Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks

Jameel, Abdul Gani Abdul; Oudenhoven, Vincent C.O. van; Emwas, Abdul‐Hamid; Sarathy, S. Mani

doi:10.1021/acs.energyfuels.8b00556

Cited by 116 publications

(64 citation statements)

References 70 publications

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“…Although comparing between models is always tricky since datasets are often dissimilar, our ANN model seems to give smaller error than other models containing blends and FACE gasolines [18,32] and as good as studies on PRF/TPRF blends [5,6]. Another advantage of using ANN is that it preformed significantly better for FACE gasoline when compared to linear regression.…”

Section: Resultsmentioning

confidence: 91%

Octane Prediction from Infrared Spectroscopic Data

Ibrahim

Farooq

2019

Energy Fuels

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A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline-ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (Fuels for Advanced Combustion Engines) gasoline blends were calculated using PIONA (Paraffin, Isoparaffin, Olefin, Naphthene, and Aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic-CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial Neural Network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity.

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Section: Resultsmentioning

confidence: 91%

Octane Prediction from Infrared Spectroscopic Data

Ibrahim

Farooq

2019

Energy Fuels

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“…Additionally, NMR allows users to explore chemistry with much greater detail than any other method. It allows users to look at intact molecules at an atomic level and to see not just the 1 H atoms but also many other kinds of atoms ( 13 C, 15 N) or biologically reactive groups, including phosphate atoms ( 31 P) [42,43,44,45,46,47]. Furthermore, NMR spectroscopy can be used to assess unique classes of metabolites (especially protein-bound metabolites such as lipoprotein particles) and to measure certain inorganic metabolites or ions (metal ions and H+ ions via pH) that cannot be done via LC-MS or GC-MS [48,49].…”

Section: Introductionmentioning

confidence: 99%

NMR Spectroscopy for Metabolomics Research

et al. 2019

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Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

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“…Most importantly, NMR provides information on the environment of specific atom sites and their neighboring attached atoms using in two dimensions [ 108 , 121 ]. Thus, NMR spectroscopy is extensively used in a wide range of applications, including organic chemistry [ 108 ], biochemistry, polymer chemistry [ 122 ], inorganic chemistry [ 122 ], structural biology [ 52 ], physics [ 61 , 123 – 127 ], biology, and drug discovery [ 52 , 128 , 129 ]. Through NMR experiments, researchers can study samples in the solid state [ 130 – 132 ], gel phase [ 133 – 136 ], tissue state [ 137 – 139 ], gas phase, and solution state [ 140 – 143 ]; these approaches have been used to investigate molecular structures, concentration levels, and molecular dynamics [ 144 – 146 ].…”

Section: Nmr Spectroscopymentioning

confidence: 99%

Using NMR spectroscopy to investigate the role played by copper in prion diseases

et al. 2020

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Prion diseases are a group of rare neurodegenerative disorders that develop as a result of the conformational conversion of normal prion protein (PrP C) to the disease-associated isoform (PrP Sc). The mechanism that actually causes disease remains unclear. However, the mechanism underlying the conformational transformation of prion protein is partially understood-in particular, there is strong evidence that copper ions play a significant functional role in prion proteins and in their conformational conversion. Various models of the interaction of copper ions with prion proteins have been proposed for the Cu (II)-binding, cell-surface glycoprotein known as prion protein (PrP). Changes in the concentration of copper ions in the brain have been associated with prion diseases and there is strong evidence that copper plays a significant functional role in the conformational conversion of PrP. Nevertheless, because copper ions have been shown to have both a positive and negative effect on prion disease onset, the role played by Cu (II) ions in these diseases remains a topic of debate. Because of the unique properties of paramagnetic Cu (II) ions in the magnetic field, their interactions with PrP can be tracked even at single atom resolution using nuclear magnetic resonance (NMR) spectroscopy. Various NMR approaches have been utilized to study the kinetic, thermodynamic, and structural properties of Cu (II)-PrP interactions. Here, we highlight the different models of copper interactions with PrP with particular focus on studies that use NMR spectroscopy to investigate the role played by copper ions in prion diseases.

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Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks

Abstract: Predicting octane number using nuclear magnetic resonance spectroscopy and artificial neural networks. Energy & Fuels.

Cited by 116 publications

References 70 publications

Octane Prediction from Infrared Spectroscopic Data

Octane Prediction from Infrared Spectroscopic Data

NMR Spectroscopy for Metabolomics Research

Using NMR spectroscopy to investigate the role played by copper in prion diseases

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