Three efficient and accurate QSPR models for predicting upper flammability limits (UFLs) of hydrocarbon mixtures were built solely based on chemical structures and mole fractions of pure compounds. Firstly, experimental UFLs of 78 blended fuels were gathered from a single reference and molecular descriptors of the nine pure chemicals were calculated by Gaussian 09. Then support vector machine (SVM) analysis was carried out for the development of QSPR models and three totally different external validation strategies were applied for testing the true external predictive capabilities of the models. It was found that all the models perform well, and hence, they are qualified for predicting UFLs of blended hydrocarbon fuels without experimental ...
International audiencePhysical hazards of chemical mixtures, associated for example with their fire ...
Due to the fast development and availability of computers, predictive approaches are increasingly us...
The new EU regulation REACH requires the evaluation of the physico-chemical properties of a large nu...
In this study, machine learning algorithms, such as support vector machine (SVM), k-nearest-neighbor...
The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire a...
Quantitative structure property relationships (QSPR) are increasingly used for the prediction of phy...
A quantitative structure−property relationship (QSPR) study was performed to develop a model for pre...
Promoted by REACH regulation and by the development of computational capabilities, QSPR are increasi...
Safety-related properties like lower flammable limit (LFL), upper flammable limit (UFL), auto-igniti...
Quantitative Structure Property Relationships (QSPR) are predictive methods of macroscopic propertie...
Quantitative Structure-Property Relationship models (QSPR) are predictive methods based on correlati...
New Quantitative Structure‐Property Relationships (QSPR) are presented to predict the flash point of...
Fire and explosions are the dominant hazards in many industry sectors, especially oil and gas. The ...
Quantitative Structure-Property Relationships (QSPR) are predictive methods of macroscopic propertie...
International audiencePhysical hazards of chemical mixtures, associated for example with their fire ...
Due to the fast development and availability of computers, predictive approaches are increasingly us...
The new EU regulation REACH requires the evaluation of the physico-chemical properties of a large nu...
In this study, machine learning algorithms, such as support vector machine (SVM), k-nearest-neighbor...
The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire a...
Quantitative structure property relationships (QSPR) are increasingly used for the prediction of phy...
A quantitative structure−property relationship (QSPR) study was performed to develop a model for pre...
Promoted by REACH regulation and by the development of computational capabilities, QSPR are increasi...
Safety-related properties like lower flammable limit (LFL), upper flammable limit (UFL), auto-igniti...
Quantitative Structure Property Relationships (QSPR) are predictive methods of macroscopic propertie...
Quantitative Structure-Property Relationship models (QSPR) are predictive methods based on correlati...
New Quantitative Structure‐Property Relationships (QSPR) are presented to predict the flash point of...
Fire and explosions are the dominant hazards in many industry sectors, especially oil and gas. The ...
Quantitative Structure-Property Relationships (QSPR) are predictive methods of macroscopic propertie...
International audiencePhysical hazards of chemical mixtures, associated for example with their fire ...
Due to the fast development and availability of computers, predictive approaches are increasingly us...
The new EU regulation REACH requires the evaluation of the physico-chemical properties of a large nu...