Surrogate Generation and Evaluation for Biodiesel and Its Mixtures

May 2, 2017 - PEPs - Products, Environment, and Processes, Department of Chemical Engineering, University of Liège, Liège, Belgium. ABSTRACT: In this ...
0 downloads 0 Views 1MB Size
Download PDF
Article pubs.acs.org/EF

Surrogate Generation and Evaluation for Biodiesel and Its Mixtures with Fossil Diesel Anton M. Reiter,† Nikolai Schubert,‡ Andreas Pfennig,†,§ and Thomas Wallek*,† †

Institute of Chemical Engineering and Environmental Technology, NAWI Graz, Graz University of Technology, Graz, Austria OMV Refining & Marketing GmbH, Vienna, Austria § PEPs - Products, Environment, and Processes, Department of Chemical Engineering, University of Liège, Liège, Belgium ‡

ABSTRACT: In this paper a precedently developed surrogate optimization algorithm for fossil fuels, which originally allowed simultaneous fitting of the true boiling point (TBP) curve, the liquid density at 15 °C, and the cetane number, is refined toward its application to biodiesel and its mixtures with fossil diesel. For this purpose, the algorithm is extended (1) to also include fitting of the kinematic viscosity at 40 °C and (2) to account for peculiarities of biodiesel concerning its narrow boiling range and compensation of systematic errors of measured boiling curves. To illustrate these improvements, first, the algorithm is applied to property estimation and surrogate optimization of three different biodiesel fuels, for which surrogates consisting of one to three components are proposed. Second, a surrogate for a commercial European fossil diesel is calculated and produced in lab-scale. Finally, the algorithm is used for surrogate optimization and property estimation of mixtures of biodiesel and fossil diesel, considering fractions of biodiesel of 7% and 20% per volume. It is shown that the improved algorithm is capable of reliably optimizing surrogates for fuels containing both biogenic and fossil components.

■

FUEL CHARACTERIZATION BY SURROGATES State-of-the-art engine and combustion development approaches frequently rely on simulations,1 aiming at optimizing efficiency and emissions while shortening development time. One requirement for realistic engine and combustion simulations is a proper fuel characterization.2 Conventional petroleum-derived fuels contain several hundreds of individual components which cannot be represented in detail within simulation software tools, as several chemical and physical properties for most of the fuel components are unknown3−5 and, furthermore, such a detailed fuel characterization would require enormous computational efforts. To overcome these limitations, surrogates composed of only a few chemical components can be used as an alternative approach for a simplified yet acceptably accurate estimation of physical and chemical properties of a provided target fuel.1,5 Surrogates for petroleum derived diesel fuels proposed in the literature are usually optimized toward the representation of physical and chemical properties.4,6−8 However, when it comes to surrogates for biodiesel, the focus is on representing its chemical kinetic mechanisms, for which frequently esters with shorter chain lengths than those typically found in biodiesel are used. Beyond that, much effort has been put in the development of chemical reaction mechanisms for the long-chained esters contained in biodiesel.9−16 For the simulation of blends with fossil diesel, typically only a single component such as n-heptane is used to represent the petroleum-derived fraction.17,18 To also account for a detailed representation of physical properties of surrogates for nonfossil fuel components, this paper extends a previously proposed approach for surrogate optimization of fossil fuels19 toward application to biodiesel and its mixtures with fossil diesel. To illustrate the change of fuel properties with an increasing fraction of biodiesel, blends with 7% and 20% biodiesel are investigated. The first blend represents a typical commercial European diesel according to DIN EN 590;20 © XXXX American Chemical Society

the corresponding U.S. standard ASTM D975 restricts the biodiesel fraction to 5% per volume, given that the requirements defined in the standard are fulfilled.21,22 The second blend considers the fact that fuels containing up to 20% biodiesel per volume can be used in most diesel equipment with minor or even no modifications.23

■

ALGORITHM FOR SURROGATE OPTIMIZATION The algorithm used in this paper is a further development of a previously proposed approach which was initially used for the characterization of crude oil in terms of surrogates7 and was then significantly extended for application to diesel fuel along with the involved estimation of physical and chemical properties of these surrogates.19 The composition of the surrogate is obtained in the course of an optimization by adjusting the surrogate’s composition to meet selected physical bulk properties of the target fuel. The properties originally used in the objective function were the true boiling point (TBP) curve according to ASTM D2887,21 the liquid density at 15 °C according to DIN EN 12185 (equivalent to ASTM D4052), and the derived cetane number according to DIN EN 15195 (equivalent to ASTM D6890).19 In some applications this set of target properties shows limitations to represent the kinematic viscosity of the target fuel. Therefore, in this paper the kinematic viscosity at 40 °C according to ON EN ISO 3104 (equivalent to ASTM D445) is added as a fourth target property. In the following, the possible components for surrogates are discussed, the estimation models for the involved physical and chemical properties are presented and the objective function is introduced. Received: February 27, 2017 Revised: April 27, 2017 Published: May 2, 2017 A

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

xhydrocarbons, and the viscosity of biodiesel, ηFAME, holding a molar fraction of xFAME in the mixture, according to

Components for Surrogates. Diesel fuel typically contains components with carbon numbers ranging from C10 to C22.6 Based on several articles and reviews2,4−6,24 a set of 49 components for surrogate generation of fossil diesel fuel was proposed previously.19 For surrogate generation of biodiesel and its mixtures with fossil diesel, these basic components are complemented with a set of fatty acid methyl esters (FAME) typically contained in biodiesel, comprising 6 saturated and 6 unsaturated components. The complete set of potential surrogate components is given in Table 1.

η = exp(xhydrocarbons ln ηhydrocarbons + x FAME ln ηFAME)

The pure component viscosity of the individual hydrocarbons is calculated with the Andrade equation28

⎛A ⎞ η = exp⎜ + B⎟ ⎝T ⎠

⎛ B ⎞ η = exp⎜A + ⎟ T − T0 ⎠ ⎝

components

n-alkanes

all n-alkanes from n-C7 to n-C24

iso-alkanes

iso-octane and iso-cetane

alkenes

1-octadecene

naphthenes

all n-alkylcyclohexanes from methyl- to butyl-cyclohexane, cisdecalin, trans-decalin, cyclohexylcyclohexane and cyclooctane

1-methylnaphthalene

saturated FAME methyl-laurate (C12:0), methyl-myristate (C14:0), methylpalmitate (C16:0), methyl-stearate (C18:0), methyleicosenoate (C20:0), methyl-docosenoate (C22:0) unsaturated FAME

methyl-palmitoleate (C16:1), methyl-oleate (C18:1), methyllinoleate (C18:2), methyl-linolenate (C18:3), methyl-cis-11eicosenoate (C20:1), methyl-cis-13-docosenoate (C22:1)

Modeling of Fitted Properties. The modeling approaches for the TBP-curve, the liquid density at 15 °C, and the derived cetane number were adopted from a previous paper19 because they can directly be applied to biodiesel as well. However, for the TBP-curve of the surrogate, which is based on a stepwise function to approximate the smooth boiling curve of the target fuel, an offset correction was introduced when applied to pure biodiesel, as shown later. As an additional fitting criterion in the objective function, the kinematic viscosity, ν, at 40 °C is introduced. It is determined according to ON EN ISO 3104 (equivalent to ASTM D445) and can be calculated from the dynamic viscosity η and the liquid density ρ according to η ν= ρ (1)

Figure 1. Kinematic viscosity at 40 °C of the hydrocarbons used for surrogate generation listed in Table 1 plotted as a function of the normal boiling point.

This figure illustrates that only components with a normal boiling point higher than 300 °C show a kinematic viscosity higher than 3 mm2/s. Therefore, for high viscosities a large share of high boiling components is necessary which in turn causes issues to correctly represent other properties, as discussed elsewhere.19 To overcome this shortcoming, the current objective function allows simultaneous fitting of the TBP-curve, liquid density at 15 °C, ρ, the cetane number, CN, and the kinematic viscosity at 40 °C, ν:

For a mixture of n hydrocarbons the mixing rule ηhydrocarbons

⎛ n ⎞3 1/3⎟ ⎜ = ⎜∑ xi·ηi ⎟ ⎝ i=1 ⎠ 25

(2)

26

suggested by API and Riazi is used. For the calculation of the dynamic viscosity of biodiesel a different mixing rule, namely ⎛ n ⎞ ηFAME = exp⎜⎜∑ xi·ln ηi⎟⎟ ⎝ i=1 ⎠

(6)

where the empirical parameters A, B, and T0 are taken from the literature.30,31 Objective Function. The objective function used for calculation of the surrogate composition was adopted from a previous paper19 and extended by the kinematic viscosity at 40 °C as an additional fitting criterion. This additional criterion is suggested because considerable differences between the kinematic viscosity of a surrogate and the corresponding target fuel were observed for a commercial European fuel in another paper.19 It was observed that such differences occur especially for fuels with higher viscosity, which can be explained by the physical-property data of the potential surrogate components shown in Figure 1.

mono aromatics all n-alkylbenzenes from ethyl- to pentadecyl-benzene, tetralin, o-, m-, p-xylene and 1,2,4-trimethylbenzene poly aromatics

(5)

where the empirical parameters A and B are determined with the ECN-method.29 The pure component viscosity of the FAME is calculated with the Vogel equation

Table 1. Potential Surrogate Components for Diesel Fuel and FAME Typically Contained in Biodiesel substance group

(4)

2 ⎛ ΔT ⎞2 ⎛ ρcalc − ρexp ⎞ ⎛ CNcalc − CNexp ⎞2 ⎜ ⎟ f (wi) = ⎜ ⎟ +⎜ ⎟ ⎟ +⎜ ΔCNref ⎝ ΔTref ⎠ ⎝ ⎠ ⎝ Δρref ⎠

(3)

as suggested by Riazi26 and Gmehling et al.27 is used, where xi represents the molar fraction and ηi the dynamic viscosity of each individual component in the mixture. The dynamic viscosity of mixtures composed of hydrocarbons and biodiesel is calculated from the respective viscosities of the hydrocarbon mixture, ηhydrocarbons with its fraction represented by

⎛ νcalc − νexp ⎞2 ×⎜ ⎟ → min ⎝ Δνref ⎠ (7) B

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

viscosity at 40 °C, cetane number, flash point, cloud point, and boiling curve for all three fuels. Additionally, the lower heating values as well as the temperature dependency of liquid density and kinematic viscosity in the range of 15 to 80 °C were evaluated for biodiesel RME2 and TME. Liquid Density and Kinematic Viscosity. The liquid density at 15 °C was determined according to ON EN ISO 12185 (comparable to ASTM D4052), and the kinematic viscosity at 40 °C was determined according to ON EN ISO 3104 (comparable to ASTM D445). In Figure 2, the obtained experimental data are

The respective numerator of each term represents the difference between the experimental value of the target fuel, index exp, and the calculation for the surrogate, index calc. For the liquid density, cetane number and viscosity, these values can be calculated in a straightforward way, whereas the difference obtained for fitting of the boiling curve is highly linked to the modeling approach of the boiling curve.19 The denominator is used for relative weighting the individual optimization criteria and represents a reference value. The optimization problem given by the objective function (eq 7) is solved through variation of the composition of the surrogate. In the present case, a Fortran implementation32,33 of the simulated annealing algorithm by Corana et al.34 is used.

■

APPLICATION OF THE ALGORITHM The extended objective function (eq 7) is applied to surrogate generation of three different biodiesel fuels, one fossil diesel, and two different mixtures of fossil diesel and biodiesel. First of all, the methods and models for property estimation are applied to these three fuels to prove their applicability to FAME mixtures. Second, biodiesel surrogates consisting of one to three components are calculated to discuss the minimum number of components required for an adequate representation of biodiesel. In a next step, a surrogate for fossil diesel is determined. Finally, this fossil surrogate is used as the basis for the generation of mixtures with biodiesel, considering volumetric fractions of 7% and 20% biodiesel. Biodiesel. To evaluate the estimation methods for property prediction, three different biodiesel fuels were used. Their composition is based on the respective fatty-acid profile and determined according to Ö N EN ISO 14103, as summarized in Table 2.

Figure 2. Liquid density at 15 °C and kinematic viscosity at 40 °C of biodiesel RME1, RME2, and TME.

compared to calculated values based on the compositions given in Table 2. The estimated uncertainty of the experimental data is indicated by error bars. The liquid density is satisfactorily represented by the applied model, showing deviations between experimental data and calculated values of 2 kg/m3 for RME1, 0.2 kg/m3 for RME2 and 0.5 kg/m3 for TME. The uncertainty of experimental data was estimated based on ASTM D4052 which assumes a reproducibility of 0.5 kg/m3 and additionally estimates a bias of 0.5 kg/m3. Also the estimation of kinematic viscosity at 40 °C is adequate, showing deviations between experimental data and calculation of 0.135 mm2/s for RME1, 0.036 mm2/s for RME2, and 0.005 mm2/s for TME. The shown reproducibility of the experimental data was determined according to ASTM D445. Additionally, the temperature dependency of density and viscosity in the range between 15 and 80 °C was evaluated for biodiesel RME2 and TME. The provided experimental data represent the mean value of two different measurements determined with a Stabinger viscosimeter SVM 3000 by Anton Paar.35 The experimental and calculated data are shown in Figure 3. The calculated density curve shows a slight offset to the experimental data which increases with temperature. However, the absolute average deviation (AAD) is 1.37 kg/m3, which is rather acceptable. Also the kinematic viscosity can be predicted excellently by the model. The AAD for kinematic viscosity is 0.06 mm2/s. The reproducibility of experimental data was obtained from the specifications of the measurement equipment. Additionally, analog to ASTM D4052, a bias of 0.5 kg/m3 for density measurement was assumed. Cetane Number. The cetane numbers for the three evaluated biodiesel fuels are illustrated in Figure 4. The calculated cetane number for RME1 is approximately 2 units lower than the experimental value, which is still within the reproducibility of the experimental data, which were determined according to ASTM D6890. The difference between experimentally determined cetane

Table 2. Three Evaluated Biodiesel Fuels, Including Two Rapeseed Methyl Ester (RME) and One Tallow Methyl Ester (TME), Composition in Mass-%a component C14:0 C16:0 C18:0 C20:0 C22:0 C24:0 C16:1 C18:1 C18:2 C18:3 C20:1 C22:1 sum

RME1

RME2

FAME - Saturated 0.0 0.2 4.5 7.0 1.5 2.4 0.5 0.1 0.3 0.0 0.1 0.0 FAME - Unsaturated 0.2 0.3 64.5 60.8 19.5 21.1 7.4 7.8 1.2 0.3 0.1 0.0 99.8 100.0

TME 2.3 25.4 18.2 0.1 0.0 0.0 2.8 41.1 8.4 1.1 0.4 0.0 99.8

a

Additionally, the sum of the species mass fractions is given, which differs slightly from 100 due to rounding errors.

The compositions of the two rapeseed-based fuels are similar, i.e., 7−10% saturated share, whereas the tallow-based biodiesel shows a significantly different composition, i.e., 46% of saturated components. With that, a wide range of possible biodiesel compositions is covered. Property Estimation. The evaluation of methods for property estimation is applied to liquid density at 15 °C, kinematic C

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

that the applied model gives a good estimate for the experimental data. However, the reproducibility for biodiesel RME is exceeded by approximately 0.2 K and for biodiesel RME2 by 2.7 K. The calculated cloud point for biodiesel TME is within experimental reproducibility. Boiling Curve. The boiling curve of biodiesel covers a very small boiling range and shows a small curvature compared to that of petroleum-derived diesel. Figure 6 illustrates (1) the experimentally obtained boiling curves determined by hightemperature gas chromatography, (2) the calculated boiling curves as a step-like approximation based on the normal boiling points of the pure components and their share in the mixtures, and (3) a modified calculated boiling curve which considers a bias as explained in the following. Regarding the calculated boiling curve, it is evident from Figure 6 that this approach underestimates temperatures throughout, yielding an AAD of 13.6 K for RME1, 10.6 K for RME2, and 10.8 K for TME in the range of 5-% vaporized to 95-% vaporized. To explain this effect, it should be recalled that in the course of high-temperature gas chromatography the retention time of the sample is determined as primary data and then used for calculation of the boiling curve. The actual chromatographic analysis of the biodiesel was conducted on the basis of ASTM D2887, a standard method for fossil diesel. For ASTM D2887 it is known that for some components, such as poly aromatics, a systematic difference between normal boiling temperature of the component and obtained boiling temperature may occur. This effect is explained by the different elution times of components with a similar normal boiling point but a different chemical structure.21 The observed offset between calculated and experimentally determined boiling curves for biodiesel can be attributed to such systematic effects. Based on the data for biodiesel TME the systematic offset was quantified, assuming that all FAME contained in biodiesel show identical bias. It turned out that the best representation of the boiling curve can be archived if a bias of 11.1 K is applied to all FAME. This value was also applied to biodiesel RME1 and RME2, reducing the AAD to 2.8 K for RME1, 2.5 K for RME2 and 2.4 K for TME in the range of 5-% vaporized to 95-% vaporized. Figure 6 illustrates the improvement of the boiling curve representation by applying this modification. Based on this modified boiling curve representation, the surrogate optimization algorithm is applied to biodiesel, focusing on the determination of the minimum number of components required for an accurate property estimation by the resulting surrogate. Surrogate Generation. The small boiling range of biodiesel already discussed suggests a dedicated approach for surrogate generation, differing from that for fossil diesel with a wide boiling range. Recalling the objective function for surrogate generation, eq 7, boiling curve, liquid density, kinematic viscosity, and cetane number are the criteria used to determine surrogates containing a maximum of three components. The selection of the individual components is based on a forward selection37 approach, with the function value of the objective function as decision criterion to decide which component should be added. This approach was applied to biodiesel RME2 and TME. For algebraic representation of the boiling curve, a prerequisite for application of the algorithm, a polynomial of degree seven was used:

Figure 3. Temperature dependency of liquid density and kinematic viscosity of biodiesel RME2 and TME.

Figure 4. Cetane number for biodiesel RME1, RME2, and TME.

number and calculated value for biodiesel TME is 5, which slightly exceeds the experimental reproducibility. It is worth mentioning that although the evaluated biodiesel fuels have significantly different compositions, the experimentally determined cetane numbers are rather similar. Especially TME contains a high fraction of saturated FAME, which typically have a considerably higher cetane number than the unsaturated components, which is evident from pure component data.36 However, the higher share of saturated components cannot be seen to the extent expected in the resulting cetane number. Flash Point, Lower Heating Value and Cloud Point. The comparisons for flash point, lower heating value, and cloud point are shown in Figure 5. The reproducibility of experimental data was determined according to ASTM D93 for the flash point, ASTM D240 for the lower heating value, and ASTM D2500 for the cloud point. The flash point can essentially be predicted; however, the difference between calculated value and experimental data for biodiesel RME2 slightly exceeds experimental reproducibility. The evaluation of the cloud-point data shows D

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 5. Flash point, lower heating value, and cloud point of biodiesel RME1, RME2, and TME.

Table 3. Parameters to Represent the Boiling Curves for Biodiesel RME2 and TME with eq 8 a b c d e f g h

RME2

TME

309.747 6.393 −369.162 × 10−3 11.059743 × 10−3 −183.89 × 10−6 1.70943 × 10−6 −8.2767 × 10−9 16.1583 × 10−12

303.202 7.155 −733.925 × 10−3 36.854634 × 10−3 −941.24 × 10−6 12.7599 × 10−6 −87.78 × 10−9 241.476 × 10−12

Table 4. Reference Values for Weighting of the Criteria in the Objective Function for Calculation of Surrogates for Biodiesel RME2 and TME property liquid density at 15 °C in kg/m kinematic viscosity at 40 °C in mm2/s cetane number TBP-curve in K 3

RME2

TME

0.5 0.1 0.5 1.5

0.8 0.2 0.8 3.0

The residua for the four fitting criteria, boiling curve, liquid density at 15 °C, kinematic viscosity at 40 °C, and cetane number are shown in Figure 7. The best single-component surrogate for both biodiesel RME2 and TME is represented by component C18:1 (methyloleate), yielding acceptable deviations. As a second component for the RME2 surrogate, the component C18:3 (methyl-linolenate) is added. With this additional component, the residua of liquid density and kinematic viscosity, which are shown in Figure 7, can be reduced significantly. The optimal two-component surrogate for biodiesel TME is composed of C18:1 and C16:0 (methyl-palmitate). By adding this component, the characterization of the boiling curve and liquid density is improved considerably, however, representation of kinematic viscosity is still limited. The residua for the two-component surrogates are already in the same order of magnitude as the reproducibility of both experimental data and uncertainty of the calculation models. Therefore, the quality of the surrogate cannot be improved significantly by adding more components. Furthermore, the inclusion of a third component already significantly depends on the selection of the reference values, because, due to the nature of the objective function, only a slight change of the reference value may lead to a different component selected.

Figure 6. Simulated distillation boiling curve of biodiesel RME1, RME2, and TME.

TBP(x) = a + b ·x + c·x 2 + d·x 3 + e·x 4 + f ·x 5 + g ·x 6 + h·x 7 (8)

The according parameters of this polynomial for biodiesel RME2 and TME are given in Table 3. The reference values used for weighting the different criteria in the objective function are given in Table 4. Based on these input data, surrogates containing exactly 1, 2, or 3 components for biodiesel RME2 and TME were calculated. The resulting optimal surrogate compositions are given in Table 5. E

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

However, the previously suggested surrogate for this fuel showed significant shortcomings in the characterization of the kinematic viscosity at 40 °C and the content of aromatics and poly aromatics. To allow a better characterization of the content of poly aromatics, it was necessary to limit the concentration of the only poly aromatic component, 1-methylnaphthalene, to a maximum fraction of 8 mass-%. The parameters of the description of the boiling curve with eq 8 are given in Table 6.

Table 5. Compositions of Optimal Surrogates Containing 1, 2, or 3 Components for Biodiesel RME2 and TME species fraction in mass-% C14:0

C16:0

C18:1

C18:3

Biodiesel RME2 RME2-S1 RME2-S2 RME2-S3 TME-S1 TME-S2 TME-S3

17.67 Biodiesel TME

1.55

19.21 17.68

100.0 80.26 62.93

12.74 19.40

Table 6. Parameters for eq 8 to Represent the Boiling Curve According to ASTM D2887 for the Petroleum Derived Target Fuel

100.0 80.79 80.76

B0-target-fuel a b c d e f g h FitStdErr

110.405 17.621 −1.166 46.141998 × 10−3 −1.04070 × 10−3 13.2323 × 10−6 −88.136 × 10−9 239.325 × 10−12 1.55

The reference values used for surrogate optimization of surrogate B0 and the resulting residua for the obtained eightcomponent surrogate are given in Table 7. Table 7. Reference Values Used and Obtained Residua for Surrogate B0 property

reference value

residuum

TBP-curve in °C liquid density at 15 °C in kg/m3 kinematic viscosity at 40 °C in mm2/s cetane number

5.0 2.5 0.4 2.5

9.5 0.3 0.4 0.1

In Table 8 the resulting compositions for the surrogates B0, B7, and B20 are provided. Several properties of these three mixtures were determined experimentally. In a next step, the obtained experimental data are compared to estimated values based on the compositions of Table 8. Boiling Curve ASTM D2887. The boiling curves according to ASTM D2887 of surrogate B0, B7, and B20 are given in Figure 8. The reproducibility of the experimental data was determined according to ASTM D2887 and is indicated by error bars. The calculated boiling curve of surrogate B0 excellently matches both the corresponding experimental data and the smooth boiling curve of the B0-target-fuel. Within the range of 5-% to 95-% vaporized, the AAD between the measured properties of the target fuel and that of the surrogate is 9.5 K, which corresponds to the estimated value of 9.47 K obtained in the course of surrogate generation. The AAD between the calculated boiling curve for surrogate B0 and the experimental data is 6.3 K. For the two surrogates B7 and B20, the estimated values are compared to experimental data, showing an excellent match for both fuels. In the range of 5-% to 95-% vaporized the AAD between the experimental data and the calculated values is 4.9 K for surrogate B7 and 6.1 K for surrogate B20. Figure 8 clearly illustrates the effect of the higher share of biodiesel in the range of 60-% to 80-% vaporized. In particular, this adds up to a flattening of the boiling curve at a temperature of

Figure 7. Development of the residua for the surrogates based on adding further components.

To conclude, the physical properties of biodiesel can be satisfactorily emulated by two-component surrogates. In a next step, property estimation and surrogate optimization for a fossil diesel and its mixtures with biodiesel are discussed. Fossil Diesel and Its Mixtures with Biodiesel. For the representation of physical properties, the characteristics considered are the boiling curves according to ASTM D2887 and ASTM D86, liquid density at 15 °C, cetane number, kinematic viscosity at 40 °C, flash point, and cloud point. The content of aromatics is additionally evaluated for the surrogate of the purely fossil target fuel (B0-target-fuel). Based on surrogate B0, the calculation models are evaluated and the measured properties of the B0-target-fuel are compared to those of the surrogates and predictions based on the surrogates’ compositions. The mixtures containing a volumetric fraction of biodiesel RME1 of 7% (surrogate B7) respectively 20% (surrogate B20) are also used for evaluation of the calculation models and, furthermore, to quantify the effect of an increased fraction of biodiesel on the fuel properties. The measured physical-property data of the petroleum derived target fuel were already summarized in another paper.19 F

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

approximately 350 °C, which corresponds to degrees of vaporization of 60-% to 80-% for surrogate B20. Boiling Curve ASTM D86. The boiling curve according to ASTM D86 is shown in Figure 9. The reproducibility of the

Table 8. Compositions of Surrogates B0, B7, and B20 in Mass-%a component

B0

Hydrocarbons cyclooctane 4.93 tetralin 11.57 1-methylnaphthalene 8.00 2,2,4,4,6,8,8-heptamethylnonane 14.11 n-hexadecane 21.02 1-octadecene 19.64 n-eicosane 12.70 n-docosane 8.02 FAME - Saturated C14:0 0.0 C16:0 0.0 C18:0 0.0 C20:0 0.0 FAME - Unsaturated C16:1 0.0 C18:1 0.0 C18:2 0.0 C18:3 0.0 C20:1 0.0 sum 99.99

B7

B20

4.57 10.71 7.40 13.05 19.45 18.18 11.75 7.42

3.89 9.12 6.31 11.12 16.58 15.49 10.02 6.33

0.0 0.52 0.18 0.01

0.00 1.48 0.51 0.02

0.02 4.54 1.58 0.58 0.02 99.98

0.06 12.85 4.46 1.65 0.06 99.95

Figure 9. Boiling curve ASTM D86 (equivalent to EN ISO 3405) of the surrogates B0, B7, and B20 to show the influence of an increased share of biodiesel.

a

Additionally, the sum of the species mass fractions is given, which differs slightly from 100 due to rounding errors.

experimental data was calculated according to ASTM D86 and is indicated by error bars. The comparison of experimental data and calculated values shows a good match for each of the surrogates. In the range of 5-% to 95-% vaporized the AAD between experimental data and estimation is 1.8 K for surrogate B0, 2.3 K for mixture B7, and 2.1 K for mixture B20. The differences between experimental data and calculated data of the surrogates B0 and B7 are within experimental reproducibility. Regarding mixture B20, the differences between experimental data and calculation slightly exceed the reproducibility at a degree of vaporization of 50-%, while deviations at all other points are within the reproducibility. Furthermore, Figure 9 illustrates the effects of an increasing share of biodiesel. By adding 7-% biodiesel per volume, the boiling temperature at a degree of vaporization of 50% increases from 281.2 to 287.8 °C. If 20-% biodiesel per volume are added, the temperature at this degree of vaporization increases to 296.5 °C. Liquid Density and Kinematic Viscosity. Experimental and estimated data of the liquid density at 15 °C and the kinematic viscosity at 40 °C are given in Figure 10. The reproducibility of the experimental data was obtained according to ASTM D4052 for liquid density and according to ASTM D445 for the kinematic viscosity. The liquid density is very well described for all three mixtures. The differences between experimental and calculated data are within experimental reproducibility. Figure 10 also indicates an increase of density by a higher share of biodiesel. The kinematic viscosity also shows a good match between experimental data and calculation, although a small offset of approximately 0.1 mm2/s is observed. Adding biodiesel to the surrogate B0 increases the viscosity of the resulting mixture. The viscosities of the surrogate B0 and the B0-target-fuel still differ significantly, which is attributed to the available components for surrogate generation as discussed in a previous paper.19 Cetane Number. Figure 11 shows the cetane number of surrogates B0, B7, and B20. The reproducibility of the experimental

Figure 8. Comparison of the boiling curve according to ASTM D2887 of the petroleum derived target fuel to data of surrogate B0 as well as experimental data and calculated values for the mixtures B7 and B20. G

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

The cloud point measurements yielded one and the same value for all mixtures. The difference between experimental data and calculation is within experimental uncertainty for surrogates B0 and B7. In the case of B20 the reproducibility is exceeded by 0.1 K. Although the calculation model is able to represent the experimental value, the surrogate is not able to reproduce the cloud point of the target fuel. This can be related to the availability and selection of components used for surrogate generation and was discussed in detail elsewhere.19 Content of Aromatics. The content of aromatics is only evaluated for surrogate B0 and is shown in Figure 13. Figure 10. Liquid density at 15 °C and kinematic viscosity at 40 °C of the petroleum derived B0-target-fuel as well as surrogates B0, B7, and B20.

Figure 13. Content of aromatics for surrogate B0.

The content of monoaromatics in the B0-target-fuel is nicely represented by the surrogate. However, the surrogate shows a content of total aromatics which is significantly higher than that of the B0-target-fuel.

Figure 11. Cetane number for surrogate B0, B7, and B20 as well as the petroleum derived B0-target-fuel.

■

data indicated by error bars was obtained according to ASTM D6890. The evaluation of surrogate B0 shows a difference of 4.7 between calculated value and experimental data. Although this value slightly exceeds the experimental uncertainty, an acceptable characterization of the B0-target-fuel is achieved. Evaluation of the other two mixtures shows a good match and indicates a decreasing cetane number when biodiesel is added. Flash Point and Cloud Point. Flash point and cloud point are addressed in Figure 12. The experimental uncertainties were

CONCLUSION Extending the portfolio of optimization criteria used hitherto boiling curve, density, and cetane numberby adding the kinematic viscosity, as well as accounting for the narrow boiling range of biodiesel, enhances the surrogate optimization algorithm toward application to biodiesel and its mixtures with fossil fuel. The quality of the surrogates proposed suggests to use these instead of empirically determined single-component surrogates to get a better understanding of fuel chemistry and combustion behavior, avoiding effects related to imprecise or oversimplified fuel characterization. Furthermore, it has been shown that the algorithm can be used to systematically investigate the effects of adding or removing individual chemical components on the fuel’s bulk properties. In this context, for future research the investigation of carburetion behavior based on surrogates should be considered.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +43-0-316-873-7966. Fax: +43-0-316-873-7469. ORCID

Thomas Wallek: 0000-0001-9687-106X

Figure 12. Flash point and cloud point of the surrogates B0, B7, and B20 and the petroleum derived B0-target-fuel.

Notes

The authors declare no competing financial interest.

■

determined according to ASTM D93 for flash point data and according to ASTM D2500 for cloud point data. The flash point is nicely represented with the applied calculation model. For all mixtures, the differences between experimental data and calculation are within experimental uncertainty. By increasing the share of biodiesel the flash point also slightly increases.

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

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

■

(21) ASTM International, Annual Book of ASTM Standards; ASTM International: West Conshohocken, PA, 2010; Vol. 05.01. (22) NREL, NREL/TP-540-43672: Biodiesel Handling and Use Guide; 2009. (23) NREL, Biodiesel Blends. 2005; http://www.nrel.gov/docs/ fy05osti/37136.pdf, access on 20.04.2016. (24) Chevron, Diesel Fuels Technical Review. 2007; http://www. chevron.com/products/prodserv/fuels/documents/Diesel_Fuel_ Tech_Review.pdf, retrieved on 2011-11-30. (25) American Petroleum Institute. Refining Department, Technical Data Book - Petroleum Refining, 6th ed.; American Petroleum Institute: 1997. (26) Riazi, M. R. Characterization and Properties of Petroleum Fractions; ASTM manual series MNL 50; ASTM International: West Conshohocken, PA, 2005. (27) Gmehling, J.; Kolbe, B.; Kleiber, M.; Rarey, J. Chemical Thermodynamics for Process Simulation; Wiley: Weinheim, Deutschland, 2012. (28) Poling, B.; Prausnitz, J.; Connell, J. The Properties of Gases and Liquids; McGraw Hill Professional: New York, 2000. (29) Krishnamoorthy, V.; Miller, S. F.; Miller, B. G. Empirical approach for predicting viscosities of liquid hydrocarbon systems: Defined compounds and coal liquids and fractions. Energy Fuels 2010, 24, 5624− 5633. (30) Pratas, M. J.; Freitas, S.; Oliveira, M. B.; Monteiro, S. C.; Lima, Á S.; Coutinho, J. A. P. Densities and viscosities of fatty acid methyl and ethyl esters. J. Chem. Eng. Data 2010, 55, 3983−3990. (31) Pratas, M. J.; Freitas, S.; Oliveira, M. B.; Monteiro, S. C.; Lima, Á S.; Coutinho, J. A. P. Densities and viscosities of minority fatty acid methyl and ethyl esters present in biodiesel. J. Chem. Eng. Data 2011, 56, 2175−2180. (32) Goffe, W. L.; Ferrier, G. D.; Rogers, J. Global optimization of statistical functions with simulated annealing. J. Econ. 1994, 60, 65−99. (33) Goffe, B. 1994; www.netlib.org/opt/simann.f; www.netlib.org Keywords: simann version 3.2; date 01-22-1994, retrieved on 2013-0131. (34) 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. (35) Anton Paar, SVM 3000 Stabinger Viskosimeter. 2016; http:// www.anton-paar.com/?eID=documentsDownload&document= 1039&L=8, accessed on 2016-04-20. (36) Yanowitz, J.; Ratcliff, M.; McCormick, R.; Taylor, J.; Murphy, M. Compendium of Experimental Cetane Numbers; NREL, 2014. (37) Thompson, M. L. Selection of variables in multiple regression: Part I. A review and evaluation. International Statistical Review/Revue Internationale de Statistique 1978, 46, 1−19.

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 Tech. Pap. Ser. 2007, 1−27. (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) 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 formulating diesel surrogate fuels with accurate compositional, ignition-quality, and volatility characteristics. Energy Fuels 2012, 26, 3284−3303. (7) Reiter, A.; Wallek, T.; Mair-Zelenka, P.; Siebenhofer, M.; Reinberger, P. Characterization of crude oil by real-component surrogates. Energy Fuels 2014, 28, 5565−5571. (8) Mueller, C. J.; Cannella, W. J.; Bays, J. T.; Bruno, T. J.; DeFabio, K.; Dettman, H. D.; Gieleciak, R. M.; Huber, M. L.; Kweon, C.-B.; McConnell, S. S.; Pitz, W. J.; Ratcliff, M. A. Diesel Surrogate Fuels for Engine Testing and Chemical-Kinetic Modeling: Compositions and Properties. Energy Fuels 2016, 30, 1445−1461. (9) Lai, J. Y.; Lin, K. C.; Violi, A. Biodiesel combustion: Advances in chemical kinetic modeling. Prog. Energy Combust. Sci. 2011, 37, 1−14. (10) Westbrook, C.; Naik, C.; Herbinet, O.; Pitz, W.; Mehl, M.; Sarathy, S.; Curran, H. Detailed chemical kinetic reaction mechanisms for soy and rapeseed biodiesel fuels. Combust. Flame 2011, 158, 742− 755. Special Issue on Kinetics. (11) Herbinet, O.; Biet, J.; Hakka, M. H.; Warth, V.; Glaude, P.-A.; Nicolle, A.; Battin-Leclerc, F. Modeling study of the low-temperature oxidation of large methyl esters from {C11} to {C19}. Proc. Combust. Inst. 2011, 33, 391−398. (12) Naik, C.; Westbrook, C.; Herbinet, O.; Pitz, W.; Mehl, M. Detailed chemical kinetic reaction mechanism for biodiesel components methyl stearate and methyl oleate. Proc. Combust. Inst. 2011, 33, 383− 389. (13) Wang, X.; Liu, H.; Zheng, Z.; Yao, M. A Skeletal Mechanism of a Biodiesel Surrogate Fuel for Compression Ignition Engines. Energy Fuels 2015, 29, 1160−1171. (14) Chang, Y.; Jia, M.; Li, Y.; Zhang, Y.; Xie, M.; Wang, H.; Reitz, R. D. Development of a skeletal oxidation mechanism for biodiesel surrogate. Proc. Combust. Inst. 2015, 35, 3037−3044. (15) Liu, T.; E, J.; Yang, W.; Hui, A.; Cai, H. Development of a skeletal mechanism for biodiesel blend surrogates with varying fatty acid methyl esters proportion. Appl. Energy 2016, 162, 278−288. (16) Agarwal, A.; Agarwal, R.; Gupta, T.; Gurjar, B. Biofuels: Technology, Challenges and Prospects; Green Energy and Technology; Springer: Singapore, 2017. (17) Herbinet, O.; Pitz, W. J.; Westbrook, C. K. Detailed chemical kinetic mechanism for the oxidation of biodiesel fuels blend surrogate. Combust. Flame 2010, 157, 893−908. (18) Hakka, M. H.; Glaude, P.-A.; Herbinet, O.; Battin-Leclerc, F. Experimental study of the oxidation of large surrogates for diesel and biodiesel fuels. Combust. Flame 2009, 156, 2129−2144. (19) Reiter, A. M.; Wallek, T.; Pfennig, A.; Zeymer, M. Surrogate Generation and Evaluation for Diesel Fuel. Energy Fuels 2015, 29, 4181−4192. (20) DIN EN 590:2014, Automotive fuels - Diesel fuel - Requirements and test methods. 2014. I

DOI: 10.1021/acs.energyfuels.7b00603 Energy Fuels XXXX, XXX, XXX−XXX