Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
---|---|---|---|---|
Training set | training | 70 | 0.922 | 15.609 |
Validation set | external validation | 15 | 0.921 | 20.919 |
Neural network (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
---|---|---|---|---|
Training set | training | 70 | 0.950 | 12.403 |
Validation set | external validation | 15 | 0.901 | 22.423 |
Regression model (regression)
Open in:QDB ExplorerQDB 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 |
Neural network (regression)
Open in:QDB ExplorerQDB 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 |
When using this QDB archive, please cite (see details) it together with the original article:
Piir, G. Data for: Estimation of flash point and autoignition temperature of organic sulfur chemicals. QsarDB repository, QDB.130. 2015. http://dx.doi.org/10.15152/QDB.130
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. http://dx.doi.org/10.1016/j.enconman.2012.01.014
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 |
Name | Description | Format | Size | View |
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2012ECM185.zip | QSPR models for organic sulfur chemicals' flash point and auto-ignition temperature | application/zip | 20.40Kb | View/ |