Predicting flammability-leading properties for liquid aerosol safety via machine learning

Abstract

Flammable and explosive hazards, which have been well studied, are major safety concerns in industrial processes. However, the liquid aerosolization phenomenon, which increases the fire and explosion hazard of bulk liquids, has not been widely recognized. This work aims at identifying the contributors influencing liquid aerosol flammability and solving their data deficiencies by developing quantitative structure − property relationship (QSPR) models. 1215 liquid chemicals and 14 predictors have been input to train the developed machine learning models via k-fold cross validation with the consideration of principal component analysis. Three rounds of model performance comparisons are conducted to find the optimal models for liquid dynamic viscosity (LDV), surface tension (ST), and liquid vapor pressure (LVP). The most persuasive model for LDV is obtained by the exponential Gaussian process regression (GPR) approach with seven principal components, while the Matern 5/2 GPR algorithm is the most robust model for predicting ST and LVP. Due to their good interpretation and prediction performance, the optimized machine learning models can be used to predict flammability-leading properties for aerosols and can therefore help design inherently safer processes involving potential liquid aerosolization.