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 [1]. Additional strategies are also being developed to infer the functional groups [2] 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.

(a) Calculated (using a regularized linear model) vs. actual molecular weight for fuels in the training dataset. (b) Example spectra of three jet fuels (top); Model-selected wavelengths and their contribution to the variation of MW (bottom). [1]

To learn more, check out some of our publications:

[1] 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

[2] 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