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dc.contributor.authorAbdul Jameel, Abdul Gani
dc.contributor.authorOudenhoven, Vincent Van
dc.contributor.authorEmwas, Abdul-Hamid M.
dc.contributor.authorSarathy, Mani
dc.date.accessioned2018-04-30T06:58:23Z
dc.date.available2018-04-30T06:58:23Z
dc.date.issued2018-04-17
dc.identifier.citationAbdul Jameel AG, Oudenhoven VV, Emwas A-H, Sarathy SM (2018) Predicting octane number using nuclear magnetic resonance spectroscopy and artificial neural networks. Energy & Fuels. Available: http://dx.doi.org/10.1021/acs.energyfuels.8b00556.
dc.identifier.issn0887-0624
dc.identifier.issn1520-5029
dc.identifier.doi10.1021/acs.energyfuels.8b00556
dc.identifier.urihttp://hdl.handle.net/10754/627695
dc.description.abstractMachine learning algorithms are attracting significant interest for predicting complex chemical phenomenon. In this work, a model to predict research octane number (RON) and motor octane number (MON) of pure hydrocarbons, hydrocarbon-ethanol blends and gasoline-ethanol blends has been developed using artificial neural networks (ANN) and molecular parameters from 1H nuclear Magnetic Resonance (NMR) spectroscopy. RON and MON of 128 pure hydrocarbons, 123 hydrocarbon-ethanol blends of known composition and 30 FACE (fuels for advanced combustion engines) gasoline-ethanol blends were utilized as a dataset to develop the ANN model. The effect of weight % of seven functional groups including paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups and ethanolic OH groups on RON and MON was studied. The effect of branching (i.e., methyl substitution), denoted by a parameter termed as branching index (BI), and molecular weight (MW) were included as inputs along with the seven functional groups to predict RON and MON. The topology of the developed ANN models for RON (9-540-314-1) and MON (9-340-603-1) have two hidden layers and a large number of nodes, and was validated against experimentally measured RON and MON of pure hydrocarbons, hydrocarbon-ethanol and gasoline-ethanol blends; a good correlation (R2=0.99) between the predicted and the experimental data was obtained. The average error of prediction for both RON and MON was found to be 1.2 which is close to the range of experimental uncertainty. This shows that the functional groups in a molecule or fuel can be used to predict its ON, and the complex relationship between them can be captured by tools like ANN.
dc.description.sponsorshipThis work was supported by the Saudi Aramco R&DC and Clean Combustion Research Center (CCRC) at King Abdullah University of Science and Technology (KAUST) under the FUELCOM Research Program. The work was also funded by KAUST competitive research funding awarded to the CCRC.
dc.publisherAmerican Chemical Society (ACS)
dc.relation.urlhttps://pubs.acs.org/doi/10.1021/acs.energyfuels.8b00556
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.8b00556.
dc.subjectRON
dc.subjectMON
dc.subjectfunctional group
dc.subject1H NMR
dc.subjectgasoline ethanol
dc.subjectmachine learning
dc.titlePredicting octane number using nuclear magnetic resonance spectroscopy and artificial neural networks
dc.typeArticle
dc.contributor.departmentChemical Engineering Program
dc.contributor.departmentClean Combustion Research Center
dc.contributor.departmentCombustion and Pyrolysis Chemistry (CPC) Group
dc.contributor.departmentImaging and Characterization Core Lab
dc.contributor.departmentNMR
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalEnergy & Fuels
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer and Electrical Engineering, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1
kaust.personAbdul Jameel, Abdul Gani
kaust.personOudenhoven, Vincent Van
kaust.personEmwas, Abdul-Hamid M.
kaust.personSarathy, Mani
refterms.dateFOA2019-04-17T00:00:00Z
dc.date.published-online2018-04-17
dc.date.published-print2018-05-17


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