Development of Flammable Dispersion Quantitative Property–Consequence Relationship Models Using Extreme Gradient Boosting

Uncontrolled release
of flammable gases and liquids can lead to
the formation of flammable vapor clouds. When their concentrations
are above the lower flammable limit (LFL), or 1/2 LFL for conservative
evaluation, fires and explosions can happen in the presence of an
ignition source. The objective of this work is to develop highly efficient
consequence models to precisely predict the downwind maximum distance,
minimum distance, and maximum vapor cloud width within the flammable
limit. In this work, the novel methodology named quantitative property–consequence
relationship (QPCR) is proposed and constructed to precisely predict
flammable dispersion consequences in a machine learning and data-driven
manner. A flammable dispersion database consisting of 450 leak scenarios
of 41 flammable chemicals was constructed using PHAST simulations.
A state-of-art machine learning regression method, the extreme gradient
boosting algorithm, was implemented to develop models. The coefficient
of determination (R2) and root-mean-square
error (RMSE) were calculated for statistical assessment, and the developed
QPCR models achieved satisfactory predictive capabilities. All developed
models had high precision, with the overall RMSE of three models being
0.0811, 0.0741, and 0.0964, respectively. The developed QPCR models
can be used to obtain instant flammable dispersion estimations for
other flammable chemicals and mixtures at much lower computational
costs.