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dc.contributor.authorAl Ibrahim, Emad
dc.contributor.authorFarooq, Aamir
dc.date.accessioned2019-11-25T12:37:06Z
dc.date.available2019-11-25T12:37:06Z
dc.date.issued2019-10-22
dc.identifier.citationAl Ibrahim, E., & Farooq, A. (2019). Octane Prediction from Infrared Spectroscopic Data. Energy & Fuels. doi:10.1021/acs.energyfuels.9b02816
dc.identifier.doi10.1021/acs.energyfuels.9b02816
dc.identifier.urihttp://hdl.handle.net/10754/660233
dc.description.abstractA 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. ©
dc.description.sponsorshipThis 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.
dc.publisherAmerican Chemical Society (ACS)
dc.relation.urlhttps://pubs.acs.org/doi/10.1021/acs.energyfuels.9b02816
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in Energy and Fuels, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acs.energyfuels.9b02816.
dc.titleOctane prediction from infrared spectroscopic data
dc.typeArticle
dc.contributor.departmentChemical Kinetics & Laser Sensors Laboratory
dc.contributor.departmentClean Combustion Research Center
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalEnergy and Fuels
dc.rights.embargodate2020-01-01
dc.eprint.versionPost-print
kaust.personAl Ibrahim, Emad
kaust.personFarooq, Aamir
refterms.dateFOA2020-01-01T00:00:00Z
kaust.acknowledged.supportUnitOffice of Sponsored Research
dc.date.published-online2019-10-22
dc.date.published-print2020-01-16


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