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

TL;DR

This work addresses the need for accurate and interpretable predictions of sustainable aviation fuel properties across neat molecules, aviation fuels, and blends. It introduces a structured framework that uses liquid-phase FTIR spectra decomposed by non-negative matrix factorization to generate additive, interpretable spectral features, which are then fed into ensemble regression learners to predict five key properties. The approach yields models whose predictions align well with experimental data and provides chemical insight by linking spectral features to functional groups such as aromatics and cycloalkanes; the results are made accessible through an interactive web tool. Overall, the method enables rapid, small-sample-property prediction with interpretable spectral attributes, supporting accelerated SAF research and development.

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.

Figures (8)

• Figure 1: Schematic of the model development process
• Figure 2: Parity plots showing experimental and predicted values of all models, split by training, validation, and testing sets.
• Figure 3: Model feature importances for all models.
• Figure 4: Highest ranked aromatic features for flash point and viscosity models.
• Figure 5: Highest ranked aromatic features for freezing point and density models.