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Bagheri, M.; Borhani, T. N. G.; Zahedi, G. Estimation of flash point and autoignition temperature of organic sulfur chemicals. Energy Convers. Manage. 2012, 58, 185–196.

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Bagheri, M.; Borhani, T. N. G.; Zahedi, G. Estimation of flash point and autoignition temperature of organic sulfur chemicals. Energy Convers. Manage. 2012, 58, 185–196.

QDB archive DOI: 10.15152/QDB.130   DOWNLOAD

QsarDB content

Property FP: Flash point [K]

Compounds: 85 | Models: 2 | Predictions: 4

Eq6: PSO-MLR model for flash point i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 70 0.922 15.609
Validation set external validation 15 0.921 20.919
Tab5: FFNN model for flash point i

Neural network (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 70 0.950 12.403
Validation set external validation 15 0.901 22.423

Property AIT: Auto-Ignition Temperature [K]

Compounds: 46 | Models: 2 | Predictions: 6

Eq7: PSO-MLR model for auto-ignition temperature i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 39 0.906 18.074
Validation set i external validation 7 0.974 15.532
Prediction set i testing 39 N/A N/A
Tab6: FFNN model for auto-ignition temperature i

Neural network (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 39 0.931 15.581
Validation set external validation 7 0.978 14.224
Prediction set i testing 39 N/A N/A

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Title: Bagheri, M.; Borhani, T. N. G.; Zahedi, G. Estimation of flash point and autoignition temperature of organic sulfur chemicals. Energy Convers. Manage. 2012, 58, 185–196.
Abstract: The combustible nature of organic sulfur containing chemicals demands an accurate hazardous knowledge for their safe handling and application in industries and researches. In this work, a quantitative structure-property relationship (QSPR) study was performed to thoroughly investigate such crucial hazardous properties i.e., flash point (FP) and autoignition temperature (AIT) of the organic sulfur chemicals which are comprising a wide range of mercaptans, sulfides/thiophenes, polyfunctional C,H,O,S material classes. Based on multivariate linear regression (MLR) the multivariate model was gained using a robust binary particle swarm optimization (PSO) for the feature selection step, the three molecular descriptors were realized as the most responsible descriptors for the flammability behaviors of such chemicals. Next, a three-layer feed-forward neural network model (ANN model) was utilized. The implemented multivariate linear regression and three-layer feed-forward neural network models were practically able to predict the flammability characteristics of a diverse range organic sulfur containing chemicals with high accuracy.
URI: http://hdl.handle.net/10967/130
http://dx.doi.org/10.15152/QDB.130
Date: 2015-01-05


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