Improved Prediction of Hydrocarbon Flash Points from Boiling Point Data

Carroll, Felix A.; Lin, Chung-Yon; Quina, Frank H.

doi:10.1021/ef1005836

Cited by 36 publications

(35 citation statements)

References 21 publications

(14 reference statements)

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“…A sim ilar regression for 2,2,4,4,6,8,8-heptamethylnonane based on data from Korosi and Kovats [24] predicts that the value of surface ten sion at 21.2°C should be 24.2mN m-1, which agrees with the data reported herein of 24.1 ± 0.2 mN mT1. The flashpoint value of 135 ± 1 °C for hexadecane agrees with the reported values of 134 ± 2 and 135 °C [26,27], and the flashpoint value of 94 ± 2 °C for 2,2,4,4,6,8,8-heptamethylnonane agrees with the reported val ues of 95 and 96 °C [28,29].The flash point and surface tension values are given in Table 7, Annex. As the volume fraction of isocetane increases, the surface tension (Fig.…”

Section: Fig 4 S U Rfa C E Te N S Io N O F M Ix Tu Re S O F Is O C supporting

confidence: 84%

An Experimental and Modeling Study Into Using Normal and Isocetane Fuel Blends as a Surrogate for a Hydroprocessed Renewable Diesel Fuel

Cowart

Raynes

Hamilton

et al. 2014

Journal of Energy Resources Technology

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A new hydroprocessed renewable diesel (HRD) fuel comprised both straight chain and branched alkane fuel components. In an effort to find a research surrogate for this fuel, single cylinder engine testing was performed with various blends o f n-hexadecane (cetane) and isocetane in order to find a binary surrogate mixture with similar perform ance characteristics to that of the HRD. A blend of approximately two-thirds n-hexade cane with one-third isocetane showed the most similar behavior based on conventional combustion metrics. Companion combustion modeling was then pursued using a com bined detailed chemical kinetic mechanism for both n-hexadecane and isocetane. These modeling results show both the importance of isocetane in lengthening ignition delay (IGD), as well as the overall importance of chemical ignition delay as the dominating effect in the overall ignition delay of these binary blend fuels.

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Section: Fig 4 S U Rfa C E Te N S Io N O F M Ix Tu Re S O F Is O C supporting

confidence: 84%

An Experimental and Modeling Study Into Using Normal and Isocetane Fuel Blends as a Surrogate for a Hydroprocessed Renewable Diesel Fuel

Cowart

Raynes

Hamilton

et al. 2014

Journal of Energy Resources Technology

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show abstract

“…We collected data from academic papers, the Gelest chemical catalogue, the DIPPR database, Lange's Handbook of Chemistry, the Hazardous Chemicals Handbook, and the PubChem chemical database . Basic properties of the flash point datasets from each paper (size, average, and standard deviation) are given in Table .…”

Section: Methodsmentioning

confidence: 99%

“…Comparisons were made to the best model from each paper. The primary papers that we compared against can be divided into two categories with respect to their testing: The first category used an 80 % training 20 % test splitting method (“80/20 split” papers) without providing the exact test set. Note that we used Katrziky07’s ANN dataset partitioning method as we compared to their ANN modeling.…”

Section: Methodsmentioning

confidence: 99%

Assessing Graph‐based Deep Learning Models for Predicting Flash Point

Sun

Krakauer

Politowicz

et al. 2020

Molecular Informatics

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Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, messagepassing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R ! and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3% lower and 2.0K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R ! of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.

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“…The aim of the present study is to investigate the ways which can improve the predictability of FP with QSPR method. The main focus would be on a more efficient strategy to select the descriptors and find the relationship between the FP and model inputs as well as considering a combination of selected descriptors and NBP as model inputs which has been found to be an efficient way to remarkably enhance the FP predictability ,,,. Using a combination of experimentally determined properties and calculated molecular descriptors as model inputs and improving the variable selection and relationship mapping efficiency studied in the current work, not only can result in a more accurate prediction of the FP as an important flammability property, but also can be used to improve the performance of QSPR in predicting other properties.…”

Section: Introductionmentioning

confidence: 97%

A Neural Network QSPR Model for Accurate Prediction of Flash Point of Pure Hydrocarbons

Mirshahvalad

Ghasemiasl

Raoufi

et al. 2018

Molecular Informatics

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The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the most effective out of 1926 calculated descriptors. The selected descriptors and their combination with the normal boiling point data were used as model inputs and their correlation with FP was mapped using feedforward artificial neural networks. Study-ing various models, the best result was obtained by a neural network with 2 neurons in the hidden layer for which a combination of the selected descriptors and normal boiling point data were used as model inputs. Evaluating the performance of this model for a dataset of 727 compounds resulted in average absolute relative errors of of 1.36 %, 1.34 %, 1.44 % and 1.42 % and average absolute deviations of 4.48, 4.41, 4.75 and 4.66 K for the overall, training, validation, and test datasets, respectively.

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Improved Prediction of Hydrocarbon Flash Points from Boiling Point Data

Cited by 36 publications

References 21 publications

An Experimental and Modeling Study Into Using Normal and Isocetane Fuel Blends as a Surrogate for a Hydroprocessed Renewable Diesel Fuel

An Experimental and Modeling Study Into Using Normal and Isocetane Fuel Blends as a Surrogate for a Hydroprocessed Renewable Diesel Fuel

Assessing Graph‐based Deep Learning Models for Predicting Flash Point

A Neural Network QSPR Model for Accurate Prediction of Flash Point of Pure Hydrocarbons

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