Predicting fuel low-temperature combustion performance using Fourier-transform infrared absorption spectra of neat hydrocarbons

Daly SR, Tran K, Niemeyer KE, Cannella WJ, Hagen CL. Fuel 242 :343-344 (2019).
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This work uses support vector machine regression to correlate infrared absorption spectra to a metric representing low temperature combustion engine (LTC) performance, the LTC index: a singular value encapsulating achievable engine loads, combustion phasing, and efficiency. 313 total fuels informed the model, including mixtures and surrogate gasoline fuels containing n-heptane, isooctane (i.e., 2,2,4-trimethylpentane), toluene, ethanol, methylcyclohexane, xylene(s), 2-methybutane, and 2-methylhexane. We predicted LTC indices of the FACE (Fuels for Advanced Combustion Engines) gasolines A–J within ±6.0 units. The proposed methodology can be used to both predict gasoline LTC performance and also identify important hydrocarbon components that most improve (or reduce) LTC engine performance.


  Author = {Shane R. Daly and Khang Tran and Kyle E. Niemeyer and William J. Cannella and Christopher L. Hagen},
  Title = {Predicting fuel low-temperature combustion performance using {Fourier}-transform infrared absorption spectra of neat hydrocarbons},
  Journal = {Fuel},
  Pages = {343--344},
  Volume = {242},
  Year = 2019,
  doi = {10.1016/j.fuel.2019.01.054}