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dc.contributor.authorAl Ibrahim, Emad
dc.contributor.authorFarooq, Aamir
dc.date.accessioned2021-05-03T07:39:57Z
dc.date.available2021-05-03T07:39:57Z
dc.date.issued2021-04-23
dc.date.submitted2020-11-19
dc.identifier.citationAl Ibrahim, E., & Farooq, A. (2021). Prediction of the Derived Cetane Number and Carbon/Hydrogen Ratio from Infrared Spectroscopic Data. Energy & Fuels. doi:10.1021/acs.energyfuels.0c03899
dc.identifier.issn0887-0624
dc.identifier.issn1520-5029
dc.identifier.doi10.1021/acs.energyfuels.0c03899
dc.identifier.urihttp://hdl.handle.net/10754/669059
dc.description.abstractA model for the prediction of the derived cetane number (DCN) and carbon/hydrogen ratio (C/H) of hydrocarbon mixtures, diesel fuels, and diesel–gasoline blends has been developed on the basis of infrared (IR) spectroscopy data of pure components. IR spectra of 65 neat hydrocarbon species were used to generate spectra of 127 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 44 real fuels were calculated using n-paraffin, isoparaffin, olefin, naphthene, aromatic, and oxygenate (PIONA-O) class averages of pure components. It is shown that this strategy retains knowledge of C/H, an important indicator of the chemical structure. Three methods were compared to assess the prediction of DCN and C/H ratio from the assembled IR spectra, i.e., partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). It was found that ANNs gave the best performance with DCN prediction errors of ±1.1 on average and C/H prediction errors of ∼0.8%. Lasso-regularized linear models were also used to find simple combinations of wavenumbers that yield acceptable estimations.
dc.description.sponsorshipThis work was funded by the Office of Sponsored Research at King Abdullah University of Science and Technology (KAUST). The authors are thankful to Prof. Mani Sarathy and Dr. Abdul Gani Abdul Jameel for helpful discussions. The authors are also thankful to Huda Badghaish for her help with the TOC graphic.
dc.publisherAmerican Chemical Society (ACS)
dc.relation.urlhttps://pubs.acs.org/doi/10.1021/acs.energyfuels.0c03899
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in Energy & 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.0c03899.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titlePrediction of the Derived Cetane Number and Carbon/Hydrogen Ratio from Infrared Spectroscopic Data
dc.typeArticle
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentClean Combustion Research Center
dc.identifier.journalEnergy & Fuels
dc.rights.embargodate2022-04-23
dc.eprint.versionPost-print
kaust.personAl Ibrahim, Emad
kaust.personFarooq, Aamir
refterms.dateFOA2021-05-04T06:41:58Z
kaust.acknowledged.supportUnitOffice of Sponsored Research


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This document is the Accepted Manuscript version of a Published Work that appeared in final form in Energy & 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.0c03899.
Except where otherwise noted, this item's license is described as This document is the Accepted Manuscript version of a Published Work that appeared in final form in Energy & 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.0c03899.