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    Octane prediction from infrared spectroscopic data

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    Octane_Paper_RevisedOct22 (1).pdf
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    Type
    Article
    Authors
    Al Ibrahim, Emad
    Farooq, Aamir cc
    KAUST Department
    Chemical Kinetics & Laser Sensors Laboratory
    Clean Combustion Research Center
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2019-10-22
    Online Publication Date
    2019-10-22
    Print Publication Date
    2020-01-16
    Embargo End Date
    2020-01-01
    Permanent link to this record
    http://hdl.handle.net/10754/660233
    
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    Abstract
    A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline-ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (fuels for advanced combustion engines) gasoline blends were calculated using PIONA (paraffin, isoparaffin, olefin, naphthene, and aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH═CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial neural network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity. ©
    Citation
    Al Ibrahim, E., & Farooq, A. (2019). Octane Prediction from Infrared Spectroscopic Data. Energy & Fuels. doi:10.1021/acs.energyfuels.9b02816
    Sponsors
    This work was funded by the Office of Sponsored Research at King Abdullah University of Science and Technology (KAUST). We are thankful to Prof. Mani Sarathy and Dr. Abdul Gani Abdul Jameel for helpful discussions.
    Publisher
    American Chemical Society (ACS)
    Journal
    Energy and Fuels
    DOI
    10.1021/acs.energyfuels.9b02816
    Additional Links
    https://pubs.acs.org/doi/10.1021/acs.energyfuels.9b02816
    ae974a485f413a2113503eed53cd6c53
    10.1021/acs.energyfuels.9b02816
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center

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