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This presentation demonstrates equivalence of DCN (D6890) and ICN (D8183) for measuring reactivity of jet fuels.
This report is an updated version of the 2014 Compendium of Experimental Cetane Number Data and presents a compilation of measured cetane numbers for pure chemical compounds. It includes all available single-compound cetane number data found in the scientific literature up until December 2016 as well as a number of previously unpublished values, most measured over the past decade at the National Renewable Energy Laboratory. This version of the compendium contains cetane values for 496 pure compounds, including 204 hydrocarbons and 292 oxygenates. 176 individual measurements are new to this version of the compendium, all of them collected using ASTM Method D6890, which utilizes an Ignition Quality Tester (IQT) a type of constant-volume combustion chamber. For many compounds, numerous measurements are included, often collected by different researchers using different methods. The text of this document is unchanged from the 2014 version, except for the numbers of compounds in Section 3.1, the Appendices, Table 1. Primary Cetane Number Data Sources and Table 2. Number of Measurements Included in Compendium. Cetane number is a relative ranking of a fuel's autoignition characteristics for use in compression ignition engines. It is based on the amount of time between fuel injection and ignition, also known as ignition delay. The cetane number is typically measured either in a single-cylinder engine or a constant-volume combustion chamber. Values in the previous compendium derived from octane numbers have been removed and replaced with a brief analysis of the correlation between cetane numbers and octane numbers. The discussion on the accuracy and precision of the most commonly used methods for measuring cetane number has been expanded, and the data have been annotated extensively to provide additional information that will help the reader judge the relative reliability of individual results.
This report is an updated version of the 2004 Compendium of Experimental Cetane Number Data and presents a compilation of measured cetane numbers for pure chemical compounds. It includes all available single compound cetane number data found in the scientific literature up until March 2014 as well as a number of unpublished values, most measured over the past decade at the National Renewable Energy Laboratory. This Compendium contains cetane values for 389 pure compounds, including 189 hydrocarbons and 201 oxygenates. More than 250 individual measurements are new to this version of the Compendium. For many compounds, numerous measurements are included, often collected by different researchers using different methods. Cetane number is a relative ranking of a fuel's autoignition characteristics for use in compression ignition engines; it is based on the amount of time between fuel injection and ignition, also known as ignition delay. The cetane number is typically measured either in a single-cylinder engine or a constant volume combustion chamber. Values in the previous Compendium derived from octane numbers have been removed, and replaced with a brief analysis of the correlation between cetane numbers and octane numbers. The discussion on the accuracy and precision of the most commonly used methods for measuring cetane has been expanded and the data has been annotated extensively to provide additional information that will help the reader judge the relative reliability of individual results.
Cetane number is one of the most important fuel performance metrics for mixing controlled compression-ignition “diesel” engines, quantifying a fuel’s propensity for autoignition when injected into end-of-compression-type temperature and pressure conditions. The historical default and referee method on a Cooperative Fuel Research (CFR) engine configured with indirect fuel injection and variable compression ratio is cetane number (CN) rating. A subject fuel is evaluated against primary reference fuel blends, with heptamethylnonane defining a low-reactivity endpoint of CN = 15 and hexadecane defining a high-reactivity endpoint of CN = 100. While the CN scale covers the range from zero (0) to 100, typical testing is in the range of 30 to 65 CN. Alternatively, several constant-volume combustion chamber (CVCC)-based cetane rating devices have been developed to rate fuels with an equivalent derived cetane number (DCN) or indicated cetane number (ICN). These devices measure ignition delay for fuel injected into a fixed volume of high-temperature and high-pressure air to simulate end-of-compression-type conditions. In this study, a range of novel fuel compounds are evaluated across three CVCC methods: the Ignition Quality Tester (IQT), Fuel Ignition Tester (FIT), and Advanced Fuel Ignition Delay Analyzer (AFIDA). Resulting DCNs and ICNs are compared for fuels within the normal diesel fuel range of reactivity, as well as very high (~100) and very low DCNs/ICNs (~5). Distinct differences between results from various devices are discussed. This is important to consider because some new, high-efficiency advanced compression-ignition (CI) engine combustion strategies operate with more kinetically controlled distributed combustion as opposed to mixing controlled diffusion flames. These advanced combustion strategies may benefit from new fuel chemistries, but current rating methods of CN, DCN, and ICN may not fully describe their performance. In addition, recent evidence suggests ignition delay in modern on-road diesel engines with high-pressure common rail fuel injection systems may no longer directly correlate to traditional CN fuel ratings. Simulated end-of-compression conditions are compared for CN, DCN, and ICN and discussed in the context of modern diesel engines to provide additional insight. Results highlight the potential need for revised and/or multiple fuel test conditions to measure fuel performance for advanced CI strategies.
The next generation of alternative fuels is being explored through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels offers a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Current work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present report improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5'-(furan-2-ylmethylene)bis(2-methylfuran), 5,5'-((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl)methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furan-based molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements presented in this paper, enables chemists to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.
Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.
Experiments using pure compounds, National Jet Fuels Combustion Program (NJFCP) test fuels, and commercial jet fuels were conducted to demonstrate the equivalence of the indicated cetane number (ICN) and derived cetane number (DCN) for jet fuels. The calibrated range for ICN was also extended to lower cetane number (CN) values (5 to 35) to allow CN quantification for jet fuel synthetic blending components (SBCs) with low CN. ICN and DCN were shown to be highly correlated for values above about 30. This study presents the most comprehensive comparison of these two methods published to date. Because of the importance of low-volume test methods for early-stage SBC production process development, we demonstrated that ICN and DCN can be accurately measured with 15 mL of fuel, well below 40 to 100 mL required by standard methods. ICN or DCN is important for jet fuels because fuels with lower CN are more prone to lean blowout (LBO), an undesirable operational failure in a jet engine. Comparing data on a fuel-to-air ratio (Φ) at LBO for the NJFCP fuels shows similar linear correlations for ICN and DCN. Ignition delay measurements at lower-pressure and higher-temperature conditions may be more directly relevant to LBO. At 675 °C, 0.5 MPa, and a global Φ of roughly 0.68, ignition delay time correlations to LBO were similar to those produced from DCN and ICN. A much weaker correlation was obtained with a global Φ value of 0.34.
Our study investigates the autoignition behavior of two gasoline surrogates doped with an alkyl nitrate cetane enhancer, 2-ethyl-hexyl nitrate (2EHN) to better understand dopant interactions with the fuels, including influences of accelerating kinetic pathways and enhanced exothermicity. A primary reference fuel (PRF) blend of n-heptane/iso-octane, and a toluene reference fuel (TRF) blend of n-heptane/iso-octane/toluene are used where the aromatic fraction of the latter is set to 20% (liquid volume), while the content of n-heptane is adjusted so that the overall reactivity of the undoped fuels is similar, e.g., Anti-Knock Index (AKI) of similar to 91, Cetane Number (CN) similar to 25. Doping levels of 0.1, 1.0 and 3.0% (liquid volume basis) are used where tests are conducted within a rapid compression machine (RCM) at a compressed pressure of 21 bar, covering temperatures from 675 to 1025 K with stoichiometric fuel-oxygen ratios at O-2 = 11.4%. At the experimental conditions, it is found that the doping effectiveness of 2EHN is fairly similar between the two fuels, though 2EHN is more effective in the aromatic blend at the lowest temperatures, while it is slightly more effective in the non-aromatic blend at intermediate temperatures. Furthermore, kinetic modeling of the experiments indicates that although some of the reactivity trends can be captured using a detailed model, the extents of predicted Cetane Number enhancement by 2EHN are too large, while differences in fuel interactions for the two fuels result in excessive stimulation of the non-aromatic blend. Sensitivity analysis using the kinetic model indicates that the CH 2 O and CH 3 O 2 chemistry are very sensitive to the dopant at all conditions. The rate of 2EHN decomposition is only important at low temperatures where its decomposition rate is slow due to the high activation energy of the reaction. At higher temperatures, dopant-derived 3-heptyl radicals are predicted to play an important role stimulating ignition. Finally, nitrogen chemistry is important through the 'NO - NO 2 loop' where this can generate substantial amounts of OH. But, at the highest doping levels the formation of methyl and ethyl nitrite, and nitric acid significantly competes with this so that less OH is generated and this constrains the reactivity enhancement of 2EHN.
Much of the research on advanced biofuels is devoted to the study of novel chemical pathways for converting nonfood biomass into liquid fuels that can be blended with existing transportation fuels. Many compounds under consideration are not found in the existing fuel supplies. Often, the physical properties needed to assess the viability of a potential biofuel are not available. The only reliable information available may be the molecular structure. Group contribution methods for estimating physical properties from molecular structure have been used for more than 60 years. The most common application is estimation of thermodynamic properties. More recently, group contribution methods have been developed for estimating rate dependent properties including cetane and octane numbers. Often, published group contribution methods are limited in terms of types of function groups and range of applicability. In this study, a new, broadly-applicable group contribution method based on an artificial neural network was developed to estimate cetane number research octane number, and motor octane numbers of hydrocarbons and oxygenated hydrocarbons. The new method is more accurate over a greater range molecular weights and structural complexity than existing group contribution methods for estimating cetane and octane numbers.
Abstract Discovering renewable fuels and fuel additives is paramount in reducing carbon emissions from internal combustion engines. Terpenes, a group of compounds that can be synthesized from plant matter and microorganisms, have gained significant interest in recent years as promising candidates for fuels/additives. Terpenes are a diverse class of compounds that contain rings and methyl branches, resulting in high energy densities and optimal cold weather behavior. Their variation in bond order, carbon chains, and functional groups lead to varying degrees of soot formation and performance in existing engines. The present work leverages predictive models, namely artificial neural networks, to predict the cetane number (CN), sooting tendency (quantified with yield sooting index, YSI), and energy density (quantified with lower heating value, LHV) of terpenes and hydrogenated terpenes whose sooting propensities were previously determined through experimental means. Predicted sooting propensities of these terpenes are compared with experimental values, and predicted cetane numbers and energy densities are used to comment on the compounds’ ability to act as fuels/additives. Expected prediction errors for CN, YSI, and LHV, defined by blind test set median absolute error, are within 5.56 cetane units, 3.63 yield sooting index units, and 0.77 MJ/kg respectively. Additionally, the present work investigates a variety of correlation/dependence metrics for property-property relationships, furthering our understanding of how combustion-relevant properties are related.
Single pulse shock tube experiments were conducted at 50 atm nominal pressure and 4 ms nominal reaction time over a temperature range of 900–1800 K, to study the pyrolysis speciation of a multi-component jet fuel, F-24, and six cetane number (CN) specialty fuels - CN30, CN35, CN40, CN45, CN50, and CN55. Gas chromatography (GC) was used to qualitatively and quantitatively analyze the post shock gases. The relationship between the formation of key pyrolysis species and the chemically controlled combustion propensity as reflected by the cetane number of each fuel was examined. A surrogate-based mechanism from the CRECK Modelling Group and chemical-functional group based optimized surrogates (CFGO) were used to simulate the pyrolysis speciation results. The model was able to capture the chemistry of most species except two important pyrolysis intermediates – ethylene and acetylene. Chemical kinetic analyses were performed to identify the important reactions which affect the chemistry of these species; however, the rate parameters of critical reactions were found to be unsuitable for simulating the present high-pressure studies. Here, to address this unsuitability, a theory-based fall-off analysis for three reactions representing the decomposition of ethylene and subsequent formation of acetylene was performed, and these are included in an updated version of the CRECK mechanism. This update resolves discrepancies between the experimental results and simulations for ethylene and acetylene. Reaction flux analyses using the updated surrogate model were also performed to identify the important reaction pathways responsible for the formation of crucial species and to provide an analysis of the chemistry of complex multi-component fuel systems. The fundamental reactions responsible for driving pyrolysis chemistry were greatly influenced by the chemical functional groups present in these fuels. In addition to updating the rate parameters of specific reactions to improve modeling, this study also emphasizes the effectiveness of the fuel-surrogate approach, where surrogates representing the chemical functional group composition of the parent fuel serve as a valuable tool for predicting the combustion chemistry of novel fuels.
The focus of the present paper was to characterize Reactivity Controlled Compression Ignition (RCCI) using a single-fuel approach of gasoline and gasoline mixed with a commercially available cetane improver on a multi-cylinder engine. RCCI was achieved by port-injecting a certification grade 96 research octane gasoline and direct-injecting the same gasoline mixed with various levels of a cetane improver, 2-ethylhexyl nitrate (EHN). The EHN volume percentages investigated in the direct-injected fuel were 10, 5, and 2.5%. The combustion phasing controllability and emissions of the different fueling combinations were characterized at 2300 rpm and 4.2 bar brake mean effective pressure over a variety of parametric investigations including direct injection timing, premixed gasoline percentage, and intake temperature. Comparisons were made to gasoline/diesel RCCI operation on the same engine platform at nominally the same operating condition. The experiments were conducted on a modern four cylinder light-duty diesel engine that was modified with a port-fuel injection system while maintaining the stock direct injection fuel system. The pistons were modified for highly premixed operation and feature an open shallow bowl design. The results indicate that the authority to control the combustion phasing through the fuel delivery strategy (e.g., direct injection timing or premixed gasoline percentage) is not a strong function of the EHN concentration in the direct-injected fuel. It was also observed that NOx emissions are a strong function of the global EHN concentration in-cylinder and the combustion phasing. Finally, in general, NOx emissions are significantly elevated for gasoline/gasoline+EHN operation compared with gasoline/diesel RCCI operation at a given operating condition.
With higher volatility and longer ignition delay characteristics than typical diesel fuel, low cetane naphtha fuel has been shown to promote partially premixed combustion and produce lower soot for improved fuel economy. In this study, emission performance of low cetane, low octane naphtha (CN 35, RON 60) as a drop-in fuel was examined on a MY13 Cummins ISX15 6-cylinder heavy-duty on-highway truck engine and aftertreatment system. Using the production hardware and development calibrations, both the engine-out and tailpipe emissions of naphtha and ultra-low sulfur diesel (ULSD) fuels were examined during the EPA s heavy-duty emission testing cycles. Without any modification to the calibrations, the tailpipe emissions were comparable when using naphtha or ULSD on the heavy duty Federal Test Procedure (FTP) and ramped modal cycle (RMC) test cycles. Overall lower CO2 emissions and fuel consumption were also measured for naphtha due in part to its higher heating value and higher hydrogen to carbon ratio. Engine-out and tailpipe NOx emissions were lower for naphtha fuel at the same catalyst conversion levels and measured particulate matter (PM) emissions were also lower when using naphtha due to its higher volatility and lower aromatic content compared to ULSD. To help assess the potential impact on diesel particulate filter design and operation, engine-out PM samples were collected and characterized at the B50 operating point. A significant reduction in elemental carbon (EC) within the particulate emissions was found when using naphtha compared to ULSD.
Fundamental understanding of in-cylinder processes in diesel engines is important to screen emerging biofuels and advanced combustion modes that can reduce greenhouse gas emissions and regulated pollutants including soot. In this study, the role of fuel properties on spray development and combustion is investigated by systematically isolating chemical and thermophysical effects. Three different fuels are considered, two with similar chemical properties and two with similar thermophysical properties with one fuel common to both groups. Experiments are performed in a constant-pressure flow chamber de-signed to provide stable test conditions and facilitate acquisition of at least 150 injections in quick succession for each fuel under reacting conditions at high-pressure, high-temperature ambient conditions using a modified conventional diesel engine injector. Further, high speed optical diagnostics including rainbow schlieren deflectometry, OH* chemiluminescence, and a two-color pyrometry system are employed to simultaneously image the transient spray and reacting jets. Image analysis is performed to determine liquid length, vapor penetration length, timing and location of first stage and main ignition events, lift-off location, total soot mass, and more. Results show that fuels with similar chemical properties or cetane number (CN) exhibit similar delay times for first stage and main ignition events as may be expected, but very different liquid length, first stage and main ignition locations, lift-off length, apparent turbulent flame speed, and soot formation. As such, the ability to characterize candidate biofuels with CN or other parameters derived from simple flame configurations is called into question. In this study, thermo-physical properties controlling the liquid length are identified as the main contributing factor for the observed differences.
Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.
In an effort aimed at predicting the combustion behavior of a new fuel in a conventional diesel engine, cetane (n-hexadecane) fuel was used in a military engine across the entire speed–load operating range. The ignition delay was characterized for this fuel at each operating condition. A chemical ignition delay was also predicted across the speed–load range using a detailed chemical kinetic mechanism with a constant pressure reactor model. At each operating condition, the measured in-cylinder pressure and predicted temperature at the start of injection were applied to the detailed n-hexadecane kinetic mechanism, and the chemical ignition delay was predicted without any kinetic mechanism calibration. The modeling results show that fuel–air parcels developed from the diesel spray with an equivalence ratio of 4 are the first to ignite. The chemical ignition delay results also showed decreasing igntion delays with increasing engine load and speed, just as the experimental data revealed. At lower engine speeds and loads, the kinetic modeling results show the characteristic two-stage negative temperature coefficient behavior of hydrocarbon fuels. However, at high engine speeds and loads, the reactions do not display negative temperature coefficient behavior, as the reactions proceed directly into high-temperature pathways due to higher temperatures and pressure at injection. A moderate difference between the total and chemical ignition delays was then characterized as a phyical delay period that scales inversely with engine speed. This physical delay time is representative of the diesel spray development time and is seen to become a minority fraction of the total igntion delay at higher engine speeds. In addition, the approach used in this study suggests that the ignition delay and thus start of combustion may be predicted with reasonable accuracy using kinetic modeling to determine the chemical igntion delay. Then, in conjunction with the physical delay time (experimental or modeling based), a new fuel’s acceptability in a conventional engine could be assessed by determining that the total ignition delay is not too short or too long.
Artificial neural networks (ANNs) are exceptional at forming non-linear correlations between multivariate input and target variables; however, they are often seen as a “black box” approach, since how ANNs form these correlations is somewhat ambiguous. Furthermore, the process underlying how ANNs learn from inlier and outlier samples within the input dataset is not fully understood. Intuitively, it is expected that training ANNs with inlier samples will increase prediction accuracy and training with outlier samples will reduce prediction accuracy; though, in practice, this is not always true. The present work identifies and analyzes inliers and outliers of existing experimental cetane number (CN) data encompassing a variety of compounds and compound groups. It also investigates how ANNs trained to predict CN perform with and without outliers included in the training data, and whether a relationship exists between inliers/outliers and ANN prediction accuracy across the whole dataset and for individual samples. Additionally, individual outlier compounds are analyzed, highlighting how they structurally differ from inlier compounds.
A four-step autoignition model was used to derive an expression for the ignition delay (ID) measured in ignition quality testers (IQT) with the derived cetane number (DCN) determined from this ID using the ASTM D6890 standard correlation. The model predicts DCN for individual compounds and blends as a function of each compound's global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in each compound. Measurement data for 125 compounds and 94 binary and ternary blends (including oxygenates: alcohols, aldehydes, esters, ethers, and ketones), from literature and from measurements performed in our lab, were used to obtain the dependence of the measured ignition delay on each functional group. The new kinetic model-based group contribution method was able to predict the ignition delay of both pure compounds and blends, with an average DCN error of 4.4 (19%) and 2.8 (11%), respectively. Here, the blend model was also used to develop an ID mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule gave an average DCN error of 3.6 (16%) for blends. Both the blend model and mixing rule were found to be superior compared to standard linear by volume fraction or mole fraction mixing rules commonly used to estimate the DCN of mixtures.