Relationships between diesel fuel ignition quality indicators and composition

Cookson, David J.; Smith, Brian E.; Johnston, R R

doi:10.1016/0016-2361(93)90578-p

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…The first application of the group additivity principle to CN prediction of n -alkanes, isoalkanes, and singly substituted alkylbenzenes was reported by DeFries et al in the 1980s. Since then, different spectroscopic methods such as nuclear magnetic resonance (NMR) spectroscopy have been utilized in the correlation of fuel composition, e.g., the fraction of carbon atoms in distinct functional group categories, to CN of petroleum-derived products. ,,,,− More recently, Mueller et al have applied NMR analysis to compare and match a surrogate fuel’s compositional characteristics to those of reference diesel fuels produced from real-world refinery streams by considering 11 characteristic carbon-types, e.g., primary carbon (−CH 3 ), secondary carbon (−CH 2 −), or quaternary carbon (aliphatic carbon). The same carbon-type classification has been used in the formulation of gasoline surrogate fuels by Ahmed et al Dooley et al ,− ,, have observed nearly identical distributions of methylene (−CH 2 −), methyl (−CH 3 ) and benzyl functional groups in jet fuels and corresponding surrogate fuels.…”

Section: Structure–property Modeling Of Ignition Delay Datamentioning

confidence: 99%

A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

2015

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The assessment of the ignition quality of a wide range of oxygenated hydrocarbons is one key challenge in the identification of novel molecular entities qualifying as biofuels or biofuel blend components derived from oxygen-rich lignocellulosic feedstocks. The present contribution summarizes the results from a comprehensive experimental screening campaign targeting a diverse set of pure-component oxygenated hydrocarbon fuels and their ignition characteristics in an ASTM D6890 Ignition Quality Tester (IQT).This constant-volume combustion chamber experiment has been chosen because of its rapid screening potential. The unique collection of data is utilized for group contribution modeling with the aim of unraveling relationships between the ignition delay observed in IQT experiments and the fuel's molecular structure. We propose a simple, yet predictive estimator for the derived cetane number (DCN) of pure oxygenated hydrocarbons covering acyclic and cyclic, branched and straight, saturated and unsaturated hydrocarbons as well as alcohols, ethers, esters, ketones, aldehydes, and aromatic and polyfunctional compounds. The calculation procedure does neither require involved computational tools nor any information that could not be derived from the two-dimensional molecular graph. The model prediction yields a first estimate of the auto-ignition capability of a fuel candidate, if a sufficiently large and pure sample is not available.

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Section: Structure–property Modeling Of Ignition Delay Datamentioning

confidence: 99%

A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

2015

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“…In this study, Pearson correlation and linear regression were used to analyze the relationship between the low-temperature properties and SIMDIS distillation profile (IBP, 10~95% recovered and final boiling point (FBP)) of the blended bio-jet fuel. Pearson correlation (r) was used to explore the linear inter-correlations between the low-temperature properties and each recovered temperature of the SIMDIS distillation profile [35][36][37].…”

Section: Methods Of Correlation and Regression Analysismentioning

confidence: 99%

Analysis of the Relationship between the Low-Temperature Properties and Distillation Profiles of HEFA-Processed Bio-Jet Fuel

Jeon

Park

et al. 2023

Sustainability

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The greenhouse gas (GHG) emission mandate on jet fuel requires a gradual reduction in the fuel’s GHG emissions, up to 50%, by 2050. For this reason, the demand for bio-jet fuel blended with conventional petroleum-derived jet fuel will increase. In order to meet the quality requirement of blended fuels (ASTM D7566), modeling that can predict the correlation between properties is required. Our aim was to predict the low-temperature properties using the distillation profile results obtained from Simulated Distillation (SIMDIS) according to the carbon number and chemical compositions of bio-jet fuel through correlation and regression analysis. We used hydroprocessed ester and fatty acid (HEFA) bio-jet fuel and hydrocarbon reagents that included C8, C10, and C12 carbons and five main families of hydrocarbons for blended jet fuel. This study shows an overall trend for each component, indicating that the distilled volume fraction is more affected than the carbon number. In the case of the freezing point, by composition, n-paraffin and naphthene have regression coefficients of more than 0.85 for the 50% and 60% recovery temperatures, respectively. In terms of carbon number, the C8 sample has a significant regression coefficient for the 40% recovery temperature, and C10 has a significant regression coefficient for the initial boiling point (IBP) and 10% recovery temperature. In the case of kinematic viscosity, by composition, the regression coefficient is significant for the 20% to 40% recovery temperatures. For naphthene, the kinematic viscosity exhibited no relationship with carbon number. This information can be utilized to determine the blended ratio of bio-jet fuel and conventional jet fuel in newly certified or commercial applications.

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“…NMR has previously been applied to link chemical structures within fuels to fuel properties. Table S1 in the Supporting Information, − provides a bibliography of key references where properties important to fossil fuel production and performance, such as ignition characteristics, physical properties, distillation temperatures, soot, and several others, are related using mathematical models to resonances observed in one-dimensional (1-D) 1 H, 13 C, or two-dimensional (2-D) 1 H– 13 C HSQC NMR spectra. Establishing these mathematical relationships is possible because 13 C and 1 H NMR provide detailed, molecular-level information about a substance using very small samples.…”

Section: Introductionmentioning

confidence: 99%

“…A number of researchers have used features derived from 1-D NMR spectroscopy and 1 H and 13 C NMR to build predictive models in the areas of fuel development and characterization ( e.g. , see Cookson et al , − Meusinger and Moros, Voigt et al , and Souza et al. ).…”

Section: Introductionmentioning

confidence: 99%

Methodology for the Development of Empirical Models Relating ¹³C NMR Spectral Features to Fuel Properties

et al. 2020

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Effective formulation of new gasoline or diesel fuels for internal combustion engines would benefit from the development of reliable models for predicting key fuel properties based on a set of molecular descriptors obtained from a single measurement. This is particularly relevant in the case of renewable fuels, where the available fuel sample quantity may be limited. In this work, we present a statistically based methodology for building empirical models to predict multiple properties from onedimensional 13 C nuclear magnetic resonance (NMR) spectra measured on around 200 μL of a liquid fuel. NMR spectra contain information about the molecular composition of a sample and the carbon types and molecular substructures therein. Our approach uses this information to build sparse, interpretable models, where the predicted properties are linked to specific molecular features. The approach takes into consideration the constrained nature of the features making up the one-dimensional NMR spectrum, which, after standardization, represents a relative fuel composition. We point to the limitations in interpretability that arise when building this type of empirical predictive model and suggest how these limitations may be diminished. Among the many properties important for maximizing engine performance and minimizing emissions, we build models that predict derived cetane number and distillation temperatures, as these are of particular interest because of their links to fuel economy, drivability, and engine-out emissions. The results suggest that the properties of interest may be impacted by only a few of the 27 13 C NMR regions represented in the data, pointing to new directions for further testing in the development of improved fuels.

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Relationships between diesel fuel ignition quality indicators and composition

Cited by 7 publications

References 21 publications

A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

Analysis of the Relationship between the Low-Temperature Properties and Distillation Profiles of HEFA-Processed Bio-Jet Fuel

Methodology for the Development of Empirical Models Relating ¹³C NMR Spectral Features to Fuel Properties

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Relationships between diesel fuel ignition quality indicators and composition

Cited by 7 publications

References 21 publications

A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

Analysis of the Relationship between the Low-Temperature Properties and Distillation Profiles of HEFA-Processed Bio-Jet Fuel

Methodology for the Development of Empirical Models Relating 13C NMR Spectral Features to Fuel Properties

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Product

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Methodology for the Development of Empirical Models Relating ¹³C NMR Spectral Features to Fuel Properties