Surrogate Generation and Evaluation for Diesel Fuel - Energy & Fuels

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Surrogate generation and evaluation for diesel fuel Anton Markus Reiter, Thomas Wallek, Andreas Pfennig, and Marc Zeymer Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.5b00422 • Publication Date (Web): 02 Jun 2015 Downloaded from http://pubs.acs.org on June 8, 2015

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Surrogate generation and evaluation for diesel fuel Anton M. Reiter,† Thomas Wallek,∗,† Andreas Pfennig,†,¶ and Marc Zeymer‡ Institute of Chemical Engineering and Environmental Technology, NAWI Graz, Graz University of Technology, Graz, Austria, and OMV Refining & Marketing GmbH, Vienna, Austria E-mail: [email protected] Phone: +43-0-316-873-7966. Fax: +43-0-316-873-7469

Abstract The correct representation of a fuel, in terms of its physical and chemical properties and its combustion kinetics poses, a challenge to modern engine development when state-of-the-art simulation technology is used. In this context, a promising approach is the use of surrogates that emulate the properties of real fuels, where the surrogates are made up of a significantly lower number of components than the original fuels. The goal of this paper is to present an algorithm that can be used to generate surrogates composed of real chemical components, as opposed to pseudo components. The algorithm was developed by simultaneously fitting the True Boiling Point (TBP) curve, the liquid density at 15 ℃ and the cetane number. To illustrate the algorithm, surrogates for four different fuels were generated: a commercially available European ∗

To whom correspondence should be addressed Institute of Chemical Engineering and Environmental Technology, NAWI Graz, Graz University of Technology, Graz, Austria ‡ OMV Refining & Marketing GmbH, Vienna, Austria ¶ Department of Applied Chemistry, University of Liège, Liège, Belgium †

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diesel and three research diesel proposed by the FACE (Fuels for Advanced Combustion Engines) CRC Research Group. Two of the resulting surrogates were produced on a lab-scale and subjected to laboratory examination. For validation, the experimental data for these two surrogates were compared to those for the target fuels and to data generated by thermodynamic models on the basis of the surrogates’ compositions. Both the fitted properties and additional properties, which were not used for fitting, were compared with experimental properties such as the ASTM D86 boiling curve, content of aromatics, flash point, heating value, cloud point, viscosity, and temperature dependency of the liquid phase viscosity and density. We demonstrate that the proposed algorithm generates surrogates of approximately ten real components, which show excellent agreement with the original target fuels.

Introduction Simulations are frequently performed during modern engine and combustion development. 1 They support a shorter development time for highly efficient, low-emission engines. A crucial issue in engine and combustion simulation is to obtain a realistic fuel characterization. 2 Because conventional, petroleum-derived fuels contain thousands of individual chemical components, their complexity cannot be fully represented in simulations. The reasons for this are the enormous computational effort required and the lack of chemical and physical property data for most of the components. 3–5 A promising approach for overcoming this problem is the generation of surrogate fuels composed of only a few real components, aiming toward the best possible emulation of the target-fuel properties. 1,5 Single-component surrogates usually only represent the complex behavior of fuel on a limited scale. For example n-heptane used as a single component surrogate shows an ignition delay comparable to that of diesel, but its physical properties are considerably different from those of diesel. 1 Moreover data about, some important mixture properties such as the boiling curve cannot be collected from a single component. Therefore, the development of multi-


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component surrogates is of increasing interest. 1 Multi-component surrogates allow for a simple representation of real fuels, covering the most important fuel characteristics such as the boiling curve, selected physical and chemical property data, and combustion kinetics. In this context, the design of multi-component surrogates involves the selection of the smallest possible number of components that are required to effectively emulate the target fuel. This relatively simple composition of surrogates has several advantages: it limits simulations computing time, improves the reproducibility of the mixture for experimental purposes, and helps improve the understanding of the effects of different hydrocarbon types contained in the fuel. An overview of current trends in the field of multi-component surrogate generation has been provided by Farrell et al. 2 and Pitz and Mueller. 4

Algorithm for Surrogate Characterization The algorithm for surrogate characterization represents a further development of the algorithm for the generation of a surrogate for crude oil, which was presented in a previous paper. 6 The surrogate composition is obtained by fitting the selected physical and chemical properties based on experimental data such as the True Boiling Point (TBP) curve according to ASTM D2887, 7 liquid density at a temperature of 15 ℃ according to ON EN ISO 12185 (equivalent to ASTM D4052), and the derived cetane number according to DIN EN 15195 (equivalent to ASTM D6890). The crucial points for successful surrogate generation are discussed in the following sections, and comprise the definition of a pool of possible components to be considered, the selection and modeling of design criteria to be fitted, and the formulation of the objective function linking these criteria.


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Selection of components Conventional petroleum-derived diesel typically contains hydrocarbons with an average carbon number of C14 or C15 and with the component carbon numbers ranging from C10 to C22. 8 Based on these characteristics, a comprehensive literature research was conducted to identify possible real components to be used as candidates for the surrogate. Based on articles and reviews 2,4,5,8,9 and, in addition, data obtained from the Dortmund Data Bank (DDB), 10 about 80 potential surrogate components were identified. Due to the lack of physical property data, this set of potential components was reduced to 49 components, which are presented in Table 1. For these components, all required data were obtained from the literature, Dortmund Data Bank (DDB), 10 or were estimated. These data include Antoine parameters, critical data, acentric factors, melting points, enthalpies of fusion, liquid densities at 15 ℃, lower flammability limits, cetane numbers, viscosities at 40 ℃, and ideal gas heat capacity (cp ) parameters. The two biggest groups of components, which were available for surrogate generation, were the homologous series of n-alkanes (18 components) and n-alkylbenzenes (14 components). The group of iso-alkanes contained only two highly branched components. However, conventional diesel fuel usually contains a high number of lightly branched components with 1 to 2 methylations. 2,3 These typical diesel components are not contained in the list of potential surrogate components due to the lack of pure component data and reliable estimation methods for the properties for isomeric components.

Selection of fitted properties The basic concept followed during the design of the fitting algorithm is to use common experimental data for diesel fuel, as defined in DIN EN 590, 11 as criteria for the surrogate. One of the fundamental fuel properties is the boiling range, which describes the volatility. A standard test method to describe the boiling curve of diesel fuel is ASTM D86. 7 However, a severe drawback of the use of this method is the fact that it results in considerable uncertainties in 4 ACS Paragon Plus Environment

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temperature, has little theoretical significance, and cannot be described by a straightforward thermodynamic model. 12 Instead, Mueller et al. 8 applied a so-called advanced distillation curve first introduced by Bruno et al.. This boiling curve offers an additional compositionexplicit data channel, provides temperatures that are true thermodynamic state points, is prone to low levels of uncertainty with regard to the measurement, and is consistent with the historical data. 8,12–16 In this paper, the simulated distillation according to ASTM D2887 7 was used to characterize the volatility of the fuel. The boiling curve obtained by ASTM D2887 is essentially equivalent to the data obtained by true boiling point (TBP) distillation according to ASTM D2892. Simulated distillation is a chromatographic method where the distillation curve is generated based on the retention time distribution of the sample. Based on the retention time distribution of a calibration sample with known components and composition, containing n-alkanes, a calibration curve which links retention time and boiling point is found. 7 However, a drawback of this method is that the retention time of the components is not only a function of their normal boiling points, but also depends on the polarity. From this behavior follows, that polar component are not eluted in order of their normal boiling points compared to n-paraffins. Furthermore, due to interactions of the components with the stationary phase of the GC-column and low volatility of very high boiling components incomplete elution for these components can occur. Additionally it is possible that thermal decomposition of components at temperatures higher than 350 ℃ occurs. 17 The simulated distillation is operated at conditions with limited column efficiency and resolution which allows to obtain distillation data which are in agreement with physical distillation. 18 Based on a characterization algorithm for hydrocarbon fractions by Riazi and Daubert, 19 a boiling curve model was constructed that correlates the mass fractions and normal boiling points of the individual components with the boiling curve. It became evident during this work that only fitting the boiling curve was not sufficient to generate a surrogate that was able to correctly represent several physical properties of


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diesel simultaneously. In order to decide which additional properties could be used as fitting criteria, the cetane number, kinematic viscosity, and liquid density of the pure components were investigated with regard to the demanded value for diesel, according to DIN EN 590. 11 In Figure 1, the kinematic viscosity at 40 ℃ and the cetane number are plotted against the liquid density at 15 ℃, showing the pure component data of the substances listed in Table 1. The shaded regions indicate the requirements for European diesel, according to DIN EN 590. 11 At a temperature or 15 ℃ for which the liquid density is presented some of the components already form solids. The provided density values for these components represent liquid-phase densities which were extrapolated to the reference temperature. The used data are in line with the density data provided by API. 20 DIN EN 590 11 only specifies a minimum cetane number, but not an upper limit. Commonly, commercial diesel does not exhibit a cetane number of more than 70 and, therefore, this value is designated as the upper limit. If a surrogate component is located within the central box appearing in each of the illustrations in Figure 1, this indicates that the specifications according to DIN EN 590 for both depicted properties are fulfilled. If the surrogate component falls, however, in the shaded areas, only one criterion is met. Interestingly, none of the single components was able to fulfill the requirements of cetane number, liquid density, and kinematic viscosity simultaneously. This situation highlights the need for surrogates that contain several components. All n-alkanes exhibited a liquid density that is considerably lower than the specification, and the cetane number for most of the n-alkanes was also too high. All the mono aromatics had a similar density, but exceeded the upper limit of DIN EN 590. 11 The cetane number of most mono aromatics was quite low, but increased along with the length of the n-alkyl side chain for the homologous series of n-alkylbenzenes. The viscosity of alkanes and mono aromatics increased with molecular weight and displayed a wide range from less than 1 to more than 6 mm2 /s. The kinematic viscosity for these components was distributed more uniformly around the specified limits than the cetane number, but both properties showed similar trends as compared to liquid


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density. Based on these considerations, liquid density and cetane number were used as fitting criteria in addition to the ASTM D2887 boiling curve. For these criteria, the modeling approaches that were used in the objective function are discussed in the next section.

Modeling of fitted properties TBP curve The strategy of modeling the TBP curve with a small number of real components was based on a method proposed by Riazi and Daubert, 19 which has been explained in detail by Reiter et al. 6 and Albahri. 21 The idea behind this approach was to model the smooth TBP curve by using a step-wise approximation method, as illustrated in Figure 2. Each of the bars represents an individual real component that was used in the modeling approach. The height of each bar corresponds to the normal boiling point of the component, and the width of the bar represents its fraction in the mixture. The bars are stacked without gaps or overlaps which leads to the stepwise TBP curve approximation. By adjusting the fraction and, consequently, the position of the surrogate components along the TBP curve, the best fit of the step-wise model to the smooth TBP curve was found. Based upon this approach, a mathematical term to characterize the fitting procedure was formulated. The state-of-the-art fitting procedure typically used in this context 6,22–26 tries to adjust the fraction and position of the surrogate components along the TBP curve such that the calculated boiling temperature for each component is set equal to the normal boiling point of this component. The boiling temperature can be calculated as the integral mean value of the fraction of the TBP curve that is covered by the component. To simplify these calculations, the arithmetic mean value of the TBP temperatures at the lower and upper boundary of the component can be used. Usually both methods yield similar results. 25 A typical function for


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this fitting procedure is f (x) =

2 n X ∆Ti Tbi

i=1

−→ min

(1)

where ∆Ti describes the difference between the mean value of component i and the normal boiling point of component i. The normal boiling point, Tbi , is used to generate a dimensionless value that can be used to compare with other criteria that might be additionally considered in the objective function. 6,21,23,25 One limitation of this objective function is represented by the fact that different components exhibiting the same temperature difference, ∆Ti , unequally contribute to the function value, because the normal boiling point of the component, Tb,i , is used in the denominator of eq 1. A second, even more severe limitation of this objective function is that the obtained function value cannot be related to the quality of a TBP-fit, which limits its practicability. The reason is that, for an arbitrary number of components used, a function value of 0 will be obtained if the normal boiling points Tb,i fulfill the following criterion: R xu,i Tb,i =

xl,i

TBP(x)dx

xu,i − xl,i

(2)

TBP(x) represents the algebraic equation describing the TBP curve; xl,i is the lower degree of vaporization for component i on the TBP curve; and xu,i is the upper degree of vaporization for this component. An example for this case is given in Figure 2, where 3 components were used for fitting. Although the value of the target function to be minimized equals 0, it was clear that the smooth TBP curve could not be reproduced by only three components. Hence, the fitting function, eq 1, gave no evidence for the required minimum number of real components that could be used to fit the curvature with sufficient accuracy. To overcome the latter limitation, we propose an alternative mathematical formulation of the fitting procedure: instead of using the temperature difference between the normal boiling point and the temperature mean value for each component independently, the area 8 ACS Paragon Plus Environment

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between the stepwise approximation and the given smooth TBP curve should be used as an optimization criterion, resulting in a reliable measure of the quality of the fit. The results of the application of the proposed new approach are illustrated in Figures 2 and 3, indicated by the shaded areas. With reference to Figure 3, an arbitrary component i can have two different area-contributions. The two different areas adjoin at the degree of vaporization xTb,i where the temperature of the TBP curve equals the normal boiling point of component i. For degrees of vaporization in the range of xl,i to xTb,i the normal boiling point of the component is higher than the temperature value of the TBP curve. This area is designated A1,i for component i. In the range of xTb,i to xu,i , the normal boiling point of the component is lower than the temperature value on the TBP curve, and this area is referred to as A2,i . For component i − 1, the upper limit xu,i−1 is smaller than the corresponding xTb,i−1 would be and, therefore, only one area exists: A1,i−1 . The same holds true for component i + 1, whose lower limit xl,i+1 is higher than xTb,i+1 and, therefore, only one area exists: A2,i+1 The calculation of the areas requires some mathematical effort, but can be easily conducted if an analytical expression for the TBP curve is available. Finally, the sum of all areas is used as the fitting criterion. Because the sum of all component fractions is 1, the resulting value can be interpreted as a mean deviation between the fitted curve and the TBP curve upon which it is based. As a consequence, the value of this objective function can also be used as a decision criterion to help determine whether the selected number of components is sufficient to meet the required accuracy, or whether further components must be included.

Liquid density The second fitting criterion used in the objective function was the liquid density of the surrogate at a temperature of 15 ℃ determined as according to ON EN ISO 12185 (equivalent to ASTM D4052). The liquid density of a surrogate comprising n components was calculated by 27 ρ=

n X wi i=1

!−1

ρi


(3)

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where wi represents the mass fraction of component i and ρi , its liquid density at 15 ℃ . Cetane number The derived cetane number was chosen to represent the ignition behavior of the fuel and was experimentally determined according to DIN EN 15195 (equivalent to ASTM D6890). For the calculation of the derived cetane number of the mixture, the linear mixing rule

CN =

n X i=1

vi · CNi

(4)

was used. 8 CN represents the derived cetane number of the surrogate, composed of n components, while CNi represents the derived cetane number of component i, and vi represents the volume fraction of component i. The volume fraction of the components was calculated based on the mass fraction and liquid density at a temperature of 15 ℃, thus neglecting any mixing effects.

Objective function The objective function used for surrogate generation was derived using a least squares approach and accountsed for the TBP curve, liquid density at 15 ℃, ρ, and the cetane number, CN: 

n P

2

 i=1 (A1,i + A2,i )   + f (wi ) =    ∆Tref

ρcalc − ρexp ∆ρref

2

+

CNcalc − CNexp ∆CNref

2 −→ min

(5)

The numerator of the individual terms represents the difference between the experimental value of the target fuel and the calculated value of the surrogate. In case of the TBP curve, this difference equaled the sum of the areas between the smooth TBP curve and the stepwise approximation, as described in the previous section. For liquid density and cetane number, the difference between the value calculated from the surrogate, index ’calc’, and the 10 ACS Paragon Plus Environment

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experimental value of the target fuel, index ’exp’, was used. The denominator represents the reference value described in the previous section, allowing the three different optimization criteria to be compared and weighted. The degree of freedom is defined by the composition of the surrogate. To solve the resulting optimization task, several algorithms which did not require differentiation of the objective function were tested. Initially the Simplex algorithm of Nelder and Mead, 28 with the implementation provided by Hoffmann and Hofmann, 29 was used. The results of a multitude of calculations with different initial values indicated that this algorithm was not capable of determining a unique optimum. Re-running the algorithm with the obtained result improved the optimum, but the results showed too much variability. Another algorithm tested was the DIRECT algorithm of Jones et al.. 30 The Fortran implementation used was that of He et al.. 31 The application of this algorithm was not successful, because it was often not possible to obtain a better or equal optimum, as compared to those already found using the simplex algorithm within an acceptable computation time limit. Tuning the optimization parameters to find a better optimum frequently required extremely high amounts of memory, which could not be handled by the system, and led to an abnormal abortion of the program run. The best performance in terms of the location of a global optimum within an acceptable calculation time limit was achieved using the simulated annealing algorithm of Corana et al., 32 with the Fortran implementation of Goffe et al.. 33 Furthermore, this stochastic algorithm is unlikely to yield a local minimum, which might occur when using other algorithms. To ensure that a global optimum was found, each optimization task was evaluated several times with different initial values. All calculations were conducted using a Pentium i-5 processor with 3.2 GHz. As an example of run-time, calculations with up to 15 components could be completed within an hour, while surrogates containing more than 30 components required several hours of computation time.


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Experimental Methods and Calculation Models A crucial point for the validation of the new approach was the comparison of the experimentally determined properties of the target fuel with properties based on the calculated composition of the surrogate. This included both properties used for fitting and those not considered in the target function for optimization. Special attention was paid to the properties that are defined within DIN EN 590 and were accessible by simulation of chemical and physical processes. The evaluated properties comprised distillation curves, cetane numbers, flash points, lower heating values, cloud points, liquid densities at 15 ℃, kinematic viscosities at 40 ℃, as well as the temperature dependencies of the kinematic viscosity and liquid density. In the following section, the experimental methods and the applied calculation models are described for each of these properties.

Boiling curves: For the characterization of the fuel volatility, the boiling curves according to ASTM D2887 (Sim Dist) and ON EN ISO 3405 (equivalent to ASTM D86 34 ) were evaluated for the surrogates. The ASTM D2887 boiling curve was simulated using a step-wise approach analogous to that used in the model already described. However, for this boiling curve, it was known that the experimentally determined temperature for some components did not represent the normal boiling point. 7 This difference between the experimental temperature and normal boiling point was considered for the poly aromatic component 1-methylnaphthalene. The model of the ASTM D86 boiling curve was based on a batch distillation model as presented by Greenfield et al. 35 This model comprised three calculation units, two flash units, and a vapor-space unit, as depicted in Figure 4. The first flash unit (flash 1) represented the distillation flask, into which where a fraction of the liquid distills due to external heating. The second flash unit (flash 2) represented the walls of the distillation flask, describing heat loss through natural convection. Due to this heat loss, a small fraction of the vapor from flash 1 condenses and is collected in flash 1. The remaining vapor travels to the vapor space 12 ACS Paragon Plus Environment

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unit (vap-space) where the typical temperature is obtained using the test method ASTM D86, whereby the heat capacity of the apparatus is accounted for. This model, which had been successfully applied to a naphtha surrogate, was adapted to diesel fuel through a three-step extension. The first modification was the definition of a maximum temperature difference between the units flash1 and vap-space. This was necessary, because naphtha has a initial boiling point of about 30 ℃ while diesel has an initial boiling point of more than 150 ℃. For a naphtha sample, it was a reasonable assumption that the whole apparatus was at room temperature until vaporization of the sample begins, but for diesel, this assumption was not applicable due to the conduction of heat in the apparatus. This effect was accounted for by introducing a maximum temperature difference between the calculation units, until the boiling point of the sample was reached. A second modification was that the initial presence of air in the apparatus and solubility of air in the fuel were neglected since their influence was at most marginal. The third modification was that additional heat losses due to radiation were considered in the vap-space unit. This modified model was solved by a quasi-stationary approach. Verification of the modified model by simulating a naphtha surrogate described in Greenfield et al. 35 demonstrated reproducibility of the provided experimental data.

Cetane number: To account for the ignition behavior of the surrogate, the derived cetane number according to DIN EN 15195 (equivalent to ASTM D6890 34 ) was evaluated using eq 4.

Flash point: As a safety-related property, the flash point as evaluated using the PenskyMartens closed cup method was determined according to EN ISO 2719 (equivalent to ASTM D93 34 ). The calculation model used was derived from Gmehling and Rasmussen 36 n X

i=1

xi · γi · psat i =1 tf − 25 psys LFLi (25) − 0.182 ∆Hc,i · psys 13 ACS Paragon Plus Environment

(6)

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where tf represents the flash-point temperature in ℃ at ambient pressure psys for a mixture containing n components. xi represents the mole fraction; γi , the activity coefficient; psat i , the saturation pressure of pure component i at the flash-point temperature; LFLi (25), the lower flammability limit at a temperature of 25 ℃; and ∆Hc,i , the net heat of combustion. Aromatics, naphthene, paraffin content and heating value: The aromatics analysis complied with ON EN 12916 (comparable with ASTM D5186). The mass fraction of mono aromatics, poly aromatics, and total aromatics was determined with this analysis. The contents of naphthenes and paraffins of the target fuels were determined with ASTM D2425. Modeling of these properties was a straightforward task, because the fraction of an observed class could be calculated from the composition. The lower heating value of hydrocarbons is closely related to their content of carbon and hydrogen and was determined according to DIN 51900 (comparable with ASTM D240). The content of carbon and hydrogen for the whole mixture was calculated based on the empirical formula, Cx Hy , and the composition: Pn

xi · nC,i · 12.01 i=1 xi (nC,i · 12.01 + nH,i · 1.008)

w C = Pn

i=1

(7)

Pn

xi · nH,i · 1.008 i=1 xi (nC,i · 12.01 + nH,i · 1.008)

wH = Pn

i=1

(8)

where wC represents the mass fraction of carbon, and wH , the mass fraction of hydrogen in the surrogate composed of n components. xi represents the mole fraction; nC,i , the number of carbon atoms; and nH,i , the number of hydrogen atoms of component i. Based on the carbon and hydrogen fractions, the lower heating value (LHV) in MJ/kg was calculated with the approximation formula of Boie: 37

LHV = 34835 · wC + 93870 · wH + 10465 · wS + 6280 · wN − 10800 · wO − 2440 · wW


(9)

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where wC represents the mass fraction of carbon; wH , the mass fraction of hydrogen; wS , the mass fraction of sulfur; wN , the mass fraction of nitrogen; wO , the mass fraction of oxygen; and wW , the mass fraction of water. Because the surrogate contains solely hydrocarbons, only the first two terms are relevant.

Cloud point: The cloud point is the temperature at which solid particles begin to form as a liquid cools. 38 Experimentally, the cloud point is determined according to ON EN 23015 (equivalent to IP 219 39 and ASTM D2500 9 ), where it can be assumed that a certain subcooling is required before the first solid forms. The calculation is based on a rigorous solidliquid equilibrium calculation. In the model applied, it was assumed that the precipitated wax would consist of several pure phases. As a relevant criterion for solid phase formation, the following inequality, presented by Lira-Galeana et al., 38 was used: T ∆hf us,i 1− ≥0 zi · γi − exp − R·T Tf us,i

(10)

where zi represents the composition of the surrogate; γi , the activity coefficient at the actual temperature T ; R, the gas constant; ∆hf us,i , the enthalpy of fusion; and Tf us,i , the melting point of pure component i contained in the mixture. The initial calculation of the cloud point includes a value, T , at which no solids exist. Then the temperature value is gradually decreased, and for each new temperature value eq 10 is evaluated for all components. As soon as one component fulfills the equation, the cloud point temperature and the component forming a solid phase can be identified. Liquid density and viscosity: The liquid density at 15 ℃ was determined according to ON EN ISO 12185 (equivalent to ASTM D4052). The kinematic viscosity at 40 ℃ was experimentally obtained by applying ON EN ISO 3104 (equivalent to ASTM D445). In addition to these standardized values, the temperature dependency of liquid density and kinematic viscosity was investigated for the range of 10 to 45 ℃.


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Liquid density for the mixture at a temperature of 15 ℃ was calculated according to eq 3. Based on this value, the temperature dependency of the density in the specified temperature range was calculated using

ρT = ρT0 − (2.34 − 1.9ρT0 ) · (T − T0 )

(11)

where ρT represents the density of the mixture at temperature T , and ρT0 , the liquid density at temperature T0 . 27 The kinematic viscosity of the surrogate at a specified temperature, νm , is obtained by

νm =

ηm ρm

(12)

where ηm represents the dynamic viscosity, and ρm , the liquid density at the same temperature. 27 The dynamic viscosity of the mixture containing n components was calculated by using the Kendall-Monroe method

ηm =

n X i=1

!3 1/3

xi · ηi

(13)

as suggested by the API, 20 with ηi representing the dynamic viscosity of the pure components, and xi , the mole fraction of the components in the mixture. The temperature dependency of the viscosity of the pure components could be described by applying Andrade’s equation

ln η =

A +B T

(14)

setting the temperature T in K and defining A and B as the two fitting parameters. The latter were calculated by using the empirical effective carbon number approach. According to this approach, it is only necessary to define one viscosity value at a specified temperature to determine the values of the parameters. 40 For most of the pure components, the


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dynamic viscosity was provided at 40 ℃, but due to the lack of experimental data for some components, data at a different, but similar, temperature were taken.

Properties of Target Fuels and Calculation of Surrogates The new algorithm was applied to four different diesel fuels to generate surrogates composed of real components, resulting in proposals for the surrogate compositions. The target fuels chosen were distinctly different in their physical properties. The first target fuel was a commercially available European diesel fuel, which we obtained from our industrial partner and which is referred to as ”OMV”. The other diesels are the FACE research fuels FD3A, FD5A and FD9A with their experimental data taken from Alnajjar et al.. 41 Selected properties of all fuels are shown in Table 2. Additionally, the boiling curves according to ASTM D86 and ASTM D2887 were available as experimental data for the target fuels. The data for fuel OMV are included in the supplementary material. In Figure 5, the liquid density, kinematic viscosity, and cetane number of all target fuels are compared to the specifications for European diesel fuel 11 and US no. 2 diesel. 7 As evident from this figure, the target fuels showed distinctly different properties. Fuel OMV complied with the demanded values of ASTM D975, but had an atypically high cetane number as compared to US no. 2 diesel. Looking at the FACE fuels, diesel FD9A is close to the requirements for a European diesel. However, its liquid density was slightly too high and its cetane number was more than 5 cetane ratings too low. The properties of the other two fuels did not match satisfyingly with the requirements defined by DIN EN 590. In order to apply the presented algorithm to the target fuels, it was necessary to provide an algebraic equation for the TBP curve. To describe the boiling curves according to ASTM D2887, the following equation was used:

TBP (x) =

a + c · x + e · x2 1 + b · x + d · x2 + f · x3 17

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(15)

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The fitting parameters were computed using the software TableCurve V5, 42 and the resulting parameters together with the Fit Standard Error (FitStdErr) are included in the supplementary material. Because one goal of this work was to experimentally validate the surrogate determination, an important criterion for the selection of components was commercial availability for purchase. Considering this constraint, a set of 15 components was compiled, which are given in Table 3. Using this component set, the low boiling part of diesel was mainly represented by aromatic and naphthenic components, while the high boiling part was represented by several n-alkanes plus one alkene. In the intermediate boiling range, one poly aromatic component and one highly branched iso-alkane were selected. Based on this component set, the algorithm was applied to generate surrogates for the four target fuels. The reference values for weighting the fitting criteria in the objective function, given in eq 5, were identified by a trial-and-error procedure, used to obtain surrogates showing satisfactory residua for the fitting criteria considered. To obtain the reference values an iterative approach is suggested. For the first simulation it is recommended to use a value of 1 for each reference value. Evaluation of the obtained residua shows how far the optimization criteria are met. If one residuum is too high the reference value must be increased until the residuum is within an accepted range. Furthermore it must be considered that changing one reference value will affect all other optimization criteria as well. Therefore it is recommended only to change one reference value at a time. Additionally, one arbitrary reference value should be treated as constant and the others should be tuned one by one. During the surrogate generation of Diesel OMV, a density reference value of 3.0 kg/m3 was chosen, while the reference values for the TBP curve were set to 1.0 K and the reference value for the cetane number was set to 1.0. The resulting surrogate showed a liquid density at 15 ℃, which is 0.5 kg/m3 lower, and a cetane number which is 0.3 cetane ratings higher, than the experimental data for the target fuel. The TBP curve of the target fuel and the


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step-wise approximation showed an average deviation of 8.6 K. For the surrogate of diesel FD3A a reference value for density of 0.5 kg/m3 , for TBP curve of 1 ℃ and for cetane number a value of 0.8 was obtained. With these reference values the obtained surrogate shows a liquid density at 15 ℃, which is 0.4 kg/m3 lower than the experimental value of the target fuel. Furthermore the cetane number of the surrogate is 0.4 cetane ratings lower than the experimental value of the target fuel. The average deviation between the TBP curve of the target fuel and the step-wise approximation of the surrogate is 7.6 K. Within surrogate generation for diesel FD5A a reference value of 0.5 kg/m3 was obtained for liquid density, while the reference value for TBP curve is 1 ℃ and that for the cetane number is 0.8. The liquid density at 15 ℃ and the cetane number of the surrogate are both a bit lower than the experimental values of the target fuel, showing a deviation of 0.5 kg/m3 and 0.1 cetane ratings, while the TBP curve was fitted with an average deviation of 9.9 ℃. The fitting procedure for the fuel FD9A resulted in a reference value of 4.0 kg/m3 for liquid density, while the reference value for the TBP curve was set to 1.0 K, and the reference value for the cetane number was set to 1.0. The surrogate obtained showed a liquid density, which was 0.1 kg/m3 lower, and a cetane number, which was 2.9 cetane ratings higher, than the experimental values for the target fuel. Furthermore, an average deviation between the experimental data and the step-wise approximation of the boiling curve of 10.0 ℃ was obtained. A challenging issue with reference to this particular fuel was the low cetane number and higher final boiling point. To allow satisfactory fitting of the boiling curve the inclusion of, long-chained n-alkanes was necessary, but on the other hand these components showed a cetane number of at least 100. Therefore, to retain a satisfying fit for the boiling curve, a fairly high residuum with regard to the cetane number was accepted. The resulting compositions of the four surrogates are presented in Table 3 with the components ranked by increasing normal boiling point. Due to the high costs of some of the components, it was decided to produce only the


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surrogate for diesel OMV and FD9A. For each of these two surrogates, a sample size of about 2 liters was produced by gravimetric dosing, whereas one liter of each of the surrogates costs approximately EUR 300. For validation of the algorithm, each surrogate was analyzed according to the methods already mentioned, with an detailed analysis following in the next section. More details on the components used for surrogate production are available in the supplementary material. Furthermore the supplementary material contains evaluations for the surrogates FD3A and FD5A which give an overview about their capabilities to represent the corresponding target fuels.

Comparison of the Surrogates with the Target Fuels Firstly, the measured properties of both produced surrogates were compared to the properties calculated from the surrogate-composition, to check the performance of the simulation models used. Secondly, the experimental data of the surrogates were checked against the experimental data of the target fuels to validate the selection algorithm. The properties considered were the boiling curves according to ASTM D2887 and ASTM D86, viscosity, liquid density, and additional properties such as cetane number, heating value, flash point, and aromatic content.

Boiling curve ASTM D2887 The boiling curve ASTM D2887 was used as a fitting criterion during surrogate generation. In Figures 6 and 7, the results for both fuels are depicted, showing the experimental data together with their representation according to the algebraic expression (eq 15), the predicted boiling curve, and the experimental data for the surrogate. Further evaluations providing an estimate of the uncertainty of experimental data and evaluating the differences between the calculations for the surrogate and both experimental data sets are provided in


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the supplementary material. The prediction and the experimental data for the surrogates were well matched. Furthermore, the experimental data of the target fuel and the surrogate fit closely. The calculated mean deviation over the whole boiling range was 8.6 K and was calculated during surrogate generation. The absolute average deviation (AAD) between the experimental data of the surrogate and that of the target fuel for diesel OMV was also 8.7 K, in the range of 10 to 95 % evaporated. This evaporation range was selected for evaluation because it excludes uncertainties related to the initial and final boiling point. The calculated mean deviation between the boiling curve and the target fuel for the whole boiling range was 10.0 K. The AAD between the experimental data of the surrogate and the target fuel for diesel FD9A was 11.0 K, in the range of 10 to 95 % evaporated. The excellent agreement between the predicted and observed residua for the boiling curve demonstrates that the TBP curve fitting algorithm developed is able to predict the effective temperature difference. However, the initial and final boiling points show significantly higher deviations for both fuels. These can be explained by the component selection: For a better representation of these points, components with lower boiling points would need to be included to describe the initial boiling point more accurately, and components with a higher normal boiling point would need to be included to obtain a better match for the final boiling point. The temperatures obtained by ASTM D2887 corresponded to the normal boiling points of the pure components, with the exception of several high-boiling components with multiple-rings. 7 For these components the retention time during analysis is significantly different from the retention time of n-alkanes with the same boiling point. Therefore, for these components not the true boiling point is obtained. Within the components used for surrogate production this effect is only relevant for 1-Methylnaphthalene and must be considered for this reason.


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Boiling curve EN ISO 3405 / ASTM D86 Figures 8 and 9 show the experimental data of the target fuels together with the predicted and experimental data for the surrogates of boiling curves according to EN ISO 3405 (equivalent to ASTM D86). This type of boiling curve was not used as a fitting criterion and represents a purely predicted property. Further evaluations which show an estimate of the uncertainty of experimental data and evaluating the differences between the calculations for the surrogate and both experimental data sets are provided in the supplementary material. The predicted boiling curve and the experimental data for the surrogate for diesel OMV showed an absolute average deviation (AAD) of 1.6 K, while the AAD between the experimental data of the surrogate and the target fuel was 10.0 K, in the range of 10 to 95 % evaporated. The predicted ASTM D86 boiling curve of the surrogate of diesel FD9A fit the experimental data well and showed an AAD of 1.8 K, in the range of 10 to 95 % evaporated. The AAD between the experimental data of the target fuel and the surrogate in the same range of evaporation was 4.1 K, which was an excellent fit.

Viscosity and liquid density In this section, the data obtained for liquid density at 15 ℃ and kinematic viscosity at 40 ℃ are evaluated, and the temperature dependency of kinematic viscosity and liquid density for surrogate FD9A in a temperature range of 10 to 45 ℃ is examined. The liquid density at 15 ℃ was considered in the objective function (eq 5) during surrogate generation. For both fuels, OMV and FD9A, maximum deviations of less than 1 kg/m3 between the calculated and experimental values of the surrogate and target fuel were observed, as shown in the left part of Figure 10. The depicted estimated uncertainty was obtained based on the reproducibility provided by ASTM D4052 and an estimated bias of 0.5 kg/m3 . Because the kinematic viscosity was not considered in the objective function during 22 ACS Paragon Plus Environment

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surrogate generation, the experimental data of the surrogate represents a prediction of the viscosity of the target fuel. The data for kinematic viscosity at 40 ℃ for both fuels is shown in the right part of Figure 10, with the uncertainty of experimental data estimated according to ASTM D445. Calculated and experimental data for the surrogate correlated well with an absolute deviation of 0.09 mm2 /s for surrogate OMV, and 0.13 mm2 /s for surrogate FD9A. The comparison of the data of the surrogate and the target fuel showed an excellent fit for fuel FD9A, while the data for fuel OMV differed by more than 0.6 mm2 /s. The latter can be attributed to the average carbon number of the fuels and the surrogates, since the viscosity of hydrocarbons is mainly related to their molecular weight. 9 The average carbon number, obtained by a traditional one-dimensional GC-MS analysis, is approximately 14 for diesel FD9A. 41 The predicted average carbon number based on the composition was also close to 14. For target fuel OMV, an average carbon number of almost 17 was derived from the hydrocarbon distribution analysis using an in-house method while the average carbon number for the surrogate was below 16. This considerable deviation is comparable with the differences observed for the kinematic viscosity. The carbon number distribution of the target fuel OMV is provided in the supplementary material. In addition to the specifications on liquid density and kinematic viscosity by DIN EN 590, the temperature dependency of these properties was also examined. To get an estimate about the uncertainties of experimental data the same method as for the liquid density and kinematic viscosity at the reference temperatures was applied. In Figure 11, the predicted values and the experimental data for surrogate FD9A are presented. In the temperature range considered, the AAD for the kinematic viscosity was 0.07 mm2 /s and 1.8 kg/m3 for liquid density.

Cloud Point The cloud point data for both fuels together with the uncertainty of experimental data estimated according to ASTM D2500 are depicted in Figure 12. Calculated and experimental 23 ACS Paragon Plus Environment

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data for the surrogate agreed well and showed deviations of 1.5 ℃ for fuel OMV and 1.3 ℃ for fuel FD9A. However, the cloud point values for the surrogate and the experimental values of the target fuels differed significantly. This result is not unexceptional, since the solid-liquid equilibrium was not considered during surrogate generation. Furthermore, all high-boiling components were n-alkanes, which show higher melting points than isoparaffins with the same carbon number. 9 Therefore, the fairly high cloud point of the surrogate as compared to the data for the target fuel can also be related to the component selection, which lacks of isoparaffins with few branches, which are typically contained in diesel fuel.

Further properties Within this section, the aromatic content, lower heating values, cetane numbers, and flash points of both fuels are examined. The uncertainties of presented experimental data were estimated based on data provided by the corresponding standards. The cetane number was considered in the objective function during surrogate generation, while the other properties of the surrogate represent predicted values that emulate those of the target fuel.

Diesel OMV: In Figure 13, the mentioned properties for fuel OMV are shown. The predicted and experimental values for the content of mono aromatics matched well. The content of mono aromatics in the target fuel was a bit lower than the data for the surrogate, but was still of the same magnitude. Concerning the fraction of total aromatics, the predicted and the experimental values fit satisfactorily. However, the experimental value of the target fuel was more than 10 mass-% lower than the values for the surrogate. This shows that the fraction of 1-methylnaphthalene, which is the only poly aromatic component, was too high. All three data for the lower heating value showed an excellent match. the experimental value of the cetane number, as a fitting criterion in the objective function, showed an excellent fit for both the surrogate and target fuel, but the calculated value was 1.9 cetane ratings lower. As shown by Sattler, 43 the cetane number according to ASTM D613 correlates very well with


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the derived cetane number according to ASTM D6890. Therefore, the cetane number of the target fuel obtained by ASTM D613 was compared with the experimental values for the surrogates obtained by ASTM D6890. The flash point of the surrogate was underestimated by approximately 5 K, but both experimental values for the surrogate and the target fuel were within the reproducibility of the experimental method ASTM D93.

Diesel FD9A: In Figure 14, the aromatics content, lower heating value, cetane number, and flash point of fuel FD9A are addressed. Predicted and experimental values of the content of mono aromatics showed a good match, but were slightly lower than those of the content of mono aromatics in the target fuel. The obtained data for total aromatics for the surrogate also showed a good fit, but compared to the target fuel, contained to many aromatics. This indicates that the content of poly aromatics was significantly too high. The target fuel contains 34 vol-% of naphthenes while the corresponding surrogate contains only 4.3 vol-%. The paraffin content of the target fuel is 34 vol-% and that of the surrogate is 44.5 vol-%. Additionally the surrogate contains 15.9 vol-% of one olefin whose properties are close to that of paraffins. These high deviations in naphthene and paraffin contents are related to the component selection. The high content of paraffins is needed to represent the high boiling part of the fuel. To reproduce the given property data of the target fuel, a high amount of aromatics is needed as counterpart. This leads to the underestimation of naphthene content. The lower heating value of the predicted and experimental data for the surrogate were in perfect agreement with the lower heating value of the target fuel. The predicted and experimental values for the cetane number, which was used as a fitting criterion in the objective function, fit very well. The cetane number of the surrogate and the target fuel differed by approximately 3 in cetane number. This difference was already predicted during surrogate generation, as a residuum of -2.9 in the cetane number was obtained. The experimental value of the flash point of the surrogate was underestimated by the applied method, but the experimental values of the surrogate and target fuel again matched very


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well.

Conclusion In this paper, an algorithm for the generation of surrogates composed of real components for diesel fuel was presented and applied to four different fuels. The algorithm allowed simultaneous fitting of the TBP curve, liquid density at 15 ℃, and cetane number by constrained optimization. Based on a selection of 15 components from 49 possible candidate components, surrogates were calculated for four different diesel fuels. Two of these surrogates were produced on a lab-scale and experimental analyses according to the methods required by DIN EN 590, along with some further analyses, were conducted. Comparison between the experimental data of the surrogates and the predicted values showed excellent agreement. Furthermore, the surrogates emulated the target fuels effectively. Although the surrogates were able to represent the target fuels very well, the highly limited availability of iso-alkanes as typical diesel fuel components is an issue that must be addressed. As a result, the high-boiling part of the diesel surrogate contained mainly n-alkanes instead of iso-alkanes with few branches. As a consequence, the algorithm compensated the properties of the n-alkanes by including high fractions of highly branched alkanes and poly aromatics with mid-range boiling points. This limited the performance of the surrogates in terms of the cloud point and content of poly aromatic components. Surrogates based on real components provide a basis for better understanding fuel chemistry and combustion behavior, because the surrogates and respective target fuels have identical properties. They can be used directly during modern engine development in both computer simulations and experimental work. As a resulting innovation, the fuel used in the combustion engine had exactly the same chemical composition as the fuel used for simulations. This avoids the introduction of inaccuracies which might arise through the imprecise definition of the fuel in the engine and/or oversimplified fuel characterization within sim-


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ulation. Furthermore, such an application allows the direct comparison of data from the simulation and the engine without the imprecisions mentioned. Another advantage of surrogates is that they can be exactly reproduced based on their composition, which is not possible for crude oil-derived fuels. Particularly, surrogates are very useful for the development of highly-efficient design fuels and engines, and for the prediction of fuel properties and engine performance. This approach even enables researches to determine effects on physical properties and combustion behavior, when biogenic components are added to commercially available fuels.

Acknowledgement The authors gratefully acknowledge support from NAWI Graz and thank OMV Refining & Marketing GmbH for providing financial support and experimental data for scientific evaluation.

Supporting Information Available Boiling curve of target fuel OMV (ASTM D2887 and ASTM D86); parameters for analytical expression of ASTM D2887 boiling curves; details on pure components used for surrogate generation; carbon number distribution of target fuel OMV. Comparison of target fuel properties and calculated surrogate properties for fuels FD3A and FD5A; Evaluated properties: boiling curve according to ASTM D86 and D2887, content of mono aromatics, total aromatics, paraffins and naphthenes, lower heating value, cetane number, flash point, liquid density at 15 ℃, kinematic viscosity at 40 ℃ and cloud point; further evaluations regarding uncertainty for ASTM D86 and D2887 boiling curve for both produced surrogates and corresponding target fuels.

This material is available free of charge via the Internet at

http://pubs.acs.org/.


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References (1) Merker, G.; Teichmann, R. Grundlagen Verbrennungsmotoren. Funktionsweise, Simulation, Messtechnik, 7th ed.; ATZ/MTZ-Fachbuch; Springer Fachmedien: Wiesbaden, 2014. (2) Farrell, J. T.; Cernansky, N. P.; Dryer, F. L.; Friend, D. G.; Hergart, C. A.; Law, C. K.; McDavid, R.; Mueller, C. J.; Pitsch, H. Development of an experimental database and kinetic models for surrogate diesel fuels. SAE Technical Paper 2007-01-0201 2007, (3) Sarathy, S.; Westbrook, C.; Mehl, M.; Pitz, W.; Togbe, C.; Dagaut, P.; Wang, H.; Oehlschlaeger, M.; Niemann, U.; Seshadri, K.; Veloo, P.; Ji, C.; Egolfopoulos, F.; Lu, T. Comprehensive chemical kinetic modeling of the oxidation of 2-methylalkanes from C7 to C20. Combust. Flame 2011, 158, 2338–2357. (4) Pitz, W. J.; Mueller, C. J. Recent progress in the development of diesel surrogate fuels. Prog. Energy Combust. Sci. 2011, 37, 330–350. (5) Naik, C. V.; Puduppakkam, K.; Wang, C.; Kottalam, J.; Liang, L.; Hodgson, D.; Meeks, E. Applying detailed kinetics to realistic engine simulation: The surrogate blend optimizer and mechanism reduction strategies. SAE International Journal of Engines 2010, 3, 241–259. (6) Reiter, A.; Wallek, T.; Mair-Zelenka, P.; Siebenhofer, M.; Reinberger, P. Characterization of crude oil by real-component surrogates. Energy & Fuels 2014, 28, 5562–5571. (7) ASTM International, Annual Book of ASTM Standards; ASTM International: West Conshohocken, PA, 2010; Vol. 05.01. (8) Mueller, C. J.; Cannella, W. J.; Bruno, T. J.; Bunting, B.; Dettman, H. D.; Franz, J. A.; Huber, M. L.; Natarajan, M.; Pitz, W. J.; Ratcliff, M. A.; Wright, K. Methodology for


Page 28 of 42

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Energy & Fuels

formulating diesel surrogate fuels with accurate compositional, ignition-quality, and volatility characteristics. Energy & Fuels 2012, 26, 3284–3303. (9) Chevron, Diesel Fuels Technical Review. 2007; http://www.chevron.com/products/ prodserv/fuels/documents/Diesel_Fuel_Tech_Review.pdf, Retrieved on 2011 11 30. (10) DDBST - Dortmund Data Bank Software & Separation Technology GmbH, Dortmund Data Bank Version 2013. 2013. (11) DIN EN 590:2004, Automotive fuels - diesel fuel - requirements and test methods. 2004. (12) Bruno, T. J. Improvements in the measurement of distillation curves. 1. A compositionexplicit approach. Ind. Eng. Chem. Res. 2006, 45, 4371–4380. (13) Bruno, T. J.; Ott, L. S.; Smith, B. L.; Lovestead, T. M. Complex fluid analysis with the advanced distillation curve approach. Anal. Chem. 2010, 82, 777–783. (14) Bruno, T. J.; Smith, B. L. Improvements in the measurement of distillation curves. 2. Application to aerospace/aviation fuels RP-1 and S-8. Ind. Eng. Chem. Res. 2006, 45, 4381–4388. (15) Bruno, T. J.; Ott, L. S.; Lovestead, T. M.; Huber, M. L. The composition-explicit distillation curve technique: Relating chemical analysis and physical properties of complex fluids. J Chromatogr A 2010, 1217, 2703–2715. (16) Bruno, T. J.; Ott, L. S.; Lovestead, T. M.; Huber, M. L. Relating complex fluid composition and thermophysical properties with the advanced distillation curve approach. Chem. Eng. Technol. 2010, 33, 363–376. (17) Boczkaj, G.; Przyjazny, A.; Kamiski, M. A new procedure for the determination of distillation temperature distribution of high-boiling petroleum products and fractions. Anal Bioanal Chem 2011, 399, 3253–3260. 29 ACS Paragon Plus Environment

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

(18) Villalanti, D. C.; Raia, J. C.; Maynard, J. B. Encyclopedia of Analytical Chemistry; John Wiley & Sons, Ltd, 2006. (19) Riazi, M. R.; Daubert, T. E. Improved characterization of wide boiling range undefined petroleum fractions. Ind. Eng. Chem. Res. 1987, 26, 629–632. (20) American Petroleum Institute. Refining Department, Technical Data Book - Petroleum Refining, 6th ed.; American Petroleum Institute, 1997. (21) Albahri, T. A. Molecularly explicit characterization model (MECM) for light petroleum fractions. Ind. Eng. Chem. Res. 2005, 44, 9286–9298. (22) Hecht, G.; Kaiser, J.; Weber, K. Zur Charakterisierung von Kohlenwasserstoffkomplexgemischen durch Repräsentativkomponenten. Chem. Techn. 1985, 37, 116–118. (23) Eckert, E.; Vanˇek, T. New approach to the characterisation of petroleum mixtures used in the modelling of separation processes. Comput. Chem. Eng. 2005, 30, 343–356. (24) Eckert, E.; Vanˇek, T. Improvements in the selection of real components forming a substitute mixture for petroleum fractions. Chem. papers 2009, 63, 399–405. (25) Ba, A.; Eckert, E.; Vanˇek, T. Procedures for the selection of real components to characterize petroleum mixtures. Chem. papers 2003, 57, 53–62. (26) Albahri, T. A. Enhanced method for predicting the properties of light petroleum fractions. Fuel 2006, 85, 748–754. (27) Riazi, M. R. Characterization and Properties of Petroleum Fractions; ASTM manual series MNL 50; ASTM International: West Conshohocken, PA, 2005. (28) Nelder, J. A.; Mead, R. A simplex method for function minimization. Comput. J. 1965, 7, 308–313.


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(29) Hoffmann, U.; Hofmann, H. Einfhrung in die Optimierung; Verlag Chemie: Weinheim, 1971. (30) Jones, D.; Perttunen, C.; Stuckman, B. Lipschitzian optimization without the Lipschitz constant. J. Optimization Theory Appl. 1993, 79, 157–181. (31) He, J.; Watson, L. T.; Sosonkina, M. Algorithm 897: VTDIRECT95: Serial and parallel codes for the global optimization algorithm direct. ACM Trans. Math. Softw. 2009, 36, 17:1–17:24. (32) Corana, A.; Marchesi, M.; Martini, C.; Ridella, S. Minimizing multimodal functions of continuous variables with the simulated annealing algorithm. ACM Trans. Math. Softw. 1987, 13, 262–280. (33) Goffe, W. L.; Ferrier, G. D.; Rogers, J. Global optimization of statistical functions with simulated annealing. J. Econ. 1994, 60, 65–99. (34) Wauquier, J. Petroleum Refining. Vol. 1 Crude Oil. Petroleum Products. Process Flow´ sheets; Editions Technip: Paris, 1995. (35) Greenfield, M. L.; Lavoie, G. A.; Smith, C. S.; Curtis, E. W. Macroscopic model of the D86 fuel volatility procedure. SAE Technical Paper 982724 1998, (36) Gmehling, J.; Rasmussen, P. Flash points of flammable liquid mixtures using UNIFAC. Ind. Eng. Chem. Fundament. 1982, 21, 186–188. (37) Karl, J. Dezentrale Energiesysteme; Oldenbourg Verlag, 2004. (38) Lira-Galeana, C.; Firoozabadi, A.; Prausnitz, J. M. Thermodynamics of wax precipitation in petroleum mixtures. AIChE J. 1996, 42, 239–248. (39) IP 219:

Petroleum products - Determination of cloud point. 2013;

//www.energypublishing.org/publication/ip-standard-test-methods/ 31 ACS Paragon Plus Environment

http:

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ip-219-petroleum-products-determination-of-cloud-point,

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Retrieved

on

2014 10 24. (40) Krishnamoorthy, V.; Miller, S.; Miller, B. Empirical approach for predicting viscosities of liquid hydrocarbon systems: defined compounds and coal liquids and fractions. Energy & Fuels 2010, 24, 5624–5633. (41) Alnajjar, M.; Cannella, B.; Dettman, H.; Fairpridge, C.; Franz, J.; Gallant, T.; Gieleciak, R.; Hager, D.; Lay, C.; Lewis, S.; Ratcliff, M.; Slunder, S.; Storey, J.; Yin, H.; Zigler, B. Chemical and physical properties of the fuels for advanced combustion engines (FACE) research diesel fuels. 2010, (42) AISN Software Inc., TableCurve 2D v5 for Windows. 2000. (43) Sattler, E. Comparing methods to determine cetane ratings of fuel blends. 2009; www. dtic.mil/cgi-bin/GetTRDoc?AD=ADA513172, Retrieved on 2014 10 20.


Page 33 of 42

n-alkanes iso-alkanes alkenes naphthenes mono-aromatics poly-aromatics

cetane number

100

50

0 6 kinematic viscosity at 40 o C in mm2 /s

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

Energy & Fuels

4

2

0

700

800

900

1,000

liquid density at 15 o C in kg/m3 Figure 1: Liquid density at 15 ℃, kinematic viscosity at 40 ℃, and cetane number of the real components together with the specifications for European diesel fuel 11 with an assumed upper limit of cetane number 70. The central box in each illustration indicates that the specifications according to DIN EN 590 for both depicted properties are fulfilled. For a few components, no kinematic viscosity at 40 ℃ is available, and data for these components is provided at the following temperatures: 1,2,4-trimethylbenzene at 30 ℃; n-C21; n-C22 and n-C24 at 50 ℃; and n-C23 at 70 ℃.


Energy & Fuels

temperature in ℃

FBP Tb,3 Tb,2 Tb,1

IBP

0 xu,1 = xl,2

xu,2 = xl,3

100

degree of vaporization x in % Figure 2: Pseudo-perfect fit of the TBP curve created using the model based on mean values R xu,i TBP(x)dx x with Tb,i = l,i xu,i − xl,i

temperature in ℃

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

Page 34 of 42

A2,i+1

Tb,i+1 Tb,i Tb,i−1 A2,i−1 xi−1

IBP

0

A2,i

A1,i

xi+1

xi

xl,i−1 xu,i−1 xTb,i =xl,i

xu,i = xu,i+1 xl,i+1

degree of vaporization x in % Figure 3: TBP curve fitting approach based on the area between step-wise approximation and smooth TBP curve


Page 35 of 42

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

Energy & Fuels

Q3 Q2

V2

flash 2

V1

V3

vap-space

L2 Q1

flash 1

Figure 4: Simulation model for ASTM D86

800

810

820

830

840

o

850 3

liquid density at 15 C in kg/m

1

2

3

4 o

5 2

kin. viscosity at 40 C in mm /s

30

40

50 60 cetane number

EU-Limits OMV FD5A

70

US-Limits FD3A FD9A

Figure 5: Comparison of selected properties of the target fuels with specifications for European diesel 11 and US no. 2 diesel fuel 7


Energy & Fuels

temperature in o C

400

300

200

100

prediction surrogate target fuel 0

20 40 60 80 100 degree of vaporization in mass-%

Figure 6: ASTM D2887 boiling curve for diesel OMV

400 temperature in o C

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

Page 36 of 42

300

prediction surrogate target fuel

200

0

20 40 60 80 100 degree of vaporization in mass-%

Figure 7: ASTM D2887 boiling curve for diesel FD9A


350 300 250 prediction surrogate target fuel

200 0

20 40 60 80 degree of vaporization in vol-%

100

Figure 8: ASTM D86 boiling curve for diesel OMV

temperature in o C

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

Energy & Fuels

temperature in o C

Page 37 of 42

300


200

0

20 40 60 80 degree of vaporization in vol-%

100

Figure 9: ASTM D86 boiling curve for diesel FD9A


Energy & Fuels

3.5 kinematic viscosity at 40 ℃ in mm2 /s

840

830

2.5 2

prediction

FD9A

OMV

1.5 OMV

820

3

surrogate

FD9A

liquid density at 15 ℃ in kg/m3

850

target fuel

Figure 10: liquid density at 15 ℃ and kinematic viscosity at 40 ℃ of both fuels

860 liquid density

4

prediction surrogate 840

3 820 kinematic viscosity

2 10

20 30 40 temperature in o C

liquid density in kg/m3

kinematic viscosity in cP

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

Page 38 of 42

800 50

Figure 11: temperature dependency of kinematic viscosity and liquid density of surrogate FD9A


10 0 −10

OMV

−30

prediction surrogate target fuel FD9A

−20

Figure 12: Cloud point of both diesel fuels

80 60


40

flash point in ℃

cetane number

LHV in MJ/kg

total-aromatics in mass-%

20 mono-aromatics in mass-%

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

Energy & Fuels

cloud point in ℃

Page 39 of 42

Figure 13: Further properties of diesel OMV


Energy & Fuels

60


40

flash point in ℃

cetane number

LHV in MJ/kg

total-aromatics in mass-%

20 mono-aromatics in mass-%

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

Page 40 of 42

Figure 14: Further properties of diesel FD9A


Page 41 of 42

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

Energy & Fuels

Table 1: Potential surrogate components obtained from the database and literature substance group

components

n-alkanes iso-alkanes alkenes naphthenes

all n-alkanes from n-C7 to n-C24 iso-octane and iso-cetane 1-octadecene all n-alkylcyclohexanes from methyl- to butyl-cyclohexane, cisdecalin, trans-decalin, cyclohexylcyclohexane and cyclooctane all n-alkylbenzenes from ethyl- to pentadecyl-benzene, tetralin, o-, m-, p-xylene and 1,2,4-trimethylbenzene 1-methylnaphthalene

mono aromatics poly aromatics

Table 2: Common properties of the target fuels property

unit

OMV

FD3A

FD5A

FD9A

liquid density at 15 ℃ kg/m3 822.7 840.0 808.6 846.4 cetane number 68.4 30.7 55.0 43.5 2 kinematic viscosity at 40 ℃ mm /s 3.41 1.32 1.795 2.107 flashpoint ℃ 79.0 60.6 58.9 60.0 lower heating value MJ/kg 43.178 42.147 42.897 42.465 mono aromatics mass-% 12.3 45.5 21.1 32.3 poly aromatics mass-% 1.3 4.5 1.1 4.6 total aromatics mass-% 13.6 50.0 22.2 37.0 paraffins vol- % n.a. 31.4 52.2 34 naphthenes vol- % n.a. 25.6 29.9 34 cloud point ℃ −1.7 −35 −29 −30


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

Page 42 of 42

Table 3: Composition of the generated surrogates in mass-% together with the normal boiling points of the pure components component

Tb in ℃

Ethylbenzene 136.2 Cyclooctane 151.1 1,2,4-Trimethylbenzene 169.4 n-Butylcyclohexane 180.9 n-Butylbenzene 183.3 t-Decalin 187.0 Tetralin 207.1 n-Hexylbenzene 226.1 1-Methylnaphthalene 244.7 2,2,4,4,6,8,8-Heptamethylnonane 246.4 n-Hexadecane 286.9 1-Octadecene 314.1 n-Octadecane 316.3 n-Eicosane 343.8 n-Docosane 375.9

OMV 2.7 4.2 8.8 8.9 14.8 22.4 10.9 8.1 11.5 7.7


FD3A

FD5A

FD9A

10.9 13.4 8.6 13.4 3.1 15.0 19.7 8.4 4.4 3.0 -

-

4.2 4.2 10.7 13.2 11.5 18.5 10.0 14.9 6.4 6.3

7.5 26.1 3.5 17.2 12.3 26.1 4.3 3.0 -

Surrogate Generation and Evaluation for Diesel Fuel - Energy & Fuels

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