New FT-IR Model Offers Breakthrough in Sustainable Aviation Fuel Analysis
Stanford University researchers introduce advanced predictive model for sustainable aviation fuels, significantly improving accuracy and efficiency.
More people and cargo are flying via airplane than ever before. According to the Federal Aviation Administration (FAA), there is an average of 45,000 daily flights (16,405,000 flights over the course of a year, with approximately 10 million of those being passenger flights) (1). The FAA estimates that 44.5 trillion pounds of cargo are transported via airplane per year, which is a staggering number that continues to increase, especially as trade between countries separated by bodies of water and continents increases (1).
It is also true that because of all the flights taking off each day, there is a growing concern about the environmental impact the aviation industry has on our planet, particularly when it comes to its carbon emissions. As a result, scientists are exploring ways to improve emissions of aircraft to reduce the deleterious impact on the environment. Researchers from Stanford University, led by Vivek Boddapati, recently explored this topic.
Passengers commercial airplane flying above clouds | Image Credit: © Jag_cz - stock.adobe.com
Their study, which was published in Fuel, presented a novel Fourier transform infrared (FT-IR) spectra-based prescreening approach that could revolutionize the analysis of sustainable aviation fuels (SAFs) (2). This approach was demonstrated to enhance the prediction accuracy of crucial physical and chemical properties of SAFs, which is a critical factor in the transition towards more sustainable aviation (2).
The new prescreening method relies on FT-IR spectroscopy, a technique that measures the absorption of IR light by molecules, providing a molecular "fingerprint" of the chemical composition of a substance (2). Boddapati and his team utilized vapor-phase FT-IR absorption spectra, covering an extensive wavelength range from 2 to 15.38 micrometers, (5000 cm⁻¹ to 650.20 cm⁻¹ ) to predict nine important properties of SAFs (2). The main properties explored in the study were density, molecular weight, hydrogen-to-carbon (H/C) ratio, net heat of combustion (NHC), flash point, derived cetane number (DCN), threshold sooting index (TSI), initial boiling point (IBP), and kinematic viscosity (KV).
The research team compiled a comprehensive training data set that included the FT-IR spectra of pure hydrocarbons, blends of neat hydrocarbons, conventional jet fuels, and SAFs (2). The physical and chemical properties of these fuels were either sourced from existing experimental data or calculated using relevant property blending correlations. By leveraging this extensive data set, the researchers trained elastic-net-regularized linear models (elastic net partial least squares [PLS]) for each of the nine properties, optimizing the model parameters through a cross-validated grid search process (2).
The new models were rigorously tested on three candidate SAFs, demonstrating higher prediction accuracy for all nine properties compared to the previous models (2). The higher prediction accuracy helps ensure that SAFs can be effectively evaluated and optimized for use in aviation, potentially leading to faster certification and adoption of new fuel candidates (2).
By providing a more accurate and efficient method for characterizing SAFs, the FT-IR model developed by Boddapati and his team could play a pivotal role in the broader adoption of sustainable fuels in the aviation industry (2). The ability to predict the properties of new SAF candidates with high accuracy and low sample volumes means that fuel developers can more rapidly screen and optimize potential fuels, reducing the time and cost associated with their development (2).
This study was also significant in that the model demonstrated an ability to handle many different fuel types. These fuel types ranged from simple hydrocarbon mixtures to complex distillate jet fuels (2). Because of the method’s versatility, it could lead to its adoption as a standard tool in fuel analysis, further improving the sustainability of the aviation industry.
References
(1) Federal Aviation Administration, Air Traffic By The Numbers. FAA.gov. Available at: https://www.faa.gov/air_traffic/by_the_numbers (accessed 2024-08-29).
(2) Boddapati, V.; Ferris, A. M.; Hanson, R. K. Predicting the Physical and Chemical Properties of Sustainable Aviation Fuels Using Elastic-Net-Regularized Linear Models based on Extended-Wavelength FT-IR Spectra. Fuel 2024, 356, 129557. DOI: 10.1016/j.fuel.2023.129557
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