Prescreening of Alternative Fuels using IR Spectral Analysis
Current efforts to mitigate transportation-related emissions via the design and adoption of sustainable alternative fuels are hindered by costly and time-consuming regulatory approval processes.
With the goal of streamlining the testing process for next-generation fuel candidates (e.g., sustainable aviation fuels, or SAFs), we are developing an economical, low-volume prescreening technology to directly predict the physical and chemical properties of fuels based on their infrared absorption spectra. We use different classes of machine learning models, trained on a database containing the Fourier Transform Infrared (FTIR) spectra and property values for a broad range of fuels and fuel components, to make accurate physical/chemical property predictions . Additional strategies are also being developed to infer the functional groups  and molecular composition of fuels from their FTIR spectra. With these techniques, our group brings down the development costs and testing time of next-generation fuels considerably, thereby driving the aviation industry towards 100% SAF usage.
To learn more, check out some of our publications:
 Y. Wang, Y. Ding, W. Wei, Y. Cao, D. F. Davidson, R. K. Hanson, “On estimating physical and chemical properties of hydrocarbon fuels using mid-infrared FTIR spectra and regularized linear models,” Fuel, Vol. 255 (2019) 115715. DOI: 10.1016/j.fuel.2019.115715
 Y. Wang, W. Wei, Y. Zhang, R. K. Hanson, “A new strategy of characterizing hydrocarbon fuels using FTIR spectra and generalized linear model with grouped-Lasso regularization,” Fuel, Vol. 287 (2021) 119419. DOI: 10.1016/j.fuel.2020.119419