An accurate model for prediction of autoignition temperature of pure compounds

J Hazard Mater. 2011 May 15;189(1-2):211-21. doi: 10.1016/j.jhazmat.2011.02.014. Epub 2011 Feb 15.

Abstract

Accurate prediction of pure compounds autoignition temperature (AIT) is of great importance. In this study, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to evaluate the AIT of pure compounds. 1025 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient of 0.984, root mean square error of 15.44K, and average percent error of 1.6% for the experimental values.

MeSH terms

  • Chemical Phenomena
  • Fires*
  • Models, Chemical*
  • Neural Networks, Computer*
  • Quantitative Structure-Activity Relationship
  • Temperature*