Prediction of cetane number of diesel fuels from carbon type structural composition determined by proton NMR spectroscopy

Gülder, Ömer L.; Glavinčevski, Boris M.

doi:10.1021/i300022a005

Cited by 36 publications

(19 citation statements)

References 3 publications

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“…10) were all recorded. The peaks for methylene and methyl protons were located at 1.2-1.25 and 0.85-0.95 ppm respectively, in line with previous work 42 .…”

Section: H Nmr Spectrasupporting

confidence: 91%

1H and 13C solution- and solid-state NMR investigation into wax products from the Fischer–Tropsch process

Speight

Rourke

Wong

et al. 2011

Solid State Nuclear Magnetic Resonance

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H and 13C solid-and solution-state NMR have been used to characterise waxes produced in the Fischer-Tropsch reaction, using Co-based catalysts either unpromoted or promoted with approximately 1 wt% of either cerium or rhenium. The aim was to measure average structural information at the submolecular level of the hydrocarbon waxes produced, along with identification of the minor products, such as oxygenates and olefins, which are typically observed in these waxes. A parameter of key interest is the average number of carbon atoms within the hydrocarbon chain (N C ). A wax prepared using an unpromoted Co/Al 2 O 3 catalyst had N C ~20, whilst waxes made using rhenium-or cerium-promoted Co/Al 2 O 3 catalysts were found to both have N C ~21. All three samples contained small amounts of oxygenates and alkenes. The subtle differences found in the waxes, in particular the minor species produced, demonstrate that the different promoters have different effects during the reaction, with the Re-promoted catalyst producing the fewest by-products. From a technique point of view it is shown that the longer chain (compared to the lengths of chain in previous studies) waxes that the lack of resolution and the complexities added by the differential cross-polarisation (CP) dynamics mean that it is difficult to accurately determine N c from this approach. However the N c determined by 13 C CP magic angle spinning NMR is broadly consistent with the more accurate solution approaches used and suggest that the wax characteristics do not change in solution.On this basis an alternative approach for determining N c is suggested based on 1 H solution state NMR that provides a higher degree of accuracy of the chain length as well as information on the minor constituents.3

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“…10) were all recorded. The peaks for methylene and methyl protons were located at 1.2-1.25 and 0.85-0.95 ppm respectively, in line with previous work 42 .…”

Section: H Nmr Spectrasupporting

confidence: 91%

1H and 13C solution- and solid-state NMR investigation into wax products from the Fischer–Tropsch process

Speight

Rourke

Wong

et al. 2011

Solid State Nuclear Magnetic Resonance

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“…Several methods and correlations have been developed to predict ignition quality (i.e. D/CN) of n-paraffins [41], pure hydrocarbons [35,37,[42][43][44], blends of pure hydrocarbons [37], oxygenated hydrocarbons [34,39,[45][46][47], diesel fuels [36,37,[48][49][50][51][52][53][54][55][56][57][58], synthetic diesels [59], biofuel candidates [33], furanic biofuel additives [16] and biodiesel [38,[60][61][62]. The D/CN prediction models reported in the literature have used both physical and chemical properties of the fuel as model inputs.…”

Section: Introductionmentioning

confidence: 99%

“…Physical properties like density [36], viscosity [63], distillation characteristics [36] and aniline point [57] have been used. Molecular structure based techniques like quantitative structure property relationships (QSPR) [16,34,35,42,44], group contribution [39,43], hydrocarbon lumps [54], functional groups [37,58,59,64], carbon groups [53] and principal component analysis [45], have also been employed for predicting D/CN. Analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy [37,45,51,53,58,65], gas chromatography coupled with mass spectroscopy (GC-MS) [56], near infra-red spectroscopy (NIR) [50] and high performance liquid chromatography (HPLC) [51] have also found applications in developing chemical composition based techniques for D/CN prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Jameel

Oudenhoven

Naser

et al. 2021

SAE Int. J. Fuels Lubr.

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Artificial intelligence based computing systems like artificial neural networks (ANN) have recently found increasing applications in predicting complex chemical phenomena like combustion properties. The present work deals with the development of an ANN model which can predict the derived cetane number (DCN) of oxygenated fuels containing alcohol and ether functionalities. Experimental DCN's of 499 fuels comprising of 116 pure compounds, 222 pure compound blends, and 159 real fuel blends were used as the dataset for model development. DCN measurements of sixty new fuels were carried out in the present work and the data for the rest was collected from the literature. Fuel chemical composition expressed in the form of eight functional groups namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups and ether O groups, along with two structural parameters namely, molecular weight and branching index (BI), were used as the ten input features of the model. The qualitative and quantitative determination of functional groups present in real fuels was performed using 1 H nuclear magnetic resonance (NMR) spectroscopy. A robust ANN methodology was followed to prevent over fitting using a multilevel grid search and genetic algorithm. The final developed model with two hidden layers was tested with 15 % of randomly generated unseen points from the dataset and a regression coefficient (R 2 ) of 0.992 was observed between the experimental and predicted DCN values. An average absolute error of 0.91 obtained from the test set indicates that the developed ANN model is successful in predicting the DCN of oxygenated fuels and captures the dependence of the fuel's ignition quality (i.e. DCN) on its constituent functional groups.

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“…Although the ASTM D613 method is the widely accepted test method for CN, it exhibits several inherent disadvantages, including a considerable amount of fuel sample requirement (*1 L), a time-consuming process, a relatively high reproducibility error, and a relatively high cost [4]. Therefore, there have been many attempts to develop theoretical models to predict the CN quickly and reliably from bulk properties of diesel such as density, viscosity, aniline point, distillation temperatures, chemical composition, saponification number, iodine value, and so on [5][6][7][8]. Ladommatos et al [7] tested the accuracy of 22 equations for predicting the CN of diesel from the above mentioned properties, by comparing the predicted values of 563 fuels with the measured values.…”

Section: Introductionmentioning

confidence: 99%

Cetane Number Prediction of Biodiesel from the Composition of the Fatty Acid Methyl Esters

Tong

Jiang

et al. 2010

J Americ Oil Chem Soc

108

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In the present work, the measured cetane numbers (CN) of pure fatty acid methyl esters (FAME), as well as the FAME compositions and the reported CN of 59 kinds of biodiesels collected from literature were used to develop a simple model involving as more FAME component as possible for predicting CN of biodiesel from its FAME composition. Two different regression equations correlating the CN of pure FAME with the carbon number of fatty acid chain were obtained by regression analysis, which shows that the dependence of the CN on the carbon number varies with the unsaturated degree of fatty acid chain. The 59 biodiesels were divided into two categories and used, respectively to develop and test a multiple linear regression model (MLRM) correlating the CN of biodiesel with its FAME composition. A simple and convenient regression equation with a high accuracy and a good reproducibility (average absolute error of 0.49 CN for testing set and 1.52 CN for all data) were developed, showing excellent correlation (R 2 : 0.9904 for testing set). The model developed in the present work can be used conveniently to give a satisfactory predicted CN of biodiesel from the FAME composition.

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Prediction of cetane number of diesel fuels from carbon type structural composition determined by proton NMR spectroscopy

Cited by 36 publications

References 3 publications

1H and 13C solution- and solid-state NMR investigation into wax products from the Fischer–Tropsch process

1H and 13C solution- and solid-state NMR investigation into wax products from the Fischer–Tropsch process

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Cetane Number Prediction of Biodiesel from the Composition of the Fatty Acid Methyl Esters

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