Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

Handle URI:
http://hdl.handle.net/10754/622452
Title:
Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression
Authors:
Abdul Jameel, Abdul Gani; Naser, Nimal ( 0000-0002-2740-2179 ) ; Emwas, Abdul-Hamid M.; Dooley, Stephen; Sarathy, Mani ( 0000-0002-3975-6206 )
Abstract:
An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.
KAUST Department:
Clean Combustion Research Center; Imaging and Characterization Core Lab
Citation:
Abdul Jameel AG, Naser N, Emwas A-H, Dooley S, Sarathy SM (2016) Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression. Energy & Fuels 30: 9819–9835. Available: http://dx.doi.org/10.1021/acs.energyfuels.6b01690.
Publisher:
American Chemical Society (ACS)
Journal:
Energy & Fuels
Issue Date:
14-Sep-2016
DOI:
10.1021/acs.energyfuels.6b01690
Type:
Article
ISSN:
0887-0624; 1520-5029
Sponsors:
This work was supported by the Saudi Aramco R&DC and Clean Combustion Research Center 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 Clean Combustion Research Center.
Additional Links:
http://pubs.acs.org/doi/full/10.1021/acs.energyfuels.6b01690
Appears in Collections:
Articles; Advanced Nanofabrication, Imaging and Characterization Core Lab; Clean Combustion Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorAbdul Jameel, Abdul Ganien
dc.contributor.authorNaser, Nimalen
dc.contributor.authorEmwas, Abdul-Hamid M.en
dc.contributor.authorDooley, Stephenen
dc.contributor.authorSarathy, Manien
dc.date.accessioned2017-01-02T09:28:32Z-
dc.date.available2017-01-02T09:28:32Z-
dc.date.issued2016-09-14en
dc.identifier.citationAbdul Jameel AG, Naser N, Emwas A-H, Dooley S, Sarathy SM (2016) Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression. Energy & Fuels 30: 9819–9835. Available: http://dx.doi.org/10.1021/acs.energyfuels.6b01690.en
dc.identifier.issn0887-0624en
dc.identifier.issn1520-5029en
dc.identifier.doi10.1021/acs.energyfuels.6b01690en
dc.identifier.urihttp://hdl.handle.net/10754/622452-
dc.description.abstractAn improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.en
dc.description.sponsorshipThis work was supported by the Saudi Aramco R&DC and Clean Combustion Research Center 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 Clean Combustion Research Center.en
dc.publisherAmerican Chemical Society (ACS)en
dc.relation.urlhttp://pubs.acs.org/doi/full/10.1021/acs.energyfuels.6b01690en
dc.titlePredicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regressionen
dc.typeArticleen
dc.contributor.departmentClean Combustion Research Centeren
dc.contributor.departmentImaging and Characterization Core Laben
dc.identifier.journalEnergy & Fuelsen
dc.contributor.institutionDepartment of Chemical and Environmental Sciences, University of Limerick, Limerick, Irelanden
kaust.authorAbdul Jameel, Abdul Ganien
kaust.authorNaser, Nimalen
kaust.authorEmwas, Abdul-Hamid M.en
kaust.authorSarathy, Manien
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