(116s) Optimal Machine Learning Models for Liquid Aerosolization Safety Contributors

Ji, C. - Presenter, Mary Kay O'Connor Process Safety Center
Jiao, Z. - Presenter, Texas A&M University
Yuan, S., Texas A&M University
Sun, Y., Mary Kay O'Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering
El-Halwagi, M., Texas A&M University
Wang, Q., Texas A&M University
To date, many attentions have been paid to the new types of marine fuels to meet the requirement of the long-term sustainable strategy issued by the International Maritime Organization. Speaking to marine fuel selection, the safety factors should always be top prioritized. Flammable and explosive hazards, which have been well studied, are the major concerns of safety aspects of fuel application. However, the liquid aerosolization issue, making bulk liquids more hazardous on combustion and explosion, has not widely recognized in industry or academia.

This study is aimed at identifying the contributors of liquid aerosol formulation and solving their data deficiencies by developing the corresponding quantitative structure−property relationship models. 1215 liquid chemical substances and 14 predictors have been input to train the machine learning models via k-fold cross validation with the consideration of principal component analysis. Three rounds of comparisons were executed to find the final optimal models for liquid dynamic viscosity (LDV), surface tension (ST) and liquid vapor pressure (LVP). The most persuasive model for LDV is obtained by exponential Gaussian process regression (GPR) approach with seven principal components while the Matern 5/2 GPR algorithm is the most robust one for ST and LVP to formulate the optimal models. Since the reasonably good interpretation and prediction performance provided by the developed models, they can be served as effective tools to expand the database for the liquid aerosol formulation properties of organic compounds.


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