Can machine learning predict fuel properties accurately?

Publication Type

Conference Paper

Date Published

10/2019

Abstract

High-potential molecules derived from biomass sources may suitably replace or supple-ment traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing cost. Ex-perimental property testing of these bioproducts is usually conducted years after initial bench-scale experiments, due to high experimental costs and/or high volume requirements. However, neglecting to conduct property testing early in the pathway development cycle can lead to investments spent on scaling-up production of bioproducts and biofuels that do not perform as expected. Instead, machine-learning techniques can be used to develop quantitative structure–property relationships for molecules using a relatively large training set of molecular descriptor data. For this study, we compiled measured properties, IR spectra, and molecular descriptors of bio-based molecules from databases and published studies for training models of bioproduct properties. We trained regres-sion models with molecular descriptors and will compare results of different estimators. This study describes the first steps towards a performance prediction tool for bio-based alternative fuels. Keywords: Machine learning, biofuels, jet fuels, fuel properties

Conference Name

2019 WSSCI Fall Technical Meeting

Year of Publication

2019

Refereed Designation

Non-Refereed