Insight into the contribution of individual functional groups to the flash point of organic compounds.

Flash point temperatures of organic compounds are predicted on the basis of a power law involving 21 additive contributions associated with non-hydrogen atoms and ring structures. The model is parametrized against a previous data set of 287 simple organic molecules. An average absolute error of 8.6K and a maximal error of about 50K are obtained when applying this model to an external test set of 488 compounds within its applicability domain. The overall performances of the method are remarkable given its simplicity and the small number of parameters involved. In addition, the present work provides valuable insight into the influence of individual functional groups to flash point temperatures.

[1]  Suhani J. Patel,et al.  QSPR Flash Point Prediction of Solvents Using Topological Indices for Application in Computer Aided Molecular Design , 2009 .

[2]  Kang Xu,et al.  Nonaqueous liquid electrolytes for lithium-based rechargeable batteries. , 2004, Chemical reviews.

[3]  Ali Eslamimanesh,et al.  Empirical Method for Representing the Flash-Point Temperature of Pure Compounds , 2011 .

[4]  F. Quina,et al.  Simple Method to Evaluate and to Predict Flash Points of Organic Compounds , 2011 .

[5]  D. Mathieu Simple Alternative to Neural Networks for Predicting Sublimation Enthalpies from Fragment Contributions , 2012 .

[6]  Laurent Catoire,et al.  A Unique Equation to Estimate Flash Points of Selected Pure Liquids Application to the Correction of Probably Erroneous Flash Point Values , 2004 .

[7]  Leonidas Constantinou,et al.  A Group-Contribution Method for Predicting Pure Component Properties of Biochemical and Safety Interest , 2004 .

[8]  Zhenyi Liu,et al.  Research Progress on Flash Point Prediction , 2010 .

[9]  J. Cummings The Chemical Database , 2013 .

[10]  Iva B. Stoyanova-Slavova,et al.  QSPR modeling of flash points: an update. , 2007, Journal of Molecular Graphics and Modelling.

[11]  J. Goodenough Challenges for Rechargeable Li Batteries , 2010 .

[12]  F. Gharagheizi,et al.  A simple accurate model for prediction of flash point temperature of pure compounds , 2012, Journal of Thermal Analysis and Calorimetry.

[13]  John L. Oscarson,et al.  Flash Point: Evaluation, Experimentation and Estimation , 2010 .

[14]  Mehdi Bagheri,et al.  Nonlinear molecular based modeling of the flash point for application in inherently safer design , 2012 .

[15]  Richard L. Rowley,et al.  Estimation of the flash point of pure organic chemicals from structural contributions , 2010 .

[16]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[17]  D. Mathieu,et al.  Flash Points of Organosilicon Compounds: How Data for Alkanes Combined with Custom Additive Fragments Can Expedite the Development of Predictive Models , 2012 .

[18]  Optimal partitioning of molecular properties into additive contributions: the case of crystal volumes. , 2007, Acta crystallographica. Section B, Structural science.

[19]  Qiang Wang,et al.  Prediction of the Flash Point Temperature of Organic Compounds with the Positional Distributive Contribution Method , 2012 .

[20]  Farhad Gharagheizi,et al.  A New Neural Network−Group Contribution Method for Estimation of Flash Point Temperature of Pure Components , 2008 .

[21]  Ludivine Pidol,et al.  Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods , 2011 .

[22]  M. Mannan,et al.  A review of estimation methods for flash points and flammability limits , 2004 .

[23]  Suhani J. Patel,et al.  Prediction models for the flash point of pure components , 2011 .

[24]  Frank H. Quina,et al.  Development of a Simple Method to Predict Boiling Points and Flash Points of Acyclic Alkenes , 2011 .

[25]  Gürkan Sin,et al.  Group-contribution+ (GC+) based estimation of properties of pure components: Improved property estimation and uncertainty analysis , 2012 .

[26]  Zhirong Wang,et al.  Quantitative structure-property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network. , 2007, Journal of hazardous materials.

[27]  Didier Mathieu Power Law Expressions for Predicting Lower and Upper Flammability Limit Temperatures , 2013 .

[28]  Yong Pan,et al.  Quantitative Structure–Property Relationship Studies for Predicting Flash Points of Organic Compounds using Support Vector Machines , 2008 .

[29]  Jae Wook Ko,et al.  Flash point prediction of organic compounds using a group contribution and support vector machine , 2012, Korean Journal of Chemical Engineering.

[30]  D. Mathieu Inductive modeling of physico-chemical properties: flash point of alkanes. , 2010, Journal of hazardous materials.