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Group-contribution based property estimation and uncertainty analysis for flammability-related properties.

Jérôme Frutiger1, Camille Marcarie1, Jens Abildskov1

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Summary

New group contribution models accurately predict flammability properties like Lower Flammability Limits (LFL) and Upper Flammability Limits (UFL), providing crucial uncertainty information for safety assessments.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Safety Engineering

Background:

  • Accurate prediction of flammability properties is essential for chemical safety.
  • Existing models often lack sufficient accuracy and uncertainty quantification.

Purpose of the Study:

  • Develop new group contribution (GC) models for predicting Lower and Upper Flammability Limits (LFL and UFL), Flash Point (FP), and Auto Ignition Temperature (AIT).
  • Incorporate uncertainty information (95%-confidence intervals) into property predictions.
  • Investigate the temperature dependence of LFL.

Main Methods:

  • Application of the Marrero/Gani (MG) method for group contribution modeling.
  • Utilized robust regression and outlier treatment for accurate parameter estimation.
  • Employed linear error propagation using covariance matrices for uncertainty analysis.

Main Results:

  • Developed GC models demonstrate high accuracy with low average relative errors (e.g., 2.0% for FP, 6.4% for AIT).
  • Models provide 95%-confidence intervals for predicted flammability properties.
  • Established a model to estimate the temperature-dependent proportionality constant (K(LFL)) for LFL.

Conclusions:

  • The new MG GC models offer improved accuracy and simplicity for predicting flammability properties.
  • Uncertainty quantification enhances the reliability of predictions for risk assessment.
  • These models support qualitative and quantitative safety-related risk assessments in chemical industries.