self Cite

View full text Add to dashboard Cite

“…The third model used was developed by Gharagheizi [24] introduced to find correlations with molecular structure [62,63].…”

Section: Qspr Models For Pure Compoundsmentioning

confidence: 99%

Combining mixing rules with QSPR models for pure chemicals to predict the flash points of binary organic liquid mixtures

Gaudin¹,

Rotureau²,

Fayet³

2015

Fire Safety Journal

View full text Add to dashboard Cite

Flash point is a key property of liquids to evaluate the safety of industrial processes. Mixing rules are commonly used to calculate the flash point of liquid mixtures, but they need knowledge of the ones of pure compounds. Theoretical methods notably based on quantitative structure property relationships (QSPR) already exist to predict flash points of pure compounds. So, in this paper, direct combination of these two types of approaches was investigated to achieve predictions even when the flash points of pure compounds were unknown. Three relevant mixing rules and four QSPR models, based on simple constitutional descriptors, were considered. Based on a data set of 284 experimental data of binary mixtures extracted from literature, two reliable combinations were highlighted. The most accurate one reached an error in prediction of only 2.9°C but needed knowledge of the boiling point and Antoine's coefficients of each component of the mixture. A new full-predictive method was in particular proposed with also a low error in prediction (4.4°C), requiring only knowledge of the molecular structure of each pure compound and molar fraction of the mixture. Errors in each predictive method keep quite reasonable against expected accuracies of direct measurements of flash point of binary mixtures.

show abstract

Kinney Revisited: An Improved Group Contribution Method for the Prediction of Boiling Points of Acyclic Alkanes

Cited by 16 publications

References 18 publications

Strategies to develop robust neural network models: Prediction of flash point as a case study

Strategies to develop robust neural network models: Prediction of flash point as a case study

A simple method to evaluate, correlate and predict boiling and flash points of alkynes

Combining mixing rules with QSPR models for pure chemicals to predict the flash points of binary organic liquid mixtures

Contact Info

Product

Resources

About