Group-Contribution based Property Estimation and Uncertainty analysis for Flammability-related Properties

Jerome Frutiger, Camille Marcarie, Jens Abildskov, Gürkan Sin

Research output: Contribution to journalJournal articleResearchpeer-review

493 Downloads (Pure)


This study presents new group contribution (GC) models for the prediction of Lower and Upper Flammability Limits (LFL and UFL), Flash Point (FP) and Auto Ignition Temperature (AIT) of organic chemicals applying the Marrero/Gani (MG) method. Advanced methods for parameter estimation using robust regression and outlier treatment have been applied to achieve high accuracy. Furthermore, linear error propagation based on covariance matrix of estimated parameters was performed. Therefore, every estimated property value of the flammability-related properties is reported together with its corresponding 95%-confidence interval of the prediction. Compared to existing models the developed ones have a higher accuracy, are simple to apply and provide uncertainty information on the calculated prediction. The average relative error and correlation coefficient are 11.5% and 0.99 for LFL, 15.9% and 0.91 for UFL, 2.0% and 0.99 for FP as well as 6.4% and 0.76 for AIT. Moreover, the temperature-dependence of LFL property was studied. A compound specific proportionality constant (KLFL) between LFL and temperature is introduced and an MG GC model to estimate KLFL is developed. Overall the ability to predict flammability-related properties including the corresponding uncertainty of the prediction can provide important information for a qualitative and quantitative safety-related risk assessment studies.
Original languageEnglish
JournalJournal of Hazardous Materials
Pages (from-to)783–793
Publication statusPublished - 2016


  • Group contribution
  • Uncertainty
  • Flammability limit
  • Flash point
  • Auto ignition temperature


Dive into the research topics of 'Group-Contribution based Property Estimation and Uncertainty analysis for Flammability-related Properties'. Together they form a unique fingerprint.

Cite this