Liquid–Liquid Equilibrium in Ternary Systems Present in Biodiesel

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Liquid−Liquid Equilibrium in Ternary Systems Present in Biodiesel Purification from Soybean Oil and Castor Oil at (298.2 and 333.2) K Yurany C. Ardila,* Alex B. Machado, Glaucia Maria F. Pinto, Rubens Maciel Filho, and Maria R. Wolf Maciel School of Chemical Engineering, State University of Campinas, São Paulo, Brazil 6066, 13081-970 ABSTRACT: Liquid−liquid equilibrium (LLE) data for the soybean oil biodiesel (BIO-SO) + ethanol + water and castor oil biodiesel (BIO−CO) + ethanol + water systems at (298.2 and 333.2) K and atmospheric pressure were determined by gas chromatography and volumetric Karl Fischer titration. The degree of consistency of the experimental LLE data was ascertained by applying the Hand and Othmer-Tobias correlations. Ethanol distribution coefficients and water selectivity were evaluated for the immiscibility region. The experimental data were also compared with the values correlated by the NRTL and UNIQUAC activity coefficient models. For all systems studied, the average deviations found for the UNIQUAC model are larger than those found for the NRTL model.

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INTRODUCTION Several studies demonstrate that the replacement (even partially) of diesel by biodiesel reduces emissions of pollutants such as SO2, CO, CO2, and particulate matter.1−3 Besides the esters, the outlet stream of the transesterification reactor contains glycerol (a byproduct of the reaction), excess of alcohol, water, and small amounts of catalyst. Water is present in the outlet stream of the transesterification reactor as an impurity. It is also used in a later stage of purification of the esters (washing) to remove other impurities present in the reaction medium, such as catalyst, salts of fatty acids, tri-, di-, and monoacylglycerol, excess of alcohol, and glycerol remaining in the ester-rich phase after separation of the glycerol-rich phase.4 As a result of this washing with water, biodiesel contains water which must be separated.5 Haq et al., 6 Rahayu et al.,7 and Chongkhong et al.8 studied the biodiesel production washing process using deionized or distilled water to remove impurities present in the biodiesel and studied the significance of the variables as temperature and amount of solvent during extraction. In the literature, liquid−liquid equilibrium (LLE) data are reported for the products found during the transesterification reaction in biodiesel production, and in particular data for systems that satisfy the biodiesel or fatty acid esters + alcohol + glycerol systems as reported by Andreatta et al.,9 Franca et al.,10 Liu et al.,11 Mesquita et al.,12 Follegatti-Romero et al.,13 and Machado et al.14,15 Although the analysis of the water interaction as a solvent in the biodiesel purification has not been studied, Follegati-Romero et al.16,17 recently presented LLE data, including that of esters such as ethyl myristate, ethyl laureate, ethyl oleate, ethyl linoleate and ethyl palmitate in mixtures with ethanol and water, but a general study of biodiesel with a good amount of impurities extracted with water has not been studied. It is therefore necessary to understand the liquid−liquid phase equilibrium formed with the mixtures involved in order to proceed © 2013 American Chemical Society

with the purification of biodiesel. Such data are not currently found in the literature, making impracticable the real understanding of phase separation and the construction of virtual plants for computer simulations. In a previous paper,18 the authors have studied the solubility curves of the systems soybean oil biodiesel + ethanol + water at different temperatures, but without determining LLE data. In this work, LLE data for castor oil biodiesel (BIO−CO) + ethanol + water and soybean oil biodiesel BIO-SO + ethanol + water systems at 298.2 K and 333.2 K and atmospheric pressure were determined by gas chromatography and volumetric Karl Fischer titration. The influence of temperature on partition coefficient (K) and on selectivity (S) was studied. Hand and Othmer-Tobias correlations were used to test the data quality, which presented R2 > 0.97 for all systems. LLE systems presented in this paper show the behavior of the solvent (water) in the separation of ethanol and also the behavior of water after the transesterification reaction. Using Aspen Plus V7.3 simulator,19 we correlated the experimental data through the molecular NRTL and UNIQUAC models for the calculation of binary interaction parameters. The results were considered satisfactory, observing that NRTL was capable of representing better the equilibrium data of the studied systems.

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EXPERIMENTAL SECTION Chemicals. Biodiesel from soybean oil and castor oil were produced and characterized according to a previous work.18 Ethanol (purity 0.995) was purchased from Synth. Water was purified by using a Milli-Q system (resistivity 0.97 and the small values of the σOT and σH indicate the high degree of consistency of the experimental LLE data. NRTL and UNIQUAC models22,23 were used to correlate the LLE data in the present work. In these models, the adjustable parameters were defined as follows: τij = Aij +

Figure 6. Othmer-Tobias plot for LLE data determined in this work: ▲···, BIO-SO system at T = 298.2 K; ○, BIO-SO system at T = 333.2 K; Δ---, BIO−CO system at T = 298.2 K; ●·--, BIO−CO system at T = 332.2 K.

Bij T

(6)

τij = exp(Aij + (Bij /T ))

(7)

In the NRTL and UNIQUAC models, the binary interaction parameters (Aij, Aij, Bij/T, Bij/T) were calculated using Aspen Plus V7.3 simulator. The regression method used in the simulator was the generalized least-squares method based on the maximum likelihood principle.20,21,24 The new Britt-Luecke algorithm was employed to obtain the model parameters with the Deming initialization method. The objective function used is expt calc 2 ⎤ ⎡ expt (wijk ) − wijk (Tk − Tkcalc)2 ⎢ ⎥ OF = ∑ ∑ ∑ + 2 2 ⎢ ⎥⎦ σ σ T w ⎣ k=1 j=1 i=1 M

2

2

(8)

where M is the number of tie-lines, wexpt and Texpt indicate the experimental mass fraction and temperature, and wcalc and Tcalc are the calculated mass fraction and temperature, respectively. The subscripts i, j, and k denote, respectively, the component, phase, and tie-line. σT and σw are the standard deviation of the temperature and the mass fraction of component i, respectively. In the regression, these values were taken as: σT = 0.01K, σw = 0.001. The data were correlated using two stages for the NRTL model to increase the regression accuracy. First, the parameters Aij, Aji, Bij, and Bji were regressed and the value of the nonrandomness parameter (αij) was kept constant and equal to

Figure 7. Hand plot for LLE data determined in this work: ▲···, BIOSO system at T = 298.2 K; ○, BIO-SO system at T = 333.2 K; Δ---, BIO−CO system at T = 298.2 K; ●·--, BIO−CO system at T = 332.2 K.

0.2.12,14,15,25 Second, all parameters were regressed simultaneously, including αij, which was varied from 0 to 0.5. 608

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As shown by França et al.,10 Mesquita et al.,12 Conceiçaõ et al.,26 and a previous work,18 the biodiesel is a mixture of esters and some of them are represented almost entirely in the case of BiO−CO ((88−90) % ricinoleate) and BiO-SO ((21−23) % oleate and (47−52) % linoleate). The Aspen Plus V7.3 simulator does not contain BIO-SO and BIO-CO in its compounds database. To represent them it is necessary to treat the systems as pseudoternary by adding the main molecule as ethyl linoleate and ethyl ricinoleate to perform the calculations of biodiesel soybean oil and castor oil biodiesel, respectively The ester molecule was constructed with the help of the ChemOffice Software.27,28 This software allowed the insertion of the molecule into the Aspen Plus compound database, and so it was possible to obtain binary parameters using data regression. The prediction of the thermodynamic properties of BIO-CO (ethyl ricinoleate) and BIO-SO (ethyl linoleate) were accomplished using the Gani method.29 For estimating the parameters of the UNIQUAC model the van der Waals volume of the molecule i(ri) and van der Waals surface of the molecule i(qi) are needed. The values of ri and qi were calculated using the Bondi method,28,30 in the Aspen plus V7.3 simulator. The values ri and qi can be seen in Table 4.

Table 6. UNIQUAC Binary Interaction Parameters for the Systems Studied components i

components

ri

qi

13.78 14.31 2.105 0.9200

11.32 11.81 1.972 1.400

1 1 2

2 3 3

1 1 2

2 3 3

NRTL

j

Aij

Bij/T

Bji/T 17875.0 −33.7027 4376.96 −3889.94 1007.81 −230.714

UNIQUAC

Root mean square deviation (rmsd), given by eq 9. bαij = 0.2. cαij obtained by regression. a

⎛ ∑ ∑ ∑ (w expt − w calc) ⎞1/2 ijk ijk i j k ⎟ rmsd = 100⎜ ⎟ ⎜ 6 M ⎠ ⎝

NRTL parameters Aij

Bij/T

BIO−CO (1) + Ethanol (2) + Water (3) 5.646 5.327b 5.395c BIO-SO (1) + Ethanol (2) + Water (3) 1.397b 1.939 1.931c

Table 5. NRTL Binary Interaction Parameters for the Systems Studied

i

Aij

BIO-SO (1) + Ethanol (2) + Water (3) −4.3237 −61.0914 2336.00 −3.1616 0.2692 137.569 1.5420 −9.9852 −960.551 BIO−CO (1) + Ethanol (2) + Water (3) −1.4996 13.030 2740.19 −5.9536 −3.6916 1402.66 5.0456 8.6874 −1396.00

Table 7. Root Mean Square Deviation (rmsd)a for the NRTL and UNIQUAC Models

Tables 5 and 6 show the values of the fitting parameters obtained using the NRTL and UNIQUAC models to correlate

components

UNIQUAC parameters Aij

based on the NRTL and UNIQUAC models are plotted in Figures 1−4 together with the experimental tie-line data. In this work, the root-mean-square deviation (rmsd) in the phase composition was calculated according to eq 9 and the resulting values are listed in Table 7.

Table 4. Parameters of the UNIQUAC Model ri and qi for the Systems Studied soybean oil biodiesel castor oil biodiesel ethanol water

j

(9)

where w is the mass fraction and the subscripts i, j, and k represent the component, phase, and tie-line, respectively. The value of M is the number of tie-lines. As shown in Figures 1−4, there is good agreement between experimental and calculated points with NRTL and UNIQUAC models. Both of them show a good fit to the experimental data, but it is observed that the NRTL model is superior to the UNIQUAC model, because it is able to represent better the system, getting closer to the experimental points. This can be seen more clearly in the deviations shown in Table 7.

Bji/T

BIO-SO (1) + Ethanol (2) + Water (3) (αij = 0.2)a 1 2 43.742 28.654 −15447.7 −6457.45 1 3 9.2225 6.6520 −2397.74 617.119 2 3 −36.163 77.360 11659.6 −25504.8 BIO-SO (1) + Ethanol (2) + Water (3) (α12 = 0.438 α13 = 0.346 α23 = 0.373)b 1 2 21.610 7.2700 −6041.95 −593.460 1 3 4.3700 −2.0300 −20248.5 3690.67 2 3 −8.7900 68.100 3683.16 −113.110 BIO−CO (1) + Ethanol (2) + Water (3) (αij = 0.2)a 1 2 79.666 −7.9541 −27711.0 4072.18 1 3 −9.4914 32.186 4080.18 −5210.63 2 3 15.869 87.316 −5741.04 −28402.0 BIO−CO (1) + Ethanol (2) + Water (3) (α12 = 0.452 α13 = 0.149 α23 = 0.180)b 1 2 −37.884 −3.0448 12160.8 1996.06 1 3 −3.0059 −4.5095 365.647 10461.6 2 3 1.1857 0.60662 1087.21 1919.60

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CONCLUSIONS

LLE data for ternary castor oil biodiesel + ethanol + water and soybean oil biodiesel + ethanol + water systems at (298.2 and 333.2) K and atmospheric pressure were obtained. These systems present higher values of ethanol distribution and selectivity at 333.2 K for castor oil biodiesel and 298.2 K for soybean oil biodiesel. The degree of consistency of the experimental LLE data was ascertained by applying the Othmer-Tobias and Hand correlations (R2 > 0.97). The experimental data were correlated using NRTL and UNIQUAC models. Both of them show a good fit to the experimental data. To increase the regression accuracy with the NRTL model two cases were studied, in the first one the value of αij was kept fixed and in the other case this value was

a

Values of the nonrandomness parameter were taken from refs 12, 14, 15, and 25. bValues of the nonrandomness parameter αij were obtained by regression.

the experimental LLE data for the systems BIO-SO + ethanol + water and BIO−CO + water + ethanol in the two temperatures of study, respectively. The calculated tie lines from the correlations 609

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varied. The best results for the NRTL model were found when the alpha value was kept constant at 0.2. Root mean square errors (rmse) are between (1.397 and 5.327) % for the NRTL model and (1.934 and 5.646) % for the UNIQUAC model.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Funding

The financial support of FAPESP and CNPq (Finantial Brazilian agencies) are gratefully acknowledged. Notes

The authors declare no competing financial interest.

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REFERENCES

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