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Phase equilibria of ethanol fuel blends
Fluid Phase Equilibria 228–229 (2005) 27–40
Phase equilibria of ethanol fuel blends Raymond French∗ , Patrick Malone Shell Global Solutions (US) Inc., Westhollow Technology Center, 3333 Hwy. 6 South, Houston, TX 77092, USA
Abstract The blending of ethanol into fuels is expected to increase significantly in the coming years. In some regions of the United States, ethanol has already replaced MTBE as the oxygenate blended into gasoline. Biofuel blends, such as blends of gasoline with ethanol made from biomass, can play an important role in helping governments and corporations meet sustainability targets. However, the introduction of ethanol fuel blends into the market place requires addressing several issues related to phase equilibria. The highly non-ideal mixing of ethanol with hydrocarbons has a dramatic impact on phase behavior, with consequences for the production, storage, distribution, and use of ethanol-blended fuels. In this paper, we will (a) illustrate the impact of ethanol use on the transportation fuels industry, (b) use thermodynamics as a unifying theme to understand these phenomena, and (c) review available data and the state-of-the-art in modeling these effects. Topics will include (a) volatility characteristics (e.g. Reid vapor pressure, ASTM D-86 Distillation, vapor–liquid ratio, and evaporative emissions), (b) phase separation effects (e.g. water tolerance and enhanced solubility of aromatic fuel components in groundwater), (c) commingling of ethanol and non-ethanol fuels, and (d) materials compatibility (e.g. increased swelling and permeation). © 2004 Shell Global Solutions (US) Inc. Published by Elsevier B.V. All rights reserved. Keywords: Model; Vapor–liquid equilibria; Liquid–liquid equilibria; Ethanol; Gasoline; Swelling
1. Introduction “There is fuel in every bit of the vegetable matter that can be fermented. There’s enough alcohol in one year’s yield of an acre of potatoes to drive the machinery necessary to cultivate the fields for a 100 years.” — Henry Ford to the New York Times in 1925. Since Ford’s quote, the search for alternative sustainable fuels has grown enormously, especially in the 1990s as attention has been focused on environmental issues. At the forefront currently for additives to motor gasolines is ethanol. Ethanol is renewable and has the potential of reducing carbon dioxide emissions as compared to traditional fuels. It can be used in non-adapted, current vehicles at levels up to 10% by volume [1]. Cost and the environmental or health effects of other fuel blend components have ultimately dictated the fuels of choice ∗
Corresponding author. Tel.: +1 281 5448382; fax: +1 281 5447705. E-mail address: [email protected] (R. French).
in the marketplace. The discovery of tetra-ethyl lead’s (TEL) ability to raise the anti-knock quality of gasoline effectively left ethanol fuels “on the shelf” for over 50 years. The adverse health effects of TEL have caused it to be banned in many countries. Such changes have put the need for alternative additives back in the spotlight [1]. Environmental concerns have lead to the desire to add oxygenates to traditional gasolines. In the US and other parts of the world, methyl tert-butyl ether (MTBE) was accepted as the most economical oxygenate. Unfortunately, MTBE entered groundwater in some areas of the US, and its very low threshold for taste and odor have rendered the water undesirable for human consumption. The removal of MTBE opens a gap for an environmentally acceptable high-octane component for gasoline and is a key driving force in the search for alternative components. Ethanol is emerging as the additive to fill the MTBE gap and, it is being phased into California and the rest of USA where oxygenated fuels are required. Specifications for gasolines are numerous as illustrated in Tables 1 and 2 for a reformulated blendstock for oxy-
0378-3812/$ – see front matter © 2004 Shell Global Solutions (US) Inc. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.fluid.2004.09.012
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R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Table 1 Partial listing of specifications for reformulated regular gasoline blendstock (RBOB) for blending with 5.7% denatured fuel ethanol (92% purity) as defined in ASTM D-4806 [2] Product property
ASTM test method
Fungible only requirements Mercaptan sulfur (wt.%) Existent gum (m/g 100 ml) Oxidation stability (min) Phosphorous (g/gal)
D-3227 D-381 D-525 D-3231
Volatility Drivability index Distillation (◦ C or ◦ F) at percentage evaporated Vapor/liquid ratio, V/L (◦ C or ◦ F) at 20 Benzene (vol.%) Octane RON MON (R + M)/2 (R + M)/2 after blending E200 (vol.%) E300 (vol.%) Non-VOC controlled requirements, RVP (psi)
Minimum
Test results maximum 0.002 4
240 0.004
D-4814 D-86 D-2533, D-5188 D-3606 D-2699 D-2700
See Table 2 See Table 2 See Table 2 1.30 Report 81.0 85.4 87.0 30 70
D-86 D-86 D-5191
Grades E3, 3E (non-VOC controlled) E4, 4E (non-VOC controlled) E5, 5E (non-VOC controlled)
70 100
11.5 13.5 15.0
genated blending or RBOB [2]. From the phase equilibria standpoint, key motor gasoline specs in the US include Reid vapor pressure (RVP), T10 (D-86 temperature at 10 vol.%), T50 (D-86 temperature at 50 vol.%), and T90 (D-86 temperature at 90 vol.%), E200 (D-86200 ◦ F vol.%), E300 (D-86300 ◦ F vol.%), temperature at vapor/liquid ratio of 20 (T at V/L20 ), and driveability index. These volatility properties determine the evaporative emissions of the gasoline, performance during engine startup, and overall driveability. Reid vapor pressure (typically ASTM D-323 or D-5191) is a measure of the vapor pressure of the fuel at 100 ◦ F in a vessel with a 4/1 vapor/liquid volume ratio. The ASTM D-86 is a batch distillation using specific heat up rates and glassware requirements. The distillation properties depend on the distribution of hydrocarbons in the gasoline and have been shown to correlate with the “driveability” or performance of the fuel. Temperature at vapor/liquid ratio of 20 or V/L20 (typically ASTM D-5188 or D-2533) is the temperature needed to obtain a vapor to liquid volume ratio of 20. Temperature at V/L20 is a measure of the tendency of a fuel to vapor lock.
To most consumers, it appears that there are only three grades of gasoline determined by octane number: super, midgrade, and regular. Actually, gasoline volatility is adjusted seasonally to provide good driveability: due to the colder temperature, winter gasoline is more volatile than summer gasoline. Tables 3 and 4 from ASTM D-4814 define the various classes of gasoline specifications for vapor pressure, distillation, and for vapor lock protection. Table 5 (a portion of the complete table from ASTM D-4814) gives an idea of the various gasoline specifications required for different regions of the US at different times of the year. More than 20 distinct volatility classes are seen in the complete ASTM 4814 table. Combining these 20 classes with three different octane grades yields 60 different “gasolines”. In addition to ASTM classifications, the 1990 Clear Air Act Amendment authorizes EPA and state governments to impose regulations on gasoline to reduce vehicular emissions. These environmental regulations lead to a whole category of different gasolines: reformulated, conventional, oxygenated, low RVP, low sulfur, etc. This complexity has led to the term “boutique” gasoline. The addition of oxygenates at various levels complicates the situation even further.
Table 2 Drivability and D-86 distillation specifications Grades
Drivability index
D-86, 10 vol.%, maximum
D-86, 50 vol.% Minimum
Maximum
E1, E2 E3 E4 E5
1250 1230 1220 1200
70 (158) 60 (140) 55 (131) 50 (122)
70 (158) 70 (158) 70 (158) 70 (158)
121 (250) 116 (240) 113 (235) 110 (230)
D-86, 90 vol.%, maximum
D-86, end point, maximum
V/L, minimum
190 (374) 185 (365) 185 (365) 185 (365)
221 (430) 221 (430) 221 (430) 221 (430)
56 (133) 51 (124) 47 (116) 41 (105)
29
2 2 2 2 2 2
Distillation temperatures at percentage evaporated, 50 vol.%, minimum, ◦ C (◦ F) 77 (170) 77 (170) 77 (170) 77 (170) 66 (150) 66 (150)
The addition of ethanol to hydrocarbons can significantly affect the volatility properties. Mixtures of ethanol with hydrocarbons exhibit large deviations from ideal mixing as defined by Raoult’s law. An example of the non-ideal behavior of such mixtures is seen in Fig. 1 for the ethanol–heptane binary [3]. Fig. 1 depicts classic homogeneous pressure maximum (Type I) azeotropic behavior. Such non-ideal behavior can be reasonably predicted for binary systems using standard activity coefficient models and newly developed equations of state applicable for associating molecules (e.g. CPA and SAFT). Gasoline consists of a large variety of hydrocarbons (typically 200 + distinct compounds) [4]. Gasoline–ethanol mixtures can be modeled similar to the ethanol–hydrocarbon binary systems with the gasoline either defined as a set of discrete components or, less accurately, as a set of pseudo-components or continuous distribution of hydrocarbons. Much of the challenge in predicting key properties of gasoline–ethanol mixtures is that, at least in the short term, the tools need to be adaptable to current gasoline blending tools in use at the refinery. This often requires developing more “empirical” correlations that can still reasonably capture the key thermodynamics of the more fundamental models. A simplified typical gasoline distribution system is seen in Fig. 2 [5]. Multiple streams from the refinery are blended together to make gasoline with the correct specifications. These streams range from butane to heavy catalytic cracked gasoline with hydrocarbons to carbon number 12. In the US, in Table 4 Vapor lock protection class requirements from ASTM 4814 Vapor lock protection class
Vapor–liquid ratio (V/L), test temperature, ◦ C (◦ F)
Vapor–liquid ratio (V/L), maximum
1 2 3 4 5 6
60 (140) 56 (133) 51 (134) 47 (116) 41 (105) 35 (95)
20 20 20 20 20 20
AA A B C D E
Vapor pressare, maximum, kPa (psi) 54 (7.8) 62 (9.0) 69 (10) 79 (11.5) 93 (13.5) 102 (15.0)
Distillation temperatures at percentage evaporated, 10 vol.%, maximum, ◦ C (◦ F) 70 (158) 70 (158) 65 (149) 60 (140) 55 (131) 50 (122)
Fig. 1. Vapor liquid equilibria for the [C2 H5 OH + C7 H16 ] binary system at 1 atm pressure.
Vapor pressure/distillation class
Table 3 Vapor pressure and distillation class requirements from ASTM D-4814
Distillation temperatures, at percentage evaporated, 50 vol.%, maximum, ◦ C (◦ F) 121 (250) 121 (250) 118 (245) 116 (240) 113 (235) 110 (230)
Distillation temperatures at percentage evaporated, 90 vol.%, maximum, ◦ C (◦ F) 190 (374) 190 (374) 190 (374) 185 (365) 183 (365) 185 (365)
Distillation temperatures at percentage evaporated, end point, maximum, ◦ C (◦ F) 225 (437) 225 (437) 225 (437) 225 (437) 225 (437) 225 (437)
Distillation residue, maximum (vol.%)
Drivability index, maximum, derived, ◦ C (◦ F) 597 (1250) 597 (1250) 591 (1240) 586 (1230) 580 (1220) 569 (1200)
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
30
State
January
February
March
April
May
June
July
August
1–15 September
16–30 September
October
November
December
Alabama Alaska
D-4 E-6
D-4 E-6
D-4/C-3 E-6
C-3/A-3 E-6
A-3 (C-3) E-6/D-4
A-3 D-4
A-3 D-4
A-2 D-4
A-2 D-4
A-2/C-3 D-4/E-6
C-3 E-6
C-3/D-4 E-6
D-4 E-6
D-4
D-4
D-4/C-3
C-3/A-2
A-2 (B-2)
A-1
A-1
A-1
A-2
A-2/B-2
B-2/C-3
C-3/D-4
D-4
D-4 E-5/D-4
D-4/C-3 D-4
C-3/B-2 D-4/C-3
B-2/A-2 C-3/A-3
A-2 (B-2) A-3 (C-3)
A-1 A-3
A-1a A-2
A-1 A-2
A-1 A-2
A-1 A-2/C-3
A-1/B-2 C-3/D-4
B-2/C-3 D-4
C-3/D-4 D-4/3-5
E-5/D-4 D-4 D-4 E-5/D-4 E-5 E-5 E-5 E-5
D-4 D-4 D-4/C-3 D-4 D-5/D-4 E-5 E-5 E-5/D-4
D-4 D-4/C-3 C-3/B-2 D-4 D-4/C-3 E-5/D-4 E-5/D-4 D-4
D-4/A-3 C-3/A-3 B-2/A-2 D-4/A-3 C-3/A-3 D-4/A-4 D-4/A-4 D-4/A-3
A-3 (C-3) A-3 (C-3) A-2 (B-2) A-3 (C-3) A-3 A-4 (D-4) A-4 (D-4) A-3 (C-3)
A-3 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2/B-2 A-2/B-2 A-1 A-2/B-2 A-2/B-2 A-3/D-4 A-3/C-3 A-3/C-3
B-2/C-3 B-2/C-3 A-1/B-2 B-2/C-3 B-2/C-3 D-4 C-3/D-4 C-3/D-4
C-3/D-4 C-3/D-4 B-2/C-3 C-3/D-4 C-3/D-4 D-4/E-5 D-4/E-5 D-4/E-5
D-4/E-5 D-4 C-3/D-4 D-4/E-5 D-4/E-5 E-5 E-5 E-5
D-4 D-4 C-3
D-4 D-4 C-3
D-4/C-3 D-4/C-3 C-3
C-3/A-3 C-3/A-3 C-3
A-3 (C-3) A-3 (C-3) C-3
A-3 A-3 C-3
A-3 A-3 C-3
A-3 A-2 C-3
A-3 A-2 C-3
A-3/C-3 A-2/C-3 C-3
C-3 C-3 C-3
C-3/D-4 C-3/D-4 C-3
D-4 D-4 C-3
Arizona 34◦ N latitude and 111◦ E longitude Remainder of state Arkansas California North coast South coast Southeast Interior Colorado Connecticut Delaware District Columbia Florida Georgia Hawaii
of
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Table 5 Partial schedule of seasonal and geographical volatility classes from ASTM D 4814
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
order to minimize the risk of phase separation in the distribution system, ethanol blending will typically be done at the terminal outside the refinery gate. Phase separation can cause reduction of ethanol in the fuel, due to solubility of ethanol in the aqueous phase, potentially causing the gasoline to be off-spec. Because only the hydrocarbon portion of the gasoline will be produced at the refinery, it is even more important to have capabilities to predict the change in properties due to ethanol addition. Property specs will be tested with hand blends, but such testing allows for only “feed backward” control of the final product. Accurate models of ethanol blending can aid in more “feed forward” control. The gasoline blending streams will change due to changes in crude diet or changes in refinery operations. Gasoline blending needs to be as accurate as possible to optimize the economics of the refinery. Typically, approximately half of the input streams to the refinery eventually become part of the motor gasoline pool. Accurate controls to meet the specifications of motor gasoline allow less “give away“of valuable streams and can significantly impact the refinery economics. Therefore, the ability to predict the change in key gasoline specifications due to addition of ethanol is important to overall refinery economics.
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2. Volatility properties As indicated in the last section, prediction of gasoline–ethanol blend properties is important to refinery economics. With the exception of octane number specifications, the most important properties defining current “boutique” gasolines are volatility properties. Here we discuss the main issues for the key volatility properties: Reid vapor pressure, distillation, T at V/L20 , driveability index, and emissions. 2.1. Reid vapor pressure Reid vapor pressure (RVP) of a fuel is defined by ASTM test procedures D-323 or D-5191. The applicable procedure depends on the fuel and EPA guidelines. These various test methods ultimately obtain an estimate of the partial pressure of the gasoline components at 100 ◦ F in a vessel with a 4/1 vapor to liquid volume ratio. Detailed RVP models are available [6]. The hydrocarbons in the gasoline can be modeled as a detailed set of discrete components, if the information is available. Typically, detailed GC data is not available, and the hydrocarbons are represented as pseudo-components using D-86 and
Fig. 2. Typical gasoline distribution system.
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R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Fig. 3. Effect of C2 H5 OH concentration on Reid vapor pressure [8].
API gravity as input. Alternate methods for describing the pseudo-components have been tried [7]. For RBOBs (no oxygenates) equation of state models can be used to predict the vapor–liquid equilibrium. Using discrete component data for the lightest hydrocarbons can significantly improve these estimates. For ethanol-blended fuels, detailed models use activity coefficient methods or associating fluid equations of state. As long as the hydrocarbon-ethanol phase behavior is accurately modeled, reasonable bubble point pressures and RVP estimates can be obtained. As with the non-oxygenate fuels, more detailed characterization of the light components can improve the estimates. While the detailed models are helpful to understand the effect of ethanol addition on the RVP of base gasolines, at this time, it is normally not practical to incorporate such detailed models into the existing blending models used at the refineries. In the short term, empirical yet accurate methods are much more likely to be used. An example of changes in RVP with addition of ethanol is seen in Fig. 3 [8]. The increase in RVP sharply rises until it hits a maximum at approximately 4–5 vol.% ethanol and very gradually decreases at levels higher than 4–5 vol.%. Because in the US, levels of ethanol addition are currently limited to below 10 vol.%, empirical correlations such as the California Model for California Reformulated Gasoline Blendstocks for Oxygenate Blending (CARBOB) [9] have been produced. The increase in RVP following addition of ethanol is considered just a linear function dependent only on the RBOB RVP (and not ethanol content): RVPBlend (psi) = 1.446 + 0.961 RVPBase
(1)
While such empiricism appears simplistic, the CARBOB RVP method can be useful when incorporated with the existing refinery gasoline blending tools. Improvements may include using the ethanol concentration and potentially API gravity of the RBOB as additional parameters.
Fig. 4. TBP curve for petroleum fraction with C2 H5 OH (adapted from Glindemann et al. [14]).
The maximum in RVP depicted in Fig. 3 causes another issue for concern. Mixing equal volumes of RBOB (0% ethanol) with a gasoline containing 10 vol.% will produce a fuel with 5 vol.% ethanol. The resulting RVP of the 5 vol.% mixture would be greater than either of the two blend components. This is one example where “commingling” of ethanol fuels can cause unexpected consequences, potentially leading to off-spec gasoline [8,10,11]. 2.2. Distillation Key distillation properties for gasoline are derived from ASTM D-86. As with RVP, fundamental thermodynamic models of the D-86 can be combined with activity coefficient or associating fluid equations of state to model gasoline–ethanol mixture D-86 behavior. Developing a detailed D-86 batch distillation model has been done by others [7,12]. Also like the detailed RVP model, these fundamental models can be very helpful to understand the key drivers for changes in D-86 properties. However, it is unlikely, in the short term, these complex models can be incorporated into the existing gasoline blending tools used at the refinery. More empirical models for estimating D-86 from TBP properties have been developed by the API [13]. The TBP represents an infinitely sharp separation while the D-86 is less sharp. Unfortunately these methods were developed for hydrocarbon streams only. The effects of blending alcohols with hydrocarbons on the TBP was studied by Glindemann et al. Key conclusions of the study are illustrated in Fig. 4 [14]. The “fictitious” TBP curve is the curve representing the components separating at their individual boiling points. In reality, the alcohol–hydrocarbon binaries produce azeotropes and even at infinitely sharp separation, the alcohol–hydrocarbons are boiled off at their respective azeotropic temperature and azeotropic composition. Thus, the TBP curve for an RBOB–ethanol mixture contains a “step change”. Once the ethanol producing the azeotropes is boiled off, the remaining
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
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Fig. 5. D-86 data for two RBOBs and 10 vol.% C2 H5 OH blends.
hydrocarbon mixture boils off at the individual hydrocarbon boiling points. The D-86 is similar to the TBP (see Fig. 5), having a “plateau” region during the ethanol–hydrocarbon azeotropes ‘region’. However, the “step change” is now a “transition region” due to the sloppy separation of the D-86. The D86 for a gasoline–ethanol blend has three distinct regions: plateau or azeotropic region, transition region, and dilutiononly region. The “dilution-only” region is designated as such because the only difference as compared to the D-86 of the straight gasoline is that the ethanol added has been distilled and the remaining hydrocarbons distilled at the same temperature but a different vol.% off point due to the “dilution” of the hydrocarbons with ethanol. Key distillation specification points in the US include T10, T50, T90, E200 and E300. As seen in Fig. 5, T10 is basically always in the plateau region. T90 and E300 are always in the dilution-only region. Similar to RVP, these points can be reasonably estimated with empirical methods for gasoline–ethanol blends. The CARB T90 model is an example: T90Blend (◦ F) = 1.493 + 0.964 × T90Base + 0.04668 × T50Base − 0.473 × EtOH vol.%
(2)
T50 and E200 present a much more difficult challenge. Depending on the gasoline and amount of ethanol, T50 can be in any of the three regions described above. Compared to the RBOB, large changes in T50 occur in the plateau region, small changes occur in the dilution-only region and the transition region varies between these two extremes. Thus, the challenge is to find accurate empirical methods to estimate T50 and E200 for gasoline–ethanol blends.
Fig. 5 emphasizes the difficulty in this problem. Gasoline A and B without ethanol have nearly identical T50 values. With 10 vol.% ethanol added, gasoline A’s T50 has changed over 20 ◦ F while gasoline B’s T50 has changed only 5 ◦ F. Models based solely on amount of ethanol added and T50 of the original RBOB are too limiting and cannot accurately predict the T50 of the blend after ethanol addition. 2.3. Vapor/liquid ratio Vapor/liquid ratio is another volatility measurement designed to minimize problems with vapor lock for fuels. As previously described, typical specifications are seen in Table 4. Two standard ASTM tests can be used for V/L: ASTM D 2533 and ASTM D 5188. For D 5188, a known volume of air-saturated fuel is injected into an evacuated chamber of known constant volume. The volume of fuel injected is chosen to give the desired V/L ratio (typically 20–1). The temperature is adjusted to produce ambient pressure in the chamber. The result is the temperature at V/L = 20. V/L can be modeled with standard thermodynamic models as mentioned previously. ASTM 4814 contains a V/L estimation method based on RVP and D-86. This estimation method is applicable to non-oxygenate gasolines. For ethanol blended fuels there is no ASTM standard estimation method at this time. The effect due to ethanol addition on V/L can be seen in Fig. 6 [5]. As expected, an increase in the RVP upon ethanol addition leads to a lowering of the temperature at V/L = 20. More work is required to test the existing empirical methods and potentially develop new ones applicable to ethanol blends. 2.4. Driveability Numerous studies have been conducted to develop a measure of the driveability based on the volatility properties of
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R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Fig. 6. Effect of C2 H5 OH concentration on vapor liquid ratio (adapted from [5]).
gasoline (see [15] for an overview). The most common specification for driveability is the driveability index, DI. DI is defined as 1.5 × T10 + 3 × T50 + T90 with all temperatures from the D-86. Increasing DI can lead to unacceptable customer satisfaction with a given fuel. The DI was developed for non-oxygenated fuels with carburetted cars. Applicability of DI to ethanol-blended fuels has been researched. While other drivability equations can be used, for a 10 vol.% blend with ethanol, the DI with an offset constant of 86 appears to reasonably model the available data [5,15]. Since the driveability is only a function of D-86 data, conclusions reached in the distillation section above are applicable. T10 and T90 are reasonably correlated for ethanol blends with empirical methods. Unfortunately, T50 is not, and improvements in T50 correlations will also improve driveability index estimates. 2.5. Evaporative emissions Evaporative emissions are categorized into three types: hot soak, diurnal, and running loss. Hot soak describes emissions after a vehicle has completed a trip. Diurnal describes long term emissions of a parked vehicle which experiences ambient temperature swings. Running loss describes nonexhaust emissions while the vehicle is running. With the use of catalytic converters, evaporative emissions have become a significant percentage of the total emissions produced from gasoline use. In the US, all of these emissions are modeled by various complex methods as defined by the EPA. One of the keys to evaporative emissions is the vapor pressure of the fuel. RVP for ethanol blends, as discussed above, is reasonably correlated empirically. Addition of 10 vol.% ethanol to an RBOB will increase the RVP and thus increase evaporative emissions [16]. Also, as described above, commingling ethanol blends resulting in higher RVP is an issue that needs to be
Fig. 7. Liquid–liquid equilibria of the [v1 H2 O + v2 C2 H5 OH + (1 − v1 − v2 )C5 H9 (CH3 )3 ] ternary system at 25 ◦ C; data from Peschke and Sandler [20].
addressed. Further work is needed to help verify the existing models with ethanol blends. 3. Phase separation Phase separation in the presence of water is arguably the most serious technical problem associated with ethanol–gasoline blends. As mentioned above, to avoid the potential of phase separation, most refiners do not transport ethanol–gasoline blends by pipeline. Ethanol is usually added to the gasoline at terminals to minimize exposure to water that can infiltrate the gasoline distribution and storage infrastructure. To understand the issue further, let us review the LLE of ternary systems comprising ethanol, water and hydrocarbons. Volume 69 of the IUPAC Solubility Data Series compiles evaluated ternary LLE data for ethanol–hydrocarbon–water systems through 1992 [17]. Several sets of ternary and quaternary LLE data have been reported since 1992 [18–24]. Data are available for about 22 hydrocarbons in the typical molecular weight range found in gasoline. Ethanol is completely miscible with water and with hydrocarbons in this range. Thus, ethanol–hydrocarbon–water systems are classified as type 1, with hydrocarbon–water as the partially miscible pair. For illustration, we will use data reported by Sandler and coworkers in 1995 [20,21]. Figs. 7 and 8 show LLE for ethanol + water + either toluene or 2,2,4-trimethylpentane at 25 ◦ C plotted on a volume fraction basis. The two-liquid phase region is larger and the tie-lines are steeper for 2,2,4trimethylpentane relative to toluene. Water solubility in hydrocarbons, even aromatics, is very low. Thus, the amount of water that can be picked-up by gasoline is very low. Such low levels of water content are not a significant concern for gasoline performance (e.g. driveability). Water ingress into distribution and storage winds-up as pooled water at the bottom of storage tanks or pipes. Thus, there is no incentive to keep dry the distribution and storage system for gasoline.
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Fig. 8. Liquid–liquid equilibria of the [v1 H2 O + v2 C2 H5 OH + (1 − v1 − v2 )C6 H5 (CH3 )] ternary system at 25 ◦ C; data from Peschke and Sandler [20].
Fig. 10. Liquid–liquid equilibria of the [x1 H2 O + x2 C2 H5 OH + (1 − x1 − x2 )C6 H5 (CH3 )] ternary system at 5 and 40 ◦ C; data from Wagner and Sandler [21].
The impact of ethanol on water tolerance can be seen in the hydrocarbon-rich corner of the LLE diagram. Even small amounts of ethanol significantly shift the phase boundary to higher water content. This implies that ethanol–gasoline blends can tolerate significantly higher water content before phase separation. Such blends with higher water content can cause driveability problems if they wind-up in vehicles. However before winding-up in vehicles, it is relatively easy for a gasoline–ethanol blend to encounter sufficient water in the gasoline distribution system to cause phase separation. To understand why the occurrence of phase separation is more of a problem with ethanol blends than with neat gasoline, we need to look at the tie-lines. Ethanol partitions preferentially into the aqueous phase. This extraction of ethanol from the gasoline can result in gasoline that does not meet specs (e.g. octane). Potential extraction of gasoline additives into the aqueous phase is a further risk. The aqueous phase that drops out can be rather large, containing virtually all the water, most of the ethanol and non-negligible amounts of hydrocarbons (due to cosolvency).
Figs. 9 and 10 illustrate the temperature effect on our model ternary systems. As temperature decreases, the twophase region increases. Thus, water tolerance decreases with decreasing temperature. This implies that a drop in temperature can cause phase separation. The shape of the phase boundary in the hydrocarbon-rich corner provides guidance on the mixing of blends with different ethanol content. The characteristic curvature of the phase boundary indicates that water tolerance is not linear with ethanol content. In other words, the water tolerance of a 10% ethanol blend is more than twice the water tolerance of a 5% ethanol blend. Thus, dilution of a blend that is near saturation can give a new blend that is above saturation, leading to phase separation. Thus, phase separation is a further risk associated with the commingling of gasolines of variable ethanol content. There are several studies of LLE for ethanol + water + gasoline [25–33]. These results are often represented in ternary diagrams with the gasoline treated as a single component. While such an approach is convenient and captures key features, it can be misleading as the measured phase boundary and tie-lines depend on the relative amount of the phases. Fig. 11 shows water tolerance as a function of temperature for three ethanol contents in gasolines with three aromatic contents [26]. The effects of temperature, aromaticity, and ethanol content are in qualitative agreement with the trends inferred from the phase diagrams of the model ternary systems. The LLE behavior of ethanol + water + hydrocarbons is a key factor in assessing the impact of spills involving ethanol–gasoline blends or neat ethanol. If an ethanol–gasoline blend comes into contact with groundwater, the ethanol will partition preferentially into the water. This will change the polarity of the aqueous phase. The tie-lines in Figs. 7 and 8 indicate that aromatic hydrocarbons, such as toluene, are more soluble in water containing ethanol than in pure water. This cosolvency effect is a function of the amount of ethanol in the aqueous phase.
Fig. 9. Liquid–liquid equilibria of the [x1 H2 O + x2 C2 H5 OH + (1 − x1 − x2 )C5 H9 (CH3 )3 ] ternary system at 5 and 40 ◦ C; data from Wagner and Sandler [21].
36
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Fig. 11. Water tolerance of ethanol–gasoline blends as a function of volume percent ethanol in the blend and volume percent aromatic hydrocarbon in the pre-blended gasoline [26].
Heermann and Powers [34] adapted the ‘log-linear’ cosolubility model to characterize the ethanol effect on BTEX partitioning between gasolines and water. The cosolvency effect of ethanol is a standard aspect of studies of the transport and fate of benzene, toluene, ethylbenzene and xylenes (BTEX) in groundwater contaminated by ethanol–gasoline or neat ethanol blends [35–38]. The potential for ethanol to solubilize hydrophobic organic compounds previously sorbed on soil has been recognized as a secondary source of contamination [39]. As a cosolvent, ethanol would be expected to lower the interfacial tension between the aqueous and nonaqueous phases, leading to increased mobility of the nonaqueous phase [40]. The cosolvency effect is strongly dependent upon the concentration of ethanol in the aqueous phase. The fuel to water ratio in a spill scenario determines the ethanol concentration in the aqueous phase, and hence, the impact of the cosolvency effect. In a 2001 review, Powers et al. [41] concluded that “Unless there is a neat ethanol spill, aqueous phase ethanol concentrations (are) unlikely to exceed 10% in contaminated sites. Therefore, it is unlikely that cosolvent-related increases in BTEX concentrations will be significant relative to other processes that affect field-scale concentrations following a spill of ethanol-blended gasoline.”
4. Materials compatibility In the gasoline storage and distribution system, thermosets, thermoplastics and elastomers are used for storage tanks, tank and pipe linings, hoses, sealants, and O-rings. Moreover, plastics and elastomers are found throughout vehicle fuel systems. These materials must retain adequate dimension, flexibility, and strength after extended exposure to fuel and, in some cases, elevated temperature. Loss of fuel through leakage and/or permeation is a major contributor to evapo-
rative emissions. Substantial progress has been made in recent years in reducing vehicle evaporative emissions through the use of multilayer technology for plastic fuel tanks and fuel lines. However, the compatibility and selection of nonmetallic materials for fuel systems remains an active area of research for auto and original equipment manufacturers [42,43]. A critical performance factor, often used in screening, for a polymeric material exposed to a solvent is the degree of solvent absorption by the material. As little as 20% volume swell can reduce physical properties such as hardness, strength, and tear resistance by 60% [44]. Furthermore, swelling influences permeation by increasing solubility and diffusion coefficients. Many of the non-metallic materials historically used in service with neat gasoline are not recommended for use with ethanol–gasoline blends. In some cases, materials that are compatible with both neat gasoline and neat ethanol are incompatible with ethanol–gasoline blends. Testing with ethanol–gasoline blends is a standard part of evaluating materials for use in vehicles and the distribution system. Fig. 12 shows data from Abu-Isa [45] for swelling of several materials in ethanol–gasoline blends. These materials show maxima in swelling between 5 and 25 vol.% ethanol in the blend. Historically, the swelling of polymeric materials in solvents has been addressed using solubility parameters of one or higher dimension [46–48]. Mixed solvents are usually treated as single liquids with the solubility parameter(s) of the ‘pseudo-solvent’ estimated as a volume fraction average of the solubility parameters of the component solvents. Under this approach, a maximum in swelling occurs when the solubility parameter(s) of the ‘pseudo-solvent’ matches the solubility parameter(s) of the polymeric material. Application of this approach is hindered by the difficulty in determining solubility parameters of for example, crosslinked rubbers or thermosets. Often these are assigned based on swelling in mixed solvents.
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Fig. 12. Swelling of typical elastomers in contact with ethanol–gasoline blends [45].
Westbrook and French [49] demonstrated that swelling of an elastomer in a solvent mixture can be estimated from the swelling of the elastomer in the pure solvents and the activities of the solvents in the mixture. Knowledge of the elastomer’s solubility parameter is not required. The swelling is expressed as the volume fraction of solvent in the elastomer: vm,e =
ai,s vi,e
(3)
i
where vm,e is the volume fraction of solvent in the elastomer in contact with the mixture (m), vi,e the volume fraction of solvent in the elastomer in contact with pure solvent i, and ai,s the activity of component i in the mixture. The parameter vi,e is essentially a partition coefficient that encompasses all contributions to the Gibbs free energy balance in the swollen elastomer from entropy, enthalpy and elastic retraction energies. This approach predicted the occurrence of swelling maxima in ethanol–gasoline blends as shown in Fig. 13. The gasoline was modeled as a blend of benzene and octane. Component activities in the mixture were calculated at 25 ◦ C using NRTL. This demonstrates that the enhanced swelling of polymeric materials in contact with ethanol–gasoline blends is driven by the non-ideal mixing of ethanol with hydrocarbons, independent of the nature of the polymeric material. The swelling maxima in Fig. 13 resemble the maximum in RVP shown in Fig. 3, underscoring that both phenomena reflect the same driving force. Eq. (3) defines swelling as an elastomer–liquid equilibrium problem in a manner that resembles the traditional expression used to represent vapor–liquid equilibrium in multicomponent mixtures: Ptot =
i
ai,s Pi
(4)
37
Fig. 13. Swelling of typical elastomers in contact with ethanol–gasoline blends [45]; data converted to volume fraction of solvent in the elastomer; lines show predictions from [49].
Pure component vapor pressure, Pi , and the pure component swelling parameter, vi,e , represent partition coefficients which quantify a solvent’s tendency to leave the liquid phase and enter the vapor phase and elastomer phase, respectively. Because an elastomer is crosslinked, it cannot partition into the liquid phase. However, low molecular weight additives, such as plasticizers, can be extracted from elastomers. Their partitioning is determined by their interaction with the solvents.
5. Available models Gibbs energy models, such as Wilson, NRTL and UNIQUAC have typically been used to correlate the VLE of ethanol with hydrocarbons [50–53]. As these models require binary interaction parameters for each pair, their use can be problematic in modeling mixtures with a large number of components, such as gasoline. Therefore, various versions of the UNIFAC group contribution approach have been used in most studies of ethanol–gasoline volatility [54,55]. However, the accuracy of UNIFAC predictions for ethanol–gasoline volatility remains a concern [56,57]. Cubic equations of state, such as Soave–Redlich–Kwong and Peng–Robinson, with conventional van der Waals one-fluid mixing rules have historically been used in the petroleum industry. However, this approach cannot adequately correlate the VLE in mixtures of ethanol plus hydrocarbons over a wide composition range [58,59]. The API Technical Data Book [60] provides binary interaction parameters for ethanol with thirteen gasoline-range hydrocarbons for use with the SRK model, but the parameters “should be used only when the liquid phase composition contains less than 20% by weight of (ethanol)”. More advanced mixing
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rules have been developed which should be better able to represent the non-ideal behavior of alkanols with hydrocarbons [61]. However, these have not yet been applied to model ethanol–gasoline volatility. Models that explicitly account for association provide a sounder basis for representing ethanol–hydrocarbon mixtures. Models using either chemical theory (see [62] for an overview) or statistical mechanics (see [63] for an overview) are available. Goral et al. have used an equation of state with association term to evaluate the VLE data of binary alkanol–alkane systems and recommend data for these systems [64,3,65]. A strong incentive for using models that explicitly account for association is their potential to represent VLE and LLE using the same set of parameters. Voutsas et al. applied the Cubic Plus Association Equation of State (CPA) to mixtures of ethanol with alkanes and water [66]. Browarzik developed an association model based on continuous thermodynamics and applied it to ethanol–alkane (VLE) and methanol–alkane (VLLE) systems [67]. The most common approach in modeling gasoline for volatility applications is to represent it as a multicomponent mixture using a set of model hydrocarbons. Ethanol–gasoline blends can also be treated as pseudo-binary systems [68,69]. Recently, continuous thermodynamics has been applied to model fuel fractions, but blends with ethanol were not included [70]. Glindemann et al. [14] used continuous thermodynamics to represent hydrocarbon mixtures for modeling TBP distillation of the hydrocarbon mixtures with discrete alcohols. Modeling multicomponent LLE is more challenging than modeling VLE (see [71] for an overview). Ternary systems of ethanol/water/hydrocarbon, with one pair of partially miscible components (water/hydrocarbon), are particularly challenging. Quantitative predictions using binary parameters derived from binary data alone are not recommended. Ternary data is regularly used to generate the binary parameters for models such as NRTL and UNIQUAC when correlating ethanol/water/hydrocarbon LLE [20–24,32]. Because both ethanol and water are capable of association, models that explicitly account for association would be expected to have more ‘predictive’ capability for ethanol/water/hydrocarbon LLE. Prikhodko et al. demonstrated the predictive capability of the associated perturbed anisotropic chain theory (APACT) and the hole quasilattice group contribution model (HM) for such systems [72]. The widely used UNIFAC model gives qualitatively correct predictions for ethanol/water/hydrocarbon systems [20–24], but cannot be relied on for quantitative predictions. Various UNIFAC parameter sets have been developed for and successfully used on environmental applications. However, for modeling the partitioning of hydrocarbons into groundwater from spills of ethanol–gasoline blends, empirical models of the log-linear type are recommended. The log-linear approach was originally developed to model cosolvent effects
on pharmaceutical solubility, and it has been adapted for environmental applications (see [36,73] for an overview). The equilibrium swelling of an elastomer reflects a balance among solvent–solvent and solvent–polymer interactions and the elastic retraction energy arising from the elatomer’s network structure. Traditionally, the solvent–solvent and solvent–polymer interactions have been modeled using the Flory–Huggins or related approach. Often, the interaction parameters (e.g. χ) in the system are addressed using solubility parameters [48]. Solubility parameters do not represent well systems in which association is important. Many elastomers (e.g. fluoroelastomers) contain polar sites. While such materials are resistant to hydrocarbons, the polarity provides sites for association with ethanol. Thus, an improved predictive model for swelling of materials in contact with ethanol–gasoline blends should use associationbased models, such as SAFT, to represent the solvent–solvent and polymer–solvent interactions. Chapman et al. [74] recently summarized the predictive ability of SAFT for systems of solvents and polymers with association and polar interactions.
6. Conclusion The blending of ethanol into fuels has a dramatic impact on the production, storage and distribution of ethanolblended fuels. The effects of ethanol on volatility properties, phase separation tendency and materials compatibility arise from the non-ideal mixing of ethanol with hydrocarbons. The VLE and LLE behavior of binary and ternary systems of ethanol with model hydrocarbons are well characterized and can be adequately modeled, particularly using association-based models. However, the application of this technology in industry remains a challenge, because implementation requires (1) representing highly non-ideal behavior in mixtures of ethanol with hydrocarbons and sometimes water, (2) representing a complex mixture like gasoline based on standard characterization data, and (3) modeling empirically defined properties such as the Reid vapor pressure and the D-86 distillation. While significant progress is being made on all three factors, further work is needed to integrate the ‘best’ technology for these factors into practical tools.
Acknowledgements The authors express appreciation to Betty Biddle, Steve Hess and George Koplos for help in preparing this manuscript, to Dave Barker, Lionel Clarke, George Deeley, King Eng, Mike Evans, Nino Leonardi-Cattolica, Chuck Lieder, and Paul Westbrook for many helpful discussions, and to Diana Davis, Steve Hess, George Koplos and Rene Lopez for help in generating and analyzing data.
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Phase equilibria of ethanol fuel blends Raymond French∗ , Patrick Malone Shell Global Solutions (US) Inc., Westhollow Technology Center, 3333 Hwy. 6 South, Houston, TX 77092, USA
Abstract The blending of ethanol into fuels is expected to increase significantly in the coming years. In some regions of the United States, ethanol has already replaced MTBE as the oxygenate blended into gasoline. Biofuel blends, such as blends of gasoline with ethanol made from biomass, can play an important role in helping governments and corporations meet sustainability targets. However, the introduction of ethanol fuel blends into the market place requires addressing several issues related to phase equilibria. The highly non-ideal mixing of ethanol with hydrocarbons has a dramatic impact on phase behavior, with consequences for the production, storage, distribution, and use of ethanol-blended fuels. In this paper, we will (a) illustrate the impact of ethanol use on the transportation fuels industry, (b) use thermodynamics as a unifying theme to understand these phenomena, and (c) review available data and the state-of-the-art in modeling these effects. Topics will include (a) volatility characteristics (e.g. Reid vapor pressure, ASTM D-86 Distillation, vapor–liquid ratio, and evaporative emissions), (b) phase separation effects (e.g. water tolerance and enhanced solubility of aromatic fuel components in groundwater), (c) commingling of ethanol and non-ethanol fuels, and (d) materials compatibility (e.g. increased swelling and permeation). © 2004 Shell Global Solutions (US) Inc. Published by Elsevier B.V. All rights reserved. Keywords: Model; Vapor–liquid equilibria; Liquid–liquid equilibria; Ethanol; Gasoline; Swelling
1. Introduction “There is fuel in every bit of the vegetable matter that can be fermented. There’s enough alcohol in one year’s yield of an acre of potatoes to drive the machinery necessary to cultivate the fields for a 100 years.” — Henry Ford to the New York Times in 1925. Since Ford’s quote, the search for alternative sustainable fuels has grown enormously, especially in the 1990s as attention has been focused on environmental issues. At the forefront currently for additives to motor gasolines is ethanol. Ethanol is renewable and has the potential of reducing carbon dioxide emissions as compared to traditional fuels. It can be used in non-adapted, current vehicles at levels up to 10% by volume [1]. Cost and the environmental or health effects of other fuel blend components have ultimately dictated the fuels of choice ∗
Corresponding author. Tel.: +1 281 5448382; fax: +1 281 5447705. E-mail address: [email protected] (R. French).
in the marketplace. The discovery of tetra-ethyl lead’s (TEL) ability to raise the anti-knock quality of gasoline effectively left ethanol fuels “on the shelf” for over 50 years. The adverse health effects of TEL have caused it to be banned in many countries. Such changes have put the need for alternative additives back in the spotlight [1]. Environmental concerns have lead to the desire to add oxygenates to traditional gasolines. In the US and other parts of the world, methyl tert-butyl ether (MTBE) was accepted as the most economical oxygenate. Unfortunately, MTBE entered groundwater in some areas of the US, and its very low threshold for taste and odor have rendered the water undesirable for human consumption. The removal of MTBE opens a gap for an environmentally acceptable high-octane component for gasoline and is a key driving force in the search for alternative components. Ethanol is emerging as the additive to fill the MTBE gap and, it is being phased into California and the rest of USA where oxygenated fuels are required. Specifications for gasolines are numerous as illustrated in Tables 1 and 2 for a reformulated blendstock for oxy-
0378-3812/$ – see front matter © 2004 Shell Global Solutions (US) Inc. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.fluid.2004.09.012
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Table 1 Partial listing of specifications for reformulated regular gasoline blendstock (RBOB) for blending with 5.7% denatured fuel ethanol (92% purity) as defined in ASTM D-4806 [2] Product property
ASTM test method
Fungible only requirements Mercaptan sulfur (wt.%) Existent gum (m/g 100 ml) Oxidation stability (min) Phosphorous (g/gal)
D-3227 D-381 D-525 D-3231
Volatility Drivability index Distillation (◦ C or ◦ F) at percentage evaporated Vapor/liquid ratio, V/L (◦ C or ◦ F) at 20 Benzene (vol.%) Octane RON MON (R + M)/2 (R + M)/2 after blending E200 (vol.%) E300 (vol.%) Non-VOC controlled requirements, RVP (psi)
Minimum
Test results maximum 0.002 4
240 0.004
D-4814 D-86 D-2533, D-5188 D-3606 D-2699 D-2700
See Table 2 See Table 2 See Table 2 1.30 Report 81.0 85.4 87.0 30 70
D-86 D-86 D-5191
Grades E3, 3E (non-VOC controlled) E4, 4E (non-VOC controlled) E5, 5E (non-VOC controlled)
70 100
11.5 13.5 15.0
genated blending or RBOB [2]. From the phase equilibria standpoint, key motor gasoline specs in the US include Reid vapor pressure (RVP), T10 (D-86 temperature at 10 vol.%), T50 (D-86 temperature at 50 vol.%), and T90 (D-86 temperature at 90 vol.%), E200 (D-86200 ◦ F vol.%), E300 (D-86300 ◦ F vol.%), temperature at vapor/liquid ratio of 20 (T at V/L20 ), and driveability index. These volatility properties determine the evaporative emissions of the gasoline, performance during engine startup, and overall driveability. Reid vapor pressure (typically ASTM D-323 or D-5191) is a measure of the vapor pressure of the fuel at 100 ◦ F in a vessel with a 4/1 vapor/liquid volume ratio. The ASTM D-86 is a batch distillation using specific heat up rates and glassware requirements. The distillation properties depend on the distribution of hydrocarbons in the gasoline and have been shown to correlate with the “driveability” or performance of the fuel. Temperature at vapor/liquid ratio of 20 or V/L20 (typically ASTM D-5188 or D-2533) is the temperature needed to obtain a vapor to liquid volume ratio of 20. Temperature at V/L20 is a measure of the tendency of a fuel to vapor lock.
To most consumers, it appears that there are only three grades of gasoline determined by octane number: super, midgrade, and regular. Actually, gasoline volatility is adjusted seasonally to provide good driveability: due to the colder temperature, winter gasoline is more volatile than summer gasoline. Tables 3 and 4 from ASTM D-4814 define the various classes of gasoline specifications for vapor pressure, distillation, and for vapor lock protection. Table 5 (a portion of the complete table from ASTM D-4814) gives an idea of the various gasoline specifications required for different regions of the US at different times of the year. More than 20 distinct volatility classes are seen in the complete ASTM 4814 table. Combining these 20 classes with three different octane grades yields 60 different “gasolines”. In addition to ASTM classifications, the 1990 Clear Air Act Amendment authorizes EPA and state governments to impose regulations on gasoline to reduce vehicular emissions. These environmental regulations lead to a whole category of different gasolines: reformulated, conventional, oxygenated, low RVP, low sulfur, etc. This complexity has led to the term “boutique” gasoline. The addition of oxygenates at various levels complicates the situation even further.
Table 2 Drivability and D-86 distillation specifications Grades
Drivability index
D-86, 10 vol.%, maximum
D-86, 50 vol.% Minimum
Maximum
E1, E2 E3 E4 E5
1250 1230 1220 1200
70 (158) 60 (140) 55 (131) 50 (122)
70 (158) 70 (158) 70 (158) 70 (158)
121 (250) 116 (240) 113 (235) 110 (230)
D-86, 90 vol.%, maximum
D-86, end point, maximum
V/L, minimum
190 (374) 185 (365) 185 (365) 185 (365)
221 (430) 221 (430) 221 (430) 221 (430)
56 (133) 51 (124) 47 (116) 41 (105)
29
2 2 2 2 2 2
Distillation temperatures at percentage evaporated, 50 vol.%, minimum, ◦ C (◦ F) 77 (170) 77 (170) 77 (170) 77 (170) 66 (150) 66 (150)
The addition of ethanol to hydrocarbons can significantly affect the volatility properties. Mixtures of ethanol with hydrocarbons exhibit large deviations from ideal mixing as defined by Raoult’s law. An example of the non-ideal behavior of such mixtures is seen in Fig. 1 for the ethanol–heptane binary [3]. Fig. 1 depicts classic homogeneous pressure maximum (Type I) azeotropic behavior. Such non-ideal behavior can be reasonably predicted for binary systems using standard activity coefficient models and newly developed equations of state applicable for associating molecules (e.g. CPA and SAFT). Gasoline consists of a large variety of hydrocarbons (typically 200 + distinct compounds) [4]. Gasoline–ethanol mixtures can be modeled similar to the ethanol–hydrocarbon binary systems with the gasoline either defined as a set of discrete components or, less accurately, as a set of pseudo-components or continuous distribution of hydrocarbons. Much of the challenge in predicting key properties of gasoline–ethanol mixtures is that, at least in the short term, the tools need to be adaptable to current gasoline blending tools in use at the refinery. This often requires developing more “empirical” correlations that can still reasonably capture the key thermodynamics of the more fundamental models. A simplified typical gasoline distribution system is seen in Fig. 2 [5]. Multiple streams from the refinery are blended together to make gasoline with the correct specifications. These streams range from butane to heavy catalytic cracked gasoline with hydrocarbons to carbon number 12. In the US, in Table 4 Vapor lock protection class requirements from ASTM 4814 Vapor lock protection class
Vapor–liquid ratio (V/L), test temperature, ◦ C (◦ F)
Vapor–liquid ratio (V/L), maximum
1 2 3 4 5 6
60 (140) 56 (133) 51 (134) 47 (116) 41 (105) 35 (95)
20 20 20 20 20 20
AA A B C D E
Vapor pressare, maximum, kPa (psi) 54 (7.8) 62 (9.0) 69 (10) 79 (11.5) 93 (13.5) 102 (15.0)
Distillation temperatures at percentage evaporated, 10 vol.%, maximum, ◦ C (◦ F) 70 (158) 70 (158) 65 (149) 60 (140) 55 (131) 50 (122)
Fig. 1. Vapor liquid equilibria for the [C2 H5 OH + C7 H16 ] binary system at 1 atm pressure.
Vapor pressure/distillation class
Table 3 Vapor pressure and distillation class requirements from ASTM D-4814
Distillation temperatures, at percentage evaporated, 50 vol.%, maximum, ◦ C (◦ F) 121 (250) 121 (250) 118 (245) 116 (240) 113 (235) 110 (230)
Distillation temperatures at percentage evaporated, 90 vol.%, maximum, ◦ C (◦ F) 190 (374) 190 (374) 190 (374) 185 (365) 183 (365) 185 (365)
Distillation temperatures at percentage evaporated, end point, maximum, ◦ C (◦ F) 225 (437) 225 (437) 225 (437) 225 (437) 225 (437) 225 (437)
Distillation residue, maximum (vol.%)
Drivability index, maximum, derived, ◦ C (◦ F) 597 (1250) 597 (1250) 591 (1240) 586 (1230) 580 (1220) 569 (1200)
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
30
State
January
February
March
April
May
June
July
August
1–15 September
16–30 September
October
November
December
Alabama Alaska
D-4 E-6
D-4 E-6
D-4/C-3 E-6
C-3/A-3 E-6
A-3 (C-3) E-6/D-4
A-3 D-4
A-3 D-4
A-2 D-4
A-2 D-4
A-2/C-3 D-4/E-6
C-3 E-6
C-3/D-4 E-6
D-4 E-6
D-4
D-4
D-4/C-3
C-3/A-2
A-2 (B-2)
A-1
A-1
A-1
A-2
A-2/B-2
B-2/C-3
C-3/D-4
D-4
D-4 E-5/D-4
D-4/C-3 D-4
C-3/B-2 D-4/C-3
B-2/A-2 C-3/A-3
A-2 (B-2) A-3 (C-3)
A-1 A-3
A-1a A-2
A-1 A-2
A-1 A-2
A-1 A-2/C-3
A-1/B-2 C-3/D-4
B-2/C-3 D-4
C-3/D-4 D-4/3-5
E-5/D-4 D-4 D-4 E-5/D-4 E-5 E-5 E-5 E-5
D-4 D-4 D-4/C-3 D-4 D-5/D-4 E-5 E-5 E-5/D-4
D-4 D-4/C-3 C-3/B-2 D-4 D-4/C-3 E-5/D-4 E-5/D-4 D-4
D-4/A-3 C-3/A-3 B-2/A-2 D-4/A-3 C-3/A-3 D-4/A-4 D-4/A-4 D-4/A-3
A-3 (C-3) A-3 (C-3) A-2 (B-2) A-3 (C-3) A-3 A-4 (D-4) A-4 (D-4) A-3 (C-3)
A-3 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2 A-2 A-1 A-2 A-2 A-3 A-3 A-3
A-2/B-2 A-2/B-2 A-1 A-2/B-2 A-2/B-2 A-3/D-4 A-3/C-3 A-3/C-3
B-2/C-3 B-2/C-3 A-1/B-2 B-2/C-3 B-2/C-3 D-4 C-3/D-4 C-3/D-4
C-3/D-4 C-3/D-4 B-2/C-3 C-3/D-4 C-3/D-4 D-4/E-5 D-4/E-5 D-4/E-5
D-4/E-5 D-4 C-3/D-4 D-4/E-5 D-4/E-5 E-5 E-5 E-5
D-4 D-4 C-3
D-4 D-4 C-3
D-4/C-3 D-4/C-3 C-3
C-3/A-3 C-3/A-3 C-3
A-3 (C-3) A-3 (C-3) C-3
A-3 A-3 C-3
A-3 A-3 C-3
A-3 A-2 C-3
A-3 A-2 C-3
A-3/C-3 A-2/C-3 C-3
C-3 C-3 C-3
C-3/D-4 C-3/D-4 C-3
D-4 D-4 C-3
Arizona 34◦ N latitude and 111◦ E longitude Remainder of state Arkansas California North coast South coast Southeast Interior Colorado Connecticut Delaware District Columbia Florida Georgia Hawaii
of
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Table 5 Partial schedule of seasonal and geographical volatility classes from ASTM D 4814
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
order to minimize the risk of phase separation in the distribution system, ethanol blending will typically be done at the terminal outside the refinery gate. Phase separation can cause reduction of ethanol in the fuel, due to solubility of ethanol in the aqueous phase, potentially causing the gasoline to be off-spec. Because only the hydrocarbon portion of the gasoline will be produced at the refinery, it is even more important to have capabilities to predict the change in properties due to ethanol addition. Property specs will be tested with hand blends, but such testing allows for only “feed backward” control of the final product. Accurate models of ethanol blending can aid in more “feed forward” control. The gasoline blending streams will change due to changes in crude diet or changes in refinery operations. Gasoline blending needs to be as accurate as possible to optimize the economics of the refinery. Typically, approximately half of the input streams to the refinery eventually become part of the motor gasoline pool. Accurate controls to meet the specifications of motor gasoline allow less “give away“of valuable streams and can significantly impact the refinery economics. Therefore, the ability to predict the change in key gasoline specifications due to addition of ethanol is important to overall refinery economics.
31
2. Volatility properties As indicated in the last section, prediction of gasoline–ethanol blend properties is important to refinery economics. With the exception of octane number specifications, the most important properties defining current “boutique” gasolines are volatility properties. Here we discuss the main issues for the key volatility properties: Reid vapor pressure, distillation, T at V/L20 , driveability index, and emissions. 2.1. Reid vapor pressure Reid vapor pressure (RVP) of a fuel is defined by ASTM test procedures D-323 or D-5191. The applicable procedure depends on the fuel and EPA guidelines. These various test methods ultimately obtain an estimate of the partial pressure of the gasoline components at 100 ◦ F in a vessel with a 4/1 vapor to liquid volume ratio. Detailed RVP models are available [6]. The hydrocarbons in the gasoline can be modeled as a detailed set of discrete components, if the information is available. Typically, detailed GC data is not available, and the hydrocarbons are represented as pseudo-components using D-86 and
Fig. 2. Typical gasoline distribution system.
32
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Fig. 3. Effect of C2 H5 OH concentration on Reid vapor pressure [8].
API gravity as input. Alternate methods for describing the pseudo-components have been tried [7]. For RBOBs (no oxygenates) equation of state models can be used to predict the vapor–liquid equilibrium. Using discrete component data for the lightest hydrocarbons can significantly improve these estimates. For ethanol-blended fuels, detailed models use activity coefficient methods or associating fluid equations of state. As long as the hydrocarbon-ethanol phase behavior is accurately modeled, reasonable bubble point pressures and RVP estimates can be obtained. As with the non-oxygenate fuels, more detailed characterization of the light components can improve the estimates. While the detailed models are helpful to understand the effect of ethanol addition on the RVP of base gasolines, at this time, it is normally not practical to incorporate such detailed models into the existing blending models used at the refineries. In the short term, empirical yet accurate methods are much more likely to be used. An example of changes in RVP with addition of ethanol is seen in Fig. 3 [8]. The increase in RVP sharply rises until it hits a maximum at approximately 4–5 vol.% ethanol and very gradually decreases at levels higher than 4–5 vol.%. Because in the US, levels of ethanol addition are currently limited to below 10 vol.%, empirical correlations such as the California Model for California Reformulated Gasoline Blendstocks for Oxygenate Blending (CARBOB) [9] have been produced. The increase in RVP following addition of ethanol is considered just a linear function dependent only on the RBOB RVP (and not ethanol content): RVPBlend (psi) = 1.446 + 0.961 RVPBase
(1)
While such empiricism appears simplistic, the CARBOB RVP method can be useful when incorporated with the existing refinery gasoline blending tools. Improvements may include using the ethanol concentration and potentially API gravity of the RBOB as additional parameters.
Fig. 4. TBP curve for petroleum fraction with C2 H5 OH (adapted from Glindemann et al. [14]).
The maximum in RVP depicted in Fig. 3 causes another issue for concern. Mixing equal volumes of RBOB (0% ethanol) with a gasoline containing 10 vol.% will produce a fuel with 5 vol.% ethanol. The resulting RVP of the 5 vol.% mixture would be greater than either of the two blend components. This is one example where “commingling” of ethanol fuels can cause unexpected consequences, potentially leading to off-spec gasoline [8,10,11]. 2.2. Distillation Key distillation properties for gasoline are derived from ASTM D-86. As with RVP, fundamental thermodynamic models of the D-86 can be combined with activity coefficient or associating fluid equations of state to model gasoline–ethanol mixture D-86 behavior. Developing a detailed D-86 batch distillation model has been done by others [7,12]. Also like the detailed RVP model, these fundamental models can be very helpful to understand the key drivers for changes in D-86 properties. However, it is unlikely, in the short term, these complex models can be incorporated into the existing gasoline blending tools used at the refinery. More empirical models for estimating D-86 from TBP properties have been developed by the API [13]. The TBP represents an infinitely sharp separation while the D-86 is less sharp. Unfortunately these methods were developed for hydrocarbon streams only. The effects of blending alcohols with hydrocarbons on the TBP was studied by Glindemann et al. Key conclusions of the study are illustrated in Fig. 4 [14]. The “fictitious” TBP curve is the curve representing the components separating at their individual boiling points. In reality, the alcohol–hydrocarbon binaries produce azeotropes and even at infinitely sharp separation, the alcohol–hydrocarbons are boiled off at their respective azeotropic temperature and azeotropic composition. Thus, the TBP curve for an RBOB–ethanol mixture contains a “step change”. Once the ethanol producing the azeotropes is boiled off, the remaining
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
33
Fig. 5. D-86 data for two RBOBs and 10 vol.% C2 H5 OH blends.
hydrocarbon mixture boils off at the individual hydrocarbon boiling points. The D-86 is similar to the TBP (see Fig. 5), having a “plateau” region during the ethanol–hydrocarbon azeotropes ‘region’. However, the “step change” is now a “transition region” due to the sloppy separation of the D-86. The D86 for a gasoline–ethanol blend has three distinct regions: plateau or azeotropic region, transition region, and dilutiononly region. The “dilution-only” region is designated as such because the only difference as compared to the D-86 of the straight gasoline is that the ethanol added has been distilled and the remaining hydrocarbons distilled at the same temperature but a different vol.% off point due to the “dilution” of the hydrocarbons with ethanol. Key distillation specification points in the US include T10, T50, T90, E200 and E300. As seen in Fig. 5, T10 is basically always in the plateau region. T90 and E300 are always in the dilution-only region. Similar to RVP, these points can be reasonably estimated with empirical methods for gasoline–ethanol blends. The CARB T90 model is an example: T90Blend (◦ F) = 1.493 + 0.964 × T90Base + 0.04668 × T50Base − 0.473 × EtOH vol.%
(2)
T50 and E200 present a much more difficult challenge. Depending on the gasoline and amount of ethanol, T50 can be in any of the three regions described above. Compared to the RBOB, large changes in T50 occur in the plateau region, small changes occur in the dilution-only region and the transition region varies between these two extremes. Thus, the challenge is to find accurate empirical methods to estimate T50 and E200 for gasoline–ethanol blends.
Fig. 5 emphasizes the difficulty in this problem. Gasoline A and B without ethanol have nearly identical T50 values. With 10 vol.% ethanol added, gasoline A’s T50 has changed over 20 ◦ F while gasoline B’s T50 has changed only 5 ◦ F. Models based solely on amount of ethanol added and T50 of the original RBOB are too limiting and cannot accurately predict the T50 of the blend after ethanol addition. 2.3. Vapor/liquid ratio Vapor/liquid ratio is another volatility measurement designed to minimize problems with vapor lock for fuels. As previously described, typical specifications are seen in Table 4. Two standard ASTM tests can be used for V/L: ASTM D 2533 and ASTM D 5188. For D 5188, a known volume of air-saturated fuel is injected into an evacuated chamber of known constant volume. The volume of fuel injected is chosen to give the desired V/L ratio (typically 20–1). The temperature is adjusted to produce ambient pressure in the chamber. The result is the temperature at V/L = 20. V/L can be modeled with standard thermodynamic models as mentioned previously. ASTM 4814 contains a V/L estimation method based on RVP and D-86. This estimation method is applicable to non-oxygenate gasolines. For ethanol blended fuels there is no ASTM standard estimation method at this time. The effect due to ethanol addition on V/L can be seen in Fig. 6 [5]. As expected, an increase in the RVP upon ethanol addition leads to a lowering of the temperature at V/L = 20. More work is required to test the existing empirical methods and potentially develop new ones applicable to ethanol blends. 2.4. Driveability Numerous studies have been conducted to develop a measure of the driveability based on the volatility properties of
34
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Fig. 6. Effect of C2 H5 OH concentration on vapor liquid ratio (adapted from [5]).
gasoline (see [15] for an overview). The most common specification for driveability is the driveability index, DI. DI is defined as 1.5 × T10 + 3 × T50 + T90 with all temperatures from the D-86. Increasing DI can lead to unacceptable customer satisfaction with a given fuel. The DI was developed for non-oxygenated fuels with carburetted cars. Applicability of DI to ethanol-blended fuels has been researched. While other drivability equations can be used, for a 10 vol.% blend with ethanol, the DI with an offset constant of 86 appears to reasonably model the available data [5,15]. Since the driveability is only a function of D-86 data, conclusions reached in the distillation section above are applicable. T10 and T90 are reasonably correlated for ethanol blends with empirical methods. Unfortunately, T50 is not, and improvements in T50 correlations will also improve driveability index estimates. 2.5. Evaporative emissions Evaporative emissions are categorized into three types: hot soak, diurnal, and running loss. Hot soak describes emissions after a vehicle has completed a trip. Diurnal describes long term emissions of a parked vehicle which experiences ambient temperature swings. Running loss describes nonexhaust emissions while the vehicle is running. With the use of catalytic converters, evaporative emissions have become a significant percentage of the total emissions produced from gasoline use. In the US, all of these emissions are modeled by various complex methods as defined by the EPA. One of the keys to evaporative emissions is the vapor pressure of the fuel. RVP for ethanol blends, as discussed above, is reasonably correlated empirically. Addition of 10 vol.% ethanol to an RBOB will increase the RVP and thus increase evaporative emissions [16]. Also, as described above, commingling ethanol blends resulting in higher RVP is an issue that needs to be
Fig. 7. Liquid–liquid equilibria of the [v1 H2 O + v2 C2 H5 OH + (1 − v1 − v2 )C5 H9 (CH3 )3 ] ternary system at 25 ◦ C; data from Peschke and Sandler [20].
addressed. Further work is needed to help verify the existing models with ethanol blends. 3. Phase separation Phase separation in the presence of water is arguably the most serious technical problem associated with ethanol–gasoline blends. As mentioned above, to avoid the potential of phase separation, most refiners do not transport ethanol–gasoline blends by pipeline. Ethanol is usually added to the gasoline at terminals to minimize exposure to water that can infiltrate the gasoline distribution and storage infrastructure. To understand the issue further, let us review the LLE of ternary systems comprising ethanol, water and hydrocarbons. Volume 69 of the IUPAC Solubility Data Series compiles evaluated ternary LLE data for ethanol–hydrocarbon–water systems through 1992 [17]. Several sets of ternary and quaternary LLE data have been reported since 1992 [18–24]. Data are available for about 22 hydrocarbons in the typical molecular weight range found in gasoline. Ethanol is completely miscible with water and with hydrocarbons in this range. Thus, ethanol–hydrocarbon–water systems are classified as type 1, with hydrocarbon–water as the partially miscible pair. For illustration, we will use data reported by Sandler and coworkers in 1995 [20,21]. Figs. 7 and 8 show LLE for ethanol + water + either toluene or 2,2,4-trimethylpentane at 25 ◦ C plotted on a volume fraction basis. The two-liquid phase region is larger and the tie-lines are steeper for 2,2,4trimethylpentane relative to toluene. Water solubility in hydrocarbons, even aromatics, is very low. Thus, the amount of water that can be picked-up by gasoline is very low. Such low levels of water content are not a significant concern for gasoline performance (e.g. driveability). Water ingress into distribution and storage winds-up as pooled water at the bottom of storage tanks or pipes. Thus, there is no incentive to keep dry the distribution and storage system for gasoline.
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
35
Fig. 8. Liquid–liquid equilibria of the [v1 H2 O + v2 C2 H5 OH + (1 − v1 − v2 )C6 H5 (CH3 )] ternary system at 25 ◦ C; data from Peschke and Sandler [20].
Fig. 10. Liquid–liquid equilibria of the [x1 H2 O + x2 C2 H5 OH + (1 − x1 − x2 )C6 H5 (CH3 )] ternary system at 5 and 40 ◦ C; data from Wagner and Sandler [21].
The impact of ethanol on water tolerance can be seen in the hydrocarbon-rich corner of the LLE diagram. Even small amounts of ethanol significantly shift the phase boundary to higher water content. This implies that ethanol–gasoline blends can tolerate significantly higher water content before phase separation. Such blends with higher water content can cause driveability problems if they wind-up in vehicles. However before winding-up in vehicles, it is relatively easy for a gasoline–ethanol blend to encounter sufficient water in the gasoline distribution system to cause phase separation. To understand why the occurrence of phase separation is more of a problem with ethanol blends than with neat gasoline, we need to look at the tie-lines. Ethanol partitions preferentially into the aqueous phase. This extraction of ethanol from the gasoline can result in gasoline that does not meet specs (e.g. octane). Potential extraction of gasoline additives into the aqueous phase is a further risk. The aqueous phase that drops out can be rather large, containing virtually all the water, most of the ethanol and non-negligible amounts of hydrocarbons (due to cosolvency).
Figs. 9 and 10 illustrate the temperature effect on our model ternary systems. As temperature decreases, the twophase region increases. Thus, water tolerance decreases with decreasing temperature. This implies that a drop in temperature can cause phase separation. The shape of the phase boundary in the hydrocarbon-rich corner provides guidance on the mixing of blends with different ethanol content. The characteristic curvature of the phase boundary indicates that water tolerance is not linear with ethanol content. In other words, the water tolerance of a 10% ethanol blend is more than twice the water tolerance of a 5% ethanol blend. Thus, dilution of a blend that is near saturation can give a new blend that is above saturation, leading to phase separation. Thus, phase separation is a further risk associated with the commingling of gasolines of variable ethanol content. There are several studies of LLE for ethanol + water + gasoline [25–33]. These results are often represented in ternary diagrams with the gasoline treated as a single component. While such an approach is convenient and captures key features, it can be misleading as the measured phase boundary and tie-lines depend on the relative amount of the phases. Fig. 11 shows water tolerance as a function of temperature for three ethanol contents in gasolines with three aromatic contents [26]. The effects of temperature, aromaticity, and ethanol content are in qualitative agreement with the trends inferred from the phase diagrams of the model ternary systems. The LLE behavior of ethanol + water + hydrocarbons is a key factor in assessing the impact of spills involving ethanol–gasoline blends or neat ethanol. If an ethanol–gasoline blend comes into contact with groundwater, the ethanol will partition preferentially into the water. This will change the polarity of the aqueous phase. The tie-lines in Figs. 7 and 8 indicate that aromatic hydrocarbons, such as toluene, are more soluble in water containing ethanol than in pure water. This cosolvency effect is a function of the amount of ethanol in the aqueous phase.
Fig. 9. Liquid–liquid equilibria of the [x1 H2 O + x2 C2 H5 OH + (1 − x1 − x2 )C5 H9 (CH3 )3 ] ternary system at 5 and 40 ◦ C; data from Wagner and Sandler [21].
36
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Fig. 11. Water tolerance of ethanol–gasoline blends as a function of volume percent ethanol in the blend and volume percent aromatic hydrocarbon in the pre-blended gasoline [26].
Heermann and Powers [34] adapted the ‘log-linear’ cosolubility model to characterize the ethanol effect on BTEX partitioning between gasolines and water. The cosolvency effect of ethanol is a standard aspect of studies of the transport and fate of benzene, toluene, ethylbenzene and xylenes (BTEX) in groundwater contaminated by ethanol–gasoline or neat ethanol blends [35–38]. The potential for ethanol to solubilize hydrophobic organic compounds previously sorbed on soil has been recognized as a secondary source of contamination [39]. As a cosolvent, ethanol would be expected to lower the interfacial tension between the aqueous and nonaqueous phases, leading to increased mobility of the nonaqueous phase [40]. The cosolvency effect is strongly dependent upon the concentration of ethanol in the aqueous phase. The fuel to water ratio in a spill scenario determines the ethanol concentration in the aqueous phase, and hence, the impact of the cosolvency effect. In a 2001 review, Powers et al. [41] concluded that “Unless there is a neat ethanol spill, aqueous phase ethanol concentrations (are) unlikely to exceed 10% in contaminated sites. Therefore, it is unlikely that cosolvent-related increases in BTEX concentrations will be significant relative to other processes that affect field-scale concentrations following a spill of ethanol-blended gasoline.”
4. Materials compatibility In the gasoline storage and distribution system, thermosets, thermoplastics and elastomers are used for storage tanks, tank and pipe linings, hoses, sealants, and O-rings. Moreover, plastics and elastomers are found throughout vehicle fuel systems. These materials must retain adequate dimension, flexibility, and strength after extended exposure to fuel and, in some cases, elevated temperature. Loss of fuel through leakage and/or permeation is a major contributor to evapo-
rative emissions. Substantial progress has been made in recent years in reducing vehicle evaporative emissions through the use of multilayer technology for plastic fuel tanks and fuel lines. However, the compatibility and selection of nonmetallic materials for fuel systems remains an active area of research for auto and original equipment manufacturers [42,43]. A critical performance factor, often used in screening, for a polymeric material exposed to a solvent is the degree of solvent absorption by the material. As little as 20% volume swell can reduce physical properties such as hardness, strength, and tear resistance by 60% [44]. Furthermore, swelling influences permeation by increasing solubility and diffusion coefficients. Many of the non-metallic materials historically used in service with neat gasoline are not recommended for use with ethanol–gasoline blends. In some cases, materials that are compatible with both neat gasoline and neat ethanol are incompatible with ethanol–gasoline blends. Testing with ethanol–gasoline blends is a standard part of evaluating materials for use in vehicles and the distribution system. Fig. 12 shows data from Abu-Isa [45] for swelling of several materials in ethanol–gasoline blends. These materials show maxima in swelling between 5 and 25 vol.% ethanol in the blend. Historically, the swelling of polymeric materials in solvents has been addressed using solubility parameters of one or higher dimension [46–48]. Mixed solvents are usually treated as single liquids with the solubility parameter(s) of the ‘pseudo-solvent’ estimated as a volume fraction average of the solubility parameters of the component solvents. Under this approach, a maximum in swelling occurs when the solubility parameter(s) of the ‘pseudo-solvent’ matches the solubility parameter(s) of the polymeric material. Application of this approach is hindered by the difficulty in determining solubility parameters of for example, crosslinked rubbers or thermosets. Often these are assigned based on swelling in mixed solvents.
R. French, P. Malone / Fluid Phase Equilibria 228–229 (2005) 27–40
Fig. 12. Swelling of typical elastomers in contact with ethanol–gasoline blends [45].
Westbrook and French [49] demonstrated that swelling of an elastomer in a solvent mixture can be estimated from the swelling of the elastomer in the pure solvents and the activities of the solvents in the mixture. Knowledge of the elastomer’s solubility parameter is not required. The swelling is expressed as the volume fraction of solvent in the elastomer: vm,e =
ai,s vi,e
(3)
i
where vm,e is the volume fraction of solvent in the elastomer in contact with the mixture (m), vi,e the volume fraction of solvent in the elastomer in contact with pure solvent i, and ai,s the activity of component i in the mixture. The parameter vi,e is essentially a partition coefficient that encompasses all contributions to the Gibbs free energy balance in the swollen elastomer from entropy, enthalpy and elastic retraction energies. This approach predicted the occurrence of swelling maxima in ethanol–gasoline blends as shown in Fig. 13. The gasoline was modeled as a blend of benzene and octane. Component activities in the mixture were calculated at 25 ◦ C using NRTL. This demonstrates that the enhanced swelling of polymeric materials in contact with ethanol–gasoline blends is driven by the non-ideal mixing of ethanol with hydrocarbons, independent of the nature of the polymeric material. The swelling maxima in Fig. 13 resemble the maximum in RVP shown in Fig. 3, underscoring that both phenomena reflect the same driving force. Eq. (3) defines swelling as an elastomer–liquid equilibrium problem in a manner that resembles the traditional expression used to represent vapor–liquid equilibrium in multicomponent mixtures: Ptot =
i
ai,s Pi
(4)
37
Fig. 13. Swelling of typical elastomers in contact with ethanol–gasoline blends [45]; data converted to volume fraction of solvent in the elastomer; lines show predictions from [49].
Pure component vapor pressure, Pi , and the pure component swelling parameter, vi,e , represent partition coefficients which quantify a solvent’s tendency to leave the liquid phase and enter the vapor phase and elastomer phase, respectively. Because an elastomer is crosslinked, it cannot partition into the liquid phase. However, low molecular weight additives, such as plasticizers, can be extracted from elastomers. Their partitioning is determined by their interaction with the solvents.
5. Available models Gibbs energy models, such as Wilson, NRTL and UNIQUAC have typically been used to correlate the VLE of ethanol with hydrocarbons [50–53]. As these models require binary interaction parameters for each pair, their use can be problematic in modeling mixtures with a large number of components, such as gasoline. Therefore, various versions of the UNIFAC group contribution approach have been used in most studies of ethanol–gasoline volatility [54,55]. However, the accuracy of UNIFAC predictions for ethanol–gasoline volatility remains a concern [56,57]. Cubic equations of state, such as Soave–Redlich–Kwong and Peng–Robinson, with conventional van der Waals one-fluid mixing rules have historically been used in the petroleum industry. However, this approach cannot adequately correlate the VLE in mixtures of ethanol plus hydrocarbons over a wide composition range [58,59]. The API Technical Data Book [60] provides binary interaction parameters for ethanol with thirteen gasoline-range hydrocarbons for use with the SRK model, but the parameters “should be used only when the liquid phase composition contains less than 20% by weight of (ethanol)”. More advanced mixing
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rules have been developed which should be better able to represent the non-ideal behavior of alkanols with hydrocarbons [61]. However, these have not yet been applied to model ethanol–gasoline volatility. Models that explicitly account for association provide a sounder basis for representing ethanol–hydrocarbon mixtures. Models using either chemical theory (see [62] for an overview) or statistical mechanics (see [63] for an overview) are available. Goral et al. have used an equation of state with association term to evaluate the VLE data of binary alkanol–alkane systems and recommend data for these systems [64,3,65]. A strong incentive for using models that explicitly account for association is their potential to represent VLE and LLE using the same set of parameters. Voutsas et al. applied the Cubic Plus Association Equation of State (CPA) to mixtures of ethanol with alkanes and water [66]. Browarzik developed an association model based on continuous thermodynamics and applied it to ethanol–alkane (VLE) and methanol–alkane (VLLE) systems [67]. The most common approach in modeling gasoline for volatility applications is to represent it as a multicomponent mixture using a set of model hydrocarbons. Ethanol–gasoline blends can also be treated as pseudo-binary systems [68,69]. Recently, continuous thermodynamics has been applied to model fuel fractions, but blends with ethanol were not included [70]. Glindemann et al. [14] used continuous thermodynamics to represent hydrocarbon mixtures for modeling TBP distillation of the hydrocarbon mixtures with discrete alcohols. Modeling multicomponent LLE is more challenging than modeling VLE (see [71] for an overview). Ternary systems of ethanol/water/hydrocarbon, with one pair of partially miscible components (water/hydrocarbon), are particularly challenging. Quantitative predictions using binary parameters derived from binary data alone are not recommended. Ternary data is regularly used to generate the binary parameters for models such as NRTL and UNIQUAC when correlating ethanol/water/hydrocarbon LLE [20–24,32]. Because both ethanol and water are capable of association, models that explicitly account for association would be expected to have more ‘predictive’ capability for ethanol/water/hydrocarbon LLE. Prikhodko et al. demonstrated the predictive capability of the associated perturbed anisotropic chain theory (APACT) and the hole quasilattice group contribution model (HM) for such systems [72]. The widely used UNIFAC model gives qualitatively correct predictions for ethanol/water/hydrocarbon systems [20–24], but cannot be relied on for quantitative predictions. Various UNIFAC parameter sets have been developed for and successfully used on environmental applications. However, for modeling the partitioning of hydrocarbons into groundwater from spills of ethanol–gasoline blends, empirical models of the log-linear type are recommended. The log-linear approach was originally developed to model cosolvent effects
on pharmaceutical solubility, and it has been adapted for environmental applications (see [36,73] for an overview). The equilibrium swelling of an elastomer reflects a balance among solvent–solvent and solvent–polymer interactions and the elastic retraction energy arising from the elatomer’s network structure. Traditionally, the solvent–solvent and solvent–polymer interactions have been modeled using the Flory–Huggins or related approach. Often, the interaction parameters (e.g. χ) in the system are addressed using solubility parameters [48]. Solubility parameters do not represent well systems in which association is important. Many elastomers (e.g. fluoroelastomers) contain polar sites. While such materials are resistant to hydrocarbons, the polarity provides sites for association with ethanol. Thus, an improved predictive model for swelling of materials in contact with ethanol–gasoline blends should use associationbased models, such as SAFT, to represent the solvent–solvent and polymer–solvent interactions. Chapman et al. [74] recently summarized the predictive ability of SAFT for systems of solvents and polymers with association and polar interactions.
6. Conclusion The blending of ethanol into fuels has a dramatic impact on the production, storage and distribution of ethanolblended fuels. The effects of ethanol on volatility properties, phase separation tendency and materials compatibility arise from the non-ideal mixing of ethanol with hydrocarbons. The VLE and LLE behavior of binary and ternary systems of ethanol with model hydrocarbons are well characterized and can be adequately modeled, particularly using association-based models. However, the application of this technology in industry remains a challenge, because implementation requires (1) representing highly non-ideal behavior in mixtures of ethanol with hydrocarbons and sometimes water, (2) representing a complex mixture like gasoline based on standard characterization data, and (3) modeling empirically defined properties such as the Reid vapor pressure and the D-86 distillation. While significant progress is being made on all three factors, further work is needed to integrate the ‘best’ technology for these factors into practical tools.
Acknowledgements The authors express appreciation to Betty Biddle, Steve Hess and George Koplos for help in preparing this manuscript, to Dave Barker, Lionel Clarke, George Deeley, King Eng, Mike Evans, Nino Leonardi-Cattolica, Chuck Lieder, and Paul Westbrook for many helpful discussions, and to Diana Davis, Steve Hess, George Koplos and Rene Lopez for help in generating and analyzing data.
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