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Surrogate formulation for marine diesel considering some important fuel physical-chemical properties Zhiyong Wu, Yebing Mao, Liang Yu, Sixu Wang, Jin Xia, Yong Qian, and Xingcai Lu Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b04253 • Publication Date (Web): 28 Mar 2019 Downloaded from http://pubs.acs.org on March 29, 2019

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Energy & Fuels

Surrogate formulation for marine diesel considering some important fuel physical-chemical properties Zhiyong Wu, Yebing Mao, Liang Yu, Sixu Wang, Jin Xia, Yong Qian, Xingcai Lu* Key Laboratory for Power Machinery and Engineering of Ministry of Education, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China

Abstract

The extremely complex composition of practical fuels highlights the significance of surrogate fuels for numerical simulation and experimental research in engine development. Heavy fuel oil (HFO) plays an important role in oceangoing transportation but the surrogate fuels to mimic its chemical and physical properties are seldom proposed. In this study, a promising approach was utilized to formulate surrogate fuels for a widely-used heavy marine diesel oil based on some important fuel physical-chemical properties. Some novel methods were applied to characterize a sample of RMG180 residual marine fuel, including GC×GC–TOFMS to characterize the composition and the simulated distillation method to characterize the volatility. Two surrogate fuels were designed for different targets: one heavy six-component surrogate was designed for premixed and spray-guided modes by emulating both real fuel’s physical, and chemical properties, including the distillation curve, cetane number (CN), and liquid density at 20 ℃; while another, lighter four-component surrogate was designed only for premixed combustion mode by simultaneously fitting H/C ratio, CN, and liquid density at 20 ℃. For validation, both surrogate fuels were produced on a lab scale and tested on the same conditions as that of the target fuel. The experimental results of the surrogates were compared with the target fuel and the values predicted by thermodynamic models based on the surrogate composition, which showed promising results.

*

Corresponding author. Tel.: +86-21-34206039, E-mail address: [email protected] (Xingcai Lu) 1

ACS Paragon Plus Environment

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Keywords: Marine diesel; Surrogate formulation; Fuel properties; Regression model

1. Introduction

Currently, over 80 percent of global trade by volume, and more than 70 percent by value is transported by ships, and seaborne trade will continue to grow annually at an estimated compound rate of 3.8 percent between 2018 and 2023.1,2 The majority of maritime transport vessels are powered by marine diesel engines,3 which plays an important role in global transportation. Interest in achieving high efficiency and low emissions combustion of marine diesel engines have increased with concerns about energy security and environmental quality. Since engine combustion is governed by complex fuel dependent physical and chemical phenomena, good knowledge of chemical reaction kinetics and key physical properties (e.g. atomization characteristics) of fuels can provide insight to the improvement of engine performance and emissions. Recently, computational fluid dynamics (CFD) coupled with detailed chemical kinetic models of real fuels provides a promising approach for modern engine optimization and combustion development.4,5 For marine engine development, this method is especially important because of the huge size, high operational difficulties and expensive cost of real world experiments. Moreover, HFO is a low-cost blend of residual fuel oils from the petroleum refining and distillate process and is an extremely complicated mixture containing thousands of hydrocarbons. As HFO is the main fuel of marine engines, being able to accurately simulate its combustion properties and characteristics is of particular importance. Current engine level CFD applications are currently unable to simulate the interaction of these complex real fuels. As a result, surrogate fuels with a limited number of components capable of emulating some physical and/or chemical properties of HFO are essential to modeling the combustion process within CFD simulations.6 For a given surrogate formulation, target properties to be matched differ depending on the intended applications. For diesel fuels, the important physical properties may include volatility (the distillation curve), density, viscosity, surface tension, cloud point, and thermal conductivity; while important chemical properties may include ignition quality (CN or derived cetane number, DCN), H/C ratio, molecular weight, lower heating value, and adiabatic flame 2


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temperature.4,6 Generally, the physical characteristics of fuels determine the evaporation of spray, the fuel-air mixing process, and the oil-gas distribution within the combustion chamber. The chemical properties of the fuel directly affect the ignition timing, soot formation, combustion rate, and combustion duration.7-10 Since the engine combustion process is closely related with the real fuel properties, a proper surrogate fuel should be able to match the key properties of the target fuel. The different combinations of the above properties lead to different surrogate formulation methods. There have been many previous studies on the surrogate formulation. Mueller et al.11,12 proposed a systematic and automated method of creating diesel surrogate fuels to match the distillation characteristics, molecular structure, DCN, and density by employing a regression model. Reiter et al.13 introduced a surrogate generation algorithm by fitting the selected liquid density, DCN, and the true boiling point (TBP) curve, creating one multicomponent surrogate for each of four target diesel fuels, respectively. The two above-mentioned studies take the physical and chemical property matching targets into account for the surrogate formulation. Dooley et al.14,15 described a four-constraint way mainly concentrating on chemical properties, by matching the DCN, H/C ratio, average molecular weight (MW), and threshold sooting index (TSI) to formulate jet fuel surrogates. However, research on surrogate formulation for HFO are scarce up to now. Currently, CFD simulations of evaporation, ignition and combustion process of marine diesel engines basically adopt some simple multi-component or single-component surrogate-fuel mechanisms.16-18 It is obvious that the over-simplification of surrogates within simulations cannot accurately represent the in-cylinder burning process of HFO. Firstly, this is because the surrogates currently used in marine diesel engine simulations cannot match key physical-chemical properties. Secondly, the components selected for simple surrogates are not the representative components of target fuel, because heavy fuel contains a large amount of heavy components (mostly falling in a carbon range of 18-2819) which is also where the challenges of surrogate designing lie. Studies on hydrocarbons in this range are still insufficient, and many compounds even have no reaction kinetics or thermodynamic and transport data. In addition, these compounds are also less common and generally difficult to obtain. Finally, few surrogate fuels can cover all the representative hydrocarbon classes contained in target fuel. The present study applied a promising approach to design surrogate fuels for a chosen heavy marine diesel oil by matching some key properties. Firstly, the main properties of target fuel, including composition, volatility, ignition

3


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quality etc, were characterized by some advanced methods. Then, a six-component large-molecule surrogate fuel(M1) and a four-component light-molecule surrogate fuel(M2) were generated in an automatic way by employing a regression model. Wherein, the long-term surrogate M1 was obtained by matching the distillation curve, ignition quality and density of the target fuel to emulate the physical and chemical properties simultaneously. To match the volatility of target fuel, M1 should contain some large components with extremely high normal boiling points. However, the kinetic models for these compounds are not currently available. Thus, in order to develop a recently available kinetic mechanism for HFO, the near-term surrogate M2 to emulate gas-phase combustion properties was obtained by matching the ignition quality, H/C ratio, and density of the target fuel. M2 contains four relatively light compounds whose chemical kinetic mechanisms are readily available. Both surrogates were produced on a lab scale to validate the matching targets.

2. Material and Method 2.1 Target Fuel The target fuel in this study is a residual marine fuel (RMG180) specified in the ISO8217,20 which is defined principally as a blend of residual oil and the distillate fuel with a maximum viscosity of 180 𝑚𝑚2/𝑠(cSt) at 50 ℃ and maximum density of 0.991 g/𝑐𝑚3 at 15 ℃. RMG180 is an important power source of cargo ships sailing on the open seas. One of the objectives of surrogate fuel is to match the properties of the target fuel, so firstly some main properties of RMG180 must be characterized. The fuel properties quantified in this work include the compositional characteristics, distillation curve, CN, H/C ratio, and density at 20 ℃, which are the important surrogate-design matching targets. The main test results are shown in section 3.1. 2.2 Surrogate Formulation Method 2.2.1 Selection of Surrogate Components and Design Properties The surrogate component selection is a balancing act of weighing the pros and cons of multiple characteristics, 4


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including: molecular structure, molecular weight, ignition quality, boiling point, density, melting point, viscosity, availability, purity (including isomeric), cost, safety, and availability of detailed and/or reduced chemical-kinetic oxidation mechanisms.11 There are some basic principles for the selection process: a) Each selected compound should be the representative of its corresponding hydrocarbon type, and all the compounds should cover the main hydrocarbon types found in the commercial fuel: n-alkanes, iso-alkanes, cycloalkanes, and aromatics b) The compounds can realize matching targets, with known properties and mature chemical-reaction kinetic mechanisms. c) The number of surrogate fuel compounds should be kept to the minimum required to adequately match the property targets, in order to reduce the complexity of the detailed kinetic mechanism.21 The detailed compound selection results, based on the test results of target fuel and selected design properties, are shown in section 3.2. In order to accurately predict spray characteristics and combustion process under engine relevant conditions, the physical and chemical properties should be simultaneously kept as the matching targets in the surrogate formulation. To meet the fuel evaporation process target, surrogate models should emphasize the emulation of the distillation curve.22 Meanwhile, the density of liquid fuel is an important property for spray prediction in engine conditions.23 The chemical reaction process associated with ignition is not only strongly influenced by the ambient conditions but also by the fuel chemical properties, including CN, H/C ratio.24 In this study, the surrogate fuel M1 was designed by matching the distillation curve, CN, and density. In order to mimic the high-temperature distillation range of RMG180, some heavy compounds with high-boiling point should be chosen as the surrogate candidates. However, the chemical-kinetic oxidation mechanisms of these compounds contained in M1 still need further developments. Therefore, a relatively light surrogate fuel M2 was formulated by fitting CN, H/C ratio, density, and its chemical-kinetic oxidation mechanism can be developed. 5


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2.2.2 Modeling for Design Properties Distillation Curve. The modeling for distillation curve in this work applies a stepwise approximation method proposed by Reiter et al.13 The strategy of the method for modeling the distillation curve with a small number of real components is based on the studies of Riazi et al25 and Albahri et al26. The general idea behind the method is illustrated in Figure 1. There are several bars surrounded by green lines in the figure and each of the bars represents a real component contained in the surrogate fuel. The height of each bar corresponds to the normal boiling point of the represented component, and the normal boiling point of component i is denoted by 𝑇𝑏,𝑖 on the ordinate axis. The width of each bar indicates its fraction in the mixture, and the fraction 𝑥𝑖 of component i is equal to (𝑥𝑖,𝑒 ― 𝑥𝑖,𝑠). 𝑥𝑖,𝑠 denotes the start boundary of the bar to component i and 𝑥𝑖,𝑒 denotes the end boundary. These bars are sequentially stacked according to the boiling temperature, which leads to form a step-shaped stepwise approximation model for the surrogate. By adjusting the proportion of each component in the surrogate fuel, the shape of the distillation model is changed accordingly. The distillation curve fitting approach considers the red shaded area (only shows component i, i-1, and i+1) indicated in Figure 1 as an optimization criterion. The shaded area between the step-shaped curve of surrogate and the smooth distillation curve of target fuel represents the difference of distillation behavior. An arbitrary component i can have two different area distributions adjoined at the degree of vaporization 𝑥𝑇𝑏,𝑖 where the temperature of the distillation curve equals to the normal boiling point of component i. In the vaporization range from start boundary 𝑥𝑖,𝑠 to 𝑥𝑇𝑏,𝑖, the normal boiling point of component i is higher than the smooth distillation curve. This shaded area is denoted as 𝐴𝑖,1. In the vaporization range between 𝑥𝑇𝑏,𝑖 and 𝑥𝑖,𝑒, the normal boiling point of component i is lower than the smooth distillation curve, and this area is denoted as 𝐴𝑖,2. For component i ― 1, the temperature corresponding to end boundary 𝑥𝑖 ― 1,𝑒 is higher than the distillation curve. So there exists only one area 𝐴𝑖 ― 1,1 . As to component i + 1, only the area 𝐴𝑖 + 1,2 exists because the normal boiling point is always lower than the 6


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distillation curve in the range of 𝑥𝑖 + 1,𝑠 and 𝑥𝑖 + 1,𝑒. For example, the area calculated for component i is 𝑥

𝑥

𝐴𝑖,1 + 𝐴𝑖,2 = ∫𝑥𝑇𝑏,𝑖(𝑇𝑏,𝑖 ― 𝑇(𝑥))𝑑𝑥 + ∫𝑥𝑖,𝑒 (𝑇(𝑥) ― 𝑇𝑏,𝑖)𝑑𝑥 𝑖,𝑠

(2)

𝑇𝑏,𝑖

Among the eq 2, 𝑇(𝑥) is a fitting function for the distillation curve of target fuel based on the experimental data. 𝑇 (𝑥) is described in the eq 3, and 𝑥 indicates the fraction of recovered distillate in the distillation experiment. The fitting parameters are computed utilizing the software TableCurve2D V5.01.27 𝑇(𝑥) = 𝑎 + 𝑏𝑥0.5 +𝑐𝑥 + 𝑑𝑥2.5 +𝑒𝑥3

(3)

Finally, a typical fitting function for the distillation curve is 𝑛

𝐹(𝑥) = ∑𝑖 = 1(𝐴𝑖,1 + 𝐴𝑖,2)

(4)

𝐹(𝑥) is the sum of the shaded areas for all the components, used as one of the fitting criterions in the regression model. As the sum of the components is 1, 𝐹(𝑥) can be considered as the average temperature difference between the boiling curve model of surrogate and the smooth distillation curve of target fuel. So, 𝐹(𝑥) can be regarded as the criterion for determining whether the surrogate fuel is sufficient to satisfy the required accuracy for matching distillation curve. Ignition Quality. The ignition quality characterized by CN is one of the design properties, as measured on a CFR engine. The calculation for CN of a mixture is assumed to obey a volume linear mixing rule,12 i.e.: 𝑛

𝐶𝑁𝑐𝑎𝑙 = ∑𝑖 = 1𝜐𝑖𝐶𝑁𝑖

(5)

𝐶𝑁𝑐𝑎𝑙 indicates the calculated CN of the mixture. 𝜐𝑖 is the volume fraction of component i and 𝐶𝑁𝑖 is its CN, while the index i goes over the n components. The volume fraction of components is calculated from the mass fraction and the liquid density at 20 ℃. Liquid Density. The liquid density at 20 ℃ is chosen to be the fitting criterion for the surrogate fuel. The density of a mixture of several components is calculated using 𝜌𝑐𝑎𝑙 =

1

(

)

𝑤𝑖 𝑛 ∑𝑖 = 1 𝜌 𝑖

(6)

Where 𝜌𝑐𝑎𝑙 represents the calculated value of mixture density, 𝑤𝑖 represents the mass fraction of component i and 7


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𝜌𝑖 indicates the liquid density at 20 ℃. There are n components included in the mixture. H/C Ratio. H/C Ratio used as one matching criterion is calculated with the following equation. 𝑛

(𝐻/𝐶)𝑐𝑎𝑙 =

∑1𝐻𝑖 ∗ 𝑥𝑖 𝑛

∑1𝐶𝑖 ∗ 𝑥𝑖

(7)

Where (𝐻/𝐶)𝑐𝑎𝑙 represents the calculated value of H/C ratio, and 𝑥𝑖 represents the mole fraction of component i, 𝐶𝑖 and 𝐻𝑖 represent the carbon number and the hydrogen number of component i, respectively. 2.2.3 Optimization Method of Surrogate Composition A multi-property regression optimization method is developed to formulate the surrogate composition in this study. The objective functions in the regression model are defined as follows: 𝑆𝑀1 = 𝑊𝐶𝑁𝐹2𝐶𝑁 + 𝑊𝐷𝐶𝐹2𝐷𝐶 + 𝑊𝜌𝐹2𝜌

(8)

𝑆𝑀2 = 𝑊𝐶𝑁𝐹2𝐶𝑁 + 𝑊(𝐻/𝐶)𝐹2(𝐻/𝐶) + 𝑊𝜌𝐹2𝜌

(9)

where 𝑆𝑀1 and 𝑆𝑀2 are the generation functions for M1 and M2, respectively, each 𝑊 is a weighting factor, and each 𝐹 is the difference percentage of a property between the measured value of target fuel and the calculated value of surrogate fuel. Each 𝐹 in the equation corresponds to a design property, as indicated by the subscript:𝐶𝑁 for cetane number, 𝐷𝐶 for distillation curve, 𝐻/𝐶 for H/C ratio, and 𝜌 for density. The four difference percentage sub functions are defined as follows: 𝑛

𝐹𝐷𝐶 = 100 ∗ 𝐹𝐶𝑁 = 100 ∗ 𝐹𝜌 = 100 ∗

∑𝑖 = 1(𝐴𝑖,1 + 𝐴𝑖,2)

(10)

𝑇𝑎𝑣𝑒 𝐶𝑁𝑐𝑎𝑙 ― 𝐶𝑁𝑚𝑒𝑎𝑠

(11)

𝐶𝑁𝑚𝑒𝑎𝑠

𝜌𝑐𝑎𝑙 ― 𝜌𝑚𝑒𝑎𝑠

(12)

𝜌𝑚𝑒𝑎𝑠

𝐹(𝐻/𝐶) = 100 ∗

(𝐻/𝐶)𝑐𝑎𝑙 ― (𝐻/𝐶)𝑚𝑒𝑎𝑠

(13)

(𝐻/𝐶)𝑚𝑒𝑎𝑠

1

(14)

𝑇𝑎𝑣𝑒 = ∫0𝑇(𝑥)𝑑𝑥

The numerator of function 𝐹𝐷𝐶 is the fitting function indicating the difference of DC, numerator of which has been explained in the previous section 2.2.2. In order to get the difference percentage function consistent with the other three design properties, the average distillation temperature 𝑇𝑎𝑣𝑒, used as the denominator in Eq. 10, is proposed. 8


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Energy & Fuels

The calculation formula is shown as Eq. 14, where T(x) is the fitting function of the distillation curve of target fuel. The calculated value of the design properties used in Eq. 11-13, denoted as 𝐶𝑁𝑐𝑎𝑙, 𝜌𝑐𝑎𝑙, and (𝐻/𝐶)𝑐𝑎𝑙, has been illustrated in Eq. 5-7, respectively, and the subscript ′𝑚𝑒𝑎𝑠′ refers to the experimental value of target fuel for each item. The procedures for running the regression model mainly include initialization and interactive calculations. The objective function ‘S’ indicates the total squared percentage difference of design properties between the surrogate fuel and the target fuel, which is to be minimized in the regression model. The values for the weighting factors can be adjusted based on a trial-and-error procedure to realize the desired matching targets between surrogate fuel and target fuel. Use the selected compounds introduced in Section 3.2 for the generation of surrogate M1and M2, and set the initial amounts for each compound and the initial weighting factors for each design property in the regression model. After initialization, run the regression model to produce a surrogate formulation that best matches the property targets. If a property does not reach the matching targets, the weighting factor of this design property is increased in the next calculation until the property meets the matching requirement. In the iterative calculation process, only one weighting factor was changed for the next calculation, so that the result of the next calculation and the direction of weighting factor adjustment can be predicted. 2.3 Test method 2.3.1 Techniques to Measure Fuel Compositional Characteristics The compositional characteristic of RMG180 is determined by utilizing an advanced method GC × GCTOFMS28 and a conventional method SH/T0659-1998 (equivalent to ASTM D2786-91).29 The preliminary analysis of RMG180 through the SH/T0659-1998 indicated the approximate content of each hydrocarbon type including paraffin, cycloalkane, aromatic and asphaltene. By applying a molecule-level analysis method GC×GC-TOFMS, it realizes a high-resolution separation and identification of the components included in RMG180. GC×GC uses a nonpolar column for the first dimension to separate the constituent compounds by boiling point and a polar column for 9


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second dimension to separate by polarity, and is able to reveal the detailed chemical structures of the molecules included. Recently, GC×GC – TOFMS has been applied in the compositional analysis of vehicle diesel fuel,30 jet fuel,31 biodegraded oil32 and crude oil.33,34 The marine diesel fuel RMG180 diluted with n-hexane was tested on a Pegasus 4D GC×GC -TOFMS instrument in this study. Though the high asphaltene content is contained in the RMG180 marine diesel fuel sample, the choice of n-hexane as a solvent can greatly reduce the possibility of introducing asphaltene into the GC column. The data processing was conducted on the ChromaTOF software, and the MAINLIB, NIST_MASS, NIST_MASS2, NIST_RI and REPLIB library database were used to determine what the detected compounds should be. Some representative components were discovered in RMG180 by this method, which is significant for deep insight into the composition of heavy marine diesel oil and the selection of candidate components for the surrogate fuels. 2.3.2 High-Temperature Simulated Distillation Curve Test Method Volatility is one of the fundamental properties of fuel. However, due to limitation of boiling points range, the considerable uncertainty, and uselessness of thermodynamic models, the commonly used distillation test method ASTM D86 cannot be utilized to characterized the volatility of RMG180.35,36 Hsieh et al.19 applied an advanced distillation curve (ADC) method, under reduced pressure, to a sample of heavy marine diesel oil IFO 380 to characterize its volatility and composition as a function of volume fraction. In this study, the volatility of RMG180 characterized by the simulated distillation curve was tested according to ASTM D7169-1637 which extends the simulated distillation to a higher boiling range. The simulated distillation is a gas chromatographic method, where a retention time calibration mixture is used to develop a retention time versus boiling point curve, and the retention times of tested sample are converted to temperature with reference to the retention time of the calibration mixture. Then the boiling points distribution of tested oil can be calculated up to the recovered amount.

10


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2.3.3 The Blending Cetane Number Test The cetane number is selected to quantify the ignition quality of target fuel, surrogate fuel, and the candidate compounds. The measurements of CN in this study were conducted on a CFR engine according to ASTM D61338. The RMG180 is a black, viscous liquid with an extremely high viscosity of about 180 𝑚𝑚2/𝑠 at 50 ℃. And RMG180 contains a large amount of asphaltenes. Usually, the low-speed marine diesel engines are equipped with HFO preheaters and larger nozzles.3,39 The fuels will be heated to a high temperature to reduce viscosity before injected. However, the CFR engine is usually used to test the ignition quality of vehicle diesel fuels or jet fuels. The viscosity of most of these light fuels is usually between 1-4 𝑚𝑚2/𝑠 at 50 ℃, which is much less than that of HFO. Thus, if the RMG180 sample is tested directly in the CFR engine, it may cause blockage in the nozzle of the test instrument. In this work, the blending CN of the blend of 20% volume fraction test fuel and 80% N-heptane was measured in a CFR engine. This can significantly reduce the viscosity of the fuels being tested. The study assumes the cetane number of the blend is a volume linear combination of the cetane number of the components, expecting a 20% blend:40 Blend CN = (0.8) × (Base Fuel CN) + (0.2) × (Test Fuel CN)

(1)

Although the typical methodology for blending cetane number results in an amplification of uncertainty, the blending cetane number data may be the only ignition-quality information available for some compounds or fuels.

3. Results and Discussion 3.1 Target Fuel Characteristics The preliminary analysis results of RMG180 fuel from SH/T0659-1998 (equivalent to ASTM D2786-91) are shown in Figure 2 which elucidate the measured contents of paraffin, naphthene, and aromatic. Figure 2 (a) illustrating the mass fraction of different hydrocarbons shows that RMG180 contains 10.6% paraffin, 28.5% naphthene, 53.3% aromatic, and 7.6% asphaltene. A stacked chart presented in Figure 2 (b) shows the breakdown of

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hydrocarbon types as a function of carbon number for the target fuel. It was found that over 45% of the total molecules fall into the range between C18 and C28. A GC × GC-TOFMS chromatogram for RMG180 marine diesel fuel is shown in Figure 3. The compounds in RMG180 were seperated to a greater extent and classified using the categories: n-alkanes, iso-alkanes, cycloalkanes,1-ring aromatics, 2-ring aromatics,and >=3-ring aromatics. The first-dimension time of chromatogram was converted into a temperature scale by correlating established boiling points of n-alkanes and their respective retention time. The area of each colored circle is proportional to the mass fraction of the corresponding compound. The chromatogram for RMG180 is found to comprise 4620 peaks, which indicates that there are more than 4620 individual compounds contained in the target fuel, as each peak corresponds to at least one compound in the chromatogram. The lightest n-alkanes detected in RMG180 was n-octane (𝐶8𝐻18) eluted at 540s from the primary column , and the heaviest n-alkanes n-hexatriacontane (𝐶36𝐻74) was detected to be eluted at 4724s from the primary column. It was found that the detected compounds contain a large number of impure elements, including: sulfur, nitrogen, oxygen, etc. The GC×GC-TOFMS test results revealed some representative structural characteristics contained in the RMG180, and the information can be very instructive for the selections of candidate components for surrogate fuel. Several identified compounds are presented in Table.1. All the listed compounds, with similarity to the library mass spectra of more than 700 (most are more than 850), are the relatively high-content species in their corresponding hydrocarbon type. The main compounds of the n-alkane class which included tetracosane (𝐶24𝐻50), heneicosane (𝐶21 𝐻44), heptacosane (𝐶27𝐻56), and nonadecane (𝐶19𝐻40), exhibited a consistent mass fraction distribution with C atoms number to the preliminary analysis result of SH/T0659-1998. Many of the branched alkanes contained in RMG180 have one or multiple methyl groups as branches, such as the typical molecules listed in Table 1. For the detected 1ring cycloalkanes, cyclohexane with long chain branching, such as pentadecylcyclohexane and octylcyclohexane, is a representative type of compound structure. Species of decalin with side chains and cycloalkanes of multi-ring linked 12


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by alkyl chain are also the common structure of multi-ring cycloalkanes in RMG180. Aromatics were the biggest hydrocarbon species found in the target fuel. The long-sidechain 1-ring aromatic, such as n-heptadecylbenzene and 1-methylundecylbenzene, presented a typical class of monocyclic aromatic compounds. A large number of naphthalene compounds and biphenyl compounds were detected in the experiment, for instance, 1methylnaphthalene and diphenylmethane. Phenanthrene is one of the most common compounds found in RMG180. Most of the tricyclic aromatic hydrocarbons are found to be composed of phenanthrene-linked alkyl side chains. The compound class of chrysene-linked alkyl side chains, such as 6-methylchrysene, also demonstrated a typical structure of the tetracyclic aromatic hydrocarbon commonly included in the RMG180 marine diesel fuel. The density of the target fuel was tested according to ISO 12185:1996,41 and the elemental analysis was conducted by the standard method ASTM D5291-16.42 The measured physical and chemical properties of the sample, summarized in Table.2, are typical of RMG180 marine diesel fuel. 3.2 Surrogate Candidate Components and Their Properties The selected components for surrogate fuels in this study are illustrated in Figure 4. Some properties of the selected components are provided in Table.3. For the large-molecule surrogate fuel M1, whose fitting targets aim at the distillation curve, CN, and density is composed of n-hexatriacontane (NHTA), pentadecylcyclohexane (PTCH), 5-phenyleicosane (5PHE), 1-methylnaphthalene (1MTN), phenanthrene (PNT), and chrysene (CHRS). The light surrogate fuel M2, matching the CN, H/C ratio, and density of target fuel, contains four constituents: n-hexadecane (NHED), 2,2,4,4,6,8,8-heptamethylnonane (HMN), decalin (DEC) and 1-methylnaphthalene (1MTN). The compounds were selected based on their ability to represent the structural characteristics of the target fuel and the selected matching targets. On the whole, all the selected molecules represented the typical structural characteristics of the target fuel showed in Table.1, and covered each type of hydrocarbon found in the target fuel illustrated in Figure 2 and Figure 3. Likewise, all the selected components are commercially available, and the properties of the compounds are known or predictable. The last column of Table 3 shows the availability of the chemical kinetics 13


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mechanism (CKM). For near-term surrogate fuel M2 aiming at the development of a detailed reaction mechanism for RMG180, the selected four components, including NHED, HMN, DEC, and 1MTN, could be provided with a relatively mature detailed CKM, while the CKM for five of the selected compounds for the long-term surrogate fuel M1 still need further development. The challenge in the selection of surrogate fuel compounds for M1 is to simultaneously meet the high boiling point to match the distillation curve, and low CN to match the ignition quality. Most of the alternative high boiling point (>350 ℃) compounds are either n-alkanes or long-chain branched aromatics or long-chain branched cycloalkanes, while all of them have a relatively high CN. PNT and CHRS were chosen not only due to the representative structure of >=3ring aromatics found in RMG180 but also the high boiling point with low CN. The representative of 2-ring aromatic found in target fuel, 1MTN, was selected to match the light end of the distillation curve with a low CN. PTCH and NHTA are representative of n-alkane and cycloalkane, respectively, while NHTA was chosen to fit the heavy end of the distillation curve, both of which were found in target fuel shown in Figure 3 and Table.1. 5PHE was selected to be one constituent of M1 due to its function for matching the middle distillation curve, and the CN of the compound is close to RMG180. The four components selected for M2 are commonly used in surrogate fuel formulation studies. Among them, HMN was chosen as representative of iso-alkanes. Iso-alkane is a common hydrocarbon type in RMG180, but since most iso-alkane molecules are difficult to separate from isomers, they are low in purity, expensive, and not readily available. However, HMN is a primary reference fuel for diesel and is readily available with high purity at a reasonable cost. The other three were also selected to meet the matching targets, covering all the hydrocarbon types found in target fuel. 3.3 Surrogate Formulation The composition of each of the surrogate fuels is shown in Table 4. For the acquired surrogate M1, the calculated liquid density at 20 ℃ shows a 3.06% deviation, which is 28.72 Kg/m3 higher than the experimental value of RMG180, 14


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and the calculated CN is 3.53 greater than the matching target. Furthermore, the average deviation within the full distillation range between the experimental data and the step approximation is 64.3 K, whereas the average deviation in the 90% distillation range is 42.55 K. The reason for the relatively higher deviation of the heavy end of the distillation curve is caused by the lack of heavy components with boiling points greater than 500 ℃. RMG180 has an extremely high-temperature and lies at the heavy end of the distillation curve due to the high-content of asphaltene. In addition, a challenging issue is to achieve both lower CN and high distillation temperature. To fit the higher boiling points, the hydrocarbon molecules with long-sidechain alkyl were chosen, however, these compounds possess high CN. Therefore, the calculated CN is kept within an acceptable tolerance while matching the distillation curves as much as possible. For surrogate M2, the calculated CN is 0.41 lower than the experimental data of RMG180. The deviation of liquid density at 20 ℃ and of H/C ratio, between surrogate and target fuel, are 4.97% and 0.1, respectively. Figure 5 shows the predicted GC×GC-TOFMS chromatogram of surrogate fuel M1 and M2, overlaid on marine diesel RMG180 composition (in gray). The area and color of each circle correspond to its mass fraction and hydrocarbon type, respectively. The position of the selected compounds in Figure 5 is determined based on the elution time of the corresponding component found in the chromatogram of target fuel RMG180. The area of each circle is determined according to the composition shown in Table 4. The background shows the distribution of RMG180 composition, for reference. Figure 5a shows that the components of M1 exhibit a wide range of polarizability and boiling range, demonstrated by 2nd retention time in y-dimension and 1st retention time in x-dimension, respectively. Due to the matching targets selected for near-term surrogate fuel M2, Figure 5b shows that the compounds mostly distribute in low polar range with lower boiling points. It also proves that when the distillation curve is the matching criterion for surrogate fuels formulation, the acquired surrogate (M1 in this study) generation could better cover the area of the target fuel’s (RMG180 in this study) chromatogram.

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3.4 Validation 3.4.1 Distillation Curve The simulated distillation curve characterized by ASTM D7169, a gas-chromatography-based method via measured elution times through a column, was used as a fitting criterion in the regression model for the generation of surrogate fuel M1. Figure 6 shows the measured distillation curve of RMG180, measured and predicted value for M1, and predicted value for M2. The measured curve for M1 through the simulated distillation method shows a stair-step shape, and each of the ‘steps’ corresponds to one component eluted in order of increasing normal boiling point. The elution ranges for each of the compounds in surrogate are shown and labeled with their corresponding compound abbreviations. The average deviation between the measured and the predicted value (the stepwise approximation illustrated the section 2.2.2) for surrogate M1 is 8.5 K. The difference may be caused by the impurities contained in the compounds or the uncertainty of the boiling point data. The good agreement between the predicted and the measured value indicates that the stepwise approximation fitting method could be utilized to predict the temperature difference. In general, the simulated distillation data of RMG180 is higher than the measured value of M1 almost over the entire distillation range, and the average deviation from T0 to T100 is about 66.7 K. However, the average difference in the distillation range of T0-T60 between RMG180 and M1, only about 26.9 K, is much lower than that of the total range. And it is believed that due to the special shape of the simulated distillation curve, the magnitudes of the targetvs-surrogate temperature differences are larger when quantified using the simulated distillation method.11 Thus, the result shows a relatively good agreement between RMG180 and M1 within the light end and middle division of the distillation curve, while the heavy end of the distillation curve accounts for a large portion of the total deviation. On one hand, the deviation was caused by extremely high-temperatures at the heavy end of the RMG180 distillation curve which is affected by the high content of asphaltene. On the other hand, it is also the result of balancing the CN matching target and the volatility matching target, when it is not available to find a high boiling-point compound 16


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Energy & Fuels

with a relatively low CN. It is evident that the selected component NHTA, found in RMG180 by GC×GC-TOFMS, representing the n-alkane hydrocarbon class in M1 are mainly responsible for the heavy-end of distillation curve, and 1MTN improved the light-end matching quality. The simulated distillation curve of M2 distributes in a relatively low boiling range between 180 ℃ and 300 ℃. As is shown in Figure 6, the average temperature deviation between M2 and RMG180 is more than 200 K throughout the total distillation range. The four light components selected for M2 could account for this large deviation. Fundamentally, the deviation resulted from the selected matching targets and the specialized kinetic-mechanism development goal of M2. 3.4.2 Ignition Quality Figure 7 shows the ignition quality of RMG180 and each of the surrogate fuels as quantified by CN. Both of the measured values for RMG180 and the two surrogate fuels were obtained from The Blending Cetane Number Test illustrated in section 2.3.3, conducted on an ASTM D613 standard. Each fuel is measured at least three times and then averaged. The predicted CN for surrogate fuels was calculated by a volume-fraction weighted linear blending rule. The measured CNs for surrogates show a good fit to the CN of RMG180, where the CN of M1 is 2.31 higher and CN of M2 is 0.16 higher than that of the target fuel. The reason for the higher CN for M1 is believed to be caused by the matching of the heavy end of the distillation curve. The surrogate fuel M2 depicts a better match for the target value of CN which is the staple fitting criterion for the near-term surrogate M2. The predicted values for surrogates were used in the regression model as a fitting criterion. Both predicted CNs replicate the corresponding measured CN of surrogates within ±1.22. 3.4.3 Density Figure 8 shows the measured and predicted results of target and surrogate-fuel densities at 20 ℃. The predicted

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densities of M1 and M2 are 968.7 kg/m3 and 893.3 kg/m3 , respectively. Both predicted values as a fitting criterion were kept within ±5% in the regression model compared to the measured density of RMG180. The experimental results of M1 and M2 are both 1-2% lower than the corresponding predicted value respectively, possibly due to the incomplete information for the candidate compounds. The measured density of M1 is 1.7% higher than the density of RMG180, relating to the target of matching distillation curve which required heavy compounds, like PNT, CHRS, and 1MTN. The experimental data of M2 is 6.04% lower than that of RMG180, which is expected because the components chosen for M2 are basically lighter than the target value except for 1MTN. 3.4.4 Elemental Analysis Figure 9 shows the target and surrogate-fuel carbon and hydrogen mass fractions. The experimental data of RMG180 was quantified using ASTM D5291, and the mass fractions of surrogate fuels were calculated based on their known compositions. As the H/C ratio is one of the fitting targets for the generation of M2, the mass fraction of carbon (hydrogen) contained in M2 falls within 0.7% (-0.7%) of the measured value for RMG180, showing a good agreement. While for M2, the matching targets of CN and distillation curve require highly unsaturated compounds, like the polycyclic aromatic hydrocarbons, which cause a relatively higher carbon content in M1.

4. Conclusions

In this study, two surrogate fuels for a heavy marine diesel from real-world refinery streams were generated by emulating the representative properties and compositional characteristics of the target fuel. The first step of the surrogate formulation approach was the selection and characterization of target fuel. Residual marine fuel (RMG180) was chosen and its composition was preliminarily analyzed by an MS method offering the content of hydrocarbon types including paraffins, cycloalkanes, aromatics and asphaltenes. A moleculelevel advanced analysis method GC×GC-TOFMS was applied to give further insight into the representative molecule structures contained in the heavy marine diesel fuel. The typical compounds identified were significantly supportive 18


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Energy & Fuels

for the selection of surrogate components. As a result of the high viscosity, high density and high impurity content of RMG180, the simulated distillation method and The Blending Cetane Number Test were performed to characterize its volatility and ignition quality. And some other key properties were also tested by some standard methods. Especially, a stepwise approximation method was used for modeling the volatility difference between surrogates and target fuel. The regression model, indicating the differences between the predicted properties of surrogate fuels and corresponding measured properties of target fuel, could automatically determine the composition of surrogate fuels in the process of iterative calculation. Two surrogate fuels were formulated for different matching targets, one (M1) was generated based on fitting volatility (distillation curve), density at 20 ℃, and ignition quality (CN), while another (M2) was obtained by fitting ignition quality, density at 20 ℃, and H/C ratio. Furthermore, M2 could be currently used for chemical kinetic mechanism development of the target fuel. The numerical work of modeling for the fuel properties was an important part of the final regression model. Finally, the fitting properties of the surrogate fuels were measured compared to those of RMG180. The primary results of the study are shown in the following parts: 1. The six-component surrogate (M1), matching volatility, ignition quality, and density, contained all the major hydrocarbon class including paraffin, cycloalkane, 1-ring aromatic, 2-ring aromatic and >=3 ring aromatic. The four-component surrogate (M2), matching ignition quality, density, and H/C ratio, contained n-alkane, iso-alkane, cycloalkane, and aromatic; 2. For M1, the average deviation of distillation points between the measured and the predicted value was 8.5 K. The average deviation between the simulated distillation data as measured for RMG180 and the measured value of M1 from T0 to T100 was about 66.7 K. However, the average value of that difference in the distillation range of T0-T60 between RMG180 and M1 was only about 26.9 K. The CN of M1 was 2.31 higher than RMG180 and the density of M1 was 1.7% higher; 3. For M2, CN was 0.16 higher than target fuel RMG180, and density of M2 was 6.04% lower. The mass fraction 19


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of carbon(hydrogen) contained in M2 fell within 0.7% (-0.7%) of the measured value for RMG180; Based on the above results, the two surrogate fuels generally achieved their respective matching targets.

Acknowledgment

The authors gratefully acknowledge the financial support from the Key Project of National Natural Science Foundation of China (No. 51425602) and the High Technology Ship Research Program-Marine Low-Speed Engine (Phase I).

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(40) Murphy, M. J.; Taylor, J. D.; McCormick, R. L. Compendium of Experimental Cetane Number Data, NREL Report No. TP-5400-67585; National Renewable Energy Laboratory; Golden, CO, 2017. (41) International Organization for Standardization. Crude Petroleum and Petroleum Products -- Determination of Density -- Oscillating U-Tube Method, ISO Standard 12185, International Organization for Standardization:1996. (42) Standard Test Methods for Instrumental Determination of Carbon, Hydrogen, and Nitrogen in Petroleum Products and Lubricants, ASTM Standard D5291-16; ASTM International: West Conshohocken, PA, USA, 2016; DOI: 10.1520/D5291. (43) Kukkadapu, G.; Wagnon, S. W.; Mehl, M.; Zhang, K.; Westbrook, C. K.; Pitz, W. J.; Mcnenly, M. J.; Sarathy, S. M.; Rodriguez, A.; Herbinet, O.; et al. An Updated Comprehensive Chemical Kinetic Model of C8-C20 n-Alkanes. 10th U.S. National Combustion Meeting 2017. (44) Sarathy, S. M.; Westbrook, C. K.; Mehl, M.; Pitz, W. J.; Togbe, C.; Dagaut, P.; Wang, H.; Oehlschlaeger, M. A.; Niemann, U.; Seshadri, K.; et al. Comprehensive Chemical Kinetic Modeling of the Oxidation of 2-Methylalkanes from C7 to C20. Combust. Flame 2011, 158 (12), 2338–2357. (45) Westbrook, C. K.; Pitz, W. J.; Mehl, M.; Curran, H. J. Detailed Chemical Kinetic Reaction Mechanisms for Primary Reference Fuels for Diesel Cetane Number and Spark-Ignition Octane Number. Proc. Combust. Inst. 2011, 33 (1), 185–192. (46) Yu, L.; Qiu, Y.; Mao, Y.; Wang, S.; Ruan, C.; Tao, W.; Qian, Y.; Lu, X., A study on the low-to-intermediate temperature ignition delays of long chain branched paraffin: Iso-cetane, Proc. Combust. Inst. 2018, https://doi.org/10.1016/j.proci.2018.08.039. (47) Oehlschlaeger, M. A.; Steinberg, J.; Westbrook, C. K.; Pitz, W. J. The Autoignition of Iso-Cetane at High to Moderate Temperatures and Elevated Pressures: Shock Tube Experiments and Kinetic Modeling. Combust. Flame 2009, 156 (11), 2165–2172. (48) Yu, L.; Wu, Z.; Qiu, Y.; Qian, Y.; Mao, Y.; Lu, X. Ignition Delay Times of Decalin over Low-to-Intermediate Temperature Ranges: Rapid Compression Machine Measurement and Modeling Study. Combust. Flame 2018, 196, 160–173. (49) Wang, M.; Zhang, K.; Kukkadapu, G.; Wagnon, S. W.; Mehl, M.; Pitz, W. J.; Sung, C. J. Autoignition of TransDecalin, a Diesel Surrogate Compound: Rapid Compression Machine Experiments and Chemical Kinetic Modeling. Combust. Flame 2018, 194, 152–163. 22


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Energy & Fuels

(50) Sun, S.; Yu, L.; Wang, S.; Mao, Y.; Lu, X. Experimental and Kinetic Modeling Study on Self-Ignition of αMethylnaphthalene in a Heated Rapid Compression Machine. Energy Fuels 2017, 31 (10), 11304–11314. (51) Wang, H.; Warner, S. J.; Oehlschlaeger, M. A.; Bounaceur, R.; Biet, J.; Glaude, P. A.; Battin-Leclerc, F. An Experimental and Kinetic Modeling Study of the Autoignition of α-Methylnaphthalene/Air and αMethylnaphthalene/n-Decane/Air Mixtures at Elevated Pressures. Combust. Flame 2010, 157 (10), 1976–1988. (52) Knovel Data Analytics: NIST ThermoDynamics Pure Compounds; RELX Group, https://app.knovel.com/web/poc/ms/discovery.html. (53) Carl L. Yaws. CHEMICAL PROPERTIES Handbook; McGraw-Hill Book Co.1999.

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Table 1. Identified Compounds in RMG180 Fuel Based on GC×GC-TOFMS Analysis Table 2. Key Properties of RMG180 Table 3. Surrogate Compounds and Their Properties Table 4. Surrogate Fuel Compositions

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Energy & Fuels

Table 1. Identified Compounds in RMG180 Fuel Based on GC×GC-TOFMS Analysis compound

Classification

CAS NO·

Formula

R.Ta (x-min/y-s)

Similarityb

Alkanes Tetracosane

n-alkane

646-31-1

C24H50

54.6/2.26

855

Heneicosane

n-alkane

629-94-7

C21H44

48.2/2.2

873

Heptacosane

n-alkane

593-49-7

C27H56

59.4/2.26

886

Nonadecane

n-alkane

629-92-5

C19H40

43.53/2.18

925

Nonadecane, 2-methyl-

iso-alkane

1560-86-7

C20H42

66.33/2.48

856

Pentadecane, 2,6,10-trimethyl-

iso-alkane

3892-00-0

C18H38

52.73/2.16

885

Hexadecane, 2,6,10,14-tetramethyl-

iso-alkane

638-36-8

C20H42

49.67/2.16

874

Cycloalkanes n-Pentadecylcyclohexane

1-ring cycloalkane

6006-95-7

C21H42

50.08/2.34

866

Cyclohexane, octyl-

1-ring cycloalkane

1795-15-9

C14H28

45.4/2.34

870

C15H28

47.13/2.46

741

C25H46

56.33/2.34

790

C23H40

54.73/2.58

740

Naphthalene, decahydro-1,4a-dimethyl-7(1-methylethyl)-, [1S-(1à,4aà,7à,8aá)]Cyclopentane, 1,1'-[4-(3cyclopentylpropyl)-1,7-heptanediyl]bis-

2-ring cycloalkane

3-ring cycloalkane

30824-818 55429-351

Aromatics 1-ring aromatic

Benzene, (1-methylundecyl)-

1-ring aromatic

2719-61-1

C18H30

43.27/2.72

754

Naphthalene, 1-methyl-

2-ring aromatic

90-12-0

C11H10

26.33/4.22

926

Diphenylmethane

2-ring aromatic

101-81-5

C13H12

29.67/3.88

842

Phenanthrene

3-ring aromatic

85-01-8

C14H10

41.13/4.72

956

Chrysene, 6-methyl-

4-ring aromatic

1705-85-7

C19H14

58.6/5.26

851

a Retention b

14752-75-

n-Heptadecylbenzene

1

time in chromatogram.

The similarity reflects the degree of matching between the peak characteristics detected in the fuel chromatogram and the library

database, and its value ranges from 0-1000. The greater the similarity, the more reliable the identification result.

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Page 26 of 38

Table 2. Key Properties of RMG180 Param.

Test Method

Density(20 ℃)

ISO 12185:1996

Cetane Number

ASTM D613

Elemental Analysis

RMG180 940 kg/m³ 40.96

ASTM D5291-16 H

11.97 wt %

C

88.03 wt %

H/C

1.62

Composition

SH/T0659-1998

paraffin

10.6 wt % naphthene

28.5 wt %

1-ring aromatic

20.3 wt %

2-ring aromatic

13.7 wt %

>= 3-ring aromatic

16.5 wt %

unidentified aromatic

2.8 wt%

asphaltene

7.6 wt%

Distillation [m/m]

ASTM D7169-16 Initial

216 ℃

0.05

325 ℃

0.1

359.5 ℃

0.2

391.5 ℃

0.3

411 ℃

0.4

427.5 ℃

0.5

443.5 ℃

0.6

464 ℃

0.7

505.5 ℃

0.8

575.5 ℃

0.9

656 ℃

0.95

>700 ℃

End

>700 ℃

26


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Energy & Fuels

Table 3. Surrogate Compounds and Their Properties densityb

CKMd

H/C

mol wt

ratio

(g/mol)

544-76-3

2.13

226.43

286.88

773.57

100

yes43-45

NHTA

630-06-8

2.06

506.95

497.18

816.49

110

-

HMN

4390/4/9

2.13

226.43

246.29

784.44

15

yes45-47

Decalin

DEC

91-17-8

1.8

138.24

191.40

882.07

42

yes48,49

pentadecylcyclohexane

PTCH

6006-95-7

2

294.55

369.70

830.04

100

-

5-phenyleicosane

5PHE

2400-04-6

1.77

358.63

408.21

854.76

39

-

1-methylnaphthalene

1MTN

90-12-0

0.91

142.19

244.63

1020.23

0

Yes50,51

phenanthrene

PNT

85-01-8

0.71

178.22

340

1112.13

0

-

chrysene

CHRS

218-01-9

0.67

228.27

448

1274

0

-

Compound name

Abbre.

CAS no.

n-hexadecane

NHED

n-hexatriacontane 2,2,4,4,6,8,8heptamethylnonane

aBoiling

points at

0.1MPa.52 bDensity

BPa (°C)

(kg/m3)

CNc

Available ?

at 20 ℃ and 0.1MPa, Some density data is calculated using linear interpolation methods based

on the known data.52,53 cCetane Number.40 dChemical Kinetics Mechanism.

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Page 28 of 38

Table 4. Surrogate Fuel Compositions compound

M1

M2

Mole %

Wt%

Mole %

Wt%

0

0

17.3

24.39

10.32

19.87

0

0

2,2,4,4,6,8,8-heptamethylnonane

0

0

5.93

8.36

Decalin

0

0

30

25.83

pentadecylcyclohexane

5

5.59

0

0

5-phenyleicosane

20

27.23

0

0

1-methylnaphthalene

15

8.1

46.77

41.42

phenanthrene

20.2

13.67

0

0

chrysene

29.48

25.55

0

0

n-hexadecane n-hexatriacontane

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Energy & Fuels


Figure 1. Distillation curve fitting approach based on the area between stepwise approximation and smooth distillation curve13 Figure 2. Measuring results from SH/T0659-1998: a) Mass fraction distribution of different hydrocarbons; b) Distribution of different hydrocarbons with the number of C atoms Figure 3. GC×GC-TOFMS chromatogram for RMG180 fuel Figure 4. Selected compounds for surrogate generation Figure 5. Predicted distributions in GC×GC-TOFMS chromatogram of surrogate fuel, (a) M1 and (b) M2, overlaid on RMG180 composition (in gray) Figure 6. Fuel distillation curve as quantified by the simulated distillation method (ASTM D7169) Figure 7. Ignition quality of target fuel and surrogate fuel quantified by CN or estimated by a volume-fractionweighted linear blending rule Figure 8. Measured target-fuel density, as well as measured and predicted surrogate-fuel densities at 20 °C Figure 9. Target- and surrogate-fuel carbon and hydrogen mass fraction

*



Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1. Distillation curve fitting approach based on the area between stepwise appro ximation and smooth distillation curve13

30


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Energy & Fuels

(a)

(b) Figure 2. Measuring results from SH/T0659-1998: a) Mass fraction distribution of different hydrocarbons; b) Distribution of different hydrocarbons with the number of C atoms

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Figure 3. GC×GC-TOFMS chromatogram for RMG180 fuel

32


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Energy & Fuels

Figure 4. Selected compounds for surrogate generation

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(a) M1

(b) M2

34


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Energy & Fuels

Figure 5. Predicted distributions in GC×GC-TOFMS chromatogram of surrogate fuel, (a) M1 and (b) M2, overlaid on RMG180 composition (in gray)

Figure 6. Fuel distillation curve as quantified by the simulated distillation method (ASTM D7169)

35


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Figure 7. Ignition quality of target fuel and surrogate fuel quantified by CN or estimated by a volume-fractionweighted linear blending rule

36


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Energy & Fuels

Figure 8. Measured target-fuel density, as well as measured and predicted surrogate-fuel densities at 20 °C

37


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Figure 9. Target- and surrogate-fuel carbon and hydrogen mass fraction

38


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Surrogate formulation for marine diesel ... - ACS Publications

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