Bio-Oil Viscosity of Sisal Residue: Process and Temperature Influence

Mar 30, 2018 - The low-viscosity bio-oil was used to investigate the influence of the shear rates at its most critical flow condition (2–26 s–1) a...
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Bio-Oil Viscosity of Sisal Residue: Process and Temperature Influence Luis G. G. Pereira* and Carlos A. M. Pires Chemical Reaction Engineering Laboratory, PPEQ, Escola Politécnica, UFBA, Rua Aristides Novis, no. 2, 2° andar, Federaçaõ , Salvador, Bahia Brazil, CEP 40210-910 S Supporting Information *

ABSTRACT: The viscosity of sisal residue bio-oil was evaluated using the variation of the operational conditions of the fast pyrolysis process in a fluidized bed. The bio-oil was produced in a pilot plant from tests that were designed using the technique of experimental design. In this work, the effect of the flow rate of the inert gas (N2), the biomass mass flow rate, and the reaction temperature on the viscosity at the shear rates of 40 and 75 s−1 was investigated. The lowest viscosity was obtained when the N2 flow rate and the temperature were at their lowest values (8 N m3/h and 450 °C) and the biomass flow was at its highest value (1560 g/h). The low-viscosity bio-oil was used to investigate the influence of the shear rates at its most critical flow condition (2−26 s−1) at temperatures of 60−110 °C. It was found that at 60 °C, the bio-oil viscosity ranged from 2699 to 353 mPa·s when the shear rate increased from 2 s−1 to 26 s−1. The lower value of this viscosity (353 mPa·s) is equivalent to the higher value reported in the literature. An empirical model of viscosity versus temperature was determined and compared with existing models. This evaluation was performed at three shear rates (2 s−1, 14 s−1, and 26 s−1) and at temperatures ranging from 60 to 110 °C. The model proposed in this study showed deviations much smaller than those obtained with the application of the most used models. these studies, the work of Heo et al.10 evaluated the distribution and characteristics of the products of the fast pyrolysis of rice husks in a fluidized bed under different reaction conditions. However, no work has quantitatively assessed the influence of operating conditions and the interactions between them, for example, on the bio-oil viscosity. The lack of interest in this topic may have been due to the moderate viscosities attributed to the bio-oils produced by the more traditional biomasses, which may not be influenced by the operating conditions of the process. However, for bio-oils considered to have higher viscosities, it becomes essential to study the influence of process operating variables on the decrease of viscosity. The objective of this work was to study the influence of the operational variables of the fast pyrolysis process on the viscosity of the bio-oil produced from sisal residue. In addition, an empirical model was developed to describe the viscosity behavior of this bio-oil as a function of temperature. The bio-oil samples used in this work were produced from tests whose operational variables were arranged based on the experimental design methodology. The response surface methodology was chosen to evaluate the influence of the operational variables and their interactions on the bio-oil viscosity, and the operational conditions that produced the bio-oil of lower viscosity were also defined. A new correlation between viscosity and temperature was proposed for the bio-oil of sisal residue, and the results were compared with others from correlations found in the literature.

1. INTRODUCTION The exploitation of renewable energy sources is considered to be promising for the global production of fuel. The exploration of new sources of energy emerges as a strategy to meet the rapid increase in energy consumption and a decrease in the current reserves of fossil fuels around the world.1,2 Biomass can be conveniently converted into bio-oil by thermochemical processes such as fast pyrolysis and high-pressure liquefaction.3 Comparatively, fast pyrolysis stands out due to the high liquid yield (up to 75 wt %), which can be easily stored and transported to biorefineries for more effective conversion into fuels and various chemicals.4,5 According to Mahinpey et al.,6 the yield and properties of the products formed in the pyrolysis are strongly influenced by the configuration of the reactor, by the reaction parameters (temperature, heating rate, residence time, pressure and catalyst), and by the characteristics of the biomass, such as granulometry, shape, and structure. Therefore, the properties of the bio-oil produced, such as fluidity and viscosity, can vary significantly with the operating conditions of the process, interfering with the flow of the fluid and its subsequent application. The bio-oils commonly found in the literature have viscosity values ranging from 100 to 400 mPa·s at 40 °C.4 However, the work of Pereira and Pires7 showed that the biooil of sisal residue had very high viscosities, with values reaching 2000 mPa·s. These authors reported that bio-oil from sisal residue caused incrustations on the inner walls of the pilot plant heat exchangers, making it difficult to recover. Most of the works on bio-oil developed by researchers are related to the production of liquid from various biomasses and processes. Soria-Verdugo8 studied the effect of inert gas velocity and bed temperature during pyrolysis. Westerhof et al.9 evaluated the influence of reaction temperature on the yield, composition, and quality of the bio-oil produced. In addition to © XXXX American Chemical Society

Received: November 22, 2017 Revised: March 19, 2018

A

DOI: 10.1021/acs.energyfuels.7b03658 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels

condensers at 9 °C so that the temperature of the noncondensable gases at the outlet of the fourth condenser does not exceed 20 °C. 2.3. Experimental Procedure − Bio-Oil Production. The reactor was charged with 200 mm of silica sand with a diameter of 0.4 mm, and the cooling fluid was fed to the heat exchanger. The air was drawn into the reactor with sufficient flow to keep the bed fluidized. The heating resistances of the reactor and the air heating were actuated so that the reaction temperature was reached. When the reaction temperature was stabilized, the air was replaced by nitrogen (inert gas), and a helical bolt led the silo biomass into the reactor with a flow predefined by the test. After 1 h of reaction, the system was cooled, and the bio-oil was collected and stored at 1 °C for further analysis. 2.4. Experimental Procedure − Bio-Oil Viscosity. For measurements of shear stress and shear rate, a programmable rheometer, model DV-III + rheometer (Brookfield), was used. Before the measurements were carried out, the samples were subjected to a preshear with the objective of homogenizing the bio-oil, thus avoiding interferences in the measurements due to the presence of bubbles. Furthermore, the sample was left in contact with the thermostatic bath for approximately 10 min to establish thermal equilibrium in each test. A thermostatic bath coupled to the rheometer allowed the measurements to be carried out at different temperatures with an accuracy of ±1 °C and within a range from −100 to 150 °C. The rheometer also has software, Rheocalc v. 2.3, through which it is possible to program the range of measurements to be performed. The measurements are reported in table form, which shows the values of viscosity (mPa·s), torque (%), shear stress (N/m2), shear rate (s−1), and temperature (°C). In this work, the measurements were performed at temperatures of 60, 70, 80, 90, and 110 °C with variable shear rates from 2 s−1 to 75 s−1 and increments of 2 s−1. The shear rate range was determined, taking into account the torque exerted on the spring so as not to exceed the limit value of 100%. 2.5. Design of Experiments. The study of the influence of the operational variables of the pyrolysis process on the bio-oil viscosity was carried out based on statistical predictions. The experiments were carried out based on the “Statistical Experimental Design” technique, using the temperature of the reaction medium (°C), the biomass flow rate that fed the reactor (g/h), and the nitrogen flow rate (Nm3/h). The other variables were kept constant: the particle size of the sisal residue, the silica sand mass, and the temperature and flow rate of the condenser cooling water. The granulometry of the sisal residue was kept constant because it is intended to evaluate the natural biomass with the characteristic specification of the defibration process. The silica sand mass, as well as its specification, was also kept constant because the heat retention capacity of the sand was enough to keep the reaction system at constant temperature, even with the injection of biomass into the reactor. The temperature and flow rate of the cooling water were kept constant because the capacity of the cooling fluid exceeds the cooling needs of the reactor effluent gas, maintaining the temperature of this fluid at the unit outlet at approximately 27 °C over the entire experimental range. The tests were defined from the factorial design 23, with three replicates at the central point, in which the values of the operational variables were coded as (−1) for lower levels, (+1) for upper levels, and (0) for the central level (Table 1). The bio-oil viscosity was defined as the test responses that were influenced by the operating conditions. However, the viscosity of each test was defined as the consequence of two different shear rates at 70

2. MATERIALS AND METHODS 2.1. Sisal Residue. Sisal (Agave sisalana, Agavaceae family) is a plant of semiarid regions, and it is cultivated with the aim of producing natural fibers. Brazil was the leading producer of sisal fibers in 2014, corresponding to 55% of their world’s production.11 In Brazil, sisal production is concentrated in the state of Bahia, and its fibers are produced at little scale farms.12 The sisal fibers are obtained from scraping of sisal leaves, corresponding to 4% of their weight. Sisal production is notable for generating a substantial amount of organic residues, which is normally treated as an organic waste, or is used as a low nutritional value animal feed.12 The sisal residue used in this work was obtained by defibering leaves of Agave sisalana, which originated from the semiarid region of Bahia. The sisal residue was transported to the laboratory and stored in a refrigerator to prevent fermentation from taking place. Prior to the pyrolysis tests being performed, the biomass was dried in an oven for 4 h at 105 °C. After being cooled to room temperature, the short sisal fibers were removed from the biomass, leaving dried slices. The general properties of sisal residue, as well as those of the bio-oil produced, were reported by Pereira and Pires.7 The sisal residue used in the reaction was not submitted to any diameter adjustment and used the same granulometric distribution from the sisal leaf defibration process. Particle size was determined by sieve openings of 1/4−3/8 in. and 12 to 31/2 mesh sizes. The average particle length was determined by the average size of the sieve openings that retained the material and the size immediately preceding it. The mean size of the residue ranged from 0.15 to 5.66 mm, and approximately 90 wt% exhibited particle size between 0.60 and 4.83 mm. 2.2. Fast Pyrolysis Unit. The sisal residue bio-oil was produced in a pilot-scale fluidized-bed pyrolysis unit. Figure 1 shows a simplified

Figure 1. Pilot bio-oil production unit: (P1) fluidization gas heating furnace, (P2) biomass storage silo, (P3) pyrolysis reactor, (P4) cyclones, and (P5) product collection system. scheme of the production unit, which contains the classical elements: a biomass feeding and injection system, a bubbling fluidized bed pyrolysis reactor, a product collection system, data acquisition, and a control system. The gas heating furnace (P1) has an electrical resistance with a power of 13.5 kW/h, and it can heat the fluidized gas up to 760 °C. The silo (P2) has a storage volume of 10 kg of dry biomass. The pyrolysis reactor (P3) is made of a stainless-steel pipe with a nominal diameter of 100 mm and a height of 1000 mm. The reactor has three 1200 W collar resistors, and it is insulated with stone wool. In addition, the reactor has four thermocouples to indicate the temperature along the reactor, a bed differential pressure gauge, and a reactor pressure meter. The system has two cyclones (P4), built-in stainless steel and a collection system for liquid products (P5), consisting of four shell and tube heat exchangers that are 800 mm, 25.4 mm in diameter (tube), and 50.8 mm in diameter (hull). The collection system has a thermocouple at the input of the first condenser and a thermocouple at the output of the fourth. The cooling fluid is water, which feeds the

Table 1. Lower, Central, and Upper Limits Values for the Independent Variables

B

variable

−1

0

+1

(N2F) - N2 flow (Nm3/h) (BioF) - Biomass flow (g/h) (Temp) - Reaction temperature (°C)

8 610 450

11 1083 500

14 1560 550

DOI: 10.1021/acs.energyfuels.7b03658 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels °C (40 s−1 and 75 s−1). The shear rates used exemplify the viscosity behavior of the bio-oil in situations in which the fluid is submitted to different shear stresses. The tests performed in this work, as well as the corresponding viscosities, are shown in Table 2. The results seem logical if it is considered that the increase in shear rate causes the decrease of viscosity in all tests carried out.

the modified average sum of the squares, and the P-value for each parameter as the results of the performed analysis of variance. The experimental unit of pyrolysis was considered as a pilot due to the dimensions of the equipment and the operational conditions imposed on the process. These characteristics make the operating conditions of this unit similar to those of an industrial unit. The similarities are related to the accuracy of the data collected by the acquisition system and in the instruments, so they are related to the operational procedure for the bio-oil collection. For this reason, and because the tests had a short duration in relation to the size of the pyrolysis unit, the range of significance of the variables was increased such that the statistical confidence used was 80%. A lower P-value is attributed to a higher importance of that factor and model. P-value amounts lower than 0.2 (confidence level of 80%) indicate that the studied parameter is significant in the results of the model. 2.6. Models for Viscosity and Temperature. The theoretical determination of viscosity from fluid temperature data usually uses empirical models. In the case of liquids, the viscosity decreases with increasing temperature under isobaric conditions. The dependence of viscosity on temperature can be described by the Guzman−Andrade equation (eq 1).16,17

Table 2. Matrix of Factorial Experiment with Independent Variable Levels viscosity (mPa·s) test

N2F

BioF

temp

40 s−1

75 s−1

1 2 3 4 5 6 7 8 9 10 11

−1 +1 −1 +1 −1 +1 −1 +1 0 0 0

−1 −1 +1 +1 −1 −1 +1 +1 0 0 0

−1 −1 −1 −1 +1 +1 +1 +1 0 0 0

150.928 196.308 57.618 134.101 228.686 149.908 79.288 163.420 131.807 110.136 148.123

128.493 136.787 47.454 113.808 136.923 129.580 66.762 136.923 105.921 89.197 110.408

ln η = A + (B /T )

(1)

where η is the dynamic viscosity in mPa·s; T is the temperature of the fluid in °C; and A and B are the adjustable parameters. This equation is quite simple, and it fits in close approximation to the experimental data of a wide variety of both organic and inorganic liquids.18 For associated liquids and mixtures of high viscosity hydrocarbons, the relationship between ln η and 1/T becomes slightly curved, so the Guzman−Andrade equation cannot adequately apply. Some authors proposed modifications in this equation in order to improve the results, generally including a third constant to correct the curvature or functions of the liquid molar volume in parameters A and B.17,19 For this reason, Vogel20 proposed eq 2 and Girifalco21 proposed eq 3:

The relationship between the viscosity and the fast pyrolysis condition is complex, and the best way to evaluate this behavior is by studying the interaction effects using statistical design.13 Therefore, the response surface methodology (RSM) was chosen for this study.13−15 This approach consists of a group of statistical techniques that allows a reduction in the number of experiments and a prediction of the influence of the factors on the chosen response using a mathematical model. The latter can be graphically represented with response surfaces that show the extent of the influence of the parameters or the significance of their interactions and can then be used to provide the optimal conditions to improve a process. The best approach for comparing various measures is the analysis of variance (ANOVA).13−15 ANOVA is the statistical treatment most commonly applied to the results of experiments to determine the percentage contribution of each parameter. ANOVA helps in formally testing the significance of all main factors and their interactions by comparing the mean square against an estimate of the experimental errors at specific confidence levels. The significance of the RSM model involves the degree of freedom, the sum of squares, the contribution of each parameter in the prediction model, the modified sum of the squares,

ln η = A + [B /(C + T )]

(2)

ln η = A + (B /T ) + (C /T 2)

(3)

Poling et al.17 reported the values of the parameters A, B, and C of a range of liquid hydrocarbons in recommended temperature ranges. The models of Guzman−Andrade, Vogel and Girifalco were not applied to bio-oil, and there is no reference in the literature to any other type of model for this purpose.

Table 3. ANOVA Results of Viscositya shear rate (s−1) 40

a

75

variables

SS

df

MS

P

SS

df

MS

P

(1) N2F (2) BioF (3) Temp (1) by (2) (1) by (3) (2) by (3) (1) (2) (3) lack of fit pure error total SS R2 R2 adj

2023.05 10614.45 847.64 4705.16 1696.83

1 1 1 1 1

2023.05 10614.45 847.64 4705.16 2023.05

0.06 0.00 0.18 0.02 0.08

2362.14 3479.30 238.13 2297.17

1 1 1 1

2362.14 3479.30 238.13 2297.17

0.01 0.00 0.20 0.01

2171.63 539.75 726.29 23324.76 0.95 0.87

1 2 2 10

2171.63 269.87 363.14

0.06 0.57

276.93 249.91 9132.75 0.94 0.88

3 2 10

92.31 124.95

0.69

Shear rate: 40 s−1, and 75 s−1. (SS) sum of squares; (df) degree of freedom; (MS) mean square; (P) P-value. C

DOI: 10.1021/acs.energyfuels.7b03658 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels

3. RESULTS AND DISCUSSION 3.1. Influence of Operational Conditions on Bio-Oil Viscosity. Table 3 shows the ANOVA to aid in formally testing the significance of all main factors and their interactions. P-values that are greater than 0.2, as is the case for the first order interactions (biomass flow by reaction temperature shear rate: 40 s−1 and 75 s−1; N2 flow by reaction temperature shear rate: 75 s−1), indicate that the null hypotheses were acceptable and that the effects of those variables were removed from the calculation. P-values that are much lower than 0.2, as is the case for the other response variables, indicate that those parameters were important. The coefficient of determination (R2) indicates how well the variation in response (viscosity) was explained by the model. Larger values of predicted R2 suggest models of greater predictive ability. The model will give a better prediction of the viscosity when the regression coefficient is close to unity. The viscosity models for higher shear rates, 40 and 75 s−1, had R2 values of 95% and 94%, respectively, which implies a good adjustment of the parameters. Adjusted R2 (R2 adj) accounts for the number of predictors in the model, and it is useful for comparing models with different numbers of predictors. Table 3 shows that adjusted R2 did not stay closer to R2 when nonsignificant variables were removed from the model (R2 adj = 0.87, to 40 s−1; and R2 adj = 0.88, to 75 s−1). However, the adjusted R2 values were high, with the models becoming suitable to predict the viscosity. Pure-error and lack-of-fit are parts of the residual sums of squares that are relevant for testing a hypothesis. The pure-error is the part that cannot be predicted by any additional terms, while lack-of-fit can be predicted by including additional terms for the predictor variables in the model. Table 3 shows an insignificant P-value (>0.2) for lack-of-fit of the bath shear rate, ratifying that it is not possible to include additional terms. A mean square pure error equal to 363.14 (40 s−1) and 124.95 (75 s−1) was found, which means that observed values by the model had reasonable variability compared to the average. Figure 2 shows how much the relation between the viscosity calculated by the model and the experimental viscosity stayed closer. This result is enough to make qualitative predictions of the influence of the operational variables on the bio-oil viscosity. Figure 3 shows three-dimensional surfaces that were plotted to study the interaction between three pairs of two variables (biomass flow and N2 flow; reaction temperature and N2 flow; and reaction temperature and biomass flow) on the bio-oil viscosity. The combined effects of the biomass flow and the N2 flow were investigated to obtain the viscosity to shear rate of 40 s−1 and 75 s−1, as shown in Figure 3, panels a and d, respectively. It was observed that the viscosity decreased by approximately 58% (Figure 3a) and 53% (Figure 3d) when the N2 flow decreased from 14 N m3/h to 8 N m3/h and the biomass flow increased from 610 g/h to 1560 g/h. The combined effects of the reaction temperature and N2 flow were investigated to obtain the viscosity to shear rate of 40 s−1 and 75 s−1, as shown in Figure 3, panels b and e, respectively. It was observed that the viscosity decreased by approximately 55% (Figure 3b) and 48% (Figure 3e) when the N2 flow decreased from 14 N m3/h to 8 N m3/h, and the reaction temperature decreased from 550 to 450 °C. The combined effects of the reaction temperature and biomass flow were investigated to obtain the viscosity to shear rate of 40 s−1 and 75 s−1, as shown in Figure 3, panels c and f, respectively. It was observed that the

Figure 2. Experimental viscosity as a function of calculated: shear rate (a) 40 s−1 e (b) 75 s−1.

viscosity decreased by approximately 47% (Figure 3c) and 41% (Figure 3f) when the reaction temperature decreased from 550 to 450 °C, and the biomass flow increased from 610 g/h to 1560 g/h. These results can be explained by the residence time of the vapors in the reactor and the water content produced. When the nitrogen flow is decreased to the lowest value, the residence time of the molecules in the reactor increases. The products generated will remain in the reactor longer, resulting in a decrease in the bio-oil viscosity due to the reduction of the size of the molecules. Furthermore, the higher biomass flow rate also contributed to the reduction of viscosity due to the increase of residence time. In this case, the increased production of gases and vapors from the pyrolysis reaction increased the pressure in the reactor, causing an increase in the residence time of the molecules due to a decrease in the volumetric flow rate. This fact can be better explained by the analysis of Figure 4. With the course of the reaction, the formed gases are conveyed to the cyclones from a pipe at the reactor top. This pipe has a much smaller diameter (3 cm) than the reactor diameter (10 cm), as can be seen in the scheme of the production unit (Figure 1). Therefore, the amount of gas produced is much higher than that which leaves the reactor in the direction of the cyclones, causing the accumulation of gases inside the reactor, which results an increase in pressure as well as an increase in the residence time of the molecules. As can be seen in Figure 4, as the system pressure increases, the gas flow decreases, as well as the opposite. So, at higher pressures the gas flow rate is smaller, causing an increase in the residence time in the reactor, resulting in a less viscous bio-oil. The reduction of the reaction temperature from 550 to 450 °C favored a decrease in the bio-oil viscosity. According to Duanguppama et al.22 and Garcia-Perez et al.,5 the bio-oil viscosity increases with increasing temperature to a certain D

DOI: 10.1021/acs.energyfuels.7b03658 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels

Figure 3. Effect of variables N2 flow, biomass flow, and reaction temperatures on the viscosity of bio-oil. Shear rate of 40 s−1: (a), (b), and (c); shear rate of 75 s−1: (d), (e), and (f).

pyrolysis water yield increases at low temperatures (∼450 °C) due to the favorable dehydration and condensation reactions of the organic molecules, which favorable proceeds at lower temperatures, due to the suppression of secondary condensation reaction at higher temperatures. So, water content decreases with increasing temperature, which produces a more viscous bio-oil.

value and then decreases. Duanguppama et al.22 found a viscosity increase from 73 mPa·s to 191 mPa·s when the temperature increased from 400 to 550 °C. However, the viscosity decreased to 159 mPa·s when the temperature increased to 600 °C. These authors related the low viscosity of the bio-oil with its high water content, having as reference the work of Oasmaa et al.23 According to Asadullah et al.,24 the E

DOI: 10.1021/acs.energyfuels.7b03658 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels

system, and it is extremely important for the manufacturer to obtain this information regarding the fluid flow characteristics. 3.2. Influence of Temperature on the Viscosity of BioOil. The viscosity is the property that requires greater consideration in the study of the flow of a fluid, since it quantifies its resistance to deformation when submitted to a shear stress. Since this property is related to the internal energy of the substance or mixture, it is highly influenced by changes in temperature.25 In this section, therefore, the behavior of the bio-oil viscosity as a function of temperature will be investigated in a range that allowed its flow when submitted to a set of shear rates. The behavior of the bio-oil viscosity as a function of the shear rate at temperatures of 60, 70, 80, 90, and 110 °C was defined, and the profiles are shown in Figure 5a,b. The viscosity of sisal residue bio-oil at 60 °C (near its melting point of 55 °C) was extremely high when the shear rate was the lowest and was very high when the shear rate was the highest. Table 4 is an adaptation of the work of Gollakota et

Figure 4. Acquisition data of pressure and flow rate as a function of time.

Some tests were carried out to produce 4 additional bio-oil samples under different operating conditions. The results (Appendix A, Supporting Information) showed that the water contents of the bio-oil samples are influenced by the operational conditions of production, although the variation between the samples is small and the water content is close to the value reported by Pereira and Pires7 (biomass = 6.11 wt %; bio-oil = 5.18 wt %). This fact reinforces that smaller flow rates of drag gas were responsible for the production of a bio-oil with lower viscosity due to the increase in the residence time of the molecules in the reactor, causing the reduction of their size. Figure 3 shows that the viscosity behavior of sisal residue biooil is similar at both shear rates. However, the maximum viscosity was always lower for the shear rate of 75 s−1. The results presented in Table 2 show that at the shear rate of 40 s−1, the viscosity ranged from 57 mPa·s to 228 mPa·s, while the viscosity range corresponding to 75 s−1 was 47 mPa·s−136 mPa·s. Therefore, the decrease of the viscosity due to the increase of the shear rate to which the fluid is submitted is observed, characteristic of the pseudoplastic fluids. These results confirm the influence of the operating conditions on the viscosity of the bio-oil produced, with trials 3 and 5 producing the fluid with lower and higher viscosity, respectively. In addition, the results also allow evaluation of the bio-oil behavior when submitted to various shear stresses if applied, for example, as a fuel or lubricating oil for various equipment. Therefore, it is concluded that the forces imposed on the bio-oil can interfere in the design of the project, for example, in a pyrolysis unit flow

Table 4. Viscosity of Bio-Oils of Various Biomasses biomass Pterocarpus indicus Fraxinus mandshurica rice straw rice husk (at 60 °C) cotton stalk (at 60 °C) corn stover (at 50 °C) pine wood (at 50 °C) cashewnut shell (60 °C) soybean oil cake (at 50 °C) bagasse (20 °C) sugar cane wood pyrolysis bio-oil (at 50 °C) contaminated sawdust (40 °C)

viscosity (mPa·s) 70−350 10−70 5−10 152.32 145 60 65.136 38.493 80.125 141.082 107.101 40−100 72.7−190.6

reference Luo et al.27 Luo et al.27 Luo et al.27 Zheng28 Zheng et al.29 Yu et al.30 Hassan et al.31 Das and Ganesh32 Sensoz and Kaynar33 Mohan et al.34 Islam et al.35 Czernik and Bridgwater36 Duanguppama et al.22

al.26 with the work of Luo et al.,27 in addition to new results; the table presents the viscosity of bio-oils produced by biomass of various types. The lower viscosity of sisal residue bio-oil found at 60 °C (353 mPa·s) was equivalent to the higher viscosity recorded in Table 4, the temperature of which was not reported. However, when observing results at 60 °C, Table 4 shows viscosities that can reach values below half of that found for sisal residue bio-oil (2699 mPa·s). At temperatures above 60

Figure 5. Dynamic viscosity of sisal residue as a function of shear rate: (a) all temperatures and (b) 70−110 °C. The bio-oil used was produced under the condition of lower viscosity: N2 flow of 8 N m3/h, Biomass flow of 1560 g/h and reaction temperature of 450 °C. F

DOI: 10.1021/acs.energyfuels.7b03658 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Table 5. Parameters of the Viscosity Models model

A

B

C

D

R2

2.53 × 107

0.88 0.96 0.98 1.00

1.03 × 107

0.98 0.99 0.99 1.00

6.72 × 106

0.98 0.99 0.99 1.00

−1

a

G-Aa Girifalco Vogel eq 4

0.21 10545.30 20.57 3.98 × 10−18

G-Aa Girifalco Vogel eq 4

0.52 5.85 3.30 1.16 × 10−8

G-Aa Girifalco Vogel eq 4

0.83 5.43 1.27 1.12 × 10−5

Shear Rate 2 s 529.99 −1223.78 45.66 1.09 × 104 Shear Rate 14 s−1 402.85 11.22 159.20 4.95 × 103 Shear Rate 26 s−1 355.71 51.93 159.20 3.28 × 103

68049.27 −50.70 −9.06 × 105

15195.84 −28.19 −3.80 × 105

11787.29 −25.22 −2.47 × 105

Guzman−Andrade.

°C, the viscosity of sisal residue bio-oil ranges from 186 to 22 mPa·s over the entire range of shear rates. Unfortunately, no viscosity values were found in the literature at temperatures above 60 °C. For this reason, it can only be stated that the viscosity of sisal residue bio-oil is very high, reaching values close to those of other bio-oils only if submitted to temperatures equal to or above 70 °C. The results showed the peculiar viscous characteristic of the bio-oil under study in relation to the various bio-oils found in the literature, which present similar viscosity values among them, as shown in Table 4. Fast pyrolysis of eucalyptus was carried out in the same pilot plant, under similar operating conditions as the sisal residue tests (N2F = 11 N m3/h; BioF = 1083 g/h and temp = 500 °C), and the results showed that the viscosity values of the eucalyptus bio-oil are about 10 times smaller than those coming from the sisal residue (Appendix A, Supporting Information), being close to those found by other authors. Figure 5 shows that the viscosity of sisal residue bio-oil decreases with increasing shear rate, which confirms the yieldpseudoplastic rheological behavior of this fluid, as reported in the work of Pereira and Pires.7 It has also been observed that viscosity is influenced by temperature, and, as discussed above, the values obtained are comparable to those of other bio-oils at temperatures above 70 °C. In studies aimed at the application of bio-oil in various situations, such as the lubrication of machines or its use as a fuel, it is necessary to know the behavior of the fluid when submitted to various temperature conditions and shear stresses. Thus, it is important to qualify and quantify the influence of these variables on the bio-oil viscosity so that it behaves conveniently in each condition. 3.3. Model of Viscosity and Temperature. According to Pereira and Pires,7 the bio-oil produced from sisal residue had an incredibly waxy and viscous appearance at room temperature, which was very different from the reports in the literature regarding the bio-oil produced from other biomass sources. While some authors have reported that bio-oil behaves as a Newtonian fluid above 46 °C and as a Bingham plastic below this temperature, sisal residue bio-oil simply does not flow at temperatures lower than 55 °C. For this reason, it is important to obtain a viscosity variation model with the temperature, in order to know the range of values of these variables that allows fluid flow and, consequently, its application.

The models described by eqs 1, 2, and 3 were applied to sisal residue bio-oil, and because of this, the specific parameters of each model were determined from the fit data of viscosity as a function of temperature (Figure 5) using a nonlinear regression method based on the Levenberg−Marquardt algorithm. To improve this analysis, a new model (eq 4) was also proposed, and then the four models were compared by means of the deviations obtained between the experimental and calculated values. ln η = A + (B /T ) + (C /T 2) + (D/T 3)

(4)

The viscosity measurements were obtained at temperatures of 60, 70, 80, 90, and 110 °C. The parameter adjustments were performed for the shear rates of 2 s−1, 14 s−1, and 26 s−1, as they were considered to be representative of the higher viscosity range. The parameter values of the models are shown in Table 5. The models were adequate to represent the influence of temperature on the dynamic viscosity, based on the high magnitude values of R2. Equation 4 stood out in relation to the others with R2 equal to 1. Figure 6 shows the viscosity behavior as a function of temperature for the models under study at each shear rate. In all the graphs, it is visually observed that the four models present good results at temperatures equal to and above 80 °C for the three shear rates studied. However, below 80 °C, the models improve their estimates as the shear rate increases. Apparently, eq 4 provides more adequate estimates for all shear rates studied. To quantitatively evaluate the quality of the adjustment in each case, Figure 7 shows the behavior of the models’ deviations from the experimental viscosities with respect to the temperature. The Guzman−Andrade model was not suitable to describe the fluid at the shear rate of 2 s−1, providing very high deviations at all temperatures, especially 60 and 70 °C. According to Velzen et al.,19 the Guzman−Andrade model presented inaccuracies when used to describe associated liquids or blends of high viscosity hydrocarbons, which explains the poor fit at temperatures close to the bio-oil pour point (55 °C). The progressive drop in viscosity caused by the increased shear rate contributed to a better fit of the model, reaching deviations below 10% at temperatures of 80, 90, and 110 °C. At the rate of G

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Figure 6. Viscosity versus temperature: Shear rate of 2 s−1 (a), 14 s−1 (b) e 26 s−1 (c).

Figure 7. Relative deviations between the experimental data and the values predicted by the models: Shear rate of 2 s−1 (a), 14 s−1 (b), and 26 s−1 (c).

26 s−1, the relative deviations at 60 and 70 °C still remained high, making the model inappropriate to predict the viscosity behavior of sisal residue bio-oil in the temperature range of 60− 110 °C. The application of the Girifalco model caused a reduction in the values of the deviations at the temperatures of 60 and 70 °C in relation to the Guzman−Andrade model for all the shear rates. However, at 90 °C, the calculated deviations were higher at the three shear rates. At 80 and 110 °C, the model showed deviations similar to those of Guzman−Andrade at the shear rates of 14 s−1 and 26 s−1. Even so, the Girifalco model is suitable for calculating the viscosity as a function of the bio-oil temperature at the shear rate of 14 s−1, with the greatest deviation of 7.06%. The Vogel model showed behavior similar to the Girifalco model at the shear rates of 14 s−1 and 26 s−1. The lowest mean deviation was obtained at 26 s−1, whose result was 6.81%. At temperatures of 60 and 70 °C, this model showed more

satisfactory predictions than the first two, although it still showed relatively high deviations, such as those obtained at 70 °C. At 90 °C, the Vogel model showed poor performance in relation to the Guzman−Andrade model for the three shear rates. Equation 4 was the model that provided the smallest deviations among the models used at the temperatures and shear rates evaluated. The exception occurred at the shear rate of 26 s−1 and a temperature of 80 °C, for which the best result was the Guzman−Andrade model. A decrease in the shear rate of 26 s−1 at temperatures of 70−90 °C, with deviations below 5%, is also observed. Equation 4 presented better viscosity predictions at all shear rates studied, and it is considered the most appropriate model to determine the viscosity of sisal residue bio-oil at the temperatures evaluated. H

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4. CONCLUSIONS The bio-oil of sisal residue differs from bio-oils produced from other biomass, mainly because of its much higher pour point. This fact is associated with viscosity, whose value ranged from 2699 to 353 mPa·s at 60 °C and shear rates ranging from 2 s−1to 26 s−1. The high viscosity value directly affects the bio-oil recovery system of the fast pyrolysis unit, which makes essential the production of bio-oil with the lowest possible viscosity. This bio-oil condition was found when the pyrolysis unit was operated with the N2 flow rate and the reaction temperature at their lowest levels (8 N m3/h and 450 °C, respectively) and with the sisal residue flow rate at its highest level (1560 g/h). The lower N2 flow rate and the higher biomass flow rate are related to the longer residence time of the pyrolysis vapors and gases, which allows the molecules to breakdown from secondary reactions. The lower reaction temperature supported the production of bio-oil of lower viscosity because it also favored the reactions of dehydration and condensation of the organic molecules, which are responsible for the greater production of water. The models of Guzman−Andrade, Girifalco, and Vogel have been reported in the literature as equations capable of predicting the viscosity of an organic fluid as a function of temperature. However, there are no studies of these equations applying to bio-oil of any source. For this reason, these equations were applied to sisal residue bio-oil for three shear rates (2 s−1, 14 s−1, and 26 s−1), and the deviations were determined to be high. This result motivated the launch of a new model, which added a cubic term to the Girifalco equation. The new equation adequately reproduced the influence of temperature on the bio-oil viscosity at all the shear rates considered. This equation will bring more safety to sisal residue bio-oil flow system projects, which are usually high in viscosity.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.7b03658. Appendix A. Supplementary Data (Table A1: Viscosity of bio-oils of eucalyptus; Figure A1: Dynamic viscosity of sisal residue and eucalyptus bio-oil as a function of shear rate) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +55 71-3283 9862. Fax: +55 71-3283 9809. ORCID

Luis G. G. Pereira: 0000-0002-1055-0300 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Bahia State Foundation for Research Support−FAPESB (Project DTE: 0061/2011) and the BNB−Northeast Bank of Brazil (Covenante no. 945). I

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