Modeling of Unwashed and Washed Gum Content in Brazilian

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Modelling of Unwashed and Washed Gum Content in Brazilian GasolineEthanol Blends during Prolonged Storage: Application of Doehlert Matrix Florian Alain Yannick Pradelle, Sergio Leal Braga, Ana Rosa Fonseca de Aguiar Martins, Franck Turkovics, and Renata Nohra Chaar Pradelle Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b01379 • Publication Date (Web): 19 Jul 2016 Downloaded from http://pubs.acs.org on July 22, 2016

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Figure 1. Projection (left) and three-dimensional (right) representation of the Doehlert matrix. Figure 1 74x38mm (300 x 300 DPI)

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Figure 2. Surface and contour plots of the unwashed gum content in additivated gasoline versus aging period (X2) and temperature (X3) for X1 (ethanol content) = -0.5 (12.5 % v/v). Figure 2 149x78mm (220 x 220 DPI)


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Figure 3 (left) Normalized repeatability given by the ASTM D381 for unwashed (left) and washed gum (right). Figure 3 (left) 63x65mm (220 x 220 DPI)


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Figure 3 (right) Normalized repeatability given by the ASTM D381 for unwashed (left) and washed gum (right). Figure 3 (right) 63x65mm (220 x 220 DPI)


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Figure 4 (bottom, left) Observed versus predicted values for regular (top) and additivated (bottom) blends of unwashed (left) and washed (right) gum content (in black, the experiments from the Doehlert design; in blue, the other experiments). The ASTM repeatability is showed by the dash lines. Figure 4 (bottom, left) 81x57mm (220 x 220 DPI)


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Figure 4 (bottom, right) Observed versus predicted values for regular (top) and additivated (bottom) blends of unwashed (left) and washed (right) gum content (in black, the experiments from the Doehlert design; in blue, the other experiments). The ASTM repeatability is showed by the dash lines. Figure 4 (bottom, right) 81x57mm (220 x 220 DPI)


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Figure 4 (top, left) Observed versus predicted values for regular (top) and additivated (bottom) blends of unwashed (left) and washed (right) gum content (in black, the experiments from the Doehlert design; in blue, the other experiments). The ASTM repeatability is showed by the dash lines. Figure 4 (top, left) 80x57mm (220 x 220 DPI)


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Figure 4 (top, right) Observed versus predicted values for regular (top) and additivated (bottom) blends of unwashed (left) and washed (right) gum content (in black, the experiments from the Doehlert design; in blue, the other experiments). The ASTM repeatability is showed by the dash lines. Figure 4 (top, right) 81x57mm (220 x 220 DPI)


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Figure 5a. Pareto chart of the effects for the Doehlert designs. Figure 5a 59x29mm (300 x 300 DPI)


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Figure 5b. Pareto chart of the effects for the Doehlert designs. Figure 5b 59x29mm (300 x 300 DPI)


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Figure 5c. Pareto chart of the effects for the Doehlert designs. Figure 5c 59x29mm (300 x 300 DPI)


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Figure 5d. Pareto chart of the effects for the Doehlert designs. Figure 5d 59x29mm (300 x 300 DPI)


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Figure 6a. Representation of the direct effects (left) and the interaction effects (right). Figure 6a 59x22mm (300 x 300 DPI)


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Figure 6b. Representation of the direct effects (left) and the interaction effects (right). Figure 6b 59x22mm (300 x 300 DPI)


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Figure 6c. Representation of the direct effects (left) and the interaction effects (right). Figure 6c 59x22mm (300 x 300 DPI)


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Figure 6d. Representation of the direct effects (left) and the interaction effects (right). Figure 6d 59x22mm (300 x 300 DPI)


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Figure 7 (bottom, left) Representation of the observed values versus the predicted values E25 gasoline blends stored at 35°C at different aging period. Experimental uncertainty with a 95% confidence interval is given. Figure 7 (bottom, left) 80x59mm (220 x 220 DPI)


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Figure 7 (bottom, right) Representation of the observed values versus the predicted values E25 gasoline blends stored at 35°C at different aging period. Experimental uncertainty with a 95% confidence interval is given. Figure 7 (bottom, right) 79x59mm (220 x 220 DPI)


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Figure 7 (top, left) Representation of the observed values versus the predicted values E25 gasoline blends stored at 35°C at different aging period. Experimental uncertainty with a 95% confidence interval is given. Figure 7 (top, left) 80x60mm (220 x 220 DPI)


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Figure 7 (top, right) Representation of the observed values versus the predicted values E25 gasoline blends stored at 35°C at different aging period. Experimental uncertainty with a 95% confidence interval is given. Figure 7 (top, right) 80x60mm (220 x 220 DPI)


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Figure 7 (Caption) Representation of the observed values versus the predicted values E25 gasoline blends stored at 35°C at different aging period. Experimental uncertainty with a 95% confidence interval is given. Figure 7 (Caption) 75x4mm (136 x 136 DPI)


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Figure 8 (bottom, left) Representation of the observed values versus the predicted values E25 gasoline blends stored 75 days at 25, 35 and 45°C. Prediction uncertainty with a 95% confidence interval is given. Figure 8 (bottom, left) 80x52mm (220 x 220 DPI)


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Figure 8 (bottom, right) Representation of the observed values versus the predicted values E25 gasoline blends stored 75 days at 25, 35 and 45°C. Prediction uncertainty with a 95% confidence interval is given. Figure 8 (bottom, right) 79x53mm (220 x 220 DPI)


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Figure 8 (top, left) Representation of the observed values versus the predicted values E25 gasoline blends stored 75 days at 25, 35 and 45°C. Prediction uncertainty with a 95% confidence interval is given. Figure 8 (top, left) 80x52mm (220 x 220 DPI)


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Figure 8 (top, right) Representation of the observed values versus the predicted values E25 gasoline blends stored 75 days at 25, 35 and 45°C. Prediction uncertainty with a 95% confidence interval is given. Figure 8 (top, right) 79x52mm (220 x 220 DPI)


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Figure 8 (legend) Representation of the observed values versus the predicted values E25 gasoline blends stored 75 days at 25, 35 and 45°C. Prediction uncertainty with a 95% confidence interval is given. Figure 8 (legend) 75x4mm (136 x 136 DPI)


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Modelling of Unwashed and Washed Gum Content in Brazilian Gasoline-Ethanol Blends during Prolonged Storage: Application of Doehlert Matrix Florian Pradelle,*,† Sergio L. Braga,† Ana Rosa F. A. Martins,‡ Franck Turkovics,¶ and Renata N. C. Pradelle¶

†

Departamento de Engenharia Mecânica (DEM), Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil ‡

Departamento de Engenharia Química e de Materiais (DEQM), Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil ¶

Peugeot Citroën do Brasil Automóveis Ltda

E-mail: [email protected] Phone: +55 (21) 3527-1708

Abstract Gasoline is a volatile mixture of hydrocarbons that is used in spark ignition engines. It is a complex mixture composed of inflammable olefinic, paraffinic, naphthenic and aromatic hydrocarbons (C4-C12). It presents low contents of oxygenates and traces of sulphur, nitrogen and metals which introduce instability to the mixture. In several countries, like Brazil, ethanol is used pure as a renewable fuel or as an octane improver in blends with gasoline, especially in flex fuel engines. Nevertheless, some compounds in the fuel slowly react, at room temperature, with atmospheric oxygen and with each other. The process is observed throughout all the fuel production and use chain and increases fuel density. These high molar mass insoluble oxidation products are commonly called gums and form deposits along the vehicle fuel system. Their accumulation has adverse effects on engine efficiency, performance and durability, as incomplete combustion, engine wear and higher pollutant

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emission. Consequently, it is necessary to prevent gum formation by improving gasoline quality and using additives. A prediction of gasoline blends behaviour is also an important tool to assess critical conditions. This work studied the influence of aging period, temperature and addition of anhydrous ethanol concentration on unwashed and washed gum content of Brazilian gasoline-ethanol blends. The effect of an additive was also evaluated, comparing results of regular and additivated gasolines. This study also defined predictive mathematic models for the studied properties through a Doehlert matrix with three factors, which robustness was assessed.

1. Introduction Automotive gasoline is a fossil fuel extensively used in spark ignition engines. It is a complex volatile and inflammable mixture of olefinic, paraffinic, naphthenic and aromatic hydrocarbons in the C4-C12 range and with boiling points between 30°C and 220°C. Low contents of oxygenates and traces of sulphur, nitrogen and metals in the mixture introduce instability to the product. The composition in terms of hydrocarbons, oxygenates and trace compounds determines the physico-chemical properties of the fuel and has a great influence on engine performance. 1-4 All around the world, private and governmental programs have been established to develop alternative and ecologically friendly fuels. In several countries, ethanol is used pure or in blends, especially in flex fuel engines, as an alternative fuel and/or as octane number improver. Moreover, as ethanol is a renewable biofuel produced from agricultural feedstocks, it contributes to reduce greenhouse effect gases emissions. With the National Alcohol Program (Proálcool), launched in 1975, and the development of flexible-fuel vehicles, Brazil was the first country to implement a large-scale program for the use of alcohol as an automotive fuel, either by the use of pure aqueous ethanol or addition of anhydrous ethanol (in the range of 18-27% v/v) in gasoline blends. Moreover, three gasolines are available in Brazilian gas stations. Regular gasoline is the cheaper and most common fuel consumed by Brazilian automotive fleet. Additivated gasoline contains detergent components to increase gasoline performance and stability, and premium gasoline is a fuel with high octane number and additives. 1,2,5


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During storage, some hydrocarbons present in gasoline react, at ambient temperature, with absorbed atmospheric oxygen and with each other. These high molar mass (200 to 500 Da) insoluble oxidation products are commonly called gums. They are highly substituted aromatic compounds, with the presence of heteroatoms, in particular oxygen, nitrogen and sulphur

6-8

. Nevertheless, the severe reduction of sulphur compounds in

current gasoline composition strongly modified the nature of gums described in the 1960’s. The gum formation leads to changes in the physico-chemical characteristics of the blend, such as an increase of the fuel density, the distillation temperatures, the aromatics and oxygen concentration, and a decrease of the concentration of olefins. Consequently, as the gum content increases, the air/fuel mixture formation and combustion processes become non optimal and incomplete. Gum deposits in injection system and combustion chambers affect: (i) drivability (engine choking, hesitation), (ii) engine performance (power loss, reduced acceleration, increased fuel consumption, detonation), and (iii) exhaust gas emissions (emissions of CO, NOx, etc. due to incomplete combustion).1-4 Even if the exact mechanisms of the reactions are complex and not fully known, two different mechanisms are involved and there is a consensus on the involvement of a serie of free radical chain processes. The first mechanism is a peroxyl radical chain mechanism leading to an oxidative polymerization of olefins. The general autoxidation mechanism usually quoted in the literature is 9-12: RH → R + H (1) R + O → ROO (2) ROO + RH → ROOH + R (3) R + R → R − R (4) ROO + R → ROOR (5) ROO + ROO → Products (6) where RH is an unsaturated organic compound such as olefins, R is a free radical, ROO is the associated peroxyl radical and ROOH is the associated hydroperoxide. In step (1), a free radical is formed from the unsaturated hydrocarbon during the initiation reaction. In the propagation reactions (2) and (3), the free radical reacts with oxygen to yield a free peroxide radical which, in turn, reacts with a further unsaturated hydrocarbon molecule whereby a further free radical is produced besides a hydroperoxide. Repetition of these two steps carries on oxidation of unsaturations until the chain is terminated with one of the reactions (4), (5), or (6). These last three reactions increase the carbon chain length to form high molar mass products.


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The second type of oxidation mechanism is also a peroxyl radical chain chemistry, but involves unhindered phenols that are natural components of gasoline. This mechanism occurs at higher temperatures than storage, typically those encountered in the engine, and is known as thermal oxidative degradation. Such mechanism has been studied for years in jet fuels and diesel oil

13-24

. These phenols are oxidized into quinones

which then undergo oxidative coupling reactions which grow the molecular weight. These processes are mainly dependent on gasoline composition, related to petrol origin and type of refinement and storage room conditions (duration and temperature). Potential to form gum increases in the order of paraffins, naphthenes, isoparaffins, aromatics, monoolefins, aromatic olefins and diolefins. Among them, the cyclic and branched olefins are the main gum producers 3,4,11,25-30. The variation rate of gum content depends on the oxygen content absorbed by gasoline. The greater this proportion is, the higher the oxidation rate is and the faster the gum concentration attains the maximally permissible value

31-34

. Traces of transition metals strongly

accelerate gasoline oxidation, by catalysing the decomposition of hydroperoxides into radicalar species. The metal suffering the transference of one electron, such as copper, iron, cobalt, and manganese ions are the most effective 1,2,3,7,35,36. Consequently, some additives can be used to control the gum formation. The autoxidation process can be controlled to some extent by antioxidants (hindered phenols and/or phenylene diamine), while the thermal oxidative degradation is controlled by dispersants. In Brazil, the additive packages additives are added by the distributor companies and their formulation contains both antioxidants and dispersant. Metal deactivators are also added to gasoline to delay the process 37-40. Moreover, Kinoshita et al. showed that the superficial temperature is the most important parameter to control deposition and there are two possibilities to prevent deposit formation. The first option is to maintain the nozzle temperature lower than 90% distillation temperature of the fuel. Above this temperature, the residual fuel in the nozzle hole remained in a liquid state and the deposit precursor was easily washed away by the fuel injection. 41 The second one is to raise the surface temperature to levels high enough to gasify the deposits and/or its precursors. 42 Among the different approaches to evaluate oxidative stability and gum formation, the washed gum content in gasoline and the induction period are the most important parameters to evaluate the fuel quality. Gum contents are measured by evaporation of a small quantity of fuel under controlled conditions of temperature and


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flow of air. The residue is weighed before and after extracting with heptane to determine the unwashed and washed gum, respectively. Current Brazilian legislation regarding gasoline quality standards establishes that the maximum gum content should be inferior to 5.0 mg per 100 mL of gasoline and the induction period at 100°C should be superior to 360 minutes. 5 Even if a large number of publications is available on literature on gum formation and a recent review has been published dealing with gum formation in gasoline and its blends 43, only three papers dealt with gasoline ethanol blends and they had contradictory conclusion on the role performed on ethanol. In 2001, D'Ornellas

44

studied instability properties in gasoline blends with approximately 20% v/v of anhydrous ethanol. In presence of alcohol, she observed an increase in potential gum and washed gum after storage at ambient temperature and 43°C during 8, 16 and 24 weeks. However, filterable precipitate insolubles, wall-adhered insolubles and, consequently, insoluble portion were reduced when ethanol was added. She concluded that ethanol has a solubility effect on insoluble gums. Nevertheless, such increased washed gum content was not observed in later literature. 3,45: An eventual contamination of ethanol with copper, observed by other authors at this period, could explain the results obtained by D'Ornellas. In 2005, Pereira et al. 3 showed that the gum content decreased as the alcohol content increased. Experimental results fitted well with a dilution law based on the measured gum content in a sample with 100% gasoline. Alcohol seemed to behave neither as a catalyst nor an inhibitor of oxidation reaction of the olefins present. One of the possible explanations is related to the oxidation mechanism of ethanol into aldehyde and, later, to carboxylic acid, that is not radicalar, in contrast to the oxidation process of olefins. Nevertheless, none of these works tried to predict simultaneously the effect of several parameters on gum formation, such as temperature, aging period and ethanol content. In 2015, Pradelle et al.

33

performed two Doehlert matrix designs with regular and additivated gasoline-

ethanol blends studying the effect of temperature, aging period and aqueous ethanol content on several properties (washed gum content, water content, density and viscosity). Aqueous ethanol was added in the range of 0 to 100% v/v in initial Brazilian gasoline with 21% v/v of anhydrous ethanol while temperature and aging period varied from 20 to 40°C and 0 to 150 days, respectively. They also observed an apparent dilution effect of ethanol in gum formation in both gasolines. In regular gasoline, the dilution effect of added ethanol was predominant when compared to the impact of aging period and temperature. In additivated gasoline, all parameters had the same contribution magnitude. This showed that, in comparison to the other variables, the additive reduced the


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dilution effect of the added ethanol and increased the influence of thermodynamics variables. This difference can be assigned to the additive. In both gasolines, aging period and temperature direct effects presented a maximum for high values of these parameters, confirming the theory encountered in literature. Nevertheless, the robustness of the models was low and must be improved before further interpretations. For this purpose, the present work studied the influence of aging period, temperature and addition of aqueous ethanol concentration on unwashed and washed gum formation, mainly by autoxidation process, in Brazilian gasoline-ethanol blends. The effect of an additive was also evaluated, comparing results of regular and additivated gasolines. This study defined predictive mathematic models for the studied properties through a Doehlert matrix with three factors. The Doehlert matrix consists of 2 experiments in the centre of the domain and 12 experiments uniformly distributed in a stack of tetrahedral on the hexagonal base. The surfaces associated to each response were described. It was also possible to compare the impact of each variable and assess the robustness of the models by the analysis of variance (ANOVA).

2. Experimental Section 2.1. Materials and Chemicals Alcohol and additive-free gasoline samples with typical Brazilian profile was used. Their main physicochemical properties are presented in Table 1. Even with relatively high olefins content, properties of gasoline were representative of typical Brazilian fuels. An additive package, with antioxidants and dispersant, was added to regular gasoline to produce additivated gasoline with the same chemical composition matrix. Anhydrous ethanol was of analytical grade (B’Herzog).

Table 1: Physical-chemical properties of the studied alcohol-free gasoline

2.2. Experimental Procedure Before use, the glassware was rinsed with alcohol-free gasoline and dried in a dust free environment. Three hermetically closed transparent glass bottle of 250 mL with 150 mL of fuel samples were prepared for


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each experimental condition. Such repetition allowed the calculation of the average and the mean square values for each sample reference. The addition of aqueous ethanol at 20°C was given by the Doehlert design matrix. Moreover, additional samples were tested to define a baseline case for gasoline with 25% v/v of anhydrous ethanol. Two more samples were also tested with commercial gasoline to assess the impact of the chemical matrix. In these two situations, only two flasks were prepared. All samples were stored during the specified aging period and at the required temperature in a thermostatically-controlled water bath (only the bottle caps were not immerged). A visual control of the sample appearance was made each 25 days through photographs. All samples were analysed according to the ASTM D381 to determined unwashed and washed gum content.

2.3. Modelling Method In this work, a Doehlert matrix is used to model the unwashed and washed gum content in regular and additivated ethanol - gasoline blends. Such experimental designs give a second order empirical polynomial model with interaction of order two, which is described by the equation: Y = b + b X + b X + b X + b X + b X + b X + b X X + b X X + b X X (7) where Y is the experimental response, X , X and X represent the variables to be optimized, b is an independent term, b , b and b are coefficients of the linear terms, b , b and b

are coefficients of the

quadratic terms and b , b and b are the coefficients of the interaction term (or rectangular terms). In the Doehlert design, each independent real or effective variable x# is related to the normalized variable X # according to the following relation: x# =

X # − X# (8) ∆X #

where x# varies from -1 to +1, X# is the value of the real (or effective) variable at the centre of the experimental region (corresponding to x# = 0) and ∆X # =

&()* +&(', ' '

is the step with the maximum (X #-./ ) and

minimum (X #-#0 ) values of effective variable X # 46.


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For a number of factors k, the Doehlert matrix consists of No experiments in the centre of the domain (generally, No=3) and k²+k experiments uniformly distributed in a hypersphere of radius 1 to form a rhombic lattice (a hexagon if k=2 and a stack of tetrahedral on a hexagonal base if k=3). As the experiments are uniformly distributed in the experimental space, the Doehlert matrix design allows a good knowledge of the whole experimental domain without proposing a priori a model representing the response and the identification of critical points such as maximum, minimum or saddle points, at each step of the optimization process 47-49. In this study, the three variables investigated were ethanol concentration (x1), aging period (x2) and temperature (x3). Three levels were assigned to temperature (25, 35, 45°C) and five levels were given to added aqueous ethanol volume (0, 12.5, 25, 37.5, 50 %.v/v) whereas seven levels of aging periods were investigated (0, 25, 50, 75, 100, 125, 150 days). The response was calculated as the average of the all measures of each property Table 2 shows the experimental design matrix conditions (Sample references 1 to 14), and Figure 1 gives two representations of the matrix Doehlert.

Table 2: Doehlert matrix in term of reduced (Xi) and real (xi) variables.

{Figure 1.tif} Figure 1: Projection (left) and three-dimensional (right) representation of the Doehlert matrix.

Statistical multivariate calculus and plots were done employing the Statgraphics software package, version 5.1. (Statpoint Technologies, Warrenton, USA) and on Excel sheets. For convenience, the graphic representations of the surface were done through a Matlab ® program With three factors, it should be necessary a four dimensions space to represent simultaneously all the response in the entire experimental domain. To solve this problem, it is necessary to assign one factor and vary the others. Consequently, it was fixed five different values of aging period and anhydrous ethanol content (the surfaces for 0 and 50 % v/v are reduced to a point) and three different values of temperature.


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3. Results and Discussion 3.1. Data’s Reduction The weekly control of the sample appearance showed a slight darkening with aging period. Nevertheless, no deposit was formed in the glass flask along the experiment. Consequently, only soluble gum was formed under the investigated experimental conditions. All the average experimental results and the normalized standard deviation for unwashed and washed gum content in regular and additivated gasoline can be found in Table 3.

Table 3: Experimental and calculated values for the tested conditions.

Average unwashed gum content ranged from 2.6 to 12.1 mg/100 mL for regular gasoline and 15.4 to 37.9 mg/100 mL for additivated gasoline. Nevertheless, the higher values of unwashed gum content cannot be considered as a loss of efficiency since the additive implied an increase of this parameter and it had no relation to gum formation. Such fact was illustrated by the comparison of the regular and additivated sample without aging (samples 6, 7 and 15). Only the variation of these parameters should be considered. Meanwhile, average washed gum content ranged from 1.4 to 6.2 mg/100 mL for regular gasoline and 0.2 to 2.7 mg/100 mL for additivated gasoline. This showed that additive was efficient to prevent autoxidation of gasoline. It is important to note that only one samples with regular gasoline blends (sample 22) had values of washed gum superior to 5.0 mg/100 mL, limit defined by the main specifications in the world. For the same fuel and the same experimental condition, unwashed gum content is systematically higher than washed gum content, as expected. Assessing the effect of additives, it can be seen that unwashed gum content in additivated gasoline is always higher than in regular gasoline. Inverse observation can be done for washed gum content, with exception of samples 6, 7 and 15. Nevertheless, the values can be considered equal under the ASTM repeatability range. Dealing with uncertainties, the 95% confidence interval (2σ) obtained experimentally was most of the time lower than the repeatability given by the norm. Relative reduction up to 97% for unwashed gum and up to 50% for washed gum is observed in both kind of gasoline. In regular gasoline, around 20% of the samples presented higher uncertainties than the value given in the ASTM D381, mainly with a relative increase lower


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than 15%. The unique exception was the sample n°8, with a significant increase of the uncertainties. This can be associated to partial evaporation of the ethanol in the flask at 45°C in some samples. On the other hand, for additivated gasoline, less than 30% of the samples presented higher uncertainties than the value given in the ASTM D381. However, when the increase is observed, it is higher than for regular gasoline (up to 215%). This fact can be partially explained by the low value of washed gum (mainly lower than 2.0 mg/100 mL) and the high normalized standard deviation associated to these values. Low normalized standard deviations were observed for unwashed gum content. Moreover, even with such uncertainties, tendencies can be observed with the experimental measures.

3.2. Modelling of Unwashed and Washed Gum Contents For the two kinds of gums and both fuels, the mathematical models were a second order design with interaction of order two, as described in the equation (1). All the parameters and their standard deviation are given in the Table 4. If the parameter is positive, an increase of the variable promotes the gum formation, whereas a negative parameter is synonym of an inhibitor effect if this factor increases.

Table 4: Parameters given by the Doehlert matrix and their respective standard deviations.

The graphical representations of mathematical models are available in the Supplementary Material. One example of representation is given in Figure 2. From left to right, it can be found the perspective representation of the property (Surface) and a zoom on the projection (Contour) limited to the experimental domain. In these projections, experimental conditions given by the Doehlert design are given in red; in blue, for the baseline for E25 gasoline blends and, in green, for other samples. In each Appendix, the graphical representations were given for different conditions: 1.

Property in function of the anhydrous ethanol content (X1) and the temperature (X3) for a determined

aging period (X2 = -0.866, -0.5, 0, 0.5 and 0.866): 0, 25, 50, 75, 100, 125 and 150 days. 2.

Property in function of the aging period (X2) and the temperature (X3) for determined anhydrous

ethanol content (X1 = -0.8, -0.5, 0, 0.5 and 0.8): 5, 12.5, 25, 37.5 and 45 % v/v (as the representations of E0 and


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E50 gasoline are limited to a simple point, it has been chosen to visualize a convenient value of X1 to show the evolution with the variable x1). 3.

Property in function of the aging period (X2) and the anhydrous ethanol content (X1) for determined

temperature (X3 = -0.816, 0 and 0.816): 25, 35 and 45°C.

{Figure 2.tif} Figure 2: Surface and contour plots of the unwashed gum content in additivated gasoline versus aging period (X2) and temperature (X3) for X1 (ethanol content) = -0.5 (12.5 % v/v).

3.2.1. Unwashed Gum in Regular Gasoline For a given aging period, the profiles of unwashed gum in function of added ethanol content and temperature showed that unwashed gum content increased with temperature, as expected from literature. Dependence with ethanol content was related to the value of temperature: for low temperature, unwashed content decreased with the increasing ethanol content while the opposite behaviour was observed for high temperature. It can be supposed that ethanol had a dilution effect for low temperature, but participate to unwashed gum formation at high temperature (catalytic effect). Another alternative can be a partial evaporation of ethanol with high temperature, increasing the concentration of unwashed gum in solution. The behaviour inversion can be observed at approximately 35°C. For the representation of unwashed gum content in function of aging period and temperature, all the surfaces were pieces of saddle surface. The unwashed gum content increased with temperature and aging period, as expected from literature.

3.2.2. Washed Gum in Regular Gasoline In the profiles of washed gum in function of added ethanol content and temperature, for a given aging period, it can be seen that the region where the washed gum content is maximum was not depending on aging period and it was characterized by high temperature and medium ethanol content. For high concentration of alcohol, the dilution effect was predominant. For high temperature and low ethanol content, evaporation seemed to reduce gum formation. The other part of the experimental domain corresponded to unfavourable


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thermodynamic conditions (low temperature). Nevertheless, it can be observed the drifting of the maximum gum content in the direction of the higher temperature when ethanol content increased. Such fact can be explained by the ethanol evaporation and a probable conversion of some unwashed gum into washed gum, requiring higher activation energy. Such chemical reaction can also require the participation of ethanol, either as a catalyst or as a reagent, since solubility strongly depends on the number of heteroatoms in the gums.

3.2.3. Unwashed Gum in Additivated Gasoline All the surfaces representing unwashed gum content in function of aging period and temperature are pieces of paraboloid surface upside oriented. The unwashed gum content increased with aging period, as expected from literature. Dependence with temperature was related to the value of ethanol content: for low ethanol content, unwashed content increased with the temperature up to approximately a concentration 25.0 % v/v. Meanwhile a parabolic dependency presenting a minimum value was observed for higher ethanol content. This minimum was shifting from lower to higher temperature when more alcohol was added. It can also be observed that for low temperature, unwashed gum content was slightly increasing with alcohol content while a strong decrease was observed for higher temperature. This behaviour was the exact opposite of the profile of unwashed gum in regular gasoline. It can be supposed that additive reactivity decreased with temperature (maybe due to a partial evaporation), allowing the dilution effect of ethanol to be the most important factor. At low temperature, the addition of ethanol decreased the additive concentration and, consequently, its ability to avoid gum formation. Nevertheless, such effect was balanced by dilution effect on gum, showing a slightly increasing profile. It can be seen that, at 25°C, the main factor was aging period, while at 45°C, the main effect was due to ethanol content. At intermediate temperature, both variables contributed to unwashed gum formation, with a slight predominance of ethanol content.

3.2.4. Washed Gum in Additivated Gasoline For a given aging period, the profiles of washed gum in function of added ethanol content and temperature showed that the region where the washed gum content is maximum was characterized by medium ethanol content. However, when aging period increased, the maximum value shifted in direction to high temperature; in


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other words, in the direction of the most favourable thermodynamics conditions. Nevertheless, this shift was reduced as this region is close to the top of a large downside-oriented paraboloid. Again, for high concentration of alcohol, the dilution effect was predominant. For high temperature and low ethanol content, evaporation seemed to reduce gum formation. The other part of the experimental domain corresponded to unfavourable thermodynamic conditions (low temperature). When considering the influence of ethanol content and aging period for a determined temperature, high alcohol concentration again led to a dilution effect. Maximum gum values were observed for medium ethanol content and the maximum drifted in direction of higher aging period when temperature increased. Nevertheless, this region was near the saddle point and the profile was almost flat. For low ethanol, content, it was also observed very few washed gum. This can be explained by the lack of ethanol to solubilize the unwashed gum and by a high activity of the additive. For a fixed concentration of ethanol, this tendency was confirmed as maximum value drifted from high aging period and temperature to low values of these parameters, showing a lower importance of thermodynamics conditions.

3.3. Accuracy of the Models Table 5 gives, for both kinds of gasoline and gum, the residual standard deviation, the average residue and two coefficients of determination of models fitting (R² and R² adjusted to the degree of freedom) as calculated in Statgraphics software. From this table, it can be seen that the coefficient of determination R² vary from 43.35 to 97.41% and from 0.00 to 91.57% when R² was adjusted to the degree of freedom.

Table 5: Residual errors for all properties and both kinds of gasoline.

The values of the coefficients of determination, higher than 97% for regular gasoline, showed that the regression models used to describe the system was significant from the statistical point of view, evidencing that the models were able to explain the behaviour of the data. For additivated gasoline, the models must be improved as the determination coefficients ranged from 43.35% to 83.04% and from 0.00 to 44.89% when R² was adjusted to the degree of freedom. Considering the nature of gasoline, both kinds of gum had similar behaviour, even if better results were obtained for unwashed gum. Such results can be explained by the difference of behaviour in additivated


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gasoline: when compared to regular gasoline, a very low quantity of washed gum was formed in these blends while additive led to higher values of unwashed gum. Consequently, as given in Figure 3, relative experimental errors, mainly due to the repeatability of the ASTM D381, were lower for unwashed gum than for washed gum. When considering the effect of the additive, the best models were obtained for regular gasoline. Again, the low quantity of washed gum in additivated gasoline contributed to this fact. Moreover, the higher value of unwashed gum content implied a greater absolute value of the average residue.

{Figure 3 (left).tif}

{Figure 3 (right).tif}

Figure 3: Normalized repeatability given by the ASTM D381 for unwashed (left) and washed gum (right).

For each case, the Doehlert design models were also evaluated by comparing experimental and predicted values, as given in Figure 4. This figures confirmed the previous observation since relative good fitting was observed in regular gasoline while some additivated gasoline samples presented some values far from the bisector, even considering the ASTM D381 repeatability. The model for washed gum content in additivated clearly presented the higher dispersion of the values, but all of them can be explained by the analytical repeatability.

{Figure 4 (top, left).tif}

{Figure 4 (top, right).tif}

{Figure 4 (bottom, left).tif}

{Figure 4 (bottom, right).tif}

Figure 4: Observed versus predicted values for regular (top) and additivated (bottom) blends of unwashed (left) and washed (right) gum content (in black, the experiments from the Doehlert design; in blue, the other experiments). The ASTM repeatability is showed by the dash lines.

3.4. Impact of the Variables As shown in Figure 5, the Pareto charts showed that the variables causing significant influence on the response were the same as pointed out by the analysis of variance (ANOVA) (Tables 6 to 9). The chart is showing standardized effect estimate (absolute value), using p-value significance levels (p=0.05).


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{Figure 5a.tif} {Figure 5b.tif} {Figure 5c.tif} {Figure 5d.tif} Figure 5: Pareto chart of the effects for the Doehlert designs.

Table 6: Results obtained by applying ANOVA to the model for the unwashed gum content in regular gasoline. The significant parameters are marked in bold.

Table 7: Results obtained by applying ANOVA to the model for the washed gum content in regular gasoline. The significant parameters are marked in bold.

Table 8: Results obtained by applying ANOVA to the model for the unwashed gum content in additivated gasoline. The significant parameters are marked in bold.

Table 9: Results obtained by applying ANOVA to the model for the washed gum content in additivated gasoline. The significant parameters are marked in bold.

Another way to quantify the effect of each variable was the visualization of the direct effects (left) and the interaction effects (right) of each factor, as it is available in Figure 6. The direct effect represents the variation of the property due to the contribution of b + b# X # + b## X# for i = 1, 2 or 3 when the interaction effect shows the impact of b#1 X # X1 on the direct effect for a fixed i and for critical values of X 1 2X 1 = ±14. The following figures confirmed the observations described in this section. The parabolic behavior described formerly and the interactions were clearly appearing in these figures.


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{Figure 6a.tif} {Figure 6b.tif} {Figure 6c.tif} {Figure 6d.tif} Figure 6: Representation of the direct effects (left) and the interaction effects (right).

For unwashed gum content in regular gasoline, the Pareto chart of effects showed that the three variables affected significantly the response through six terms. As also shown in Figure 6, aging period and temperature had a direct impact while ethanol appeared through interaction terms with temperature. For washed gum content in regular gasoline, three variables affected significantly the response through five terms. Aging period and temperature had a direct impact while ethanol appeared through the quadratic terms. No interaction terms affected significantly the value using p-value significance levels (p=0.05), even if the interaction between ethanol content and temperature had a notable influence. For unwashed gum content in additivated gasoline, the Pareto chart of effects showed that only ethanol content variables affected significantly the response through the linear term. Such observation is confirmed by the Figure 6. For washed gum content in regular gasoline, no variable affected significantly the response, neither for the three variables model nor for the two variables model. These inconclusive analyses of the variables should be related to the low value of R².

3.5. Baselines for Regular and Additivated E25 Gasoline Blend The comparison of the observed values versus the predicted values for E25 gasoline blends stored at 25, 35 and 45°C at different aging period is given in Figure 7. This figure showed that the models gave the global tendency of the evolution of these properties, even if some experimental values did not fit with the model, especially for unwashed gum in additivated gasoline. As formerly explained, the best representativeness was observed for regular gasoline. Furthermore, the uncertainties were high for washed gum content. However, it can be mostly associated to the repeatability of the ASTM D381 and the fact that only two repetitions of the experiments had been done for such blends. More repetition can smooth the experimental profile and it can


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reduce the residue between predicted and observed values. For washed gum content in regular gasoline, the experimental prediction uncertainty was dominating.

{Figure 7 (top, left).tif}




{Figure 7 (legend).tif} Figure 7: Representation of the observed values versus the predicted values E25 gasoline blends stored at 35°C at different aging period. Experimental uncertainty with a 95% confidence interval is given.

The comparison of the observed values versus the predicted values for E25 gasoline blends stored for 75 days at 25, 35 and 45° is given in Figure 8. Best results were observed for unwashed gum content in regular gasoline, which is the model with the higher R². Tendencies are also correctly given for washed gum content in regular and additivated gasolines. Nevertheless, the variation of the experimental data was higher than given by the model. Adequacy for washed gum content in additivated gasoline can be balanced by a high predictive standard deviation. For unwashed gum content in additivated gasoline, the modelled tendency was opposed to the experimental observation and the residues were high. Adequacy was lower for these samples than those described in Figure 7 as only three experiments had been realized for the Doehlert design at 25 and 45°C. {Figure 8 (top, left).tif}




{Figure 8 (legend).tif} Figure 8: Representation of the observed values versus the predicted values E25 gasoline blends stored 75 days at 25, 35 and 45°C. Prediction uncertainty with a 95% confidence interval is given.

4. Conclusions The state-of-the-art shows that, in addition to room storage conditions (temperature and aging period), gasoline hydrocarbons composition, specially olefins and diolefins, the absorbed oxygen content in gasoline and


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a possible contamination with transition metal deeply influence the gum formation. The recent studies seemed to show that addition of ethanol in gasoline have a dilution effect. Based on this literature review, an experimental design has been defined to study the stability of gasolineethanol blends exposed to different representative conditions of aging, where autoxidation occured. The aging influence on blend properties of two types of gasoline (regular or additivated), the concentration of anhydrous ethanol, the temperature and the aging period were the four factors investigated. For this purpose, a Doehlert matrix designs with three factors have been used for each kind of gasoline. The impact of these variables was evaluated measuring unwashed gum and washed gum content. Second order mathematical models with an interaction of order two was obtained and used as a predictive tool. In general, more robust models were obtained for unwashed gum than for washed gum as relative experimental errors, mainly due to the repeatability of the ASTM D381, were lower. For regular gasoline, the determination coefficients higher than 97% showed that the regression model used to describe the system was significant from the statistical point of view, evidencing that the model is able to explain the behaviour of the data. For additivated gasoline, the models must be improved as the determination coefficients ranged from 43.35% to 83.04%. In order to improve the experimental procedure, increase the precision of the results and facilitate the interpretation, the following recommendations should be applied in a new study: - Repeat the experiments for better representability of the real value: This paper showed that experimental error can be high, in particular for the additivated gasoline. Consequently, it is recommended to repeat more than three times the same experiment and work with an average value. Other data reduction, such as elimination of the aberrant values, can also contribute to smooth the experimental profile. - Strengthen the mathematic model: An alternative and/or complementary option to the repetition of the experiments is to use a design of experiments that gives a mathematic model with higher intrinsic robustness, such as full factorial design, central composite or Box-Behnken designs. Nevertheless, such option implies an increase on the number of experiments. - Broaden the experimental domain from E0 to E100 using anhydrous ethanol: This article showed results given for gasoline-ethanol blends with up to 50 % v/v of ethanol, with the restriction given by the limits of the experimental domain. Consequently, it is recommended to broaden the experimental domain to cover blends with higher ethanol concentration to give more flexibility to the applications of the models as predictive tools.


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For unwashed gum content in regular gasoline, aging period and temperature had a positive direct impact, as expected from the literature, while ethanol appeared through interaction terms with temperature. Consequently, influence of ethanol content depended on the value of temperature: for low temperature, unwashed content decreased with the increasing ethanol content while the opposite behaviour was observed for high temperature. It can be supposed that ethanol had a dilution effect for low temperature, but participated to unwashed gum formation at high temperature (catalytic effect). Another alternative can be a partial evaporation of ethanol with high temperature, increasing the concentration of unwashed gum in solution. For washed gum content in regular gasoline, the three variables affected significantly the response through five terms. Aging period and temperature had a direct impact while ethanol appeared through the quadratic terms. For a given aging period, it can be seen that the region where the washed gum content is maximum was not depending from aging period and it was characterized by high temperature and medium ethanol content. For high concentration of alcohol, the dilution effect was predominant. For high temperature and low ethanol content, evaporation seemed to reduce gum formation. The other part of the experimental domain corresponded to unfavourable thermodynamic conditions (low temperature). For a determined aging period, it can be observed the drifting of the maximum gum content in the direction of the higher temperature when ethanol content increased. Such fact can be explained by the ethanol evaporation and a probable conversion of some unwashed gum into washed gum, requiring higher activation energy. Such chemical reaction can also require the participation of ethanol, either as a catalyst or as a reagent, as solubility strongly depends on the number of heteroatoms in the gums. For unwashed gum content in additivated gasoline, only ethanol content variables significantly affected the response through the linear term. This inconclusive analysis of the other variables should be related to the low value of R². The unwashed gum content increased with aging period, as expected from the literature. Dependence with temperature was related to the value of ethanol content: for low ethanol content, unwashed content increased with the temperature up to approximately a concentration 25.0 % v/v while a parabolic dependency presenting a minimum value was observed for higher ethanol content. This minimum was shifting from lower to higher temperature when more alcohol was added. For a determined aging period, it can also be observed that for low temperature, unwashed gum content slightly increased with alcohol content while a strong decrease was observed for higher temperature. This behaviour was the exact opposite of the profile of unwashed


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gum in regular gasoline. It can be supposed that additive reactivity decreased with temperature (maybe due to a partial evaporation), allowing the dilution effect of ethanol to be the most important factor. At low temperature, the addition of ethanol decreased the additive concentration and, consequently, its ability to avoid gum formation. Nevertheless, such effect was balanced by dilution effect on gum, showing an almost flat or slightly increasing profile. It can be seen that, at 25°C, the main factor was aging period while, at 45°C, the main effect was due to ethanol content. An intermediate temperature, both variables contributed to unwashed gum formation, with a slight predominance of ethanol content. For washed gum content in regular gasoline, no variable significantly affected the response. For a given aging period, the profiles of washed gum in function of added ethanol content and temperature showed that the region where the washed gum content is maximum was characterized by medium ethanol content. However, when aging period increased, the maximum value shifted in direction to high temperature, in other words, in the direction of the most favourable thermodynamics conditions. Again, for high concentration of alcohol, the dilution effect was predominant. For high temperature and low ethanol content, evaporation seems to reduce gum formation. The other part of the experimental domain corresponded to unfavourable thermodynamic conditions (low temperature). High alcohol concentration led to a dilution effect. Maximum gum values were observed for medium ethanol content and the maximum drifted in direction of higher aging period when temperature increased. For low ethanol content, it was also observed very few washed gum. This can be explained by the lack of ethanol to solubilize the unwashed gum and by a high activity of the additive. Even if the function of ethanol remained complex and required more investigations to confirm the tendencies, it is possible to use the given mathematical models as predictive tools to evaluate the typical Brazilian ethanol-gasoline blends, in particular in regular blends.

Acknowledgments The authors are indebted to CNPq/MCT, CAPES, FAPERJ and FINEP for the financial support to the Department of Mechanical Engineering (DEM) at the Pontifical Catholic University of Rio de Janeiro (PUCRio). The authors gratefully acknowledge Peugeot Citroën do Brasil Automóveis Ltda for the financial support and Lubrizol do Brasil Aditivos Ltda for its contribution to this study.


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Supporting Information Surface and contour plots for unwashed and washed gum in regular and additivated gasoline given for different conditions: 1.

Property in function of the anhydrous ethanol content and the temperature for a determined aging period: 0, 25, 50, 75, 100, 125 and 150 days.

2.

Property in function of the aging period and the temperature for determined anhydrous ethanol content: 5, 12.5, 25, 37.5 and 45 % v/v.

3.

Property in function of the aging period and the anhydrous ethanol content for determined temperature: 25, 35 and 45°C.

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List of Figures Figure 1: Projection (left) and three-dimensional (right) representation of the Doehlert matrix. Figure 2: Surface and contour plots of the unwashed gum content in additivated gasoline versus aging period (X2) and temperature (X3) for X1 (ethanol content) = -0.5 (12.5 % v/v). Figure 3: Normalized repeatability given by the ASTM D381 for unwashed (left) and washed gum (right). Figure 4: Observed versus predicted values for regular (top) and additivated (bottom) blends of unwashed (left) and washed (right) gum content (in black, the experiments from the Doehlert design; in blue, the other experiments). The ASTM repeatability is showed by the dash lines. Figure 5: Pareto chart of the effects for the Doehlert designs. Figure 6: Representation of the direct effects (left) and the interaction effects (right). Figure 7: Representation of the observed values versus the predicted values E25 gasoline blends stored at 35°C at different aging period. Experimental uncertainty with a 95% confidence interval is given. Figure 8: Representation of the observed values versus the predicted values E25 gasoline blends stored 75 days at 25, 35 and 45°C. Prediction uncertainty with a 95% confidence interval is given.

List of Tables Table 1: Physical-chemical properties of the studied alcohol-free gasoline Table 2: Doehlert matrix in term of reduced (Xi) and real (xi) variables. Table 3: Experimental and calculated values for the tested conditions. Table 4: Parameters given by the Doehlert matrix and their respective standard deviations. Table 5: Residual errors for all properties and both kinds of gasoline. Table 6: Results obtained by applying ANOVA to the model for the unwashed gum content in regular gasoline. The significant parameters are marked in bold. Table 7: Results obtained by applying ANOVA to the model for the washed gum content in regular gasoline. The significant parameters are marked in bold. Table 8: Results obtained by applying ANOVA to the model for the unwashed gum content in additivated gasoline. The significant parameters are marked in bold.


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Table 9: Results obtained by applying ANOVA to the model for the washed gum content in additivated gasoline. The significant parameters are marked in bold.


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Table 1: Physical-chemical properties of the studied alcohol-free gasoline

a

Property

Method

Colour (-)

Visual

Visual Aspect (-)

ASTM D4176

Ethanol content (% v/v) Motor Octane Number (-) IAD (-) Induction period at 100°C (min) Density at 20°C (kg/m3) Distillation temperature 10% evaporated (°C) 50% evaporated (°C) 90% evaporated (°C) Final boiling point (°C) Distillation residue (% v/v)

NBR 13992 ASTM D2700 ASTM D2699 ASTM D525 ASTM D4052 ASTM D86

Vapour pressure at 37,8°C (kPa) Gum content (mg/100mL) Corrosiveness to Copper, 3h at 50°C (-) Sulfur content (mg/kg) Benzene content (% v/v) Silicon content (mg/kg) Aromatic (% v/v) Olefinic (% v/v) Saturated (% v/v)

ASTM D5191 ASTM D381 ASTM D130 ASTM D7039 ASTM 3606 PE FGAA CG

Brazilian Specification a Without / With alcohol Colourless to slightly yellow Limpid and exempt of impurities 1 (max) - / 82.0 (min) - / 87.0 (min) - / 360 (min) -

Colourless to slightly yellow Limpid and exempt of impurities 720 728.9

65.0 (max) 120.0 / 80.0 (max) 190.0 (max) 215.0 (max) 2.0 (max)

51.8 93.2 159.4 200.7 1.0

45.0 to 62.0 5.0 (max) 1 (max) - / 50 (max) - / 1.0 (max) - / 35 (max) - / 25 (max) -

62.3 1.5 1 40.8 0.76

Modeling of Unwashed and Washed Gum Content in Brazilian

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