Energy & Fuels, volume 32, issue 5, pages 6309-6329
Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks
Publication type: Journal Article
Publication date: 2018-04-17
Journal:
Energy & Fuels
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 5.3
ISSN: 08870624, 15205029
General Chemical Engineering
Energy Engineering and Power Technology
Fuel Technology
Abstract
Machine 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 (ANNs) 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 data set to develop the ANN model. The effect of weight percent 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 w...
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GOST
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Abdul Jameel A. G. et al. Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks // Energy & Fuels. 2018. Vol. 32. No. 5. pp. 6309-6329.
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Abdul Jameel A. G., Van Oudenhoven V., Emwas A., Sarathy S. M. Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks // Energy & Fuels. 2018. Vol. 32. No. 5. pp. 6309-6329.
Cite this
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TY - JOUR
DO - 10.1021/acs.energyfuels.8b00556
UR - https://doi.org/10.1021/acs.energyfuels.8b00556
TI - Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks
T2 - Energy & Fuels
AU - Van Oudenhoven, Vincent
AU - Abdul Jameel, Abdul Gani
AU - Sarathy, S. Mani
AU - Emwas, Abdul-Hamid
PY - 2018
DA - 2018/04/17
PB - American Chemical Society (ACS)
SP - 6309-6329
IS - 5
VL - 32
SN - 0887-0624
SN - 1520-5029
ER -
Cite this
BibTex
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@article{2018_Abdul Jameel,
author = {Vincent Van Oudenhoven and Abdul Gani Abdul Jameel and S. Mani Sarathy and Abdul-Hamid Emwas},
title = {Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks},
journal = {Energy & Fuels},
year = {2018},
volume = {32},
publisher = {American Chemical Society (ACS)},
month = {apr},
url = {https://doi.org/10.1021/acs.energyfuels.8b00556},
number = {5},
pages = {6309--6329},
doi = {10.1021/acs.energyfuels.8b00556}
}
Cite this
MLA
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Abdul Jameel, Abdul Gani, et al. “Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks.” Energy & Fuels, vol. 32, no. 5, Apr. 2018, pp. 6309-6329. https://doi.org/10.1021/acs.energyfuels.8b00556.