A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning

Abstract

Sustainable aviation fuels have the potential for reducing emissions and environmental impact. To help identify viable sustainable aviation fuels and accelerate research, several machine learning models have been developed to predict relevant physiochemical properties. However, many of the models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that have limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid-phase FTIR spectra measurements can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 mL of sample. The method first decomposes FTIR spectra into fundamental building blocks using Non-negative Matrix Factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15C, and kinematic viscosity at -20C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict properties while enabling interpretation of relationships between compositional elements of a fuel, such as functional groups or chemical classes, and its properties. To support sustainable aviation fuel research and development, the models and data are available on an interactive web tool.