Diesel Fuel

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Thermodynamics, Transport, and Fluid Mechanics

Prediction of Flash Point temperature of alcohol/biodiesel/diesel fuel blends Arnaldo Álvarez, Magín Lapuerta, and John R. Agudelo Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b00843 • Publication Date (Web): 02 Apr 2019 Downloaded from http://pubs.acs.org on April 5, 2019

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Industrial & Engineering Chemistry Research

Prediction of Flash Point temperature of alcohol/biodiesel/diesel fuel blends Arnaldo Álvarez a, Magín Lapuertab, John R. Agudeloa,* a

Department of Mechanical Engineering. Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellín, Colombia b

Universidad de Castilla-La Mancha. Av. Camilo José Cela s/n, Ciudad Real, Spain

Abstract Significant advances have been achieved in the last decades for predicting flash point, not only for pure substances but also for multicomponent fuels, even if they are complex nonideal mixtures. This is the case of blends of alcohols with diesel or diesel/biodiesel fuels. Prevention of hazards is necessary when a light and volatile alcohol such as ethanol or nbutanol (with increasing interest as diesel fuel components) is included in the fuel composition. However, an accurate modelling requires detained knowledge of the diesel fuel components, which is not always available. In this work, a taylored two-option model is proposed, which can be use depending on the information available. If detailed information about the diesel fuel composition is available, a first method based on Liaw 1

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equation is recommended. If not, and extended method based on the combination of Liaw equation and Gibbs-Duhem equation is proposed instead, with diesel fuel being represented by a three-component surrogate (one component belonging to each of the main diesel-component families). These two methods were compared with experimental flash point results and it was shown that the simplified method provides results as accurate as the complete method, and only if results for ethanol blends are considered unreliable, the complete method would be slightly more accurate. Both experimental and modelled results show that the rate of decrease of flash point is sharper for ethanol than for n-butanol, but the latter presents a more noticeable minimum flash point behaviour. However, the presence of biodiesel in the blends has the potential to neutralize both hazardous behaviours.

1.

Introduction

Flash point temperature (FP) is one of the most restrictive properties to include light hydrocarbons or oxygenates in fuel blends, since it prevents from hazardous storage and transportation [1]. Keeping room temperature under FP guarantees that the mixture of air and the flammable vapours remains under the flammability limits. Depending on the molecule structure of the components, some blends can have even higher propensity to ignite than the pure components, therefore showing a Minimum Flash Point Behaviour (MFPB). In this case, the FP is higher for the blend than for its components [2-4]. 2


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Different methods have been proposed for the prediction of FP of both pure substances and blends. In the case of pure substances, the proposed methods can be classified into those based on the boiling temperature [5-7] and those based on the molecular structure [815]. Among the latter ones, different approaches have been used such as neural networks [14,15], quantitative structure-property relationships (QSPR) [12,13], and intermediate parameters such as the flash point number (Nfp), as proposed by Carroll et al. [8-11]. Among all the methods mentioned, that based on the flash point number has been proved to provide the best agreement with experimental results [8,9]. In the case of blends, also different approaches have been proposed from very long ago. Thiele et al [16] proposed a logarithmic model to predict FP in lubricant mixtures. Despite being adjusted for high molecular weight blends, they also obtained approximate results when applied their method to fuel blends. Butler et al [17], proposed a method based on the Clausius-Clapeyron equation to predict FP for middle distillates of oil from the FP values of narrow fractionation cuts. This method was later extended to hydrocarbons and petroleum products by Alqaheem et al [18] who found a constant difference of around 0.7 between boiling temperature and FP temperature. Aleme et al. [19] proposed a method to predict FP for diesel fuels from its distillation curve obtained following standard ASTM D86, after the method being trained with other distillation curves of diesel fuels with known FP. Affens et al. [20] used the Le Chatelier equation in ideal mixtures to predict FP and proved that the concentration of flammable substances in the gaseous phase was much more determinant 3



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than the composition and thermal properties of the liquid phase. White et al [21] modified the model proposed by Affens, but neglected the effect of temperature on the lower flammability limit, thus simplifying the method, and applied it to aviation fuels. Few years later, Liaw et al. [22] tested the models proposed by Affens and White and showed good prediction capability for ideal or next to ideal mixtures (such as those defining diesel and gasoline fuels), but proved that these models were inaccurate for nonideal mixtures (such as those of alcohols mixed with hydrocarbons). These tests demonstrated that the methods ignoring the excess of Gibbs enthalpy or the activity coefficients can only be effective for ideal mixtures or weakly nonideal mixtures. More recently, despite the method proposed by Liaw was already well known, some other methods have been proposed to predict FP in blends. Mejía et al, and Gülüm et al [23, 24] proposed an empirical method through regression equations to obtain FP for dieselbiodiesel blends. Although reliable, these approaches are only applicable to the fuels tested, and require experimental data from the blends. Li et al [25] proposed a simplified method to predict FP of n-alkanes, requiring the calculation of an empirical coefficient, obtained from the tested blend. Based on liquid-vapor equilibrium and Clausius-Clapeyron equations, Catoire at al [26, 27] proposed a method to predict FP of binary and ternary flammable solvent blends by introducing the temperature and enthalpy of vaporization, and the number of carbon atoms. Results exhibited differences between experimental and predicted values for some nonideal binary blends such as ethanol/n-octane (4.5ºC) and 4


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methanol/methyl acetate (6ºC). Some methods have correlated FP with the physical properties of the blend components [28, 29, 30], but they are not extensible to blends composed of other substances. Agarwal et al. [31], Liu et al. [32] and Kumar et al. [33], used neural networks to predict FP of aviation fuels and biodiesel fuels (blends of different methyl esters) and reported better results than those obtained previously from correlations. Saldana et al. [34] applied QSPR to predict FP of diesel and aviation fuels from the FP values of hydrocarbons, esters and alcohols, which may compose them. Liu et al. [35] reviewed some of the existing prediction methods for FP of both pure fuels and blends and concluded that Liaw method has nowadays the best accuracy and the strongest theoretical support. However, they pointed out that QSPR methods, combined with near-infrared spectroscopy will have even better prediction capability in the future. Also Phoon et al. [36] compared the methods based on correlations [28, 29] with those based on QSPR [37] and with those based on the Le Chatelier equation [20, 37,38], and found that the best prediction capability was obtained with the latter ones, even in the case of nonideal mixtures. Among these methods, that proposed by Liaw was proved to be the most efficient. This demonstrated that considering the vapor-liquid equilibrium is necessary to extend the range of application of the method beyond the specific components used to develop the model. Le Chatelier’s rule predicts the minimum concentration of a flammable blend in a mixture with air to ignite at a given temperature from the composition of the gaseous phase and the 5



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lower flammability limits of all the components [39]. This equation was used in some of the methods previously mentioned to determine the FP of flammable blends [20-22]. However, the difficulties for the determination of the concentrations in the gaseous phase led Liaw et al. to propose a method to determine the concentrations in the gaseous phase from those in the liquid phase, based on the vapour-liquid equilibrium condition [2, 4, 37, 38, 40, 41, 42]. Deviations with respect to ideal partial pressures can be accounted from the excess free enthalpy of the mixture, which is related to the activity coefficients. These coefficients can be obtained from a variety of methods such as those based on UNIFAC [3, 4, 37, 38, 40,43,44], UNIFAC Dortmund [45-48], Wilson [3, 4, 34, 42], UNIQUAC [4,49,50], NRTL [3,4,50] and those based on quantum chemistry [4]. Liaw et al. [38] compared the effect of using the activity coefficients obtained from UNIFAC method or from UNIFAC Dortmund method on the accuracy to predict the FP of blends. They observed that the accuracy was better when UNIFAC Dortmund was used. Vidal et al. [4] summarized the different methods to estimate FP of blends, including those which used binary interaction parameters obtained from quantum chemical methods or from molecular dynamics to estimate the activity coefficients. In any case, they concluded that either experimental or estimated vapour-liquid equilibrium data are necessary for a good estimation of FP in nonideal mixtures. In the case of multicomponent blends, some simplifications are needed. For example, Phoon et al. analysed FP of blends of B5 (5% palm biodiesel with diesel fuel) with ethyl 6


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levulinate (an ester), with dibutyl and dipentyl ethers and with alcohols from propanol to hexanol [51-53], and considered B5 as an ideal substance represented with just eight alkanes (simulating diesel fuel) and eight methyl esters (simulating biodiesel), with its activity coefficient linearly averaged from those of its components. Their prediction accuracies were acceptable for blends of B5 with ethers and esters but not with alcohols. However, even diesel/biodiesel blends are slightly nonlinear. This, together with the fact of representing diesel fuel only with alkanes, thus ignoring the different interactions of the naphtenic and aromatic groups with alcohols, could also hinder an accurate prediction. The nonlinear interactions between very different components may lead to blends with MFPB (defined above), very important for hazard prevention. Some works have tried to predict this behaviour in blends [2, 54-56]. Da Cunha associated this behaviour with the presence of azeotropic points in the blend [55] and proposed that a MFPB is expected when the difference in boiling point between the components of a blend is equal or higher than the difference between their flash points. Di Benedetto et al [57] reported experimental FP data for nonideal binary blends with azeotropic behaviour at low temperature. They recognize the necessity of running more measurements and developing predictive models of FP under different blend concentrations to avoid dangerous situations due to MFPB. The difficulties in the FP determination are even higher in the case of partially miscible blends. However, Liaw et al [58] proved that his model has good prediction capability in the cases of well-stirred partially miscible blends, or even when the blends reached equilibrium after 7



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separation [59-61], and only when the blends are still unstable right after blending, the experimental

results

were

unpredictable.

To

minimize

explosion

hazards,

they

recommended stirring samples before measuring FP, since the measured value after stirring would in any case be lower than that measured under unstable conditions. Astbury et al [62] proposed a method to predict FP of binary blends composed by water and flammable solvents for applications in pharmaceutical industry. Although this approach does not take into account the energy of excess of nonideal blends, satisfactory results were reported. Liaw et al [63] found that blends of flammable substances used in semiconductors industry containing water at 36% and above did not ignite when the FP temperature of the blend is close to water boiling point. Finally, non-idealities are not only associated with deviations of equilibrium partial pressures in the gas phase with respect the Raoult´s law. They can also be associated with transient non-equilibrium conditions. If steady vapour-liquid equilibrium conditions are reached, the composition is homogeneous in the gas phase, but when equilibrium has not been reached yet, the FP value should be obtained from the composition of the gas phase close to the liquid interphase. Considering all these previous studies, this work proposes an extension of the Liaw equation applied to complex nonideal fuel blends in order to predict the flash point temperature of alcohol-diesel-biodiesel fuel blends of practical interest. The method proposed here uses the Clausius-Clapeyron equation to obtain the vapour pressure under 8


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saturation conditions. For the estimation of the activity coefficients, two approaches were compared. The first one used the UNIFAC-Dortmund method considering the most representative hydrocarbons or oxygenates of the four main families composing diesel and biodiesel fuels: paraffins, naphtenes, aromatics and esters. The second one used UNIFACDortmund to determine the activity coefficient for alcohols (ethanol or n-butanol) as eventual components in a base fuel (diesel or diesel/biodiesel blends, in any case considered as a single component), and then, the activity coefficient of the base fuel was obtained from the Gibbs-Duhem equation. In addition, since detailed composition of diesel fuel is required to select the distribution of its most representative components, a method is proposed to obtain such distribution from the FP, the distillation curve, the mean molecular weight, and the kinematic viscosity of the base fuel. This study fills a practical gap, given that the models proposed in the literature cannot easily be applied to real blends of alcohols with multicomponent fuels, since they require specific properties of every component of such fuels, which are rarely available. Additionally, the composition of these multicomponent fuels may even be unknown. The methods proposed here provide practical approaches for both cases: in the first, through the application of the Clausius-Clapeyron equation and in the second case, through the definition of three-component surrogate plus the application of the Gibbs-Duhem equation. The methods proposed in this work have shown to be reliable for both weak and strongly nonideal complex fuel blends (alcohol/diesel and

9



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alcohol/methylesters/diesel), and do not require additional blend flash point temperature data.

2. Experimental protocol and test fuels 2.1. Experiments Flash point temperatures were measured at least three times following the procedure in standard ASTM D56 (50 mL, non-stirred sample) [64], which is applicable to fuels with flash point temperatures below 93°C. For FP temperatures above 93ºC the standard ASTM D93 (75 mL, stirred sample) [65] was used. All measurements were made in the same close cup equipment, by the same technician, under 85.5 kPa ambient pressure, and then corrected to 101 kPa. Repeatability of 1.2ºC for FP below 60ºC and 1.6ºC for FP above 60ºC, was guaranteed. For the estimation of the component distribution of each hydrocarbon family composing diesel fuel, different additional measurements were made. Distillation curve was obtained following standard ASTM D86 in a Koelher K45200 distillator [66]. Also in this case, results were after corrected to 1 atm by Sydney Young equation. Kinematic viscosity was measured following standard ASTM D445 [67], using an Ostwald-type viscosimeter Cannon for transparent liquids and a thermostatic bath at 40ºC. The average molecular weight (MW) of the diesel fuel was obtained by the vapor pressure osmometry technique using a Knauer osmometer. The sample was diluted in chloroform using benzyl as the calibration standard. All measurements were performed using standard and sample solutions of 0.005 mol/kg 10


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and 1 g/kg, respectively. Then, the average MW was obtained from the Kc/Ks ratio, where Kc is the benzyl calibration constant in kg/mol and Ks is the sample constant in kg/g. 2.2. Test fuel composition and properties Anhydrous ethanol and n-butanol, both (with purities above 99.5% w/w) were provided by a local distributor. The palm oil biodiesel was obtained from the local producer BioD S.A. Details of the production process are given as supporting information (S1). The methylester composition was determined by gas chromatography (Table 1). The ultra-low diesel fuel (ULSD) was supplied by the Colombian petroleum company (Ecopetrol). The composition of this diesel fuel was obtained by an analytical approach described in the supporting information (S2), assuming a gamma distribution for each family (paraffins, naphtenes and aromatics) [48]. This gamma distribution (see Table 2) provided the best fit between experimental and predicted data. The same concentration (33.33% w/w) was assumed for each family in the diesel fuel. Detailed composition proposed for each family is shown in supporting information (S2). Table 3 shows the most relevant properties of the test fuels used in this work. 2.3. Fuel blends preparation Binary alcohol/diesel and ternary alcohol/biodiesel/diesel fuel blends were prepared. Binary blend alcohol contents were 1%, 1.5%, 2%, 2.5%, 5%, 7.5%, 10%, 15%, 20%, 30%, 40%, 50%, 75% and 80% by volume. Separation was observed in the blends when ethanol was added to diesel fuel in concentrations higher than 15% and lower than 80%, and thus these 11



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blends were discarded. The same behavior was observed by Chotwichien et al [68]. Ternary blends were prepared from B10, B30 y B50 (where the number next to B letter indicates the volume concentration of biodiesel added to diesel fuel) with alcohol contents of 1%, 1.5%, 2%, 2.5%, 5%, 25%, 50%, 75% by volume. All fuel blends were prepared at room temperature of 20°C. Ethanol and n-butanol were added to binary biodiesel/diesel blends. B10 was selected, because it is a commercial blend all over the world (as in Colombia), B30 was selected because this blend is used in Europe for captive vehicle fleets [69]. B50 blend was selected because this allowed checking the potential of the proposed method to predict the flash point temperature even with high biodiesel concentration in the blend. Table 1. Palm oil biodiesel composition Methylester Methyl Laureate Methyl Miristate Methyl Palmitate Methyl Palmitoleate Methyl Estearate Methyl Oleate Methyl Linoleate Methyl Linolenate Total

% mass 0.362 1.078 43.502 0.156 4.302 41.002 9.359 0.24 100

molar 0.479 1.260 45.583 0.165 4.084 39.190 9.006 0.233 100

Tabla 2. Parameters α and β for gamma distribution Paraffin s 3.6 1.1

Parameter α β

Naphthene s 10.8 0.9

Aromatic s 13 0.6

Table 3. Measured test fuel properties 12


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Property Density ad 15°C (kg/m3) Kinematic viscosity at 40°C cst Lower heating value (MJ/kg) UNE 51123 C / H ratio Water content (% w/w) Molecular weight (kg/kmol) Oxygen content (% w/w) Boiling point (°C) Cetane number Latent heat of evaporation (kJ/kg) Flash point (°C)

3.

Prediction of the flash point alcohol/biodiesel/diesel blends

diesel 856 3.63

biodiesel 875.6 4.465

n-Butanol 810 2.5

Ethanol 809 1.45

42.84

37.33

33.10

25.23

0.51

Diesel Fuel

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