Illuminating Flash Point: Comprehensive Prediction Models

Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure – property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically diverse compounds, with flash point spanning from less than −130 °C to over 900 °C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials.

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

[2]  R. Prugh Estimation of flash point temperature , 1973 .

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

[4]  Frank R. Burden,et al.  Robust QSAR Models from Novel Descriptors and Bayesian Regularised Neural Networks , 2000 .

[5]  Tu C Le,et al.  Aqueous solubility prediction: do crystal lattice interactions help? , 2013, Molecular pharmaceutics.

[6]  Frank R. Burden,et al.  Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods , 2009 .

[7]  G. S. Patil Estimation of flash point , 1988 .

[8]  Igor I. Baskin,et al.  Fragmental descriptors in QSPR: flash point calculations , 2003 .

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

[10]  G. Zahedi,et al.  Estimation of flash point and autoignition temperature of organic sulfur chemicals , 2012 .

[11]  Takahiro Suzuki,et al.  Quantitative Structure-Property Relationships for the Estimation of Boiling Point and Flash Point Using a Radial Basis Function Neural Network , 1999, J. Chem. Inf. Comput. Sci..

[12]  David A. Winkler,et al.  Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds , 2013, J. Chem. Inf. Model..

[13]  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.

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

[15]  F. Gharagheizi,et al.  Prediction of Flash Point Temperature of Pure Components Using a Quantitative Structure–Property Relationship Model , 2008 .

[16]  Dave Winkler,et al.  Bayesian Regularization of Neural Networks , 2009, Artificial Neural Networks.

[17]  Frank R. Burden,et al.  New QSAR Methods Applied to Structure-Activity Mapping and Combinatorial Chemistry , 1999, J. Chem. Inf. Comput. Sci..

[18]  K. Satyanarayana,et al.  Note: Correlation of flash points , 1991 .

[19]  Ritu Jain,et al.  QSPR Analysis of Flash Points , 2001, J. Chem. Inf. Comput. Sci..

[20]  R. L. Rowley,et al.  Prediction of pure‐component flash points for organic compounds , 2011 .

[21]  A. A. Shimy,et al.  Calculating flammability characteristics of hydrocarbons and alcohols , 1970 .

[22]  Frank R. Burden,et al.  An Optimal Self‐Pruning Neural Network and Nonlinear Descriptor Selection in QSAR , 2009 .

[23]  Frank R Burden,et al.  Quantitative structure-property relationship modeling of diverse materials properties. , 2012, Chemical reviews.

[24]  F. Burden,et al.  Robust QSAR models using Bayesian regularized neural networks. , 1999, Journal of medicinal chemistry.

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

[26]  M. Keshavarz,et al.  Simple method for reliable predicting flash points of unsaturated hydrocarbons. , 2011, Journal of hazardous materials.

[27]  Fu‐Yu Hshieh Correlation of closed‐cup flash points with normal boiling points for silicone and general organic compounds , 1997 .

[28]  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 .

[29]  Diego Saldana Miranda,et al.  Prediction of Flash Points for Fuel Mixtures Using Machine Learning and a Novel Equation , 2013 .

[30]  W. Affens Flammability Properties of Hydrocarbon Fuels. Interrelations of Flammability Properties of n-Alkanes in Air. , 1966 .

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

[32]  G. Cooke,et al.  Prediction of Flash Points of Middle Distillates , 1956 .

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

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