Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression
KAUST DepartmentChemical and Biological Engineering Program
Clean Combustion Research Center
Imaging and Characterization Core Lab
Mechanical Engineering Program
Physical Sciences and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/622452
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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.
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.
SponsorsThis 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.
PublisherAmerican Chemical Society (ACS)
JournalEnergy & Fuels