This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
Article Cite This: Energy Fuels XXXX, XXX, XXX−XXX
pubs.acs.org/EF
Investigation of Water Interactions with Petroleum-Derived and Synthetic Aviation Turbine Fuels Zachary J. West,* Takahiro Yamada, Christopher R. Bruening, Rhonda L. Cook, Susan S. Mueller, Linda M. Shafer, Matthew J. DeWitt, and Steven Zabarnick University of Dayton Research Institute, 300 College Park, Dayton, Ohio 45469, United States S Supporting Information *
ABSTRACT: While undesirable in aviation fuel systems, water is both ubiquitous and tenacious; thus, interactions between water and aviation turbine fuel occur regularly. From a fuel user perspective, it is important to know, understand, and be able to predict such fuel−water interactions, e.g., water solubility, water settling rate, and interfacial tension, for proper mitigation. We explore these interactions as well as surface tension of both petroleum-derived and alternative jet fuels to compare potential differences between product compositions on these physical interactions. Observations indicate a positive, nonlinear correlation between water solubility and both aromatic content and temperature (from 0 to 50 °C). Water settling rates appear to follow a Stokes’ law model; therefore, bulk chemical composition indirectly influences settling rates via density and viscosity. Finally, surface tension appears positively correlated to sample density, while interfacial tension is correlated to both surface tension and fuel aromatic content.
■
INTRODUCTION The aviation industry has developed effective jet fuel handling methods and aircraft fuel system operating procedures for traditional crude-oil-derived fuels. A key aspect of these procedures is the management of trace water, which may be present in fuel as dissolved in solution and/or as free water droplets. The presence of trace water in aviation fuel systems is undesirable because it can cause a host of problems, e.g., reduce fuel energy content, initiate material corrosion, promote biological growth, and precipitate as ice. Alternative aviation fuels may have compositional differences relative to the conventional petroleum-based products, e.g., different proportional amounts of known hydrocarbon classes. These alternative aviation fuels have been developed as synthesized blending components to supplement the aviation fuel supply, which in the short term may have a more subtle influence on chemical composition as a result of dilution effects. Nevertheless, the presence of potentially different fuel compositions, that is, relative amounts of each hydrocarbon class, raises questions regarding the impact on water solubility characteristics and other fuel−water interactions. The water content of alternative aviation fuels may be required as a fit-forpurpose property per ASTM D4054,1 where fuels will “conform to typical response of values within engine/airframe manufacturers’ experience”, i.e., values reported in the Handbook of Aviation Fuel Properties2 [henceforth referred to as the Coordinating Research Council (CRC) Handbook]. However, the CRC Handbook only reports average solubility values for petroleum-derived fuels and lacks data on alternative aviation fuels.2 The aviation industry uses water coalescer technology to reduce the amount of trace, free water in fuel to an acceptable level, typically below 15 ppm at the point of fueling.3 Coalescer filter manufacturers have expressed a need for data to establish whether water separates from alternative aviation fuels in the © XXXX American Chemical Society
same manner as from conventional jet fuels. Experience has shown that trace impurities in fuels, e.g., surfactants from the source feedstock or the manufacturing process, have impacted coalescer performance through surface activity/disarming properties. Another common operational practice to reduce free water levels is settling. Allowing fuels to settle in tanks after bulk fuel transfers, with subsequent sumping to remove water bottoms, can be effective; however, it is desirable to determine if water settles at similar rates in alternative aviation fuels compared to conventional fuels.
■
BACKGROUND Water Solubility. The solubility of water in aviation fuels is an operational concern. The solubility of water in jet fuel increases with increasing fluid temperatures.2,4,5 When fuel is exposed to warm, humid air in aircraft ground tanks, it can solubilize water in excess of 250 ppm wt.6 Upon ascent, fuel in aircraft wing tanks will cool, thereby decreasing the solubility and causing water to separate into a second phase. When fuel tank temperatures drop sufficiently, the condensed water will transition into solid, ice particles, which can impede flow through fuel filters and other critical components.7 In addition, water can be introduced into aircraft fuel tanks during normal flight operations, e.g., intake of moist air via fuel tank pressure equilibration during aircraft descent. Previous work has estimated these levels could be on the order of 40 ppmV; however, these levels are aircraft- and weather-dependent.8 A critical evaluation of water solubility in pure hydrocarbons has been reported, in detail, in a series of publications by Maczynski and Shaw, sponsored by the International Union of Pure and Applied Chemistry (IUPAC) and the National Received: September 20, 2017 Revised: December 4, 2017
A
DOI: 10.1021/acs.energyfuels.7b02844 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels Institute of Standards and Technology (NIST).9−12 On the basis of these solubility data and the known constituents found in jet fuel, n-decane and ethylbenzene were selected as appropriate low- and high-solubility reference/validation fluids, respectively. An inspection of the sources cited in the Maczynski and Shaw series also provides an overview of experimental methods used to determine water solubility. Methods vary but often rely on direct liquid−liquid (twophase) exposure, where liquid fuel and liquid water are the two phases.13,14 These experiments are conducted under controlled temperature and pressure conditions, with subsequent analysis of one or both liquid phases after an extended static period.13−15 Quantitation techniques of the water in hydrocarbons also varies and includes direct Karl Fischer titration as well as radioactive tracer monitoring (using tritiated water as a tracer in deuterated water).13,16 Other studies have addressed water solubility in aviation turbine fuels.4−6 The work reported by Zherebtsov and Paganova5 used a novel liquid−vapor−liquid (three-phase) equilibrium cell, where the liquid fuel and liquid water phases are physically separated by a vapor ullage. The segregation of the two liquid phases eliminates the potential bias of undissolved, free water occurring in the fuel phase, which can skew solubility measurements. Lam et al.4 have reported correlations between jet fuel aromatic content and flash point of the fuel to predict water solubility as a function of the temperature. Other researchers have suggested that water solubility in hydrocarbons can be estimated as a simple function of water vapor pressure; these authors invoke a compelling argument based on thermodynamics; however, the correlations were only tested on pure substances and not complex mixtures, such as jet fuel.17 Water Settling. The rate of free water settling in aviation turbine fuel is a critical parameter for determining recommended practices regarding fuel tank contamination and cleanup procedures. The current guidance about settling rates given by the CRC Handbook shows the settling rate as a function of the particle size.2 While documentation of these settling rates is lacking, it is believed that the calculations were based on Stokes’ law, i.e., a force balance about a spherical particle falling through a viscous fluid. The spherical particle is presumed to be water (or water with iron contaminant), and the Stokes’ model assumes the following: laminar flow, smooth surfaces, homogeneous materials, and no particle−particle interactions. Stokes’ model is appealing because terminal particle velocities can be estimated using readily available physical properties of the desired fuel, i.e., density and viscosity. References of direct measurements of water settling in aviation fuels were not identified during the literature search for the current study. Without data for direct comparison, values generated for this work can only be compared on a relative basis (not absolute). However, physical models describing settling behavior can improve confidence in the collected data. Modeling the settling behavior of real hydrocarbon−water systems can become quite complex, as shown for crude oil and water systems.18,19 The methods of measuring water settling can be equally complex, e.g., use of low-field nuclear magnetic resonance (NMR); however, some researchers have taken on a simpler approach, e.g., monitoring the increase in depth of the heavier liquid in a well-mixed fluid column, and had reasonable success.20 Regardless, the water droplet size distribution is an important parameter for full understanding and can be measured directly21,22 or by calculation.20 In most cases, a
log-normal particle distribution is observed;20,21,23 these distributions are encountered frequently and have been very well characterized.24 Surface and Interfacial Tension. The surface tension (ST) of fuel−air and the interfacial tension (IFT) of fuel−water systems are important quantities that relate to the amount of cohesive energy present at these interfaces. These values are of interest to the fuel community to provide an understanding of (1) fuel atomization in airstreams and combustors, (2) gas dissolution fundamentals, e.g., outgassing, and (3) water collection and remediation technologies, e.g., filter/coalescers.2,25 The CRC Handbook2 gives guidance regarding the surface tension of petroleum-derived fuels versus temperature; however, there is much interest in extending these data to alternative fuels and to include IFT measurements of fuel− water mixtures. Evans26 outlines various methods to assess both ST and IFT that include static methods (e.g., capillary rise and drop/bubble shape), quasi-static methods (e.g., du Nouy ring and maximum bubble pressure), and various dynamic methods. Jasper et al.27 have reported on the surface tension for a homologous series of n-alkanes using the capillary rise method, while Zeppieri et al.28 have reported the IFT for a similar homologous series of nalkanes and water using a pendant drop method. The drop shape method lends itself to being able to determine both ST and IFT using the same apparatus with good precision and accuracy29,30 and enhanced control over environmental conditions. Objectives. The primary objectives of this work are to determine the influence of alternative aviation fuels, both as a blend with conventional petroleum-derived fuels and on a stand-alone basis, on the following characteristics: (1) water solubility in fuel, (2) relative water settling rate in fuel, and (3) fuel−air surface tension and fuel−water interfacial tension, i.e., how fuel properties are affected by composition and temperature. A secondary objective of this program is to investigate possible correlations or theoretical models that connect the empirical data to the known composition of the fuel samples.
■
EXPERIMENTAL SECTION
Fuel Samples and Test Methods. A total of 36 fuel samples (including blends) were identified for testing within this program to represent a broad range of composition and type; Tables 1−3 list the neat and blended fuel samples and some relevant physical and chemical properties. Each fuel sample was assigned a unique CRC sample number for this study; samples also have a corresponding POSF ID number, which is specific to the Fuels and Energy Branch of the Air Force Research Laboratory, Wright-Patterson Air Force Base (WPAFB), OH, U.S.A. Density was measured for the neat fuel samples (Table 1) using ASTM D4052.31 Density and aromatic content were calculated assuming linear volumetric mixing for blended samples (Table 3). All kinematic viscosities reported were measured using ASTM D445.32 A summary of the neat fuel and blend stock compositions is listed in Table 2 (compositions of fuel blends are assumed to have linear combinations of species); a detailed account of sample compositions can be found in the Supporting Information. CRC fuel sample numbers 1−6 are traditional, petroleum-derived aviation turbine fuels and were selected to include a low-aromatic commercial jet fuel (sample number 1), a high-aromatic commercial jet fuel (sample number 2), various grades of U.S. military jet fuel (sample numbers 3−5) that contain the military additive package, i.e., FSII, SDA, and CI/LI (no fuel contains +100 additive), and a fuel with (sample number 5) and without (sample number 6) the U.S. military additive package. Sample numbers 7−13 and 34 are synthetic-based fuels/blendstocks and include aliphatic, highly isoparaffinic mixtures B
DOI: 10.1021/acs.energyfuels.7b02844 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
coulometer; and the empty syringe was then reweighed to determine the sample mass injected. The injected sample mass was used to determine the water content and was reported in milligrams of water per kilogram of sample (ppm wt). Two identical double-jacketed, glass equilibrium cells, as shown in Figure 1, were designed and fabricated for solubility measurements. One cell was outfitted with an insulating Teflon cap (i.e., lowtemperature cell from −40 to 0 °C operation), and the other was outfitted with an aluminum cap containing a heater cartridge and thermocouple (i.e., high-temperature cell from 0 to 50 °C operation). An O-ring seal was used between the cap and glass cell to account for differences in thermal expansion. The caps included gas inlet and outlet lines, to allow purging of the cell headspace with dry nitrogen for cleaning/dehumidification and for sample transfer operation, and syringe access ports to both the upper water reservoir and the lower fuel sample reservoir. The test fluid was stirred using a magnetic stirrer. A K-type thermocouple, immersed in the fuel sample, was used to monitor and control the equilibrium temperature. A number of auxiliary pieces of equipment and subsystems accompany the equilibrium cells including: constant-temperature circulating baths, a magnetic stirrer, a sample preparation vessel, a gaseous dry nitrogen supply, a digital thermometer, and a variety of transfer lines/tubing. Fuel was dried over 3 Å molecular sieves (8−12 mesh, beads) in sample preparation vessels, which were connected directly to the equilibrium cells. Transfer of samples occurred without exposure to the ambient environment using a nitrogen head pressure. Fuel was filtered during transfer using an in-line, 20 μm sintered stainless-steel filter. Care was taken to ensure minimal bias of water measurements by keeping the sampling syringe as dry as possible; sampling equipment was stored in a desiccator when not in use. Water Settling. Water settling experiments were conducted by preparing fuel/water emulsions at three different nominal water levels (500, 1000, and 10 000 ppmV) in a cylindrical container. Samples were emulsified for about 60 s at 13 500 rpm using a high-shear contact mixer. After mixing, fuel samples were taken at a fixed location in the vessel using rigid capillary tubing and analyzed by Karl Fischer (KF) titration for the total water content. The water concentration (at a fixed height in the fluid column) as a function of time was used as a relative indicator of water settling rates in the neat fuel samples. A fuel volume of 600 mL was placed into a round, wide-mouth 1 L Teflon FEP bottle with dimensions of 91 mm inner diameter × 209 mm height (Thermo Scientific catalog number 02-924-15G). About 3 /4 of the bottle was immersed in a constant temperature bath, set at 15 °C, and the sample was allowed to temperature equilibrate for at least 1 h (but typically overnight). The fuel level reached
Table 1. List of Neat Fuel Samples and Blend Stocks CRC number
POSF number
1 2 3 4 5 6 7 8 9 10 11 12 13 34 36
12402 11769 10289 12360 8451 5237 11714 7629 12376 11498 8076 12398 7675 12918 12842
fuel type
density at 15 °C (kg/m3)
kinematic viscosity at 15 °C (mm2/s)
Chinese No. 3 Jet Jet A JP-5 F-24a JP-8b Jet A-1 SPK-HEFA SPK-FT IPK-A ATJ HDCJ SIP SK SAK AVGAS 100LL
803 812 827 814 796 796 764 760 782 761 887 770 812 875 715
1.75 1.83 2.37 2.24 1.83 1.83 2.39 1.59 1.52 2.25 2.31 3.94 2.21 1.04 n/a
a Fuel grade F-24 is defined by NATO Standard AFLP-3747 as type Jet A with the addition of FSII, SDA, and CI/LI. bCRC sample number 5 was blended on-site using CRC sample number 6 plus FSII, SDA, and CI/LI.
(sample numbers 7, 8, 10, and 12), an aliphatic, highly cycloparaffinic mixture (sample number 13), highly aromatic mixtures (sample numbers 11 and 34), and two “fully formulated” synthetic jet fuels (sample numbers 9 and 35). An aviation gasoline sample (number 36) was added later in the program for solubility measurements. Water Solubility. The approach to determine water solubility as a function of the temperature was similar to that reported by Zherebtsov and Peganova.5 The saturation limit of water in each test fluid was achieved by establishing equilibrium of the test fluid with a vapor space maintained at 100% relative humidity (RH). The 100% RH vapor was produced by having a segregated water phase within the equilibrium cell. Separation of the discrete fuel and water phases allows for saturation without the presence of free (e.g., excess) water within the fuel phase. The presence of free water in the test fluid could significantly bias measured solubility levels toward higher amounts. Fuel samples were drawn from the equilibrium chamber using a syringe through a septum port. The fuel sample and syringe were immediately weighed on a balance; then the total fuel sample was dispensed into a Mettler-Toledo (model DL39) Karl Fischer
Table 2. Summary of Fuel Bulk Chemical Compositions (via GC × GC) CRC number
fuel type
MWavg
average molecular formula
hydrogen content (wt % H)
aromatics (% vol)
cycloparaffins (% vol)
n-paraffins (% vol)
isoparaffins (% vol)
1 2a 3 4a 5b 6b 7 8 9 10a,c 11a 12 13 34
No. 3 Jet Jet A JP-5 F-24 JP-8 Jet A-1 SPK-HEFA SPK-FT IPK-A ATJ HDCJ SIP SK SAK
152 155 166 165 159
C10.9H21.5 C11.1H21.1 C11.9H22.6 C11.8H22.9 C11.4H22.3
14.3 13.8 13.7 14.0 14.1
5.9 19.9 18.6 15.8 18.8
58.7 39.0 46.8 41.6 21.2
10.3 17.3 15.0 17.3 25.8
25.1 23.6 19.6 25.4 34.2
179 154 151 178 152 212 163 125
C12.6H27.2 C10.8H23.6 C10.8H21.7 C12.6H27.2 C11.1H18.0 C15.0H32.0 C11.6H23.2 C9.3H12.7
15.3 15.5 14.5 15.3 12.0 15.2 14.4 10.3
0.1 0.5 17.7 1, then the model fit does not fully capture the data or the variance is underestimated; if χ2red = 1, then the observed and estimated data are within the variance (best fit); and if χ2red < 1, then the model is “overfitting” the data. Fitting was performed using the GRG nonlinear solver, with multistart, in MS Excel, to find a global solution for the model by iterating the two parameters, i.e., log-normal distribution mean and standard deviation, until χ2red was optimized to a value as close as possible to 1. Table 5 shows the final regressed values of χ2red
Figure 13. Inverse water concentration profiles for the 10 000 ppmV nominal initial water level at 15 °C and a depth of 7.62 cm.
Table 5. Reduced χ2 Statistic, χ2red, and Estimated Particle Distribution Arithmetic Mean, D1,0, and Volume-Weighted Mean, D4,3, Diameters for 10 000 ppmV Nominal Water CRC number
description
χ2red
D1,0
D4,3
n/a 1 2 3 4 5 6 7 8 9 10 11 12 13 34 35
n-decane No. 3 Jet Jet A JP-5 F-24 JP-8 Jet A-1 SPK-HEFA SPK-FT IPK-A ATJ HDCJ SIP SK SAK HEFA-SAK
2.26 1.73 2.35 1.00 1.19 1.87 1.00 1.02 1.72 2.07 1.00 1.00 1.00 1.00 1.37 3.64
1.9 12.2 1.7 2.2 10.9 4.0 5.9 6.2 1.7 2.2 2.7 5.3 5.2 5.4 1.7 2.1
177.9 42.6 138.2 126.9 37.2 49.4 180.2 68.8 129.4 181.0 135.8 111.6 137.8 134.1 131.5 171.0
Figure 14. Correlation between experimental and theoretical relative settling rates of water in 15 fuel samples and n-decane at 15 °C.
arbitrary; the importance is to have a consistent reference velocity that differs based on fuel composition/properties and not the drop geometry because it is assumed that a consistent droplet distribution forms during the experiment. The data shown in Figure 14 seem to indicate a correlation exists between Stokes’ law and the experimental data; that is, as the calculated terminal velocity increases, the observed relative settling rate also increases. This result is encouraging and implies that a Stokes’ law model might be useful to predict settling behavior. The connection between theory and experiment was explored further by attempting to fit Stokes’ law model (eq 6) to the empirical data (Figure 12). To do so, the water droplet distribution(s) must be estimated. It has been shown that water droplet distributions of this nature often fit a lognormal distribution; therefore, a log-normal probability distribution was assumed for the initial droplet distribution over a range of particle diameters from 0.2 to 400 μm (with a step size of 0.2 μm).21 The discrete particle distribution allowed for the calculation of terminal velocities at each particle
along with the resulting predicted arithmetic mean particle diameter, D1,0, and volume-weighted mean particle diameter, D4,3, of the water particle distributions. Definitions for mean particle diameter of log-normal distribution are given by Alderliesten24 such that
D1,0 =
∑ niDi ∑ ni
(8)
and D4,3 =
∑ niDi4 ∑ niDi3
(9)
where ni is the number of particles with a diameter of i. The data listed in Table 5 show that the model fit successfully for I
DOI: 10.1021/acs.energyfuels.7b02844 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels the highest initial water condition, 10 000 ppmV; poor model fits were observed for the majority of 500 and 1000 ppmV water cases (data not shown). This may be caused by water particle distributions that were skewed to very low mean diameters for the lower water concentration cases. These small particles exhibit mass diffusion rates greater than or equal to the associated terminal velocity; therefore, errors are expected because diffusion was not considered with the current model. Nevertheless, the 10 000 ppmV initial water case did provide good model fits. Figure 15 shows a typical predicted water
Figure 16. Water settling profiles, experimental (markers) and theoretical fits (curves), for sample numbers 1 and 10 and n-decane at 15 °C, a depth of 7.62 cm, and 10 000 ppmV initial water.
Figure 15. Estimated water droplet number probability and volume concentration profiles for CRC sample number 1 with 10 000 ppmV nominal total water.
droplet distribution for sample number 1 (No. 3 Jet) at 10 000 ppmV initial water content. The probability distribution, i.e., relative number count of particles, is shown along with the relative water concentration versus droplet diameter. The figure shows the probability function centered at a diameter of about 7 μm, whereas the concentration distribution is centered at a larger diameter of about 24 μm. The concentration distribution leads the probability distribution because the concentration is a cubic function of the diameter, whereas the number count is not a function of the diameter. Water particle sizes of this magnitude are in line with experimental observations from others,21 which gives confidence to the current predictions. Figure 16 shows an example of how Stokes’ model, using a log-normal water droplet distribution, fits the experimental data at the 10 000 ppmV water case. While best fits were not achieved for all fuels (see χ2red statistics listed in Table 5), the qualitative form of the models closely resembles the experimental data, even over a broad range of settling rates and fuel types, as demonstrated by the data shown in Figure 16. The level of agreement between the simple theoretical model and the experimental data gives confidence that the settling behavior can be represented by Stokes’ law for water distributions, where D4,3 is greater than about 40 μm, for the fuel compositions examined. Surface and Interfacial Tension. The surface tension of a reference fluid (n-decane) was measured as a function of the temperature using a pendant drop apparatus; the results are shown in Figure 17. As the figure shows, measured ST values are in agreement with reference data,35 which gives confidence in the current measurement technique (uncertainty bars for data are smaller than the marker size). The surface tension of ndecane was also verified at a single temperature using a du Nouy ring apparatus,36 further corroborating the results measured using the pendant drop technique. A similar effort
Figure 17. Surface (ST) and interfacial (IFT) tension profiles of verification fluid n-decane−air and n-decane−water, respectively. Solid markers are experimental values from current work, and line and open markers are reference values.28,35
was conducted to validate the emergent bubble technique for measurement of interfacial tension. The IFT of n-decane− water, measured via the emergent bubble technique, is also shown in Figure 17. As the figure shows, good agreement is seen between the current work and data reported by Zeppieri et al.28 Again, a du Noüy ring method37 was used as a second verification at a single temperature. The agreement between the ring method and the emergent bubble method or the reference data is not as good as for the ST data; nevertheless, the ring data are within about 2−3% of the current values. With the pendant drop/emergent bubble methods verified, measurements of ST and IFT were made on the neat fuel samples, and the results are shown in Figure 18. As the figure shows, both ST and IFT give inverse linear correlations to the temperature, i.e., the free energy of the fluid interface decreases as the temperature increases. Closer observation of surface tension data show that most fuels are within about ±2.5 mN/m J
DOI: 10.1021/acs.energyfuels.7b02844 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels γ12 = γ1 + γ2 − 2Φ γ1γ2
(10)
where γ12 is the interfacial tension, γ1 and γ2 are the surface tension of the two fluids, and Φ is a characteristic system constant. The system constant, Φ, for many pure hydrocarbon−water interfaces has been shown to range from 0.51 to 0.78, with aliphatic compounds tending toward the lower end of the range and aromatic compounds tending toward the upper end of the range.38 Equation 10 was rearranged to solve for Φ in terms of IFT and ST; the experimental IFT and ST values for jet fuel (Figure 18) were then used to determine Φ for each fuel and are shown versus the sample aromatic content in Figure 20. As the figure shows, Φ is proportional to the aromatic level and is well-described by a linear regression. The range of calculated Φ for fuel samples coincides with the range for pure hydrocarbons. Therefore, Figures 19 and 20 and eq 10 can be combined to estimate IFT of a fuel sample using density and aromatic content.
Figure 18. Measured fuel−air surface tension (ST) and fuel−water interfacial tension (IFT) profiles of various petroleum-derived and synthetic fuels, with the reference curve shown for ST of Jet A, Jet A-1, and JP-8 fuels.2
of the average, which gives a relative standard deviation of about 10% or less. Most fuels exhibit ST values at or below the values reported in the CRC Handbook.2 Sample numbers 11 (SAK) and 34 (HDCJ), which are both high density, high aromatic blend components, are exceptions and exhibit ST values about 20% higher than the other fuel samples. This is not of large concern, as finished fuels will be blended to an aromatic level of ≤25% vol, which would shift ST values closer to the average, as shown with sample number 35 (which is a blend of sample number 11 and HEFA-SPK). A similar inspection of the IFT data shows a clustering of values within a range of about 5 mN/m, with sample number 35 exhibiting an IFT of about 20% less than average. Again, blending of sample number 11 with HEFA-SPK, i.e., sample number 35, brings the IFT value closer to average. In the past, the Ramsy−Shields−Eötvös correlation has been used to estimate surface tension values of jet fuel.2 However, this correlation has limitations because it was developed for pure materials and some of the necessary variables, i.e., fuel molecular weight and critical temperature, are not readily available for most fuels. To reduce potential errors of ST estimation of jet fuel, an empirical approach has been taken; however, the reported correlations only relate ST to the temperature (by fuel grade) rather than associating ST to fuel composition.2 It is desirable, for fuel users, to have a concise correlation of ST to a common physical property, such as density, for mixtures as complex as jet fuel. Figure 19 is an attempt at such a correlation and shows the measured ST at 20 °C versus sample density. As the figure shows, jet fuel surface tension is proportional to fuel density with a correlation coefficient of about 0.911. The observed linear trend for jet fuel is similar in nature to that observed by Jasper et al.27 for pure normal alkanes; however, the constants of linear regression for fuel are understandably different from those of normal alkanes because fuel contains aromatics, isoparaffins, and cycloparaffins in addition to n-alkanes. It is also desirable to develop a concise correlation for jet fuel IFT with respect to a common/readily obtained fuel property. Girifalco and Good38 have reported on a means of estimating interfacial tension of many organic−aqueous interfaces using the following equation:
Figure 19. Correlation of fuel sample ST to density.
■
CONCLUSION Two reference fluids, i.e., n-decane and ethylbenzene, were used to verify the experimental methodology for determining water solubility in hydrocarbon fuel samples; measured solubility values of reference fluids closely matched accepted literature values, giving confidence in the current method. Water solubility values were reported for all 36 fuel samples and blends. Measured values were consistent with expected trends; e.g., solubility increased in an exponential fashion with the temperature from −40 to +50 °C, and solubility was observed to correlate with the fuel aromatic concentration. Only 2 of the 36 samples measured were consistently greater than the nominal solubility curves provided by the CRC Handbook;2 those two samples had aromatic levels much greater than the current 25% vol maximum and, therefore, would not be appropriate jet fuels without blending. Correlations to predict and interpret water solubility were explored. The exponential correlation proposed by Lam et al.4 incorporating both K
DOI: 10.1021/acs.energyfuels.7b02844 Energy Fuels XXXX, XXX, XXX−XXX
Energy & Fuels
■
Article
ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.7b02844. Fuel sample bulk hydrocarbon compositions (via GC × GC) (Tables S1−S5), experimental technique used to generate the data, and complete data, including calculated uncertainties, of water solubility (Tables S6− S13), water settling (Tables S14−S16), and surface and interfacial tension (Tables S17 and S18) (PDF)
■
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Zachary J. West: 0000-0001-7671-9781 Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS The authors thank the Coordinating Research Council, Inc. for sponsoring this work (Project AV-19-14). The authors thank Robert E. Morris (Nova Research, Inc.) and Alisdair Clark (Air BP) for useful discussions. The authors thank Tim Edwards and Miguel Maldonado (AFRL/RQTF) for use of U.S. Air Force facilities for a portion of the work (under CRADA 10-223-RZ01). Also, the authors thank Sahil Jain and Cynthia Ginestra (Shell) for enabling the SK and SAK samples to be part of this study.
Figure 20. Relationship of the characteristic interfacial constant, Φ, at 20 °C to the sample aromatic content for jet fuel−water systems.
aromatic content and temperature was found suitable for most samples at ambient temperatures. Theoretical and experimental evaluations were conducted on neat fuel samples to assess differences in water settling rates. Water droplet terminal velocities, determined using Stokes’ law, predict a variation of about ±30% in the settling rate for the six petroleum-derived fuels in this study. The eight alternative fuel samples under study exhibited a larger range in computed terminal velocities, with values as high as 83% faster than a typical Jet A and as low as 49% slower than the typical Jet A. This broad range in velocities is expected on the basis of the range of viscosity and density values. This analysis could be applied to a broader set of fuels to more comprehensively determine the variance in terminal velocities of typical fuels. Experimental settling profiles were measured at three initial water concentrations, i.e., 500, 1000, and 10,000 ppmV. Profiles at the lower two water concentrations had high uncertainty; therefore, analysis focused on the 10 000 ppmV nominal water level. Experimental settling rates varied by a factor of about 3 and exhibited some correlation to calculated terminal velocities. Stoke’s law model was fit to the experimental data, further demonstrating the usefulness of such analysis for comparison of different fuels. A pendant drop/emergent bubble technique was used to measure surface and interfacial tension of 15 fuel samples plus a reference fluid (n-decane). Reference values were in excellent agreement with the literature, giving confidence to the current method. Surface and interfacial tension values for fuel samples were observed to be linear and inversely proportional to the temperature, which was expected. Surface tension is wellcorrelated to sample density, while IFT is related to both density (via surface tension) and aromatic content.
■
L
NOMENCLATURE a = coefficient (ppm wt) b = coefficient (°C−1) C = water concentration (ppm wt) CI/LI = corrosion improver/lubricity improver additive d = compound-specific constant D = diameter (m) D1,0 = arithmetic mean diameter (m) D4,3 = volume-weighted mean diameter (m) EC = equilibrium cell FSII = fuel system icing inhibitor (i.e., diethylene glycol monomethyl ether) FT = Fischer−Tropsch g = standard acceleration of gravity (9.8 m/s2) GC × GC = two-dimensional gas chromatography H = Henry’s coefficient (kPa/mass fraction) HDCJ = hydrotreated depolymerized cellulosic jet HEFA = hydroprocessed esters and fatty acids IFT = interfacial tension IPK−A = isoparaffinic kerosene with aromatics MW = molecular weight (g/mol) n = water solubility (mass fraction = grams of water per gram) NMR = nuclear magnetic resonance ppmV = parts per million by volume (μL/L) ppm wt = parts per million by weight (μg/g) pvap = pure substance vapor pressure (kPa) RH = relative humidity RO = reverse osmosis S = water solubility (ppm wt) DOI: 10.1021/acs.energyfuels.7b02844 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
(17) Kirschenbaum, L. J.; Ruekberg, B. A. Chem. Sci. J. 2013, 2013, 1−9. (18) Grimes, B. A. J. Dispersion Sci. Technol. 2012, 33, 578−590. (19) Grimes, B. A.; Dorao, C. A.; Opedal, N. V. D. T.; Kralova, I.; Sørland, G. H.; Sjöblom, J. J. Dispersion Sci. Technol. 2012, 33, 591− 598. (20) Grossman, G. Ind. Eng. Chem. Process Des. Dev. 1972, 11 (4), 537−542. (21) Clark, A. Q.; Smith, A. G.; Threadgold, S.; Taylor, S. E. Ind. Eng. Chem. Res. 2011, 50, 5749−5765. (22) Bessee, G. B.; Moorthy, K. Determination of Water-Droplet Size Distributions in Diesel and Aviation Fuels. Proceedings of the 12th International Conference on Stability, Handling and Use of Liquid Fuels (IASH); Sarasota, FL, Oct 16−20, 2011. (23) Limpert, E.; Stahel, W. A.; Abbt, M. BioScience 2001, 51 (5), 341−352. (24) Alderliesten, M. J. Biopharm. Stat. 2005, 15, 295−325. (25) Heminghaus, G.; Boval, T.; Bacha, J.; Barnes, F.; Franklin, M.; Gibbs, L.; Hogue, N.; Jones, J.; Lesnini, D.; Lind, J.; Morris, J. Aviation Fuels Technical Review; Chevron Corporation: Houston, TX, 2007; FTR-3. (26) Evans, M. J. B. Measurement of Surface and Interfacial Tension. In Measurement of the Thermodynamic Properties of Multiple Phases; Weir, R. D., de Loos, T. W., Eds.; Elservier: New York, 2005; Vol. 7, Chapter 15, pp 384−407. (27) Jasper, J. J.; Kerr, E. R.; Gregorich, F. J. Am. Chem. Soc. 1953, 75, 5252−5254. (28) Zeppieri, S.; Rodriguez, J.; Lopez de Ramos, A. L. J. Chem. Eng. Data 2001, 46, 1086−1088. (29) Song, B.; Springer, J. J. Colloid Interface Sci. 1996, 184, 64−76. (30) Song, B.; Springer, J. J. Colloid Interface Sci. 1996, 184, 77−91. (31) ASTM International. ASTM Standard D4052-15, Standard Test Method for Density, Relative Density, and API Gravity of Liquids by Digital Density Meter; ASTM International: West Conshohocken, PA, 2015. (32) ASTM International. ASTM Standard D445-11a, Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity); ASTM International: West Conshohocken, PA, 2011. (33) Joint Committee for Guides in Metrology (JCGM). Evaluation of Measurement DataGuide to the Expression of Uncertainty in Measurement; JCGM, Sept 2008; JCGM 100:2008 (GUM 1995 with minor corrections). (34) Poling, B. E.; Thomson, G. H.; Friend, D. G.; Rowley, R. L.; Wilding, W. V. Physical and Chemical Data. In Perry’s Chemical Engineers’ Handbook, 8th ed.; Green, D. W., Perry, R. H., Eds.; McGraw-Hill: New York, 2008; section 2, p 48. (35) Mulero, A.; Cachadiña, I.; Parra, M. I. J. Phys. Chem. Ref. Data 2012, 41 (4), 043105. (36) ASTM International. ASTM Standard D1331-14, Standard Test Methods for Surface and Interfacial Tension of Solutions of Paints, Solvents, Solutions of Surface-Active Agents, and Related Materials; ASTM International: West Conshohocken, PA, 2014. (37) ASTM International. ASTM Standard D971-12, Standard Test Method for Interfacial Tension of Oil Against Water by the Ring Method; ASTM International: West Conshohocken, PA, 2012. (38) Girifalco, L. A.; Good, R. J. J. Phys. Chem. 1957, 61, 904−909.
SDA = static dissipater additive ST = surface tension SPK = synthesized paraffinic kerosene SK = synthetic kerosene SAK = synthetic aromatic kerosene SIP = synthesized isoparaffins from hydroprocessed fermented sugars T = temperature (°C or K) To = compound-specific constant (K) xwater = molar solubility of water in hydrocarbon (mole fraction = moles of water per mole) v = terminal velocity (m/s) α = aromatic concentration (% vol) γ12 = interfacial tension (mN/m) γ1 and γ2 = surface tension (mN/m) η = kinematic viscosity (mm2/s = cSt) ρs = particle density (kg/m3) ρf = fluid density (kg/m3) Φ = characteristic constant for interfacial tension χ2red = reduced χ2 statistic
■
REFERENCES
(1) ASTM International. ASTM Standard D4054-16, Standard Practice for Qualification and Approval of New Aviation Turbine Fuels and Fuel Additives; ASTM International: West Conshohocken, PA, 2016. (2) Coordinating Research Council (CRC). Handbook of Aviation Fuel Properties, 4th ed.; CRC: Alpharetta, GA, 2013; CRC Report 663. (3) Energy Institute (EI). Specifications and Qualification Procedures for Aviation Jet Fuel Filter/Separators, 5th ed.; EI: London, U.K., 2002; EI Specification 1581. (4) Lam, J. K.-W.; Carpenter, M. D.; Williams, C. A.; Hetherington, J. I. Fuel 2014, 133, 26−33. (5) Zherebtsov, V. L.; Peganova, M. M. Fuel 2012, 102, 831−834. (6) Affens, W. A.; Hazlett, R. N.; DeGuzman, J. D. The Solubility of Water in Current JP-5 Jet Turbine Fuels; Naval Research Laboratory: Washington, D.C., Aug 1981; NRL Memorandum Report 4609. (7) Baena-Zambrana, S.; Repetto, S. L.; Lawson, C. P.; Lam, J. K.-W. Progress Aerospace Sciences 2013, 60, 35−44. (8) DeWitt, M. J.; Zabarnick, S.; Williams, T. F.; West, Z.; Shafer, L.; Striebich, R.; Breitfield, S.; Adams, R.; Cook, R.; Phelps, D. K.; Delaney, C. L. Determination of the Minimum Use Level of Fuel System Icing Inhibitor (FSII) in JP-8 That Will Provide Adequate Icing Inhibition and Biostatic Protection for Air Force Aircraft; Air Force Research Laboratory: Wright-Patterson Air Force Base (WPAFB), OH, Dec 2013; Public Release Version of Technical Report AFRL-RZ-WP-TR2009-2217, AFRL-RZ-WP-TR-2013-0271, Interim Report for the Aerospace Systems Directorate. (9) Maczynski, A.; Shaw, D. G.; Goral, M.; Wisniewska-Goclowska, B.; Skrzecz, A.; Maczynska, Z.; Owczarek, I.; Blazej, K.; Haulait-Pirson, M.-C.; Kapuku, F.; Hefter, G. T.; Szafranski, A. J. Phys. Chem. Ref. Data 2005, 34 (2), 441−476. (10) Goral, M.; Wisniewska-Goclowska, B.; Skrzecz, A.; Owczarek, I.; Blazej, K.; Haulait-Pirson, M.-C.; Hefter, G. T.; Maczynska, Z.; Szafranski, A. J. Phys. Chem. Ref. Data 2005, 34 (3), 1489−1553. (11) Goral, M.; Wisniewska-Goclowska, B.; Skrzecz, A.; Owczarek, I.; Blazej, K.; Haulait-Pirson, M.-C.; Hefter, G. T.; Kapuku, F.; Maczynska, Z.; Szafranski, A. J. Phys. Chem. Ref. Data 2005, 34 (4), 2261−2298. (12) Shaw, D. G.; Maczynski, A.; Goral, M.; Wisniewska-Goclowska, B.; Skrzecz, A.; Owczarek, I.; Blazej, K.; Haulait-Pirson, M.-C.; Hefter, G. T.; Huyskens, P. L.; Kapuku, F.; Maczynska, Z.; Szafranski, A. J. Phys. Chem. Ref. Data 2006, 35 (1), 93−151. (13) Polak, J.; Lu, B. C.-Y. Can. J. Chem. 1973, 51 (24), 4018−4023. (14) Chen, H.; Wagner, J. J. Chem. Eng. Data 1994, 39, 470−474. (15) Chen, H.; Wagner, J. J. Chem. Eng. Data 1994, 39, 475−479. (16) Jones, J. R.; Monk, C. B. J. Chem. Soc. 1963, 2633−2635. M
DOI: 10.1021/acs.energyfuels.7b02844 Energy Fuels XXXX, XXX, XXX−XXX
